Prospective correlative chemosensitivity testing in high-dose intraarterial chemotherapy for liver metastases.

PubMed

Link, K H; Aigner, K R; Kuehn, W; Schwemmle, K; Kern, D H

1986-09-01

Clinical response of liver metastases treated by high-dose intraarterial chemotherapy (HDIAC) delivered via the hepatic artery was predicted by a modification of the human tumor colony-forming assay (HTCFA) originally described by Hamburger and Salmon [Science (Wash. DC), 197:461-463, 1977. In a first set of experiments, the immediate clinical response to HDIAC was determined in 12 patients with colorectal liver metastases. Biopsies were taken immediately before and after HDIAC, and cells were plated in the HTCFA. Three patients received intraoperative 4-epidoxorubicin and another 9 received mitomycin C by 15-min intraarterial infusions. Sensitivity in the HTCFA was defined as 50% inhibition of colony formation in tumors exposed to the chemotherapeutic agent, compared to the untreated controls. Clinical response was accurately predicted by the HTCFA in 11 of 12 cases. Eight patients had a regression of disease following HDIAC treatment with mitomycin C, as evidenced by either greater than 50% reduction in carcinoembryonic antigen serum level (7 patients) or regression of tumor by computed tomography scan (1 patient). Three patients had no evidence of clinical response to epidoxorubicin, and their tumors were resistant to epidoxorubicin in the HTCFA. One tumor was sensitive to mitomycin C in the HTCFA, but serum carcinoembryonic antigen in the patient continued to increase following HDIAC. The HTCFA was also performed on untreated biopsies following incubation in vitro with the drug used for HDIAC. Results correlated with clinical response in all 12 cases. In a second set of experiments, the HTCFA was used to predict the long-term clinical response to HDIAC of 30 patients with liver metastases. One patient had breast cancer metastases, one patient had carcinoid liver metastases, 4 had liver metastases of malignant melanoma, and 24 patients had colorectal liver metastases. All 21 of the patients whose tumors were sensitive in vitro had clinical response, while 6 of 9 patients predicted by the HTCFA to be resistant had no clinical response. Our results demonstrate a high correlation between the HTCFA and clinical response.

A simple prediction tool for inhaled corticosteroid response in asthmatic children.

PubMed

Wu, Yi-Fan; Su, Ming-Wei; Chiang, Bor-Luen; Yang, Yao-Hsu; Tsai, Ching-Hui; Lee, Yungling L

2017-12-07

Inhaled corticosteroids are recommended as the first-line controller medication for childhood asthma owing to their multiple clinical benefits. However, heterogeneity in the response towards these drugs remains a significant clinical problem. Children aged 5 to 18 years with mild to moderate persistent asthma were recruited into the Taiwanese Consortium of Childhood Asthma Study. Their responses to inhaled corticosteroids were assessed based on their improvements in the asthma control test and peak expiratory flow. The predictors of responsiveness were demographic and clinical features that were available in primary care settings. We have developed a prediction model using logistic regression and have simplified it to formulate a practical tool. We assessed its predictive performance using the area under the receiver operating characteristic curve. Of the 73 asthmatic children with baseline and follow-up outcome measurements for inhaled corticosteroids treatment, 24 (33%) were defined as non-responders. The tool we have developed consisted of three predictors yielding a total score between 0 and 5, which are comprised of the following parameters: the age at physician-diagnosis of asthma, sex, and exhaled nitric oxide. Sensitivity and specificity of the tool for prediction of inhaled corticosteroids non-responsiveness, for a score of 3, were 0.75 and 0.69, respectively. The areas under the receiver operating characteristic curve for the prediction tool was 0.763. Our prediction tool represents a simple and low-cost method for predicting the response of inhaled corticosteroids treatment in asthmatic children.

A Crowdsourcing Approach to Developing and Assessing Prediction Algorithms for AML Prognosis

PubMed Central

Noren, David P.; Long, Byron L.; Norel, Raquel; Rrhissorrakrai, Kahn; Hess, Kenneth; Hu, Chenyue Wendy; Bisberg, Alex J.; Schultz, Andre; Engquist, Erik; Liu, Li; Lin, Xihui; Chen, Gregory M.; Xie, Honglei; Hunter, Geoffrey A. M.; Norman, Thea; Friend, Stephen H.; Stolovitzky, Gustavo; Kornblau, Steven; Qutub, Amina A.

2016-01-01

Acute Myeloid Leukemia (AML) is a fatal hematological cancer. The genetic abnormalities underlying AML are extremely heterogeneous among patients, making prognosis and treatment selection very difficult. While clinical proteomics data has the potential to improve prognosis accuracy, thus far, the quantitative means to do so have yet to be developed. Here we report the results and insights gained from the DREAM 9 Acute Myeloid Prediction Outcome Prediction Challenge (AML-OPC), a crowdsourcing effort designed to promote the development of quantitative methods for AML prognosis prediction. We identify the most accurate and robust models in predicting patient response to therapy, remission duration, and overall survival. We further investigate patient response to therapy, a clinically actionable prediction, and find that patients that are classified as resistant to therapy are harder to predict than responsive patients across the 31 models submitted to the challenge. The top two performing models, which held a high sensitivity to these patients, substantially utilized the proteomics data to make predictions. Using these models, we also identify which signaling proteins were useful in predicting patient therapeutic response. PMID:27351836

Identification of immune signatures predictive of clinical protection from malaria.

PubMed

Valletta, John Joseph; Recker, Mario

2017-10-01

Antibodies are thought to play an essential role in naturally acquired immunity to malaria. Prospective cohort studies have frequently shown how continuous exposure to the malaria parasite Plasmodium falciparum cause an accumulation of specific responses against various antigens that correlate with a decreased risk of clinical malaria episodes. However, small effect sizes and the often polymorphic nature of immunogenic parasite proteins make the robust identification of the true targets of protective immunity ambiguous. Furthermore, the degree of individual-level protection conferred by elevated responses to these antigens has not yet been explored. Here we applied a machine learning approach to identify immune signatures predictive of individual-level protection against clinical disease. We find that commonly assumed immune correlates are poor predictors of clinical protection in children. On the other hand, antibody profiles predictive of an individual's malaria protective status can be found in data comprising responses to a large set of diverse parasite proteins. We show that this pattern emerges only after years of continuous exposure to the malaria parasite, whereas susceptibility to clinical episodes in young hosts (< 10 years) cannot be ascertained by measured antibody responses alone.

Predicting Response to Histone Deacetylase Inhibitors Using High-Throughput Genomics.

PubMed

Geeleher, Paul; Loboda, Andrey; Lenkala, Divya; Wang, Fan; LaCroix, Bonnie; Karovic, Sanja; Wang, Jacqueline; Nebozhyn, Michael; Chisamore, Michael; Hardwick, James; Maitland, Michael L; Huang, R Stephanie

2015-11-01

Many disparate biomarkers have been proposed as predictors of response to histone deacetylase inhibitors (HDI); however, all have failed when applied clinically. Rather than this being entirely an issue of reproducibility, response to the HDI vorinostat may be determined by the additive effect of multiple molecular factors, many of which have previously been demonstrated. We conducted a large-scale gene expression analysis using the Cancer Genome Project for discovery and generated another large independent cancer cell line dataset across different cancers for validation. We compared different approaches in terms of how accurately vorinostat response can be predicted on an independent out-of-batch set of samples and applied the polygenic marker prediction principles in a clinical trial. Using machine learning, the small effects that aggregate, resulting in sensitivity or resistance, can be recovered from gene expression data in a large panel of cancer cell lines.This approach can predict vorinostat response accurately, whereas single gene or pathway markers cannot. Our analyses recapitulated and contextualized many previous findings and suggest an important role for processes such as chromatin remodeling, autophagy, and apoptosis. As a proof of concept, we also discovered a novel causative role for CHD4, a helicase involved in the histone deacetylase complex that is associated with poor clinical outcome. As a clinical validation, we demonstrated that a common dose-limiting toxicity of vorinostat, thrombocytopenia, can be predicted (r = 0.55, P = .004) several days before it is detected clinically. Our work suggests a paradigm shift from single-gene/pathway evaluation to simultaneously evaluating multiple independent high-throughput gene expression datasets, which can be easily extended to other investigational compounds where similar issues are hampering clinical adoption. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Clinical utility of pretreatment prediction of chemoradiotherapy response in rectal cancer: a review.

PubMed

Yoo, Byong Chul; Yeo, Seung-Gu

2017-03-01

Approximately 20% of all patients with locally advanced rectal cancer experience pathologically complete responses following neoadjuvant chemoradiotherapy (CRT) and standard surgery. The utility of radical surgery for patients exhibiting good CRT responses has been challenged. Organ-sparing strategies for selected patients exhibiting complete clinical responses include local excision or no immediate surgery. The subjects of this tailored management are patients whose presenting disease corresponds to current indications of neoadjuvant CRT, and their post-CRT tumor response is assessed by clinical and radiological examinations. However, a model predictive of the CRT response, applied before any treatment commenced, would be valuable to facilitate such a personalized approach. This would increase organ preservation, particularly in patients for whom upfront CRT is not generally prescribed. Molecular biomarkers hold the greatest promise for development of a pretreatment predictive model of CRT response. A combination of clinicopathological, radiological, and molecular markers will be necessary to render the model robust. Molecular research will also contribute to the development of drugs that can overcome the radioresistance of rectal tumors. Current treatments for rectal cancer are based on the expected prognosis given the presenting disease extent. In the future, treatment schemes may be modified by including the predicted CRT response evaluated at presentation.

Predicting Treatment Response in Social Anxiety Disorder From Functional Magnetic Resonance Imaging

PubMed Central

Doehrmann, Oliver; Ghosh, Satrajit S.; Polli, Frida E.; Reynolds, Gretchen O.; Horn, Franziska; Keshavan, Anisha; Triantafyllou, Christina; Saygin, Zeynep M.; Whitfield-Gabrieli, Susan; Hofmann, Stefan G.; Pollack, Mark; Gabrieli, John D.

2013-01-01

Context Current behavioral measures poorly predict treatment outcome in social anxiety disorder (SAD). To our knowledge, this is the first study to examine neuroimaging-based treatment prediction in SAD. Objective To measure brain activation in patients with SAD as a biomarker to predict subsequent response to cognitive behavioral therapy (CBT). Design Functional magnetic resonance imaging (fMRI) data were collected prior to CBT intervention. Changes in clinical status were regressed on brain responses and tested for selectivity for social stimuli. Setting Patients were treated with protocol-based CBT at anxiety disorder programs at Boston University or Massachusetts General Hospital and underwent neuroimaging data collection at Massachusetts Institute of Technology. Patients Thirty-nine medication-free patients meeting DSM-IV criteria for the generalized subtype of SAD. Interventions Brain responses to angry vs neutral faces or emotional vs neutral scenes were examined with fMRI prior to initiation of CBT. Main Outcome Measures Whole-brain regression analyses with differential fMRI responses for angry vs neutral faces and changes in Liebowitz Social Anxiety Scale score as the treatment outcome measure. Results Pretreatment responses significantly predicted subsequent treatment outcome of patients selectively for social stimuli and particularly in regions of higher-order visual cortex. Combining the brain measures with information on clinical severity accounted for more than 40% of the variance in treatment response and substantially exceeded predictions based on clinical measures at baseline. Prediction success was unaffected by testing for potential confounding factors such as depression severity at baseline. Conclusions The results suggest that brain imaging can provide biomarkers that substantially improve predictions for the success of cognitive behavioral interventions and more generally suggest that such biomarkers may offer evidence-based, personalized medicine approaches for optimally selecting among treatment options for a patient. PMID:22945462

Validating genetic markers of response to recombinant human growth hormone in children with growth hormone deficiency and Turner syndrome: the PREDICT validation study

PubMed Central

Stevens, Adam; Murray, Philip; Wojcik, Jerome; Raelson, John; Koledova, Ekaterina; Chatelain, Pierre

2016-01-01

Objective Single-nucleotide polymorphisms (SNPs) associated with the response to recombinant human growth hormone (r-hGH) have previously been identified in growth hormone deficiency (GHD) and Turner syndrome (TS) children in the PREDICT long-term follow-up (LTFU) study (Nbib699855). Here, we describe the PREDICT validation (VAL) study (Nbib1419249), which aimed to confirm these genetic associations. Design and methods Children with GHD (n = 293) or TS (n = 132) were recruited retrospectively from 29 sites in nine countries. All children had completed 1 year of r-hGH therapy. 48 SNPs previously identified as associated with first year growth response to r-hGH were genotyped. Regression analysis was used to assess the association between genotype and growth response using clinical/auxological variables as covariates. Further analysis was undertaken using random forest classification. Results The children were younger, and the growth response was higher in VAL study. Direct genotype analysis did not replicate what was found in the LTFU study. However, using exploratory regression models with covariates, a consistent relationship with growth response in both VAL and LTFU was shown for four genes – SOS1 and INPPL1 in GHD and ESR1 and PTPN1 in TS. The random forest analysis demonstrated that only clinical covariates were important in the prediction of growth response in mild GHD (>4 to <10 μg/L on GH stimulation test), however, in severe GHD (≤4 μg/L) several SNPs contributed (in IGF2, GRB10, FOS, IGFBP3 and GHRHR). Conclusions The PREDICT validation study supports, in an independent cohort, the association of four of 48 genetic markers with growth response to r-hGH treatment in both pre-pubertal GHD and TS children after controlling for clinical/auxological covariates. However, the contribution of these SNPs in a prediction model of first-year response is not sufficient for routine clinical use. PMID:27651465

Validating genetic markers of response to recombinant human growth hormone in children with growth hormone deficiency and Turner syndrome: the PREDICT validation study.

PubMed

Stevens, Adam; Murray, Philip; Wojcik, Jerome; Raelson, John; Koledova, Ekaterina; Chatelain, Pierre; Clayton, Peter

2016-12-01

Single-nucleotide polymorphisms (SNPs) associated with the response to recombinant human growth hormone (r-hGH) have previously been identified in growth hormone deficiency (GHD) and Turner syndrome (TS) children in the PREDICT long-term follow-up (LTFU) study (Nbib699855). Here, we describe the PREDICT validation (VAL) study (Nbib1419249), which aimed to confirm these genetic associations. Children with GHD (n = 293) or TS (n = 132) were recruited retrospectively from 29 sites in nine countries. All children had completed 1 year of r-hGH therapy. 48 SNPs previously identified as associated with first year growth response to r-hGH were genotyped. Regression analysis was used to assess the association between genotype and growth response using clinical/auxological variables as covariates. Further analysis was undertaken using random forest classification. The children were younger, and the growth response was higher in VAL study. Direct genotype analysis did not replicate what was found in the LTFU study. However, using exploratory regression models with covariates, a consistent relationship with growth response in both VAL and LTFU was shown for four genes - SOS1 and INPPL1 in GHD and ESR1 and PTPN1 in TS. The random forest analysis demonstrated that only clinical covariates were important in the prediction of growth response in mild GHD (>4 to <10 μg/L on GH stimulation test), however, in severe GHD (≤4 μg/L) several SNPs contributed (in IGF2, GRB10, FOS, IGFBP3 and GHRHR). The PREDICT validation study supports, in an independent cohort, the association of four of 48 genetic markers with growth response to r-hGH treatment in both pre-pubertal GHD and TS children after controlling for clinical/auxological covariates. However, the contribution of these SNPs in a prediction model of first-year response is not sufficient for routine clinical use. © 2016 European Society of Endocrinology.

Metabolomics biomarkers to predict acamprosate treatment response in alcohol-dependent subjects.

PubMed

Hinton, David J; Vázquez, Marely Santiago; Geske, Jennifer R; Hitschfeld, Mario J; Ho, Ada M C; Karpyak, Victor M; Biernacka, Joanna M; Choi, Doo-Sup

2017-05-31

Precision medicine for alcohol use disorder (AUD) allows optimal treatment of the right patient with the right drug at the right time. Here, we generated multivariable models incorporating clinical information and serum metabolite levels to predict acamprosate treatment response. The sample of 120 patients was randomly split into a training set (n = 80) and test set (n = 40) five independent times. Treatment response was defined as complete abstinence (no alcohol consumption during 3 months of acamprosate treatment) while nonresponse was defined as any alcohol consumption during this period. In each of the five training sets, we built a predictive model using a least absolute shrinkage and section operator (LASSO) penalized selection method and then evaluated the predictive performance of each model in the corresponding test set. The models predicted acamprosate treatment response with a mean sensitivity and specificity in the test sets of 0.83 and 0.31, respectively, suggesting our model performed well at predicting responders, but not non-responders (i.e. many non-responders were predicted to respond). Studies with larger sample sizes and additional biomarkers will expand the clinical utility of predictive algorithms for pharmaceutical response in AUD.

Clinical assessment is an accurate predictor of which patients will need septoplasty.

PubMed

Sedaghat, Ahmad R; Busaba, Nicolas Y; Cunningham, Michael J; Kieff, David A

2013-01-01

Septoplasty is a frequently performed surgical procedure with the most common indication being nasal airway obstruction. Almost universally, health insurance companies mandate a trial of medical therapy consisting of intranasal corticosteroids prior to performance of septoplasty regardless of clinical assessment. Evidence for this requirement is lacking. We sought to evaluate the initial clinical assessment as a predictor of response to this mandated trial of medical treatment. Retrospective review of prospectively collected data on 137 consecutive patients who presented with symptoms of nasal obstruction and a deviated nasal septum on physical examination. Patients were placed into one of three cohorts based on prediction of 1) failure of medical therapy with subsequent septoplasty, 2) success of medical therapy without subsequent septoplasty, or 3) unable to make a prediction. Patients from each cohort were assessed for subsequent response to medical therapy and ultimate need for septoplasty. Overall clinical assessment had a sensitivity of 86.9%, specificity of 91.8%, positive predictive value of 93.6%, and negative predictive value of 96.4% for detecting/predicting need for septoplasty. The accuracy of the overall clinical assessment is considerably better than severe deviation at any one septal anatomical site. Of patients whose response to medical therapy could not be predicted, 61.3% failed medical therapy and needed surgery; this is statistically equivalent to a 50/50 distribution between either needing septoplasty or not. Clinical assessment at initial presentation of patients with nasal obstruction and deviated septum is highly accurate in predicting which patients will need septoplasty. Copyright © 2012 The American Laryngological, Rhinological, and Otological Society, Inc.

Serum metabolites predict response to angiotensin II receptor blockers in patients with diabetes mellitus.

PubMed

Pena, Michelle J; Heinzel, Andreas; Rossing, Peter; Parving, Hans-Henrik; Dallmann, Guido; Rossing, Kasper; Andersen, Steen; Mayer, Bernd; Heerspink, Hiddo J L

2016-07-05

Individual patients show a large variability in albuminuria response to angiotensin receptor blockers (ARB). Identifying novel biomarkers that predict ARB response may help tailor therapy. We aimed to discover and validate a serum metabolite classifier that predicts albuminuria response to ARBs in patients with diabetes mellitus and micro- or macroalbuminuria. Liquid chromatography-tandem mass spectrometry metabolomics was performed on serum samples. Data from patients with type 2 diabetes and microalbuminuria (n = 49) treated with irbesartan 300 mg/day were used for discovery. LASSO and ridge regression were performed to develop the classifier. Improvement in albuminuria response prediction was assessed by calculating differences in R(2) between a reference model of clinical parameters and a model with clinical parameters and the classifier. The classifier was externally validated in patients with type 1 diabetes and macroalbuminuria (n = 50) treated with losartan 100 mg/day. Molecular process analysis was performed to link metabolites to molecular mechanisms contributing to ARB response. In discovery, median change in urinary albumin excretion (UAE) was -42 % [Q1-Q3: -69 to -8]. The classifier, consisting of 21 metabolites, was significantly associated with UAE response to irbesartan (p < 0.001) and improved prediction of UAE response on top of the clinical reference model (R(2) increase from 0.10 to 0.70; p < 0.001). In external validation, median change in UAE was -43 % [Q1-Q35: -63 to -23]. The classifier improved prediction of UAE response to losartan (R(2) increase from 0.20 to 0.59; p < 0.001). Specifically ADMA impacting eNOS activity appears to be a relevant factor in ARB response. A serum metabolite classifier was discovered and externally validated to significantly improve prediction of albuminuria response to ARBs in diabetes mellitus.

Faecal calprotectin assay after induction with anti-Tumour Necrosis Factor α agents in inflammatory bowel disease: Prediction of clinical response and mucosal healing at one year.

PubMed

Guidi, Luisa; Marzo, Manuela; Andrisani, Gianluca; Felice, Carla; Pugliese, Daniela; Mocci, Giammarco; Nardone, Olga; De Vitis, Italo; Papa, Alfredo; Rapaccini, Gianlodovico; Forni, Franca; Armuzzi, Alessandro

2014-11-01

Faecal calprotectin levels correlate with inflammation in inflammatory bowel disease. We evaluated the role of faecal calprotectin after anti-Tumour Necrosis Factor α induction in inflammatory bowel disease patients to predict therapeutic effect at one year. Faecal calprotectin levels were measured in stools of 63 patients before and after induction of anti-Tumour Necrosis Factor α therapy. Clinical activity, measured by clinical indices, was assessed before and after biologic treatment. Clinical responders after induction were included in the study and colonoscopy was performed before and after one year of treatment to assess mucosal healing. 63 patients (44 Crohn's disease, 19 ulcerative colitis) were prospectively included (41.2% males, mean age at diagnosis 33 years). A sustained clinical response during the first year was observed in 57% of patients; median faecal calprotectin was 106 μg/g after induction versus 308 μg/g pre-induction (p<0.0001). Post-induction faecal calprotectin was significantly lower in responders versus non-responders (p=0.0002). Post-induction faecal calprotectin had 83% sensitivity and 74% specificity (cut-off ≤ 168 μg/g) for predicting a sustained clinical response at one year (p=0.0001); also, sensitivity was 79% and specificity 57% (cut-off ≤ 121 μg/g) for predicting mucosal healing (p=0.0001). In inflammatory bowel disease faecal calprotectin assay after anti-Tumour Necrosis Factor α induction can be used as a marker to predict sustained clinical response and mucosal healing at one year. Copyright © 2014 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning-An Artificial Intelligence Concept.

PubMed

Abajian, Aaron; Murali, Nikitha; Savic, Lynn Jeanette; Laage-Gaupp, Fabian Max; Nezami, Nariman; Duncan, James S; Schlachter, Todd; Lin, MingDe; Geschwind, Jean-François; Chapiro, Julius

2018-06-01

To use magnetic resonance (MR) imaging and clinical patient data to create an artificial intelligence (AI) framework for the prediction of therapeutic outcomes of transarterial chemoembolization by applying machine learning (ML) techniques. This study included 36 patients with hepatocellular carcinoma (HCC) treated with transarterial chemoembolization. The cohort (age 62 ± 8.9 years; 31 men; 13 white; 24 Eastern Cooperative Oncology Group performance status 0, 10 status 1, 2 status 2; 31 Child-Pugh stage A, 4 stage B, 1 stage C; 1 Barcelona Clinic Liver Cancer stage 0, 12 stage A, 10 stage B, 13 stage C; tumor size 5.2 ± 3.0 cm; number of tumors 2.6 ± 1.1; and 30 conventional transarterial chemoembolization, 6 with drug-eluting embolic agents). MR imaging was obtained before and 1 month after transarterial chemoembolization. Image-based tumor response to transarterial chemoembolization was assessed with the use of the 3D quantitative European Association for the Study of the Liver (qEASL) criterion. Clinical information, baseline imaging, and therapeutic features were used to train logistic regression (LR) and random forest (RF) models to predict patients as treatment responders or nonresponders under the qEASL response criterion. The performance of each model was validated using leave-one-out cross-validation. Both LR and RF models predicted transarterial chemoembolization treatment response with an overall accuracy of 78% (sensitivity 62.5%, specificity 82.1%, positive predictive value 50.0%, negative predictive value 88.5%). The strongest predictors of treatment response included a clinical variable (presence of cirrhosis) and an imaging variable (relative tumor signal intensity >27.0). Transarterial chemoembolization outcomes in patients with HCC may be predicted before procedures by combining clinical patient data and baseline MR imaging with the use of AI and ML techniques. Copyright © 2018 SIR. Published by Elsevier Inc. All rights reserved.

Accuracy of stroke volume variation in predicting fluid responsiveness: a systematic review and meta-analysis.

PubMed

Zhang, Zhongheng; Lu, Baolong; Sheng, Xiaoyan; Jin, Ni

2011-12-01

Stroke volume variation (SVV) appears to be a good predictor of fluid responsiveness in critically ill patients. However, a wide range of its predictive values has been reported in recent years. We therefore undertook a systematic review and meta-analysis of clinical trials that investigated the diagnostic value of SVV in predicting fluid responsiveness. Clinical investigations were identified from several sources, including MEDLINE, EMBASE, WANFANG, and CENTRAL. Original articles investigating the diagnostic value of SVV in predicting fluid responsiveness were considered to be eligible. Participants included critically ill patients in the intensive care unit (ICU) or operating room (OR) who require hemodynamic monitoring. A total of 568 patients from 23 studies were included in our final analysis. Baseline SVV was correlated to fluid responsiveness with a pooled correlation coefficient of 0.718. Across all settings, we found a diagnostic odds ratio of 18.4 for SVV to predict fluid responsiveness at a sensitivity of 0.81 and specificity of 0.80. The SVV was of diagnostic value for fluid responsiveness in OR or ICU patients monitored with the PiCCO or the FloTrac/Vigileo system, and in patients ventilated with tidal volume greater than 8 ml/kg. SVV is of diagnostic value in predicting fluid responsiveness in various settings.

Genomic and Histopathological Tissue Biomarkers That Predict Radiotherapy Response in Localised Prostate Cancer

PubMed Central

Wilkins, Anna; Dearnaley, David; Somaiah, Navita

2015-01-01

Localised prostate cancer, in particular, intermediate risk disease, has varied survival outcomes that cannot be predicted accurately using current clinical risk factors. External beam radiotherapy (EBRT) is one of the standard curative treatment options for localised disease and its efficacy is related to wide ranging aspects of tumour biology. Histopathological techniques including immunohistochemistry and a variety of genomic assays have been used to identify biomarkers of tumour proliferation, cell cycle checkpoints, hypoxia, DNA repair, apoptosis, and androgen synthesis, which predict response to radiotherapy. Global measures of genomic instability also show exciting capacity to predict survival outcomes following EBRT. There is also an urgent clinical need for biomarkers to predict the radiotherapy fraction sensitivity of different prostate tumours and preclinical studies point to possible candidates. Finally, the increased resolution of next generation sequencing (NGS) is likely to enable yet more precise molecular predictions of radiotherapy response and fraction sensitivity. PMID:26504789

Prediction of therapeutic response in steroid-treated pulmonary sarcoidosis. Evaluation of clinical parameters, bronchoalveolar lavage, gallium-67 lung scanning, and serum angiotensin-converting enzyme levels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hollinger, W.M.; Staton, G.W. Jr.; Fajman, W.A.

1985-07-01

To find a pretreatment predictor of steroid responsiveness in pulmonary sarcoidosis the authors studied 21 patients before and after steroid treatment by clinical evaluation, pulmonary function tests, bronchoalveolar lavage (BAL), gallium-67 lung scan, and serum angiotensin-converting enzyme (SACE) level. Although clinical score, forced vital capacity (FVC), BAL percent lymphocytes (% lymphs), quantitated gallium-67 lung uptake, and SACE levels all improved with therapy, only the pretreatment BAL % lymphs correlated with the improvement in FVC (r = 0.47, p less than 0.05). Pretreatment BAL % lymphs of greater than or equal to 35% predicted improvement in FVC of 10/11 patients, whereasmore » among 10 patients with BAL % lymphs less than 35%, 5 patients improved and 5 deteriorated. Clinical score, pulmonary function parameters, quantitated gallium-67 lung uptake, and SACE level used alone, in combination with BAL % lymphs or in combination with each other, did not improve this predictive value. The authors conclude that steroid therapy improves a number of clinical and laboratory parameters in sarcoidosis, but only the pretreatment BAL % lymphs are useful in predicting therapeutic responsiveness.« less

Neuroimaging-based biomarkers in psychiatry: clinical opportunities of a paradigm shift.

PubMed

Fu, Cynthia H Y; Costafreda, Sergi G

2013-09-01

Neuroimaging research has substantiated the functional and structural abnormalities underlying psychiatric disorders but has, thus far, failed to have a significant impact on clinical practice. Recently, neuroimaging-based diagnoses and clinical predictions derived from machine learning analysis have shown significant potential for clinical translation. This review introduces the key concepts of this approach, including how the multivariate integration of patterns of brain abnormalities is a crucial component. We survey recent findings that have potential application for diagnosis, in particular early and differential diagnoses in Alzheimer disease and schizophrenia, and the prediction of clinical response to treatment in depression. We discuss the specific clinical opportunities and the challenges for developing biomarkers for psychiatry in the absence of a diagnostic gold standard. We propose that longitudinal outcomes, such as early diagnosis and prediction of treatment response, offer definite opportunities for progress. We propose that efforts should be directed toward clinically challenging predictions in which neuroimaging may have added value, compared with the existing standard assessment. We conclude that diagnostic and prognostic biomarkers will be developed through the joint application of expert psychiatric knowledge in addition to advanced methods of analysis.

Human Papillomavirus DNA Methylation Predicts Response to Treatment Using Cidofovir and Imiquimod in Vulval Intraepithelial Neoplasia 3.

PubMed

Jones, Sadie E F; Hibbitts, Samantha; Hurt, Christopher N; Bryant, Dean; Fiander, Alison N; Powell, Ned; Tristram, Amanda J

2017-09-15

Purpose: Response rates to treatment of vulval intraepithelial neoplasia (VIN) with imiquimod and cidofovir are approximately 57% and 61%, respectively. Treatment is associated with significant side effects and, if ineffective, risk of malignant progression. Treatment response is not predicted by clinical factors. Identification of a biomarker that could predict response is an attractive prospect. This work investigated HPV DNA methylation as a potential predictive biomarker in this setting. Experimental Design: DNA from 167 cases of VIN 3 from the RT3 VIN clinical trial was assessed. HPV-positive cases were identified using Greiner PapilloCheck and HPV 16 type-specific PCR. HPV DNA methylation status was assessed in three viral regions: E2, L1/L2, and the promoter, using pyrosequencing. Results: Methylation of the HPV E2 region was associated with response to treatment. For cidofovir ( n = 30), median E2 methylation was significantly higher in patients who responded ( P ≤ 0.0001); E2 methylation >4% predicted response with 88.2% sensitivity and 84.6% specificity. For imiquimod ( n = 33), median E2 methylation was lower in patients who responded to treatment ( P = 0.03; not significant after Bonferroni correction); E2 methylation <4% predicted response with 70.6% sensitivity and 62.5% specificity. Conclusions: These data indicate that cidofovir and imiquimod may be effective in two biologically defined groups. HPV E2 DNA methylation demonstrated potential as a predictive biomarker for the treatment of VIN with cidofovir and may warrant investigation in a biomarker-guided clinical trial. Clin Cancer Res; 23(18); 5460-8. ©2017 AACR . ©2017 American Association for Cancer Research.

The reliability of the clinical examination in predicting hemodynamic status in acute febrile illness in a tropical, resource-limited setting.

PubMed

Moek, Felix; Poe, Poe; Charunwatthana, Prakaykaew; Pan-Ngum, Wirichada; Wattanagoon, Yupaporn; Chierakul, Wirongrong

2018-05-19

The clinical examination alone is widely considered unreliable when assessing fluid responsiveness in critically ill patients. Little evidence exists on the performance of the clinical examination to predict other hemodynamic derangements or more complex hemodynamic states. Patients with acute febrile illness were assessed on admission, both clinically and per non-invasive hemodynamic measurement. Correlations between clinical signs and hemodynamics patterns were analyzed, and the predictive capacity of the clinical signs was examined. Seventy-one patients were included; the most common diagnoses were bacterial sepsis, scrub typhus and dengue infection. Correlations between clinical signs and hemodynamic parameters were only statistically significant for Cardiac Index (r=0.75, p-value <0.01), Systemic Vascular Resistance Index (r=0.79, p-value <0.01) and flow time corrected (r=0.44, p-value 0.03). When assessing the predictive accuracy of clinical signs, the model identified only 62% of hemodynamic states correctly, even less if there was more than one hemodynamic abnormality. The clinical examination is not reliable to assess a patient's hemodynamic status in acute febrile illness. Fluid responsiveness, cardiodepression and more complex hemodynamic states are particularly easily missed.

Empirically and Clinically Useful Decision Making in Psychotherapy: Differential Predictions with Treatment Response Models

ERIC Educational Resources Information Center

Lutz, Wolfgang; Saunders, Stephen M.; Leon, Scott C.; Martinovich, Zoran; Kosfelder, Joachim; Schulte, Dietmar; Grawe, Klaus; Tholen, Sven

2006-01-01

In the delivery of clinical services, outcomes monitoring (i.e., repeated assessments of a patient's response to treatment) can be used to support clinical decision making (i.e., recurrent revisions of outcome expectations on the basis of that response). Outcomes monitoring can be particularly useful in the context of established practice research…

Ultrasonographic Changes at 12 Weeks of Anti-TNF Drugs Predict 1-year Sonographic Response and Clinical Outcome in Crohn's Disease: A Multicenter Study.

PubMed

Ripollés, Tomás; Paredes, José M; Martínez-Pérez, María J; Rimola, Jordi; Jauregui-Amezaga, Arantza; Bouzas, Rosa; Martin, Gregorio; Moreno-Osset, Eduardo

2016-10-01

The objective was to assess the long-term effect of biological treatment on transmural lesions of Crohn's disease evaluated with ultrasound, including contrast-enhanced ultrasound. Fifty-one patients with active Crohn's disease were included in a prospective multicenter longitudinal study. All patients underwent a clinical assessment and sonographic examination at baseline, 12 weeks after treatment initiation, and after 1-year of treatment. Patients were clinically followed at least 2 years from inclusion until the end of the study. Ultrasonographic evaluation included bowel wall thickness, color Doppler grade, parietal enhancement, and presence of transmural complications or stenosis. Sonographic changes after treatment were classified as normalization, improvement, or lack of response. Improvement at 52 weeks was more frequent in patients with improvement at final of induction (12 weeks) compared with patients who did not improve (85% versus 28%; P < 0.0001). One-year sonographic evolution correlated with clinical response; 28 of the 29 (96.5%) patients with sonographic improvement at 52 weeks showed clinical remission or response. Patients without sonographic improvement at 52 weeks of treatment were more likely to have a change or intensification in medication or surgery (13/20, 65%) during the next year of follow-up than patients with improvement on the sonography (3/28, 11%). Stricturing behavior was the only sonographic feature associated to a negative predictive value of response (P = 0.0001). Sonographic response after 12 weeks of therapy is more pronounced and predicts 1-year sonographic response. Sonographic response at 1-year examination correlates with 1-year clinical response and is a predictor of further treatment's efficacy, 1-year or longer period of follow-up.

Identification of immune signatures predictive of clinical protection from malaria

PubMed Central

2017-01-01

Antibodies are thought to play an essential role in naturally acquired immunity to malaria. Prospective cohort studies have frequently shown how continuous exposure to the malaria parasite Plasmodium falciparum cause an accumulation of specific responses against various antigens that correlate with a decreased risk of clinical malaria episodes. However, small effect sizes and the often polymorphic nature of immunogenic parasite proteins make the robust identification of the true targets of protective immunity ambiguous. Furthermore, the degree of individual-level protection conferred by elevated responses to these antigens has not yet been explored. Here we applied a machine learning approach to identify immune signatures predictive of individual-level protection against clinical disease. We find that commonly assumed immune correlates are poor predictors of clinical protection in children. On the other hand, antibody profiles predictive of an individual’s malaria protective status can be found in data comprising responses to a large set of diverse parasite proteins. We show that this pattern emerges only after years of continuous exposure to the malaria parasite, whereas susceptibility to clinical episodes in young hosts (< 10 years) cannot be ascertained by measured antibody responses alone. PMID:29065113

Pharmacogenomics of Methotrexate Membrane Transport Pathway: Can Clinical Response to Methotrexate in Rheumatoid Arthritis Be Predicted?

PubMed Central

Lima, Aurea; Bernardes, Miguel; Azevedo, Rita; Medeiros, Rui; Seabra, Vitor

2015-01-01

Background: Methotrexate (MTX) is widely used for rheumatoid arthritis (RA) treatment. Single nucleotide polymorphisms (SNPs) could be used as predictors of patients’ therapeutic outcome variability. Therefore, this study aims to evaluate the influence of SNPs in genes encoding for MTX membrane transport proteins in order to predict clinical response to MTX. Methods: Clinicopathological data from 233 RA patients treated with MTX were collected, clinical response defined, and patients genotyped for 23 SNPs. Genotype and haplotype analyses were performed using multivariate methods and a genetic risk index (GRI) for non-response was created. Results: Increased risk for non-response was associated to SLC22A11 rs11231809 T carriers; ABCC1 rs246240 G carriers; ABCC1 rs3784864 G carriers; CGG haplotype for ABCC1 rs35592, rs2074087 and rs3784864; and CGG haplotype for ABCC1 rs35592, rs246240 and rs3784864. GRI demonstrated that patients with Index 3 were 16-fold more likely to be non-responders than those with Index 1. Conclusions: This study revealed that SLC22A11 and ABCC1 may be important to identify those patients who will not benefit from MTX treatment, highlighting the relevance in translating these results to clinical practice. However, further validation by independent studies is needed to develop the field of personalized medicine to predict clinical response to MTX treatment. PMID:26086825

Pharmacogenomics of Methotrexate Membrane Transport Pathway: Can Clinical Response to Methotrexate in Rheumatoid Arthritis Be Predicted?

PubMed

Lima, Aurea; Bernardes, Miguel; Azevedo, Rita; Medeiros, Rui; Seabra, Vítor

2015-06-16

Methotrexate (MTX) is widely used for rheumatoid arthritis (RA) treatment. Single nucleotide polymorphisms (SNPs) could be used as predictors of patients' therapeutic outcome variability. Therefore, this study aims to evaluate the influence of SNPs in genes encoding for MTX membrane transport proteins in order to predict clinical response to MTX. Clinicopathological data from 233 RA patients treated with MTX were collected, clinical response defined, and patients genotyped for 23 SNPs. Genotype and haplotype analyses were performed using multivariate methods and a genetic risk index (GRI) for non-response was created. Increased risk for non-response was associated to SLC22A11 rs11231809 T carriers; ABCC1 rs246240 G carriers; ABCC1 rs3784864 G carriers; CGG haplotype for ABCC1 rs35592, rs2074087 and rs3784864; and CGG haplotype for ABCC1 rs35592, rs246240 and rs3784864. GRI demonstrated that patients with Index 3 were 16-fold more likely to be non-responders than those with Index 1. This study revealed that SLC22A11 and ABCC1 may be important to identify those patients who will not benefit from MTX treatment, highlighting the relevance in translating these results to clinical practice. However, further validation by independent studies is needed to develop the field of personalized medicine to predict clinical response to MTX treatment.

Early non-response to certolizumab pegol in rheumatoid arthritis predicts treatment failure at one year. Data from a randomised phase III clinical trial.

PubMed

Berenbaum, Francis; Pham, Thao; Claudepierre, Pascal; de Chalus, Thibault; Joubert, Jean-Michel; Saadoun, Carine; Riou França, Lionel; Fautrel, Bruno

2018-01-01

To compare different early clinical criteria of non-response determined at three months as predictors of clinical failure at one year in patients with rheumatoid arthritis starting therapy with certolizumab pegol. Data were derived from a randomised Phase III clinical trial in patients with rheumatoid arthritis who failed to respond to methotrexate monotherapy. Patients included in this post-hoc analysis were treated with certolizumab pegol (400mg qd reduced to 200mg qd after one month) and with methotrexate. The study duration was twelve months. Response at three months was determined with the American College of Rheumatology-50, Disease Assessment Score-28 ESR, Health Assessment Questionnaire and the Clinical Disease Activity Index. The performance of these measures at predicting treatment failure at twelve months defined by the American College of Rheumatology-50 criteria was determined, using the positive predictive values as the principal evaluation criterion. Three hundred and eighty two patients were available for analysis and 225 completed the twelve-month follow-up. At Week 52, 149 (38.1%) patients met the American College of Rheumatology-50 response criterion. Positive predictive values ranged from 81% for a decrease in Health Assessment Questionnaire- Disability index score since baseline >0.22 to 95% for a decrease in Disease Assessment Score-28 score since baseline≥1.2. Sensitivity was≤70% in all cases. Performance of these measures was similar irrespective of the definition of treatment failure at 12months. Simple clinical measures of disease activity can predict future treatment failure reliably and are appropriate for implementing treat-to-target treatment strategies in everyday practice. Copyright © 2017 Société française de rhumatologie. Published by Elsevier SAS. All rights reserved.

Rostral anterior cingulate cortex morphology predicts treatment response to internet-based CBT for depression

PubMed Central

Webb, Christian A.; Olson, Elizabeth A.; Killgore, William D.S.; Pizzagalli, Diego A.; Rauch, Scott L.; Rosso, Isabelle M.

2018-01-01

Background Rostral and subgenual anterior cingulate cortex (rACC and sgACC) activity and, to a lesser extent, volume have been shown to predict depressive symptom improvement across different antidepressant treatments. This study extends prior work by examining whether rACC and/or sgACC morphology predicts treatment response to internet-based cognitive behavioral therapy (iCBT) for major depressive disorder (MDD). This is the first study to examine neural predictors of response to iCBT. Methods Hierarchical linear modeling tested whether pre-treatment rACC and sgACC volumes predicted depressive symptom improvement during a 6-session (10-week) randomized clinical trial of iCBT (n = 35) vs. a monitored attention control (MAC; n = 38). Analyses also tested whether pre-treatment rACC and sgACC volumes differed between patients who achieved depression remission versus those who did not remit. Results Larger pre-treatment right rACC volume was a significant predictor of greater depressive symptom improvement in iCBT, even when controlling for demographic (age, gender, race) and clinical (baseline depression, anhedonia and anxiety) variables previously linked to treatment response. In addition, pre-treatment right rACC volume was larger among iCBT patients whose depression eventually remitted relative to those who did not remit. Corresponding analyses in the MAC group and for the sgACC were not significant. Conclusions rACC volume prior to iCBT demonstrated incremental predictive validity beyond clinical and demographic variables previously found to predict symptom improvement. Such findings may help inform our understanding of the mediating anatomy of iCBT and, if replicated, may suggest neural targets to augment treatment response (e.g., via modulation of rACC function). ClinicalTrials.gov Identifier NCT01598922 PMID:29486867

Predicting methylphenidate response in attention deficit hyperactivity disorder: a preliminary study.

PubMed

Johnston, Blair A; Coghill, David; Matthews, Keith; Steele, J Douglas

2015-01-01

Methylphenidate (MPH) is established as the main pharmacological treatment for patients with attention deficit hyperactivity disorder (ADHD). Whilst MPH is generally a highly effective treatment, not all patients respond, and some experience adverse reactions. Currently, there is no reliable method to predict how patients will respond, other than by exposure to a trial of medication. In this preliminary study, we sought to investigate whether an accurate predictor of clinical response to methylphenidate could be developed for individual patients, using sociodemographic, clinical and neuropsychological measures. Of the 43 boys with ADHD included in this proof-of-concept study, 30 were classed as responders and 13 as non-responders to MPH, with no significant differences in age nor verbal intelligence quotient (IQ) between the groups. Here we report the application of a multivariate analysis approach to the prediction of clinical response to MPH, which achieved an accuracy of 77% (p = 0.005). The most important variables to the classifier were performance on a 'go/no go' task and comorbid conduct disorder. This preliminary study suggested that further investigation is merited. Achieving a highly significant accuracy of 77% for the prediction of MPH response is an encouraging step towards finding a reliable and clinically useful method that could minimise the number of children needlessly being exposed to MPH. © The Author(s) 2014.

Nomogram predicting response after chemoradiotherapy in rectal cancer using sequential PETCT imaging: a multicentric prospective study with external validation.

PubMed

van Stiphout, Ruud G P M; Valentini, Vincenzo; Buijsen, Jeroen; Lammering, Guido; Meldolesi, Elisa; van Soest, Johan; Leccisotti, Lucia; Giordano, Alessandro; Gambacorta, Maria A; Dekker, Andre; Lambin, Philippe

2014-11-01

To develop and externally validate a predictive model for pathologic complete response (pCR) for locally advanced rectal cancer (LARC) based on clinical features and early sequential (18)F-FDG PETCT imaging. Prospective data (i.a. THUNDER trial) were used to train (N=112, MAASTRO Clinic) and validate (N=78, Università Cattolica del S. Cuore) the model for pCR (ypT0N0). All patients received long-course chemoradiotherapy (CRT) and surgery. Clinical parameters were age, gender, clinical tumour (cT) stage and clinical nodal (cN) stage. PET parameters were SUVmax, SUVmean, metabolic tumour volume (MTV) and maximal tumour diameter, for which response indices between pre-treatment and intermediate scan were calculated. Using multivariate logistic regression, three probability groups for pCR were defined. The pCR rates were 21.4% (training) and 23.1% (validation). The selected predictive features for pCR were cT-stage, cN-stage, response index of SUVmean and maximal tumour diameter during treatment. The models' performances (AUC) were 0.78 (training) and 0.70 (validation). The high probability group for pCR resulted in 100% correct predictions for training and 67% for validation. The model is available on the website www.predictcancer.org. The developed predictive model for pCR is accurate and externally validated. This model may assist in treatment decisions during CRT to select complete responders for a wait-and-see policy, good responders for extra RT boost and bad responders for additional chemotherapy. Copyright © 2014 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Shunting normal pressure hydrocephalus: the predictive value of combined clinical and CT data.

PubMed

Vanneste, J; Augustijn, P; Tan, W F; Dirven, C

1993-03-01

The value of an ordinal global scale derived from combined clinical and CT data (clin/CT scale) to predict the clinical outcome in 112 patients shunted for presumed normal pressure hydrocephalus (NPH) was analysed. The clinical data were retrospectively collected, all CT scans were re-evaluated, and the clin/CT scale was determined blind to the results of further ancillary tests and to the post-surgical outcome. The scale ranked three classes of prediction: on the basis of clinical and CT characteristics, improvement after shunting was probable, possible, or improbable. The predictive value of the clin/CT scale for the subgroup of communicating NPH was established for two different strategies, depending on the strictness of selection criteria for shunting. In the subgroup of patients with presumed communicating NPH, the prevalence of shunt responsiveness was 29%; the best strategy was to shunt only patients with probable shunt-responsive NPH: the sensitivity was 0.54, the specificity 0.84, and the predictive accuracy 0.75, with a limited number of ineffective shunts (11%) and missed improvements (13%). The study illustrates its need to assess the pre-test probability of NPH based on combined clinical and CT data, before establishing the clinical usefulness of an ancillary test.

Shunting normal pressure hydrocephalus: the predictive value of combined clinical and CT data.

PubMed Central

Vanneste, J; Augustijn, P; Tan, W F; Dirven, C

1993-01-01

The value of an ordinal global scale derived from combined clinical and CT data (clin/CT scale) to predict the clinical outcome in 112 patients shunted for presumed normal pressure hydrocephalus (NPH) was analysed. The clinical data were retrospectively collected, all CT scans were re-evaluated, and the clin/CT scale was determined blind to the results of further ancillary tests and to the post-surgical outcome. The scale ranked three classes of prediction: on the basis of clinical and CT characteristics, improvement after shunting was probable, possible, or improbable. The predictive value of the clin/CT scale for the subgroup of communicating NPH was established for two different strategies, depending on the strictness of selection criteria for shunting. In the subgroup of patients with presumed communicating NPH, the prevalence of shunt responsiveness was 29%; the best strategy was to shunt only patients with probable shunt-responsive NPH: the sensitivity was 0.54, the specificity 0.84, and the predictive accuracy 0.75, with a limited number of ineffective shunts (11%) and missed improvements (13%). The study illustrates its need to assess the pre-test probability of NPH based on combined clinical and CT data, before establishing the clinical usefulness of an ancillary test. PMID:8459240

Striatal Activation Predicts Differential Therapeutic Responses to Methylphenidate and Atomoxetine.

PubMed

Schulz, Kurt P; Bédard, Anne-Claude V; Fan, Jin; Hildebrandt, Thomas B; Stein, Mark A; Ivanov, Iliyan; Halperin, Jeffrey M; Newcorn, Jeffrey H

2017-07-01

Methylphenidate has prominent effects in the dopamine-rich striatum that are absent for the selective norepinephrine transporter inhibitor atomoxetine. This study tested whether baseline striatal activation would predict differential response to the two medications in youth with attention-deficit/hyperactivity disorder (ADHD). A total of 36 youth with ADHD performed a Go/No-Go test during functional magnetic resonance imaging at baseline and were treated with methylphenidate and atomoxetine using a randomized cross-over design. Whole-brain task-related activation was regressed on clinical response. Task-related activation in right caudate nucleus was predicted by an interaction of clinical responses to methylphenidate and atomoxetine (F 1,30  = 17.00; p < .001). Elevated caudate activation was associated with robust improvement for methylphenidate and little improvement for atomoxetine. The rate of robust response was higher for methylphenidate than for atomoxetine in youth with high (94.4% vs. 38.8%; p = .003; number needed to treat = 2, 95% CI = 1.31-3.73) but not low (33.3% vs. 50.0%; p = .375) caudate activation. Furthermore, response to atomoxetine predicted motor cortex activation (F 1,30  = 14.99; p < .001). Enhanced caudate activation for response inhibition may be a candidate biomarker of superior response to methylphenidate over atomoxetine in youth with ADHD, purportedly reflecting the dopaminergic effects of methylphenidate but not atomoxetine in the striatum, whereas motor cortex activation may predict response to atomoxetine. These data do not yet translate directly to the clinical setting, but the approach is potentially important for informing future research and illustrates that it may be possible to predict differential treatment response using a biomarker-driven approach. Stimulant Versus Nonstimulant Medication for Attention Deficit Hyperactivity Disorder in Children; https://clinicaltrials.gov/; NCT00183391. Copyright © 2017 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

Ultrasound disease activity of bilateral wrist and finger joints at three months reflects the clinical response at six months of patients with rheumatoid arthritis treated with biologic disease-modifying anti-rheumatic drugs.

PubMed

Kawashiri, Shin-Ya; Nishino, Ayako; Shimizu, Toshimasa; Umeda, Masataka; Fukui, Shoichi; Nakashima, Yoshikazu; Suzuki, Takahisa; Koga, Tomohiro; Iwamoto, Naoki; Ichinose, Kunihiro; Tamai, Mami; Nakamura, Hideki; Origuchi, Tomoki; Aoyagi, Kiyoshi; Kawakami, Atsushi

2017-03-01

We evaluated whether the early responsiveness of ultrasound synovitis can predict the clinical response in rheumatoid arthritis (RA) patients treated with biologic disease-modifying anti-rheumatic drugs (bDMARDs). Articular synovitis was assessed by ultrasound at 22 bilateral wrist and finger joints in 39 RA patients treated with bDMARDs. Each joint was assigned a gray-scale (GS) and power Doppler (PD) score from 0 to 3, and the sum of the GS or PD scores was considered to represent the ultrasound disease activity. We investigated the correlation of the change in ultrasound disease activity at three months with the EULAR response criteria at six months. GS and PD scores were significantly decreased at three months (p < 0.0001). The % changes of the GS and PD scores at three months were significantly higher at six months in moderate and good responders compared with non-responders (p < 0.05). These tendencies were numerically more prominent if clinical response was set as good responder or Disease Activity Score 28 remission. Poor improvement of ultrasound synovitis scores had good predictive value for non-responders at six months. The responsiveness of ultrasound disease activity is considered to predict further clinical response in RA patients treated with bDMARDs.

Rostral anterior cingulate cortex activity and early symptom improvement during treatment for major depressive disorder

PubMed Central

Korb, Alexander S.; Hunter, Aimee M.; Cook, Ian A.; Leuchter, Andrew F.

2011-01-01

In treatment trials for Major Depressive Disorder (MDD), early symptom improvement is predictive of eventual clinical response. Clinical response may also be predicted by elevated pretreatment theta (4-7 Hz) current density in the rostral anterior cingulate (rACC) and medial orbitofrontal cortex (mOFC). We investigated the relationship between pretreatment EEG and early improvement in predicting clinical outcome in 72 MDD subjects across three placebo-controlled treatment trials. Subjects were randomized to receive fluoxetine, venlafaxine, or placebo. Theta current density in the rACC and mOFC was computed with Low-Resolution Brain Electromagnetic Tomography (LORETA). An ANCOVA, examining week 8 Hamilton Depression Rating Scale (HamD) percent change, showed a significant effect of week-2-HamD-percent-change, and a significant three-way interaction of week-2-HamD-percent-change × Treatment × rACC. Medication subjects with robust early improvement showed almost no relationship between rACC theta current density and final clinical outcome. However, in subjects with little early improvement, rACC activity showed a strong relationship with clinical outcome. The model examining mOFC showed a trend in the three-way interaction. A combination of pretreatment rACC activity and early symptom improvement may be useful for predicting treatment response. PMID:21546222

Predictors of treatment efficacy in a clinical trial of three psychosocial treatments for adolescent depression.

PubMed

Brent, D A; Kolko, D J; Birmaher, B; Baugher, M; Bridge, J; Roth, C; Holder, D

1998-09-01

To assess the predictors of treatment outcome across treatments, as well as those associated with differential treatment response. One hundred seven adolescent outpatients, aged 13 to 18 years, with DSM-III-R major depression were randomly assigned to one of three manual-based, brief (12 to 16 sessions) psychosocial treatments: cognitive-behavioral therapy (CBT), systemic-behavioral family therapy, or nondirective supportive therapy. Those with good and poor outcomes were compared. Continued depression was predicted by clinical referral (versus via advertisement) and was in part mediated by hopelessness. Other predictors of depression were comorbid anxiety disorder and higher levels of cognitive distortion and hopelessness at intake. Achievement of clinical remission was predicted by a higher level of self-reported depression. Poorer functional status was predicted by a higher level of initial interviewer-rated depression. Comorbid anxiety and maternal depressive symptoms predicted differential treatment efficacy. CBT's performance continued to be robust with respect to nondirective supportive therapy, even in the presence of the above-noted adverse predictors. Predictors of poor outcome may give clues as to how to boost treatment response. Subjects who come to treatment for clinical trials via advertisement (versus clinical referral) may show more favorable treatment responses. CBT is likely to be a robust intervention even in more complex and difficult-to-treat patients.

Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy

PubMed Central

Mani, Subramani; Chen, Yukun; Li, Xia; Arlinghaus, Lori; Chakravarthy, A Bapsi; Abramson, Vandana; Bhave, Sandeep R; Levy, Mia A; Xu, Hua; Yankeelov, Thomas E

2013-01-01

Objective To employ machine learning methods to predict the eventual therapeutic response of breast cancer patients after a single cycle of neoadjuvant chemotherapy (NAC). Materials and methods Quantitative dynamic contrast-enhanced MRI and diffusion-weighted MRI data were acquired on 28 patients before and after one cycle of NAC. A total of 118 semiquantitative and quantitative parameters were derived from these data and combined with 11 clinical variables. We used Bayesian logistic regression in combination with feature selection using a machine learning framework for predictive model building. Results The best predictive models using feature selection obtained an area under the curve of 0.86 and an accuracy of 0.86, with a sensitivity of 0.88 and a specificity of 0.82. Discussion With the numerous options for NAC available, development of a method to predict response early in the course of therapy is needed. Unfortunately, by the time most patients are found not to be responding, their disease may no longer be surgically resectable, and this situation could be avoided by the development of techniques to assess response earlier in the treatment regimen. The method outlined here is one possible solution to this important clinical problem. Conclusions Predictive modeling approaches based on machine learning using readily available clinical and quantitative MRI data show promise in distinguishing breast cancer responders from non-responders after the first cycle of NAC. PMID:23616206

The value of integrating pre-clinical data to predict nausea and vomiting risk in humans as illustrated by AZD3514, a novel androgen receptor modulator

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grant, Claire, E-mail: claire.grant@astrazeneca.com; Ewart, Lorna; Muthas, Daniel

Nausea and vomiting are components of a complex mechanism that signals food avoidance and protection of the body against the absorption of ingested toxins. This response can also be triggered by pharmaceuticals. Predicting clinical nausea and vomiting liability for pharmaceutical agents based on pre-clinical data can be problematic as no single animal model is a universal predictor. Moreover, efforts to improve models are hampered by the lack of translational animal and human data in the public domain. AZD3514 is a novel, orally-administered compound that inhibits androgen receptor signaling and down-regulates androgen receptor expression. Here we have explored the utility ofmore » integrating data from several pre-clinical models to predict nausea and vomiting in the clinic. Single and repeat doses of AZD3514 resulted in emesis, salivation and gastrointestinal disturbances in the dog, and inhibited gastric emptying in rats after a single dose. AZD3514, at clinically relevant exposures, induced dose-responsive “pica” behaviour in rats after single and multiple daily doses, and induced retching and vomiting behaviour in ferrets after a single dose. We compare these data with the clinical manifestation of nausea and vomiting encountered in patients with castration-resistant prostate cancer receiving AZD3514. Our data reveal a striking relationship between the pre-clinical observations described and the experience of nausea and vomiting in the clinic. In conclusion, the emetic nature of AZD3514 was predicted across a range of pre-clinical models, and the approach presented provides a valuable framework for predicition of clinical nausea and vomiting. - Highlights: • Integrated pre-clinical data can be used to predict clinical nausea and vomiting. • Data integrated from standard toxicology studies is sufficient to make a prediction. • The use of the nausea algorithm developed by Parkinson (2012) aids the prediction. • Additional pre-clinical studies can be used to confirm and quantify the risk.« less

Reduced Short Interval Cortical Inhibition Correlates with Atomoxetine Response in Children with ADHD

PubMed Central

Chen, Tina H.; Wu, Steve W.; Welge, Jeffrey A.; Dixon, Stephan; Shahana, Nasrin; Huddleston, David A.; Sarvis, Adam R.; Sallee, Floyd R.; Gilbert, Donald L.

2014-01-01

Clinical trials in children with Attention Deficit Hyperactivity Disorder (ADHD) show variability in behavioral responses to the selective norepinephrine reuptake inhibitor atomoxetine (ATX). The objective of this study was to determine whether Transcranial Magnetic Stimulation (TMS)-evoked Short Interval Cortical Inhibition (SICI) might be a biomarker predicting, or correlating with, clinical ATX response. At baseline and after 4 weeks of ATX treatment in 7–12 year old children with ADHD, TMS-SICI was measured, blinded to clinical improvement. Primary analysis was by multivariate ANCOVA. Baseline SICI did not predict clinical responses. However, paradoxically, after 4 weeks of ATX, mean SICI was reduced 31.9% in responders and increased 6.1% in non-responders (ANCOVA t41=2.88; p = .0063). Percent reductions in SICI correlated with reductions in ADHD-Rating Scale (ADHDRS) (r = .50; p = .0005). In children ages 7–12 years with ADHD treated with ATX, improvements in clinical symptoms are correlated with reductions in motor cortex SICI. PMID:24413361

Predicting beneficial effects of atomoxetine and citalopram on response inhibition in Parkinson's disease with clinical and neuroimaging measures

PubMed Central

Ye, Zheng; Rae, Charlotte L.; Nombela, Cristina; Ham, Timothy; Rittman, Timothy; Jones, Peter Simon; Rodríguez, Patricia Vázquez; Coyle‐Gilchrist, Ian; Regenthal, Ralf; Altena, Ellemarije; Housden, Charlotte R.; Maxwell, Helen; Sahakian, Barbara J.; Barker, Roger A.; Robbins, Trevor W.

2016-01-01

Abstract Recent studies indicate that selective noradrenergic (atomoxetine) and serotonergic (citalopram) reuptake inhibitors may improve response inhibition in selected patients with Parkinson's disease, restoring behavioral performance and brain activity. We reassessed the behavioral efficacy of these drugs in a larger cohort and developed predictive models to identify patient responders. We used a double‐blind randomized three‐way crossover design to investigate stopping efficiency in 34 patients with idiopathic Parkinson's disease after 40 mg atomoxetine, 30 mg citalopram, or placebo. Diffusion‐weighted and functional imaging measured microstructural properties and regional brain activations, respectively. We confirmed that Parkinson's disease impairs response inhibition. Overall, drug effects on response inhibition varied substantially across patients at both behavioral and brain activity levels. We therefore built binary classifiers with leave‐one‐out cross‐validation (LOOCV) to predict patients’ responses in terms of improved stopping efficiency. We identified two optimal models: (1) a “clinical” model that predicted the response of an individual patient with 77–79% accuracy for atomoxetine and citalopram, using clinically available information including age, cognitive status, and levodopa equivalent dose, and a simple diffusion‐weighted imaging scan; and (2) a “mechanistic” model that explained the behavioral response with 85% accuracy for each drug, using drug‐induced changes of brain activations in the striatum and presupplementary motor area from functional imaging. These data support growing evidence for the role of noradrenaline and serotonin in inhibitory control. Although noradrenergic and serotonergic drugs have highly variable effects in patients with Parkinson's disease, the individual patient's response to each drug can be predicted using a pattern of clinical and neuroimaging features. Hum Brain Mapp 37:1026–1037, 2016. © 2016 Wiley Periodicals, Inc. PMID:26757216

Electroconvulsive therapy in treatment-resistant schizophrenia: prediction of response and the nature of symptomatic improvement.

PubMed

Chanpattana, Worrawat; Sackeim, Harold A

2010-12-01

The clinical features of patients with schizophrenia who respond to electroconvulsive therapy (ECT) are uncertain. There is a longstanding belief that the duration of illness and/or the presence of affective symptoms associate with good prognosis. There is also little information on the nature of symptomatic improvement with this treatment. We examined the demographic and clinical history features associated with response, the symptom profile predictive of response, and the profile of symptomatic improvement. Using a standardized protocol, 253 patients with treatment-resistant schizophrenia were prospectively treated with a combination of ECT and flupenthixol. Of this group, 138 patients (54.5%) met the response criteria. Independence of sex, longer duration of current episode, and greater severity of baseline negative symptoms were predictive of poorer outcome. Duration of illness had weak relations with outcome only among females. There were marked sex differences in other clinical features and symptoms associated with response. In contrast, no sex differences were observed in the nature of symptomatic improvement. Treatment resulted in marked improvement in specific positive symptoms, with an intermediate effect on affective symptoms and no effect or worsening of specific negative symptoms. The findings challenge recommendations that long duration of illness or absence of affective symptoms portends poor response to ECT in patients with treatment-resistant schizophrenia. Sex may play a critical role in determining the features of the illness that predict outcome.

Predicting long-term outcome of Internet-delivered cognitive behavior therapy for social anxiety disorder using fMRI and support vector machine learning.

PubMed

Månsson, K N T; Frick, A; Boraxbekk, C-J; Marquand, A F; Williams, S C R; Carlbring, P; Andersson, G; Furmark, T

2015-03-17

Cognitive behavior therapy (CBT) is an effective treatment for social anxiety disorder (SAD), but many patients do not respond sufficiently and a substantial proportion relapse after treatment has ended. Predicting an individual's long-term clinical response therefore remains an important challenge. This study aimed at assessing neural predictors of long-term treatment outcome in participants with SAD 1 year after completion of Internet-delivered CBT (iCBT). Twenty-six participants diagnosed with SAD underwent iCBT including attention bias modification for a total of 13 weeks. Support vector machines (SVMs), a supervised pattern recognition method allowing predictions at the individual level, were trained to separate long-term treatment responders from nonresponders based on blood oxygen level-dependent (BOLD) responses to self-referential criticism. The Clinical Global Impression-Improvement scale was the main instrument to determine treatment response at the 1-year follow-up. Results showed that the proportion of long-term responders was 52% (12/23). From multivariate BOLD responses in the dorsal anterior cingulate cortex (dACC) together with the amygdala, we were able to predict long-term response rate of iCBT with an accuracy of 92% (confidence interval 95% 73.2-97.6). This activation pattern was, however, not predictive of improvement in the continuous Liebowitz Social Anxiety Scale-Self-report version. Follow-up psychophysiological interaction analyses revealed that lower dACC-amygdala coupling was associated with better long-term treatment response. Thus, BOLD response patterns in the fear-expressing dACC-amygdala regions were highly predictive of long-term treatment outcome of iCBT, and the initial coupling between these regions differentiated long-term responders from nonresponders. The SVM-neuroimaging approach could be of particular clinical value as it allows for accurate prediction of treatment outcome at the level of the individual.

Evaluation of a 30-gene paclitaxel, fluorouracil, doxorubicin and cyclophosphamide chemotherapy response predictor in a multicenter randomized trial in breast cancer

PubMed Central

Tabchy, Adel; Valero, Vicente; Vidaurre, Tatiana; Lluch, Ana; Gomez, Henry; Martin, Miguel; Qi, Yuan; Barajas-Figueroa, Luis Javier; Souchon, Eduardo; Coutant, Charles; Doimi, Franco D; Ibrahim, Nuhad K; Gong, Yun; Hortobagyi, Gabriel N; Hess, Kenneth R; Symmans, W Fraser; Pusztai, Lajos

2010-01-01

Purpose We examined in a prospective, randomized, international clinical trial the performance of a previously defined 30-gene predictor (DLDA-30) of pathologic complete response (pCR) to preoperative weekly paclitaxel and fluorouracil, doxorubicin, cyclophosphamide (T/FAC) chemotherapy, and assessed if DLDA-30 also predicts increased sensitivity to FAC-only chemotherapy. We compared the pCR rates after T/FAC versus FAC×6 preoperative chemotherapy. We also performed an exploratory analysis to identify novel candidate genes that differentially predict response in the two treatment arms. Experimental Design 273 patients were randomly assigned to receive either weekly paclitaxel × 12 followed by FAC × 4 (T/FAC, n=138), or FAC × 6 (n=135) neoadjuvant chemotherapy. All patients underwent a pretreatment FNA biopsy of the tumor for gene expression profiling and treatment response prediction. Results The pCR rates were 19% and 9% in the T/FAC and FAC arms, respectively (p<0.05). In the T/FAC arm, the positive predictive value (PPV) of the genomic predictor was 38% (95%CI:21–56%), the negative predictive value (NPV) 88% (CI:77–95%) and the AUC 0.711. In the FAC arm, the PPV was 9% (CI:1–29%) and the AUC 0.584. This suggests that the genomic predictor may have regimen-specificity. Its performance was similar to a clinical variable-based predictor nomogram. Conclusions Gene expression profiling for prospective response prediction was feasible in this international trial. The 30-gene predictor can identify patients with greater than average sensitivity to T/FAC chemotherapy. However, it captured molecular equivalents of clinical phenotype. Next generation predictive markers will need to be developed separately for different molecular subsets of breast cancers. PMID:20829329

Prediction of response to PPI therapy and factors influencing treatment outcome in patients with GORD: a prospective pragmatic trial using pantoprazole

PubMed Central

2011-01-01

Background Management of patients with gastro-oesophageal reflux disease (GORD) can be assisted by information predicting the likely response to proton pump inhibitor (PPI) treatment. The aim was to undertake a study of GORD patients designed to approximate ordinary clinical practice that would identify patient characteristics predicting symptomatic response to pantoprazole treatment. Methods 1888 patients with symptoms of GORD were enrolled in a multicentre, multinational, prospective, open study of 8 weeks pantoprazole treatment, 40 mg daily. Response was assessed by using the ReQuest™ questionnaire, by the investigator making conventional clinical enquiry and by asking patients about their satisfaction with symptom control. Factors including pre-treatment oesophagitis, gender, age, body mass index (BMI), Helicobacter pylori status, anxiety and depression, and concurrent IBS symptoms were examined using logistic regression to determine if they were related to response, judged from the ReQuest™-GI score. Results Poorer treatment responses were associated with non-erosive reflux disease, female gender, lower BMI, anxiety and concurrent irritable bowel syndrome symptoms before treatment. No association was found with age, Helicobacter pylori status or oesophagitis grade. Some reflux-related symptoms were still present in 14% of patients who declared themselves 'well-satisfied' with their symptom control. Conclusions Some readily identifiable features help to predict symptomatic responses to a PPI and consequently may help in managing patient expectation. ClinicalTrial.gov identifier: NCT00312806. PMID:21569313

The Incremental Value of Subjective and Quantitative Assessment of 18F-FDG PET for the Prediction of Pathologic Complete Response to Preoperative Chemoradiotherapy in Esophageal Cancer.

PubMed

van Rossum, Peter S N; Fried, David V; Zhang, Lifei; Hofstetter, Wayne L; van Vulpen, Marco; Meijer, Gert J; Court, Laurence E; Lin, Steven H

2016-05-01

A reliable prediction of a pathologic complete response (pathCR) to chemoradiotherapy before surgery for esophageal cancer would enable investigators to study the feasibility and outcome of an organ-preserving strategy after chemoradiotherapy. So far no clinical parameters or diagnostic studies are able to accurately predict which patients will achieve a pathCR. The aim of this study was to determine whether subjective and quantitative assessment of baseline and postchemoradiation (18)F-FDG PET can improve the accuracy of predicting pathCR to preoperative chemoradiotherapy in esophageal cancer beyond clinical predictors. This retrospective study was approved by the institutional review board, and the need for written informed consent was waived. Clinical parameters along with subjective and quantitative parameters from baseline and postchemoradiation (18)F-FDG PET were derived from 217 esophageal adenocarcinoma patients who underwent chemoradiotherapy followed by surgery. The associations between these parameters and pathCR were studied in univariable and multivariable logistic regression analysis. Four prediction models were constructed and internally validated using bootstrapping to study the incremental predictive values of subjective assessment of (18)F-FDG PET, conventional quantitative metabolic features, and comprehensive (18)F-FDG PET texture/geometry features, respectively. The clinical benefit of (18)F-FDG PET was determined using decision-curve analysis. A pathCR was found in 59 (27%) patients. A clinical prediction model (corrected c-index, 0.67) was improved by adding (18)F-FDG PET-based subjective assessment of response (corrected c-index, 0.72). This latter model was slightly improved by the addition of 1 conventional quantitative metabolic feature only (i.e., postchemoradiation total lesion glycolysis; corrected c-index, 0.73), and even more by subsequently adding 4 comprehensive (18)F-FDG PET texture/geometry features (corrected c-index, 0.77). However, at a decision threshold of 0.9 or higher, representing a clinically relevant predictive value for pathCR at which one may be willing to omit surgery, there was no clear incremental value. Subjective and quantitative assessment of (18)F-FDG PET provides statistical incremental value for predicting pathCR after preoperative chemoradiotherapy in esophageal cancer. However, the discriminatory improvement beyond clinical predictors does not translate into a clinically relevant benefit that could change decision making. © 2016 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Prediction of clinical response based on pharmacokinetic/pharmacodynamic models of 5-hydroxytryptamine reuptake inhibitors in mice

PubMed Central

Kreilgaard, M; Smith, D G; Brennum, L T; Sánchez, C

2008-01-01

Background and purpose: Bridging the gap between preclinical research and clinical trials is vital for drug development. Predicting clinically relevant steady-state drug concentrations (Css) in serum from preclinical animal models may facilitate this transition. Here we used a pharmacokinetic/pharmacodynamic (PK/PD) modelling approach to evaluate the predictive validity of 5-hydroxytryptamine (5-HT; serotonin) transporter (SERT) occupancy and 5-hydroxytryptophan (5-HTP)-potentiated behavioral syndrome induced by 5-HT reuptake inhibitor (SRI) antidepressants in mice. Experimental approach: Serum and whole brain drug concentrations, cortical SERT occupancy and 5-HTP-potentiated behavioral syndrome were measured over 6 h after a single subcutaneous injection of escitalopram, paroxetine or sertraline. [3H]2-(2-dimethylaminomethylphenylsulphanyl)-5-methyl-phenylamine ([3H]MADAM) was used to assess SERT occupancy. For PK/PD modelling, an effect-compartment model was applied to collapse the hysteresis and predict the steady-state relationship between drug exposure and PD response. Key results: The predicted Css for escitalopram, paroxetine and sertraline at 80% SERT occupancy in mice are 18 ng mL−1, 18 ng mL−1 and 24 ng mL−1, respectively, with corresponding responses in the 5-HTP behavioral model being between 20–40% of the maximum. Conclusions and implications: Therapeutically effective SERT occupancy for SRIs in depressed patients is approximately 80%, and the corresponding plasma Css are 6–21 ng mL−1, 21-95 ng mL−1 and 20–48 ng mL−1 for escitalopram, paroxetine and sertraline, respectively. Thus, PK/PD modelling using SERT occupancy and 5-HTP-potentiated behavioral syndrome as response markers in mice may be a useful tool to predict clinically relevant plasma Css values. PMID:18552871

Prealbumin, platelet factor 4 and S100A12 combination at baseline predicts good response to TNF alpha inhibitors in Rheumatoid Arthritis.

PubMed

Nguyen, Minh Vu Chuong; Baillet, Athan; Romand, Xavier; Trocmé, Candice; Courtier, Anaïs; Marotte, Hubert; Thomas, Thierry; Soubrier, Martin; Miossec, Pierre; Tébib, Jacques; Grange, Laurent; Toussaint, Bertrand; Lequerré, Thierry; Vittecoq, Olivier; Gaudin, Philippe

2018-06-06

Tumour necrosis factor-alpha inhibitors (TNFi) are effective treatments for Rheumatoid Arthritis (RA). Responses to treatment are barely predictable. As these treatments are costly and may induce a number of side effects, we aimed at identifying a panel of protein biomarkers that could be used to predict clinical response to TNFi for RA patients. Baseline blood levels of C-reactive protein, platelet factor 4, apolipoprotein A1, prealbumin, α1-antitrypsin, haptoglobin, S100A8/A9 and S100A12 proteins in bDMARD naive patients at the time of TNFi treatment initiation were assessed in a multicentric prospective French cohort. Patients fulfilling good EULAR response at 6 months were considered as responders. Logistic regression was used to determine best biomarker set that could predict good clinical response to TNFi. A combination of biomarkers (prealbumin, platelet factor 4 and S100A12) was identified and could predict response to TNFi in RA with sensitivity of 78%, specificity of 77%, positive predictive values (PPV) of 72%, negative predictive values (NPV) of 82%, positive likelihood ratio (LR+) of 3.35 and negative likelihood ratio (LR-) of 0.28. Lower levels of prealbumin and S100A12 and higher level of platelet factor 4 than the determined cutoff at baseline in RA patients are good predictors for response to TNFi treatment globally as well as to Infliximab, Etanercept and Adalimumab individually. A multivariate model combining 3 biomarkers (prealbumin, platelet factor 4 and S100A12) accurately predicted response of RA patients to TNFi and has potential in a daily practice personalized treatment. Copyright © 2018. Published by Elsevier Masson SAS.

A gene expression signature of RAS pathway dependence predicts response to PI3K and RAS pathway inhibitors and expands the population of RAS pathway activated tumors.

PubMed

Loboda, Andrey; Nebozhyn, Michael; Klinghoffer, Rich; Frazier, Jason; Chastain, Michael; Arthur, William; Roberts, Brian; Zhang, Theresa; Chenard, Melissa; Haines, Brian; Andersen, Jannik; Nagashima, Kumiko; Paweletz, Cloud; Lynch, Bethany; Feldman, Igor; Dai, Hongyue; Huang, Pearl; Watters, James

2010-06-30

Hyperactivation of the Ras signaling pathway is a driver of many cancers, and RAS pathway activation can predict response to targeted therapies. Therefore, optimal methods for measuring Ras pathway activation are critical. The main focus of our work was to develop a gene expression signature that is predictive of RAS pathway dependence. We used the coherent expression of RAS pathway-related genes across multiple datasets to derive a RAS pathway gene expression signature and generate RAS pathway activation scores in pre-clinical cancer models and human tumors. We then related this signature to KRAS mutation status and drug response data in pre-clinical and clinical datasets. The RAS signature score is predictive of KRAS mutation status in lung tumors and cell lines with high (> 90%) sensitivity but relatively low (50%) specificity due to samples that have apparent RAS pathway activation in the absence of a KRAS mutation. In lung and breast cancer cell line panels, the RAS pathway signature score correlates with pMEK and pERK expression, and predicts resistance to AKT inhibition and sensitivity to MEK inhibition within both KRAS mutant and KRAS wild-type groups. The RAS pathway signature is upregulated in breast cancer cell lines that have acquired resistance to AKT inhibition, and is downregulated by inhibition of MEK. In lung cancer cell lines knockdown of KRAS using siRNA demonstrates that the RAS pathway signature is a better measure of dependence on RAS compared to KRAS mutation status. In human tumors, the RAS pathway signature is elevated in ER negative breast tumors and lung adenocarcinomas, and predicts resistance to cetuximab in metastatic colorectal cancer. These data demonstrate that the RAS pathway signature is superior to KRAS mutation status for the prediction of dependence on RAS signaling, can predict response to PI3K and RAS pathway inhibitors, and is likely to have the most clinical utility in lung and breast tumors.

Connectivity Predicts Deep Brain Stimulation Outcome in Parkinson Disease

PubMed Central

Horn, Andreas; Reich, Martin; Vorwerk, Johannes; Li, Ningfei; Wenzel, Gregor; Fang, Qianqian; Schmitz-Hübsch, Tanja; Nickl, Robert; Kupsch, Andreas; Volkmann, Jens; Kühn, Andrea A.; Fox, Michael D.

2018-01-01

Objective The benefit of deep brain stimulation (DBS) for Parkinson disease (PD) may depend on connectivity between the stimulation site and other brain regions, but which regions and whether connectivity can predict outcome in patients remain unknown. Here, we identify the structural and functional connectivity profile of effective DBS to the subthalamic nucleus (STN) and test its ability to predict outcome in an independent cohort. Methods A training dataset of 51 PD patients with STN DBS was combined with publicly available human connectome data (diffusion tractography and resting state functional connectivity) to identify connections reliably associated with clinical improvement (motor score of the Unified Parkinson Disease Rating Scale [UPDRS]). This connectivity profile was then used to predict outcome in an independent cohort of 44 patients from a different center. Results In the training dataset, connectivity between the DBS electrode and a distributed network of brain regions correlated with clinical response including structural connectivity to supplementary motor area and functional anticorrelation to primary motor cortex (p<0.001). This same connectivity profile predicted response in an independent patient cohort (p<0.01). Structural and functional connectivity were independent predictors of clinical improvement (p<0.001) and estimated response in individual patients with an average error of 15% UPDRS improvement. Results were similar using connectome data from normal subjects or a connectome age, sex, and disease matched to our DBS patients. Interpretation Effective STN DBS for PD is associated with a specific connectivity profile that can predict clinical outcome across independent cohorts. This prediction does not require specialized imaging in PD patients themselves. PMID:28586141

Using short-term evidence to predict six-month outcomes in clinical trials of signs and symptoms in rheumatoid arthritis.

PubMed

Nixon, Richard M; Bansback, Nick; Stevens, John W; Brennan, Alan; Madan, Jason

2009-01-01

A model is presented to generate a distribution for the probability of an ACR response at six months for a new treatment for rheumatoid arthritis given evidence from a one- or three-month clinical trial. The model is based on published evidence from 11 randomized controlled trials on existing treatments. A hierarchical logistic regression model is used to find the relationship between the proportion of patients achieving ACR20 and ACR50 at one and three months and the proportion at six months. The model is assessed by Bayesian predictive P-values that demonstrate that the model fits the data well. The model can be used to predict the number of patients with an ACR response for proposed six-month clinical trials given data from clinical trials of one or three months duration. Copyright 2008 John Wiley & Sons, Ltd.

Validation of biomarkers to predict response to immunotherapy in cancer: Volume I - pre-analytical and analytical validation.

PubMed

Masucci, Giuseppe V; Cesano, Alessandra; Hawtin, Rachael; Janetzki, Sylvia; Zhang, Jenny; Kirsch, Ilan; Dobbin, Kevin K; Alvarez, John; Robbins, Paul B; Selvan, Senthamil R; Streicher, Howard Z; Butterfield, Lisa H; Thurin, Magdalena

2016-01-01

Immunotherapies have emerged as one of the most promising approaches to treat patients with cancer. Recently, there have been many clinical successes using checkpoint receptor blockade, including T cell inhibitory receptors such as cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programmed cell death-1 (PD-1). Despite demonstrated successes in a variety of malignancies, responses only typically occur in a minority of patients in any given histology. Additionally, treatment is associated with inflammatory toxicity and high cost. Therefore, determining which patients would derive clinical benefit from immunotherapy is a compelling clinical question. Although numerous candidate biomarkers have been described, there are currently three FDA-approved assays based on PD-1 ligand expression (PD-L1) that have been clinically validated to identify patients who are more likely to benefit from a single-agent anti-PD-1/PD-L1 therapy. Because of the complexity of the immune response and tumor biology, it is unlikely that a single biomarker will be sufficient to predict clinical outcomes in response to immune-targeted therapy. Rather, the integration of multiple tumor and immune response parameters, such as protein expression, genomics, and transcriptomics, may be necessary for accurate prediction of clinical benefit. Before a candidate biomarker and/or new technology can be used in a clinical setting, several steps are necessary to demonstrate its clinical validity. Although regulatory guidelines provide general roadmaps for the validation process, their applicability to biomarkers in the cancer immunotherapy field is somewhat limited. Thus, Working Group 1 (WG1) of the Society for Immunotherapy of Cancer (SITC) Immune Biomarkers Task Force convened to address this need. In this two volume series, we discuss pre-analytical and analytical (Volume I) as well as clinical and regulatory (Volume II) aspects of the validation process as applied to predictive biomarkers for cancer immunotherapy. To illustrate the requirements for validation, we discuss examples of biomarker assays that have shown preliminary evidence of an association with clinical benefit from immunotherapeutic interventions. The scope includes only those assays and technologies that have established a certain level of validation for clinical use (fit-for-purpose). Recommendations to meet challenges and strategies to guide the choice of analytical and clinical validation design for specific assays are also provided.

Predictive factors for the placebo effect in clinical trials for dry eye: a pooled analysis of three clinical trials.

PubMed

Imanaka, Takahiro; Sato, Izumi; Tanaka, Shiro; Kawakami, Koji

2017-11-01

Placebo effect is one of the methodological difficulties in dry eye clinical trials. If we could elucidate the tendencies of the placebo response and find predictors, we could reduce the placebo response in clinical trials for dry eye. In this study, we investigated the predictive factors for the placebo effect in dry eye clinical trials. A total of 205 patients with dry eye assigned to the placebo arms of three placebo-controlled randomised clinical trials were analysed by simple and multivariable regression analysis. The corneal fluorescein (FL) staining score and dry eye symptoms were studied at week 4. The variables of interest included gender, age, complications of Sjögren's syndrome, Schirmer's test I value, tear break-up time and conjunctival hyperaemia score. We also conducted a stratified analysis according to the patients' age. Among all the studied endpoints, the baseline scores were significantly related to the corresponding placebo response. In addition, for the FL score and the dryness score, age was a significant predictor of the placebo response (p=0.04 and p<0.0001, respectively). Stratified analysis by age showed that patients more than 40 years of age are more likely to have a stronger placebo response in the FL and dryness scores. The baseline scores and age were predictive factors of the placebo response in frequently used endpoints, such as FL score or dryness symptoms. These patient characteristics can be controlled by study design, and our findings enable the design of more efficient placebo-controlled studies with good statistical power. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Genetic predictors of antipsychotic response to lurasidone identified in a genome wide association study and by schizophrenia risk genes.

PubMed

Li, Jiang; Yoshikawa, Akane; Brennan, Mark D; Ramsey, Timothy L; Meltzer, Herbert Y

2018-02-01

Biomarkers which predict response to atypical antipsychotic drugs (AAPDs) increases their benefit/risk ratio. We sought to identify common variants in genes which predict response to lurasidone, an AAPD, by associating genome-wide association study (GWAS) data and changes (Δ) in Positive And Negative Syndrome Scale (PANSS) scores from two 6-week randomized, placebo-controlled trials of lurasidone in schizophrenia (SCZ) patients. We also included SCZ risk SNPs identified by the Psychiatric Genomics Consortium using a polygenic risk analysis. The top genomic loci, with uncorrected p<10 -4 , include: 1) synaptic adhesion (PTPRD, LRRC4C, NRXN1, ILIRAPL1, SLITRK1) and scaffolding (MAGI1, MAGI2, NBEA) genes, both essential for synaptic function; 2) other synaptic plasticity-related genes (NRG1/3 and KALRN); 3) the neuron-specific RNA splicing regulator, RBFOX1; and 4) ion channel genes, e.g. KCNA10, KCNAB1, KCNK9 and CACNA2D3). Some genes predicted response for patients with both European and African Ancestries. We replicated some SNPs reported to predict response to other atypical APDs in other GWAS. Although none of the biomarkers reached genome-wide significance, many of the genes and associated pathways have previously been linked to SCZ. Two polygenic modeling approaches, GCTA-GREML and PLINK-Polygenic Risk Score, demonstrated that some risk genes related to neurodevelopment, synaptic biology, immune response, and histones, also contributed to prediction of response. The top hits predicting response to lurasidone did not predict improvement with placebo. This is the first evidence from clinical trials that SCZ risk SNPs are related to clinical response to an AAPD. These results need to be replicated in an independent sample. Copyright © 2017. Published by Elsevier B.V.

Clinical utility of C-reactive protein to predict treatment response during cystic fibrosis pulmonary exacerbations.

PubMed

Sharma, Ashutosh; Kirkpatrick, Gordon; Chen, Virginia; Skolnik, Kate; Hollander, Zsuzsanna; Wilcox, Pearce; Quon, Bradley S

2017-01-01

C-reactive protein (CRP) is a systemic marker of inflammation that correlates with disease status in cystic fibrosis (CF). The clinical utility of CRP measurement to guide pulmonary exacerbation (PEx) treatment decisions remains uncertain. To determine whether monitoring CRP during PEx treatment can be used to predict treatment response. We hypothesized that early changes in CRP can be used to predict treatment response. We reviewed all PEx events requiring hospitalization for intravenous (IV) antibiotics over 2 years at our institution. 83 PEx events met our eligibility criteria. CRP levels from admission to day 5 were evaluated to predict treatment non-response, using a modified version of a prior published composite definition. CRP was also evaluated to predict time until next exacerbation (TUNE). 53% of 83 PEx events were classified as treatment non-response. Paradoxically, 24% of PEx events were characterized by a ≥ 50% increase in CRP levels within the first five days of treatment. Absolute change in CRP from admission to day 5 was not associated with treatment non-response (p = 0.58). Adjusted for FEV1% predicted, admission log10 CRP was associated with treatment non-response (OR: 2.39; 95% CI: 1.14 to 5.91; p = 0.03) and shorter TUNE (HR: 1.60; 95% CI: 1.13 to 2.27; p = 0.008). The area under the receiver operating characteristics (ROC) curve of admission CRP to predict treatment non-response was 0.72 (95% CI 0.61-0.83; p<0.001). 23% of PEx events were characterized by an admission CRP of > 75 mg/L with a specificity of 90% for treatment non-response. Admission CRP predicts treatment non-response and time until next exacerbation. A very elevated admission CRP (>75mg/L) is highly specific for treatment non-response and might be used to target high-risk patients for future interventional studies aimed at improving exacerbation outcomes.

Prediction of Short- and Medium-term Efficacy of Biosimilar Infliximab Therapy. Do Trough Levels and Antidrug Antibody Levels or Clinical And Biochemical Markers Play the More Important Role?

PubMed

Gonczi, Lorant; Vegh, Zsuzsanna; Golovics, Petra Anna; Rutka, Mariann; Gecse, Krisztina Barbara; Bor, Renata; Farkas, Klaudia; Szamosi, Tamás; Bene, László; Gasztonyi, Beáta; Kristóf, Tünde; Lakatos, László; Miheller, Pál; Palatka, Károly; Papp, Mária; Patai, Árpád; Salamon, Ágnes; Tóth, Gábor Tamás; Vincze, Áron; Biro, Edina; Lovasz, Barbara Dorottya; Kurti, Zsuzsanna; Szepes, Zoltan; Molnár, Tamás; Lakatos, Péter L

2017-06-01

Biosimilar infliximab CT-P13 received European Medicines Agency [EMA] approval in June 2013 for all indications of the originator product. In the present study, we aimed to evaluate the predictors of short- and medium-term clinical outcome in patients treated with the biosimilar infliximab at the participating inflammatory bowel disease [IBD] centres in Hungary. Demographic data were collected and a harmonised monitoring strategy was applied. Clinical and biochemical activities were evaluated at Weeks 14, 30, and 54. Trough level [TL] and anti-drug antibody [ADA] concentrations were measured by enzyme-linked immunosorbent assay [ELISA] [LT-005, Theradiag, France] at baseline at 14, 30 and 54 weeks and in two centres at Weeks 2 and 6. A total of 291 consecutive IBD patients (184 Crohn's disease [CD] and 107 ulcerative colitis [UC]) were included. In UC, TLs at Week 2 predicted both clinical response and remission at Weeks 14 and 30 (clinical response/remission at Week 14: area under the curve [AUC] = 0.81, p < 0.001, cut-off: 11.5 μg/ml/AUC = 0.79, p < 0.001, cut-off: 15.3μg/ml; clinical response/remission at Week 30: AUC = 0.79, p = 0.002, cut-off: 11.5 μg/ml/AUC = 0.74, p = 0.006, cut-off: 14.5 μg/ml), whereas ADA positivity at Week 14 was inversely associated with clinical response at Week 30 [58.3% vs 84.8% ,p = 0.04]. Previous anti-tumour necrosis factor [TNF] exposure was inversely associated with short-term clinical remission [Week 2: 18.8% vs 47.8%, p = 0.03, at Week 6: 38.9% vs 69.7%, p = 0.013, at Week 14: 37.5% vs 2.5%, p = 0.06]. In CD, TLs at Week 2 predicted short-term [Week 14 response/remission, AUCTLweek2 = 0.715-0.721, p = 0.05/0.005] but not medium-term clinical efficacy. In addition, early ADA status by Week 14 [p = 0.04-0.05 for Weeks 14 and 30], early clinical response [p < 0.001 for Weeks 30/54] and normal C-reactive protein [CRP] at Week 14 [p = 0.005-0.0001] and previous anti-TNF exposure [p = 0.03-0.0001 for Weeks 14, 30, and 54] were associated with short-and medium-term clinical response and remission. In UC, early TLs were predictive for short- and medium-term clinical efficacy, whereas in CD, Week 2 TLs were associated only with short-term clinical outcomes. Copyright © 2016 European Crohn’s and Colitis Organisation (ECCO). Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com

Prediction of Response to Neoadjuvant Chemotherapy and Radiation Therapy with Baseline and Restaging 18F-FDG PET Imaging Biomarkers in Patients with Esophageal Cancer.

PubMed

Beukinga, Roelof J; Hulshoff, Jan Binne; Mul, Véronique E M; Noordzij, Walter; Kats-Ugurlu, Gursah; Slart, Riemer H J A; Plukker, John T M

2018-06-01

Purpose To assess the value of baseline and restaging fluorine 18 ( 18 F) fluorodeoxyglucose (FDG) positron emission tomography (PET) radiomics in predicting pathologic complete response to neoadjuvant chemotherapy and radiation therapy (NCRT) in patients with locally advanced esophageal cancer. Materials and Methods In this retrospective study, 73 patients with histologic analysis-confirmed T1/N1-3/M0 or T2-4a/N0-3/M0 esophageal cancer were treated with NCRT followed by surgery (Chemoradiotherapy for Esophageal Cancer followed by Surgery Study regimen) between October 2014 and August 2017. Clinical variables and radiomic features from baseline and restaging 18 F-FDG PET were selected by univariable logistic regression and least absolute shrinkage and selection operator. The selected variables were used to fit a multivariable logistic regression model, which was internally validated by using bootstrap resampling with 20 000 replicates. The performance of this model was compared with reference prediction models composed of maximum standardized uptake value metrics, clinical variables, and maximum standardized uptake value at baseline NCRT radiomic features. Outcome was defined as complete versus incomplete pathologic response (tumor regression grade 1 vs 2-5 according to the Mandard classification). Results Pathologic response was complete in 16 patients (21.9%) and incomplete in 57 patients (78.1%). A prediction model combining clinical T-stage and restaging NCRT (post-NCRT) joint maximum (quantifying image orderliness) yielded an optimism-corrected area under the receiver operating characteristics curve of 0.81. Post-NCRT joint maximum was replaceable with five other redundant post-NCRT radiomic features that provided equal model performance. All reference prediction models exhibited substantially lower discriminatory accuracy. Conclusion The combination of clinical T-staging and quantitative assessment of post-NCRT 18 F-FDG PET orderliness (joint maximum) provided high discriminatory accuracy in predicting pathologic complete response in patients with esophageal cancer. © RSNA, 2018 Online supplemental material is available for this article.

Is electromyography a predictive test of patient response to biofeedback in the treatment of fecal incontinence?

PubMed

Lacima, Gloria; Pera, Miguel; González-Argenté, Xavier; Torrents, Abiguei; Valls-Solé, Josep; Espuña-Pons, Montserrat

2016-03-01

Biofeedback is effective in more than 70% of patients with fecal incontinence. However, reliable predictors of successful treatment have not been identified. The aim was to identify clinical variables and diagnostic tests, particularly electromyography, that could predict a successful outcome. We included 135 consecutive women with fecal incontinence treated with biofeedback. Clinical evaluation, manometry, ultrasonography, electromyography, and pudendal nerve terminal motor latency were performed before therapy. Treatment outcome was assessed using a symptoms diary, Wexner incontinence score and the patient's subjective perception. According to the symptoms diaries, 106 (78.5%) women had a good clinical result and 29 (21.5%) had a poor result. There were no differences in age, severity and type of fecal incontinence. Maximum resting pressure (39.3 ± 19.1 mmHg vs. 33.7 ± 20.2 mmHg; P = 0.156) and maximum squeeze pressure (91.8 ± 33.2 mmHg vs. 79.8 ± 31.2 mmHg; P = 0.127) were higher in patients having good clinical outcome although the difference was not significant. There were no differences in the presence of sphincter defects or abnormalities in electromyographic recordings. Logistic regression analysis found no independent predictive factor for good clinical outcome. Biofeedback is effective in more than 75% of patients with fecal incontinence. Clinical characteristics of patients and results of baseline tests have no predictive value of response to therapy. Specifically, we found no association between severity of electromyographic deficit and clinical response. © 2015 Wiley Periodicals, Inc.

Maternal Responsiveness Predicts Child Language at Ages 3 and 4 in a Community-Based Sample of Slow-to-Talk Toddlers

ERIC Educational Resources Information Center

Hudson, Sophie; Levickis, Penny; Down, Kate; Nicholls, Ruth; Wake, Melissa

2015-01-01

Background: Maternal responsiveness has been shown to predict child language outcomes in clinical samples of children with language delay and non-representative samples of typically developing children. An effective and timely measure of maternal responsiveness for use at the population level has not yet been established. Aims: To determine…

Predicting the Naturalistic Course of Major Depressive Disorder Using Clinical and Multimodal Neuroimaging Information: A Multivariate Pattern Recognition Study.

PubMed

Schmaal, Lianne; Marquand, Andre F; Rhebergen, Didi; van Tol, Marie-José; Ruhé, Henricus G; van der Wee, Nic J A; Veltman, Dick J; Penninx, Brenda W J H

2015-08-15

A chronic course of major depressive disorder (MDD) is associated with profound alterations in brain volumes and emotional and cognitive processing. However, no neurobiological markers have been identified that prospectively predict MDD course trajectories. This study evaluated the prognostic value of different neuroimaging modalities, clinical characteristics, and their combination to classify MDD course trajectories. One hundred eighteen MDD patients underwent structural and functional magnetic resonance imaging (MRI) (emotional facial expressions and executive functioning) and were clinically followed-up at 2 years. Three MDD trajectories (chronic n = 23, gradual improving n = 36, and fast remission n = 59) were identified based on Life Chart Interview measuring the presence of symptoms each month. Gaussian process classifiers were employed to evaluate prognostic value of neuroimaging data and clinical characteristics (including baseline severity, duration, and comorbidity). Chronic patients could be discriminated from patients with more favorable trajectories from neural responses to various emotional faces (up to 73% accuracy) but not from structural MRI and functional MRI related to executive functioning. Chronic patients could also be discriminated from remitted patients based on clinical characteristics (accuracy 69%) but not when age differences between the groups were taken into account. Combining different task contrasts or data sources increased prediction accuracies in some but not all cases. Our findings provide evidence that the prediction of naturalistic course of depression over 2 years is improved by considering neuroimaging data especially derived from neural responses to emotional facial expressions. Neural responses to emotional salient faces more accurately predicted outcome than clinical data. Copyright © 2015 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Effects and Predictive Factors of Immunosuppressive Therapy Combined with Umbilical Cord Blood Infusion in Patients with Severe Aplastic Anemia.

PubMed

Zhang, Xia; Li, Zhangzhi; Geng, Wei; Song, Bin; Wan, Chucheng

2018-07-01

To investigate the efficacy and safety of umbilical cord blood (UCB) infusion (UCBI) plus immunosuppressive therapy (IST) treatment in comparison to IST treatment, as well as predictive factors for clinical responses, in severe aplastic anemia (SAA) patients. Totally, 93 patients with SAA were enrolled in this cohort study. In the IST group, rabbit antithymocyte globulin (r-ATG) combined with cyclosporine A (CsA) was administered, while in the IST+UBCI group, r-ATG, CsA, and UCB were used. After 6 months of treatment, UCBI+IST achieved a higher complete response (CR) rate (p=0.002) and an elevated overall response rate (ORR) (p=0.004), compared to IST. Regarding hematopoietic recovery at month 6, platelet responses in the UCBI+IST group were better than those in the IST group (p=0.002), and UCBI+IST treatment facilitated increasing trends in absolute neutrophil count (ANC) response (p=0.056). Kaplan-Meier curves illuminated UCBI+IST achieved faster ANC response (p<0.001) and platelet response (p<0.001), compared with IST therapy. There was no difference in overall survival (OS) between the two groups (p=0.620). Furthermore, logistic regression analysis demonstrated that UCBI+IST was an independent predicting factor for both CR (p=0.001) and ORR (p<0.001), compared to IST; meanwhile, very severe aplastic anemia (VSAA) and ANC could predict clinical responses as well. However, Cox proportional hazard regression indicated that VSAA (p=0.003), but not UCBI+IST, affected OS. Safety profiles showed that UCBI+IST therapy did not elevate adverse events, compared with IST treatment. UCBI+IST achieved better clinical responses and hematopoietic recovery than IST, and was well tolerated in SAA patients. © Copyright: Yonsei University College of Medicine 2018.

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder

PubMed Central

Kessler, R.C.; van Loo, H.M.; Wardenaar, K.J.; Bossarte, R.M.; Brenner, L.A.; Ebert, D.D; de Jonge, P.; Nierenberg, A.A.; Rosellini, A.J.; Sampson, N.A.; Schoevers, R.A.; Wilcox, M.A.; Zaslavsky, A.M.

2016-01-01

Aims Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful. Methods We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalized) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments. Results Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention versus control) or differential treatment outcomes (i.e., intervention A versus intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalized treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials. Conclusions Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists. PMID:26810628

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder.

PubMed

Kessler, R C; van Loo, H M; Wardenaar, K J; Bossarte, R M; Brenner, L A; Ebert, D D; de Jonge, P; Nierenberg, A A; Rosellini, A J; Sampson, N A; Schoevers, R A; Wilcox, M A; Zaslavsky, A M

2017-02-01

Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful. We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments. Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials. Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.

Quantification of hazard prediction ability at hazard prediction training (Kiken-Yochi Training: KYT) by free-response receiver-operating characteristic (FROC) analysis.

PubMed

Hashida, Masahiro; Kamezaki, Ryousuke; Goto, Makoto; Shiraishi, Junji

2017-03-01

The ability to predict hazards in possible situations in a general X-ray examination room created for Kiken-Yochi training (KYT) is quantified by use of free-response receiver-operating characteristics (FROC) analysis for determining whether the total number of years of clinical experience, involvement in general X-ray examinations, occupation, and training each have an impact on the hazard prediction ability. Twenty-three radiological technologists (RTs) (years of experience: 2-28), four nurses (years of experience: 15-19), and six RT students observed 53 scenes of KYT: 26 scenes with hazardous points (hazardous points are those that might cause injury to patients) and 27 scenes without points. Based on the results of these observations, we calculated the alternative free-response receiver-operating characteristic (AFROC) curve and the figure of merit (FOM) to quantify the hazard prediction ability. The results showed that the total number of years of clinical experience did not have any impact on hazard prediction ability, whereas recent experience with general X-ray examinations greatly influenced this ability. In addition, the hazard prediction ability varied depending on the occupations of the observers while they were observing the same scenes in KYT. The hazard prediction ability of the radiologic technology students was improved after they had undergone patient safety training. This proposed method with FROC observer study enabled the quantification and evaluation of the hazard prediction capability, and the application of this approach to clinical practice may help to ensure the safety of examinations and treatment in the radiology department.

Genetic and clinical predictors of ovarian response in assisted reproductive technology

NASA Astrophysics Data System (ADS)

Wiweko, B.; Damayanti, I.; Suryandari, D.; Natadisastra, M.; Pratama, G.; Sumapraja, K.; Meutia, K.; Iffanolia, P.; Harzief, A. K.; Hestiantoro, A.

2017-08-01

Several factors are known to influence ovarian response to rFSH stimulation such as age, antral follicle count (AFC), and basal FSH level, Mutation of allele Ser680Asn in FSHR gene was responsible to ovarian resistance toward exogenous FSH. The aim of this study is to develop a prediction model of ovarian response to COS in IVF. This study was a prospective cohort study. One hundred and thirteen women undergoing their first cycle of IVF in Yasmin IVF Clinic Jakarta were recruited to this study. Clinical datas included were age, BMI, and AFC. Basal FSH and E2 as well as serum AMH was measured from peripheral blood taken at second day of cycle. Bsr-1 enzyme is used to identify the polymorphism in exon 10 position 680 with RFLP technique. Three genotype polymorphism, Asn/Asn (255 bp ribbon), Asn/Ser (97 bp and 158 bp), and Ser/Ser (97 bp, 158 bp, and 255 bp). AFC has the highest predictor for ovarian response with AUC 0.922 (CI 95% 0.833-1.000). AMH also showed high predicting value (AUC 0.843 CI 95% 0.663-1.000). The multivariate analysis revealed combination of AFC, AMH, age, and basal FSH is a good model for ovarian response prediction (AUC=0.97). No significant relation between Asn/Asn, Asn/Ser, or Ser/Ser genotype FSHR polymorphism with ovarian response (p = 0.866) and total dose of rRSH (p = 0.08). This study showed that model combination of AFC, AMH, patient’s age and basal FSH are very good to predict number of mature oocytes.

A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI

NASA Astrophysics Data System (ADS)

Ravichandran, Kavya; Braman, Nathaniel; Janowczyk, Andrew; Madabhushi, Anant

2018-02-01

Neoadjuvant chemotherapy (NAC) is routinely used to treat breast tumors before surgery to reduce tumor size and improve outcome. However, no current clinical or imaging metrics can effectively predict before treatment which NAC recipients will achieve pathological complete response (pCR), the absence of residual invasive disease in the breast or lymph nodes following surgical resection. In this work, we developed and applied a convolu- tional neural network (CNN) to predict pCR from pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) scans on a per-voxel basis. In this study, DCE-MRI data for a total of 166 breast cancer pa- tients from the ISPY1 Clinical Trial were split into a training set of 133 patients and a testing set of 33 patients. A CNN consisting of 6 convolutional blocks was trained over 30 epochs. The pre-contrast and post-contrast DCE-MRI phases were considered in isolation and conjunction. A CNN utilizing a combination of both pre- and post-contrast images best distinguished responders, with an AUC of 0.77; 82% of the patients in the testing set were correctly classified based on their treatment response. Within the testing set, the CNN was able to produce probability heatmaps that visualized tumor regions that most strongly predicted therapeutic response. Multi- variate analysis with prognostic clinical variables (age, largest diameter, hormone receptor and HER2 status), revealed that the network was an independent predictor of response (p=0.05), and that the inclusion of HER2 status could further improve capability to predict response (AUC = 0.85, accuracy = 85%).

Clinical application of a systems model of apoptosis execution for the prediction of colorectal cancer therapy responses and personalisation of therapy.

PubMed

Hector, Suzanne; Rehm, Markus; Schmid, Jasmin; Kehoe, Joan; McCawley, Niamh; Dicker, Patrick; Murray, Frank; McNamara, Deborah; Kay, Elaine W; Concannon, Caoimhin G; Huber, Heinrich J; Prehn, Jochen H M

2012-05-01

Key to the clinical management of colorectal cancer is identifying tools which aid in assessing patient prognosis and determining more effective and personalised treatment strategies. We evaluated whether an experimental systems biology strategy which analyses the susceptibility of cancer cells to undergo caspase activation can be exploited to predict patient responses to 5-fluorouracil-based chemotherapy and to case-specifically identify potential alternative targeted treatments to reactivate apoptosis. We quantified five essential apoptosis-regulating proteins (Pro-Caspases 3 and 9, APAF-1, SMAC and XIAP) in samples of Stage II (n = 13) and III (n=17) tumour and normal colonic (n = 8) tissue using absolute quantitative immunoblotting and employed systems simulations of apoptosis signalling to predict the susceptibility of tumour cells to execute apoptosis. Additional systems analyses assessed the efficacy of novel apoptosis-inducing therapeutics such as XIAP antagonists, proteasome inhibitors and Pro-Caspase-3-activating compounds in restoring apoptosis execution in apoptosis-incompetent tumours. Comparisons of caspase activity profiles demonstrated that the likelihood of colorectal tumours to undergo apoptosis decreases with advancing disease stage. Systems-level analysis correctly predicted positive or negative outcome in 85% (p=0.004) of colorectal cancer patients receiving 5-fluorouracil based chemotherapy and significantly outperformed common uni- and multi-variate statistical approaches. Modelling of individual patient responses to novel apoptosis-inducing therapeutics revealed markedly different inter-individual responses. Our study represents the first proof-of-concept example demonstrating the significant clinical potential of systems biology-based approaches for predicting patient outcome and responsiveness to novel targeted treatment paradigms.

Supraspinal Control Predicts Locomotor Function and Forecasts Responsiveness to Training after Spinal Cord Injury

PubMed Central

Field-Fote, Edelle C.; Yang, Jaynie F.; Basso, D. Michele; Gorassini, Monica A.

2017-01-01

Abstract Restoration of walking ability is an area of great interest in the rehabilitation of persons with spinal cord injury. Because many cortical, subcortical, and spinal neural centers contribute to locomotor function, it is important that intervention strategies be designed to target neural elements at all levels of the neuraxis that are important for walking ability. While to date most strategies have focused on activation of spinal circuits, more recent studies are investigating the value of engaging supraspinal circuits. Despite the apparent potential of pharmacological, biological, and genetic approaches, as yet none has proved more effective than physical therapeutic rehabilitation strategies. By making optimal use of the potential of the nervous system to respond to training, strategies can be developed that meet the unique needs of each person. To complement the development of optimal training interventions, it is valuable to have the ability to predict future walking function based on early clinical presentation, and to forecast responsiveness to training. A number of clinical prediction rules and association models based on common clinical measures have been developed with the intent, respectively, to predict future walking function based on early clinical presentation, and to delineate characteristics associated with responsiveness to training. Further, a number of variables that are correlated with walking function have been identified. Not surprisingly, most of these prediction rules, association models, and correlated variables incorporate measures of volitional lower extremity strength, illustrating the important influence of supraspinal centers in the production of walking behavior in humans. PMID:27673569

Pharmacogenetics-based area-under-curve model can predict efficacy and adverse events from axitinib in individual patients with advanced renal cell carcinoma.

PubMed

Yamamoto, Yoshiaki; Tsunedomi, Ryouichi; Fujita, Yusuke; Otori, Toru; Ohba, Mitsuyoshi; Kawai, Yoshihisa; Hirata, Hiroshi; Matsumoto, Hiroaki; Haginaka, Jun; Suzuki, Shigeo; Dahiya, Rajvir; Hamamoto, Yoshihiko; Matsuyama, Kenji; Hazama, Shoichi; Nagano, Hiroaki; Matsuyama, Hideyasu

2018-03-30

We investigated the relationship between axitinib pharmacogenetics and clinical efficacy/adverse events in advanced renal cell carcinoma (RCC) and established a model to predict clinical efficacy and adverse events using pharmacokinetic and gene polymorphisms related to drug metabolism and efflux in a phase II trial. We prospectively evaluated the area under the plasma concentration-time curve (AUC) of axitinib, objective response rate, and adverse events in 44 consecutive advanced RCC patients treated with axitinib. To establish a model for predicting clinical efficacy and adverse events, polymorphisms in genes including ABC transporters ( ABCB1 and ABCG2 ), UGT1A , and OR2B11 were analyzed by whole-exome sequencing, Sanger sequencing, and DNA microarray. To validate this prediction model, calculated AUC by 6 gene polymorphisms was compared with actual AUC in 16 additional consecutive patients prospectively. Actual AUC significantly correlated with the objective response rate ( P = 0.0002) and adverse events (hand-foot syndrome, P = 0.0055; and hypothyroidism, P = 0.0381). Calculated AUC significantly correlated with actual AUC ( P < 0.0001), and correctly predicted objective response rate ( P = 0.0044) as well as adverse events ( P = 0.0191 and 0.0082, respectively). In the validation study, calculated AUC prior to axitinib treatment precisely predicted actual AUC after axitinib treatment ( P = 0.0066). Our pharmacogenetics-based AUC prediction model may determine the optimal initial dose of axitinib, and thus facilitate better treatment of patients with advanced RCC.

Pharmacogenetics-based area-under-curve model can predict efficacy and adverse events from axitinib in individual patients with advanced renal cell carcinoma

PubMed Central

Yamamoto, Yoshiaki; Tsunedomi, Ryouichi; Fujita, Yusuke; Otori, Toru; Ohba, Mitsuyoshi; Kawai, Yoshihisa; Hirata, Hiroshi; Matsumoto, Hiroaki; Haginaka, Jun; Suzuki, Shigeo; Dahiya, Rajvir; Hamamoto, Yoshihiko; Matsuyama, Kenji; Hazama, Shoichi; Nagano, Hiroaki; Matsuyama, Hideyasu

2018-01-01

We investigated the relationship between axitinib pharmacogenetics and clinical efficacy/adverse events in advanced renal cell carcinoma (RCC) and established a model to predict clinical efficacy and adverse events using pharmacokinetic and gene polymorphisms related to drug metabolism and efflux in a phase II trial. We prospectively evaluated the area under the plasma concentration–time curve (AUC) of axitinib, objective response rate, and adverse events in 44 consecutive advanced RCC patients treated with axitinib. To establish a model for predicting clinical efficacy and adverse events, polymorphisms in genes including ABC transporters (ABCB1 and ABCG2), UGT1A, and OR2B11 were analyzed by whole-exome sequencing, Sanger sequencing, and DNA microarray. To validate this prediction model, calculated AUC by 6 gene polymorphisms was compared with actual AUC in 16 additional consecutive patients prospectively. Actual AUC significantly correlated with the objective response rate (P = 0.0002) and adverse events (hand-foot syndrome, P = 0.0055; and hypothyroidism, P = 0.0381). Calculated AUC significantly correlated with actual AUC (P < 0.0001), and correctly predicted objective response rate (P = 0.0044) as well as adverse events (P = 0.0191 and 0.0082, respectively). In the validation study, calculated AUC prior to axitinib treatment precisely predicted actual AUC after axitinib treatment (P = 0.0066). Our pharmacogenetics-based AUC prediction model may determine the optimal initial dose of axitinib, and thus facilitate better treatment of patients with advanced RCC. PMID:29682213

Usefulness of Interim FDG-PET After Induction Chemotherapy in Patients With Locally Advanced Squamous Cell Carcinoma of the Head and Neck Receiving Sequential Induction Chemotherapy Followed by Concurrent Chemoradiotherapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoon, Dok Hyun; Cho, Yoojin; Kim, Sang Yoon

2011-09-01

Purpose: Induction chemotherapy (ICT) has been used to select patients for organ preservation and determine subsequent treatments in patients with locally advanced squamous cell carcinoma of the head and neck (LASCCHN). Still, the clinical outcomes of LASCCHN patients who showed response to ICT are heterogeneous. We evaluated the efficacy of interim 18-fluoro-2-deoxy-glucose positron emission tomography (FDG-PET) after ICT in this specific subgroup of LASCCHN patients who achieved partial response (PR) after ICT to predict clinical outcomes after concurrent chemoradiotherapy (CCRT). Methods and Materials: Twenty-one patients with LASCCHN who showed PR to ICT by Response Evaluation Criteria In Solid Tumors beforemore » definitive CCRT were chosen in this retrospective analysis. FDG-PET was performed before and 2-4 weeks after ICT to assess the extent of disease at baseline and the metabolic response to ICT, respectively. We examined the correlation of the metabolic response by the percentage decrease of maximum standardized uptake value (SUVmax) on the primary tumor or lymph node after ICT or a specific threshold of SUVmax on interim FDG-PET with clinical outcomes including complete response (CR) rate to CCRT, progression-free survival (PFS), and overall survival (OS). Results: A SUVmax of 4.8 on interim FDG-PET could predict clinical CR after CCRT (100% vs. 20%, p = 0.001), PFS (median, not reached vs. 8.5 mo, p < 0.001), and OS (median, not reached vs. 12.0 months, p = 0.001) with a median follow-up of 20.3 months in surviving patients. A 65% decrease in SUVmax after ICT from baseline also could predict clinical CR after CCRT (100% vs. 33.3%, p = 0.003), PFS (median, not reached vs. 8.9 months, p < 0.001) and OS (median, not reached vs. 24.4 months, p = 0.001) of the patients. Conclusion: These data suggest that interim FDG-PET after ICT might be a useful determinant to predict clinical outcomes in patients with LASCCHN receiving sequential ICT followed by CCRT.« less

Prediction of Response to Treatment in a Randomized Clinical Trial of Couple Therapy: A 2-Year Follow-Up

ERIC Educational Resources Information Center

Baucom, Brian R.; Atkins, David C.; Simpson, Lorelei E.; Christensen, Andrew

2009-01-01

Many studies have examined pretreatment predictors of immediate posttreatment outcome, but few studies have examined prediction of long-term treatment response to couple therapies. Four groups of predictors (demographic, intrapersonal, communication, and other interpersonal) and 2 moderators (pretreatment severity and type of therapy) were…

Predictive factors in the evaluation of treatment response to neoadjuvant chemoradiotherapy in patients with advanced esophageal squamous cell cancer

PubMed Central

Wong, Claudia

2017-01-01

Neoadjuvant therapy before esophagectomy is evidence-based, and is a standard-of-care for locally advanced and operable esophageal cancer. However response to such treatment varies in individual patients, from no clinical response to pathological complete response. It has been consistently shown that a good pathological responses is of prognostic value, but perhaps in the expense of those who do not. It is important to identify suitable predictive factors for response, so that patients are not exposed to potentially harmful chemotherapy and/or radiotherapy without benefits. Alternative management strategies can be devised. Various clinical, radiological, serological and potential molecular markers have been studied. None has been shown to be sufficiently reliable to be used in daily practice. Certainly more understanding of the molecular basis for response to chemotherapy/radiotherapy is needed, so that patient treatment can be tailored and individualized. PMID:28815073

Predicting treatment response to cognitive behavioral therapy in panic disorder with agoraphobia by integrating local neural information.

PubMed

Hahn, Tim; Kircher, Tilo; Straube, Benjamin; Wittchen, Hans-Ulrich; Konrad, Carsten; Ströhle, Andreas; Wittmann, André; Pfleiderer, Bettina; Reif, Andreas; Arolt, Volker; Lueken, Ulrike

2015-01-01

Although neuroimaging research has made substantial progress in identifying the large-scale neural substrate of anxiety disorders, its value for clinical application lags behind expectations. Machine-learning approaches have predictive potential for individual-patient prognostic purposes and might thus aid translational efforts in psychiatric research. To predict treatment response to cognitive behavioral therapy (CBT) on an individual-patient level based on functional magnetic resonance imaging data in patients with panic disorder with agoraphobia (PD/AG). We included 49 patients free of medication for at least 4 weeks and with a primary diagnosis of PD/AG in a longitudinal study performed at 8 clinical research institutes and outpatient centers across Germany. The functional magnetic resonance imaging study was conducted between July 2007 and March 2010. Twelve CBT sessions conducted 2 times a week focusing on behavioral exposure. Treatment response was defined as exceeding a 50% reduction in Hamilton Anxiety Rating Scale scores. Blood oxygenation level-dependent signal was measured during a differential fear-conditioning task. Regional and whole-brain gaussian process classifiers using a nested leave-one-out cross-validation were used to predict the treatment response from data acquired before CBT. Although no single brain region was predictive of treatment response, integrating regional classifiers based on data from the acquisition and the extinction phases of the fear-conditioning task for the whole brain yielded good predictive performance (accuracy, 82%; sensitivity, 92%; specificity, 72%; P < .001). Data from the acquisition phase enabled 73% correct individual-patient classifications (sensitivity, 80%; specificity, 67%; P < .001), whereas data from the extinction phase led to an accuracy of 74% (sensitivity, 64%; specificity, 83%; P < .001). Conservative reanalyses under consideration of potential confounders yielded nominally lower but comparable accuracy rates (acquisition phase, 70%; extinction phase, 71%; combined, 79%). Predicting treatment response to CBT based on functional neuroimaging data in PD/AG is possible with high accuracy on an individual-patient level. This novel machine-learning approach brings personalized medicine within reach, directly supporting clinical decisions for the selection of treatment options, thus helping to improve response rates.

Performance of immunological response in predicting virological failure.

PubMed

Ingole, Nayana; Mehta, Preeti; Pazare, Amar; Paranjpe, Supriya; Sarkate, Purva

2013-03-01

In HIV-infected individuals on antiretroviral therapy (ART), the decision on when to switch from first-line to second-line therapy is dictated by treatment failure, and this can be measured in three ways: clinically, immunologically, and virologically. While viral load (VL) decreases and CD4 cell increases typically occur together after starting ART, discordant responses may be seen. Hence the current study was designed to determine the immunological and virological response to ART and to evaluate the utility of immunological response to predict virological failure. All treatment-naive HIV-positive individuals aged >18 years who were eligible for ART were enrolled and assessed at baseline, 6 months, and 12 months clinically and by CD4 cell count and viral load estimations. The patients were categorized as showing concordant favorable (CF), immunological only (IO), virological only (VO), and concordant unfavorable responses (CU). The efficiency of immunological failure to predict virological failure was analyzed across various levels of virological failure (VL>50, >500, and >5,000 copies/ml). At 6 months, 87(79.81%), 7(5.5%), 13 (11.92%), and 2 (1.83%) patients and at 12 months 61(69.3%), 9(10.2%), 16 (18.2%), and 2 (2.3%) patients had CF, IO, VO, and CU responses, respectively. Immunological failure criteria had a very low sensitivity (11.1-40%) and positive predictive value (8.3-25%) to predict virological failure. Immunological criteria do not accurately predict virological failure resulting in significant misclassification of therapeutic responses. There is an urgent need for inclusion of viral load testing in the initiation and monitoring of ART.

Serial MR diffusion to predict treatment response in high-grade pediatric brain tumors: a comparison of regional and voxel-based diffusion change metrics

PubMed Central

Rodriguez Gutierrez, Daniel; Manita, Muftah; Jaspan, Tim; Dineen, Robert A.; Grundy, Richard G.; Auer, Dorothee P.

2013-01-01

Background Assessment of treatment response by measuring tumor size is known to be a late and potentially confounded response index. Serial diffusion MRI has shown potential for allowing earlier and possibly more reliable response assessment in adult patients, with limited experience in clinical settings and in pediatric brain cancer. We present a retrospective study of clinical MRI data in children with high-grade brain tumors to assess and compare the values of several diffusion change metrics to predict treatment response. Methods Eighteen patients (age range, 1.9–20.6 years) with high-grade brain tumors and serial diffusion MRI (pre- and posttreatment interval range, 1–16 weeks posttreatment) were identified after obtaining parental consent. The following diffusion change metrics were compared with the clinical response status assessed at 6 months: (1) regional change in absolute and normalized apparent diffusivity coefficient (ADC), (2) voxel-based fractional volume of increased (fiADC) and decreased ADC (fdADC), and (3) a new metric based on the slope of the first principal component of functional diffusion maps (fDM). Results Responders (n = 12) differed significantly from nonresponders (n = 6) in all 3 diffusional change metrics demonstrating higher regional ADC increase, larger fiADC, and steeper slopes (P < .05). The slope method allowed the best response prediction (P < .01, η2 = 0.78) with a classification accuracy of 83% for a slope of 58° using receiver operating characteristic (ROC) analysis. Conclusions We demonstrate that diffusion change metrics are suitable response predictors for high-grade pediatric tumors, even in the presence of variable clinical diffusion imaging protocols. PMID:23585630

Drug response in a genetically engineered mouse model of multiple myeloma is predictive of clinical efficacy

PubMed Central

Chesi, Marta; Matthews, Geoffrey M.; Garbitt, Victoria M.; Palmer, Stephen E.; Shortt, Jake; Lefebure, Marcus; Stewart, A. Keith; Johnstone, Ricky W.

2012-01-01

The attrition rate for anticancer drugs entering clinical trials is unacceptably high. For multiple myeloma (MM), we postulate that this is because of preclinical models that overemphasize the antiproliferative activity of drugs, and clinical trials performed in refractory end-stage patients. We validate the Vk*MYC transgenic mouse as a faithful model to predict single-agent drug activity in MM with a positive predictive value of 67% (4 of 6) for clinical activity, and a negative predictive value of 86% (6 of 7) for clinical inactivity. We identify 4 novel agents that should be prioritized for evaluation in clinical trials. Transplantation of Vk*MYC tumor cells into congenic mice selected for a more aggressive disease that models end-stage drug-resistant MM and responds only to combinations of drugs with single-agent activity in untreated Vk*MYC MM. We predict that combinations of standard agents, histone deacetylase inhibitors, bromodomain inhibitors, and hypoxia-activated prodrugs will demonstrate efficacy in the treatment of relapsed MM. PMID:22451422

Infants, mothers, and dyadic contributions to stability and prediction of social stress response at 6 months.

PubMed

Provenzi, Livio; Olson, Karen L; Montirosso, Rosario; Tronick, Ed

2016-01-01

The study of infants' interactive style and social stress response to repeated stress exposures is of great interest for developmental and clinical psychologists. Stable maternal and dyadic behavior is critical to sustain infants' development of an adaptive social stress response, but the association between infants' interactive style and social stress response has received scant attention in previous literature. In the present article, overtime stability of infant, maternal, and dyadic behaviors was measured across 2 social stress (i.e., Face-to-Face Still-Face, FFSF) exposures, separated by 15 days. Moreover, infant, maternal, and dyadic behaviors were simultaneously assessed as predictors of infants' social stress to both FFSF exposures. Eighty-one mother-infant dyads underwent the FFSF twice, at 6 months (Exposure 1: the first social stress) and at 6 months and 15 days (Exposure 2: repeated social stress). Infant and mother behavior and dyadic synchrony were microanalytically coded. Overall, individual behavioral stability emerged between FFSF exposures. Infants' response to the first stress was predicted by infant behavior during Exposure 1 Play. Infants' response to the repeated social stress was predicted by infants' response to the first exposure to the Still-Face and by infants' behavior and dyadic synchrony during Exposure 2 Play. Findings reveal stability for individual, but not for dyadic, behavior between 2 social stress exposures at 6 months. Infants' response to repeated social stress was predicted by infants' earlier stress response, infants' own behavior in play, and dyadic synchrony. No predictive effects of maternal behavior were found. Insights for research and clinical work are discussed. (c) 2015 APA, all rights reserved).

Clinical Factors Predict Atezolizumab Response.

PubMed

2018-04-01

Researchers have presented a new model that uses six readily available clinical factors to predict whether a patient with advanced bladder cancer who has already received platinum chemotherapy will respond to treatment with the PD-L1 inhibitor atezolizumab. The results may help patients and their doctors decide how to proceed with treatment. ©2018 American Association for Cancer Research.

Diffuse optical measurements of head and neck tumor hemodynamics for early prediction of chemoradiation therapy outcomes

NASA Astrophysics Data System (ADS)

Dong, Lixin; Kudrimoti, Mahesh; Irwin, Daniel; Chen, Li; Kumar, Sameera; Shang, Yu; Huang, Chong; Johnson, Ellis L.; Stevens, Scott D.; Shelton, Brent J.; Yu, Guoqiang

2016-08-01

This study used a hybrid near-infrared diffuse optical instrument to monitor tumor hemodynamic responses to chemoradiation therapy for early prediction of treatment outcomes in patients with head and neck cancer. Forty-seven patients were measured once per week to evaluate the hemodynamic status of clinically involved cervical lymph nodes as surrogates for the primary tumor response. Patients were classified into two groups: complete response (CR) (n=29) and incomplete response (IR) (n=18). Tumor hemodynamic responses were found to be associated with clinical outcomes (CR/IR), wherein the associations differed depending on human papillomavirus (HPV-16) status. In HPV-16 positive patients, significantly lower levels in tumor oxygenated hemoglobin concentration ([HbO2]) at weeks 1 to 3, total hemoglobin concentration at week 3, and blood oxygen saturation (StO2) at week 3 were found in the IR group. In HPV-16 negative patients, significantly higher levels in tumor blood flow index and reduced scattering coefficient (μs‧) at week 3 were observed in the IR group. These hemodynamic parameters exhibited significantly high accuracy for early prediction of clinical outcomes, within the first three weeks of therapy, with the areas under the receiver operating characteristic curves (AUCs) ranging from 0.83 to 0.96.

Diffuse optical measurements of head and neck tumor hemodynamics for early prediction of chemoradiation therapy outcomes

PubMed Central

Dong, Lixin; Kudrimoti, Mahesh; Irwin, Daniel; Chen, Li; Kumar, Sameera; Shang, Yu; Huang, Chong; Johnson, Ellis L.; Stevens, Scott D.; Shelton, Brent J.; Yu, Guoqiang

2016-01-01

Abstract. This study used a hybrid near-infrared diffuse optical instrument to monitor tumor hemodynamic responses to chemoradiation therapy for early prediction of treatment outcomes in patients with head and neck cancer. Forty-seven patients were measured once per week to evaluate the hemodynamic status of clinically involved cervical lymph nodes as surrogates for the primary tumor response. Patients were classified into two groups: complete response (CR) (n=29) and incomplete response (IR) (n=18). Tumor hemodynamic responses were found to be associated with clinical outcomes (CR/IR), wherein the associations differed depending on human papillomavirus (HPV-16) status. In HPV-16 positive patients, significantly lower levels in tumor oxygenated hemoglobin concentration ([HbO2]) at weeks 1 to 3, total hemoglobin concentration at week 3, and blood oxygen saturation (StO2) at week 3 were found in the IR group. In HPV-16 negative patients, significantly higher levels in tumor blood flow index and reduced scattering coefficient (μs′) at week 3 were observed in the IR group. These hemodynamic parameters exhibited significantly high accuracy for early prediction of clinical outcomes, within the first three weeks of therapy, with the areas under the receiver operating characteristic curves (AUCs) ranging from 0.83 to 0.96. PMID:27564315

A Prediction Algorithm for Drug Response in Patients with Mesial Temporal Lobe Epilepsy Based on Clinical and Genetic Information

PubMed Central

Carvalho, Benilton S.; Bilevicius, Elizabeth; Alvim, Marina K. M.; Lopes-Cendes, Iscia

2017-01-01

Mesial temporal lobe epilepsy is the most common form of adult epilepsy in surgical series. Currently, the only characteristic used to predict poor response to clinical treatment in this syndrome is the presence of hippocampal sclerosis. Single nucleotide polymorphisms (SNPs) located in genes encoding drug transporter and metabolism proteins could influence response to therapy. Therefore, we aimed to evaluate whether combining information from clinical variables as well as SNPs in candidate genes could improve the accuracy of predicting response to drug therapy in patients with mesial temporal lobe epilepsy. For this, we divided 237 patients into two groups: 75 responsive and 162 refractory to antiepileptic drug therapy. We genotyped 119 SNPs in ABCB1, ABCC2, CYP1A1, CYP1A2, CYP1B1, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A4, and CYP3A5 genes. We used 98 additional SNPs to evaluate population stratification. We assessed a first scenario using only clinical variables and a second one including SNP information. The random forests algorithm combined with leave-one-out cross-validation was used to identify the best predictive model in each scenario and compared their accuracies using the area under the curve statistic. Additionally, we built a variable importance plot to present the set of most relevant predictors on the best model. The selected best model included the presence of hippocampal sclerosis and 56 SNPs. Furthermore, including SNPs in the model improved accuracy from 0.4568 to 0.8177. Our findings suggest that adding genetic information provided by SNPs, located on drug transport and metabolism genes, can improve the accuracy for predicting which patients with mesial temporal lobe epilepsy are likely to be refractory to drug treatment, making it possible to identify patients who may benefit from epilepsy surgery sooner. PMID:28052106

A gene expression signature of RAS pathway dependence predicts response to PI3K and RAS pathway inhibitors and expands the population of RAS pathway activated tumors

PubMed Central

2010-01-01

Background Hyperactivation of the Ras signaling pathway is a driver of many cancers, and RAS pathway activation can predict response to targeted therapies. Therefore, optimal methods for measuring Ras pathway activation are critical. The main focus of our work was to develop a gene expression signature that is predictive of RAS pathway dependence. Methods We used the coherent expression of RAS pathway-related genes across multiple datasets to derive a RAS pathway gene expression signature and generate RAS pathway activation scores in pre-clinical cancer models and human tumors. We then related this signature to KRAS mutation status and drug response data in pre-clinical and clinical datasets. Results The RAS signature score is predictive of KRAS mutation status in lung tumors and cell lines with high (> 90%) sensitivity but relatively low (50%) specificity due to samples that have apparent RAS pathway activation in the absence of a KRAS mutation. In lung and breast cancer cell line panels, the RAS pathway signature score correlates with pMEK and pERK expression, and predicts resistance to AKT inhibition and sensitivity to MEK inhibition within both KRAS mutant and KRAS wild-type groups. The RAS pathway signature is upregulated in breast cancer cell lines that have acquired resistance to AKT inhibition, and is downregulated by inhibition of MEK. In lung cancer cell lines knockdown of KRAS using siRNA demonstrates that the RAS pathway signature is a better measure of dependence on RAS compared to KRAS mutation status. In human tumors, the RAS pathway signature is elevated in ER negative breast tumors and lung adenocarcinomas, and predicts resistance to cetuximab in metastatic colorectal cancer. Conclusions These data demonstrate that the RAS pathway signature is superior to KRAS mutation status for the prediction of dependence on RAS signaling, can predict response to PI3K and RAS pathway inhibitors, and is likely to have the most clinical utility in lung and breast tumors. PMID:20591134

Predicting factors of fistula healing and clinical remission after infliximab-based combined therapy for perianal fistulizing Crohn's disease.

PubMed

Tougeron, David; Savoye, Guillaume; Savoye-Collet, Céline; Koning, Edith; Michot, Francis; Lerebours, Eric

2009-08-01

Perianal fistulizing Crohn's disease (PFCD) treatment is based on fistula drainage, antibiotics, immunosuppressant (IS) drugs, and infliximab. Our aim was to study the effectiveness of combination therapy on PFCD and to search for clinical or imaging features associated with the initial complete clinical response and its stability overtime. All patients with PFCD treated in our tertiary center between 2000 and 2005 by infliximab in combination with seton placement and/or IS and evaluated by MRI before treatment were included in the study. Basal clinical and MRI characteristics were recorded. Response to treatment was evaluated after the infliximab induction regiment and at the end of the follow-up. Twenty-six patients were included and followed-up for an average 4.9 years. A complex fistula was present in 69% (18/26 patients) of cases and eight (8/26 patients) had an ano-vaginal fistula. After infliximab induction therapy, 13 patients (50%) achieved a complete clinical response. The initial clinical response was significantly associated with the absence of both, active intestinal disease (54% vs. 8%, P = 0.03) and active proctitis (77% vs. 23%, P = 0.01). No initial MRI characteristics were linked to the initial response. In multivariate analysis, only the presence of active proctitis was associated with the lack of response (P = 0.047). At the end of the follow-up, 42% of the patients remained in clinical remission. No clinical characteristics were associated to sustained response when among long-standing responders two exhibited a normal post-treatment MRI. An initial complete response of PFCD was observed in half of the patients after combined therapy including infliximab that decreased to 42% later on. Complete healing of fistulas on MRI was possible but unusual. The initial response seemed related to the absence of active intestinal disease, especially in the rectum, when the long-term response could not be predicted by the basal characteristics of patients.

Thymidine phosphorylase and hypoxia-inducible factor 1-α expression in clinical stage II/III rectal cancer: association with response to neoadjuvant chemoradiation therapy and prognosis.

PubMed

Lin, Shuhan; Lai, Hao; Qin, Yuzhou; Chen, Jiansi; Lin, Yuan

2015-01-01

The aim of this study was to determine whether pretreatment status of thymidine phosphorylase (TP), and hypoxia-inducible factor alpha (HIF-1α) could predict pathologic response to neoadjuvant chemoradiation therapy with oxaliplatin and capecitabine (XELOXART) and outcomes for clinical stage II/III rectal cancer patients. A total of 180 patients diagnosed with clinical stage II/III rectal cancer received XELOXART. The status of TP, and HIF-1α were determined in pretreatment biopsies by immunohistochemistry (IHC). Tumor response was assessed in resected regimens using the tumor regression grade system and TNM staging system. 5-year disease free survival (DFS) and 5-year overall survival (OS) were evaluated with the Kaplan-Meier method and were compared by the log-rank test. Over expression of TP and low expression of HIF-1α were associated with pathologic response to XELOXART and better outcomes (DFS and OS) in clinical stage II/III rectal cancer patients (P < 0.05). Our result suggested that pretreatment status of TP and HIF-1α were found to predict pathologic response and outcomes in clinical stage II/III rectal cancer received XELOXART. Additional well-designed, large sample, multicenter, prospective studies are needed to confirm the result of this study.

Clinical utility and validity of minoxidil response testing in androgenetic alopecia.

PubMed

Goren, Andy; Shapiro, Jerry; Roberts, Janet; McCoy, John; Desai, Nisha; Zarrab, Zoulikha; Pietrzak, Aldona; Lotti, Torello

2015-01-01

Clinical response to 5% topical minoxidil for the treatment of androgenetic alopecia (AGA) is typically observed after 3-6 months. Approximately 40% of patients will regrow hair. Given the prolonged treatment time required to elicit a response, a diagnostic test for ruling out nonresponders would have significant clinical utility. Two studies have previously reported that sulfotransferase enzyme activity in plucked hair follicles predicts a patient's response to topical minoxidil therapy. The aim of this study was to assess the clinical utility and validity of minoxidil response testing. In this communication, the present authors conducted an analysis of completed and ongoing studies of minoxidil response testing. The analysis confirmed the clinical utility of a sulfotransferase enzyme test in successfully ruling out 95.9% of nonresponders to topical minoxidil for the treatment of AGA. © 2014 Wiley Periodicals, Inc.

Reduced short interval cortical inhibition correlates with atomoxetine response in children with attention-deficit hyperactivity disorder (ADHD).

PubMed

Chen, Tina H; Wu, Steve W; Welge, Jeffrey A; Dixon, Stephan G; Shahana, Nasrin; Huddleston, David A; Sarvis, Adam R; Sallee, Floyd R; Gilbert, Donald L

2014-12-01

Clinical trials in children with attention-deficit hyperactivity disorder (ADHD) show variability in behavioral responses to the selective norepinephrine reuptake inhibitor atomoxetine. The objective of this study was to determine whether transcranial magnetic stimulation-evoked short interval cortical inhibition might be a biomarker predicting, or correlating with, clinical atomoxetine response. At baseline and after 4 weeks of atomoxetine treatment in 7- to 12-year-old children with ADHD, transcranial magnetic stimulation short interval cortical inhibition was measured, blinded to clinical improvement. Primary analysis was by multivariate analysis of covariance. Baseline short interval cortical inhibition did not predict clinical responses. However, paradoxically, after 4 weeks of atomoxetine, mean short interval cortical inhibition was reduced 31.9% in responders and increased 6.1% in nonresponders (analysis of covariance t 41 = 2.88; P = .0063). Percentage reductions in short interval cortical inhibition correlated with reductions in the ADHD Rating Scale (r = 0.50; P = .0005). In children ages 7 to 12 years with ADHD treated with atomoxetine, improvements in clinical symptoms are correlated with reductions in motor cortex short interval cortical inhibition. © The Author(s) 2014.

Illness severity and self-efficacy as course predictors of DSM-IV alcohol dependence in a multisite clinical sample.

PubMed

Langenbucher, J; Sulesund, D; Chung, T; Morgenstern, J

1996-01-01

Illness severity and self-efficacy are two constructs of growing interest as predictors of clinical response in alcoholism. Using alternative measures of illness severity (DSM-IV symptom count, Alcohol Dependence Scale, and Addiction Severity Index) and self-efficacy (brief version of the Situational Confidence Questionnaire) rigorously controlled for theoretically important background variables, we studied their unique contribution to multiple indices of relapse, relapse latency, and use of alternative coping behaviors in a large, heterogeneous clinical sample. The Alcohol Dependence Scale contributed to the prediction of 4 of 5 relapse indicators. The SCQ failed to predict relapse behavior or its precursor, coping response. The findings emphasize the predictive validity of severity of dependence as a course specifier and underline the need for more sensitive and externally valid measures of cognitive processes such as self-efficacy for application in future studies of posttreatment behavior.

Inhaled corticosteroids do not influence the early inflammatory response and clinical presentation of hospitalized subjects with COPD exacerbation.

PubMed

Crisafulli, Ernesto; Guerrero, Mónica; Menéndez, Rosario; Huerta, Arturo; Martinez, Raquel; Gimeno, Alexandra; Soler, Néstor; Torres, Antoni

2014-10-01

Inhaled corticosteroids are anti-inflammatory medications that can down-regulate the immunologic response in patients with COPD; however, their role at onset of COPD exacerbation is still not understood. The aim of this study was to assess the early inflammatory response and clinical presentation of patients with COPD exacerbation mediated by inhaled corticosteroids. Prospective data were collected on 123 hospitalized subjects with COPD exacerbation over a 30-month period at 2 Spanish university hospitals. Based on domiciliary use, comparative analyses were performed between subjects who did not use inhaled corticosteroids (n = 58) and subjects who did (n = 65). Measurements of serum biomarkers were recorded on admission to the hospital (day 1) and on day 3; clinical, physiological, microbiological, and severity data and mortality/readmission rates were also recorded. At days 1 and 3, both groups showed a similar inflammatory response; fluticasone produced lower levels of interleukin-8 compared with budesonide (P < .01). All clinical features considered were similar in the 2 groups; multivariate analysis predicting clinical complications on hospitalization showed air-flow obstruction severity as the only predictive factor (odds ratio 3.13, 95% CI 1.13-8.63, P = .02). Our study demonstrates a lack of inhaled corticosteroid influence in the early systemic inflammatory response to and clinical presentation of COPD exacerbation. Copyright © 2014 by Daedalus Enterprises.

Motor Asymmetry and Substantia Nigra Volume Are Related to Spatial Delayed Response Performance in Parkinson Disease

ERIC Educational Resources Information Center

Foster, Erin R.; Black, Kevin J.; Antenor-Dorsey, Jo Ann V.; Perlmutter, Joel S.; Hershey, Tamara

2008-01-01

Studies suggest motor deficit asymmetry may help predict the pattern of cognitive impairment in individuals with Parkinson disease (PD). We tested this hypothesis using a highly validated and sensitive spatial memory task, spatial delayed response (SDR), and clinical and neuroimaging measures of PD asymmetry. We predicted SDR performance would be…

Predicting therapeutic nanomedicine efficacy using a companion magnetic resonance imaging nanoparticle.

PubMed

Miller, Miles A; Gadde, Suresh; Pfirschke, Christina; Engblom, Camilla; Sprachman, Melissa M; Kohler, Rainer H; Yang, Katherine S; Laughney, Ashley M; Wojtkiewicz, Gregory; Kamaly, Nazila; Bhonagiri, Sushma; Pittet, Mikael J; Farokhzad, Omid C; Weissleder, Ralph

2015-11-18

Therapeutic nanoparticles (TNPs) have shown heterogeneous responses in human clinical trials, raising questions of whether imaging should be used to identify patients with a higher likelihood of NP accumulation and thus therapeutic response. Despite extensive debate about the enhanced permeability and retention (EPR) effect in tumors, it is increasingly clear that EPR is extremely variable; yet, little experimental data exist to predict the clinical utility of EPR and its influence on TNP efficacy. We hypothesized that a 30-nm magnetic NP (MNP) in clinical use could predict colocalization of TNPs by magnetic resonance imaging (MRI). To this end, we performed single-cell resolution imaging of fluorescently labeled MNPs and TNPs and studied their intratumoral distribution in mice. MNPs circulated in the tumor microvasculature and demonstrated sustained uptake into cells of the tumor microenvironment within minutes. MNPs could predictably demonstrate areas of colocalization for a model TNP, poly(d,l-lactic-co-glycolic acid)-b-polyethylene glycol (PLGA-PEG), within the tumor microenvironment with >85% accuracy and circulating within the microvasculature with >95% accuracy, despite their markedly different sizes and compositions. Computational analysis of NP transport enabled predictive modeling of TNP distribution based on imaging data and identified key parameters governing intratumoral NP accumulation and macrophage uptake. Finally, MRI accurately predicted initial treatment response and drug accumulation in a preclinical efficacy study using a paclitaxel-encapsulated NP in tumor-bearing mice. These approaches yield valuable insight into the in vivo kinetics of NP distribution and suggest that clinically relevant imaging modalities and agents can be used to select patients with high EPR for treatment with TNPs. Copyright © 2015, American Association for the Advancement of Science.

Qualitative radiology assessment of tumor response: does it measure up?

PubMed

Gottlieb, Ronald H; Litwin, Alan; Gupta, Bhavna; Taylor, John; Raczyk, Cheryl; Mashtare, Terry; Wilding, Gregory; Fakih, Marwan

2008-01-01

Our purpose was to assess whether a simpler qualitative evaluation of tumor response by computed tomography is as reproducible and predictive of clinical outcome as the Response Evaluation Criteria in Solid Tumors (RECIST) and World Health Organization (WHO) methods. This study was a two-reader retrospective evaluation in which qualitative assessment resulted in agreement in 21 of 23 patients with metastatic colorectal carcinoma (91.3%, kappa=0.78; 95% CI, 0.51-1.00). Hepatic metastases were classified as increased, decreased, or unchanged, compared with agreement in 20 of 23 patients (87.0%) for RECIST (kappa=0.62; 95% CI, 0.23-1.00) and WHO (kappa=0.67; 95% CI, 0.34-1.00) methods. Patients were placed into partial response, stable disease, and disease progression categories. Time to progression of disease was better predicted qualitatively than by RECIST or WHO. Our pilot data suggest that our qualitative scoring system is more reproducible and predictive of patient clinical outcome than the RECIST and WHO methods.

Prediction of remission of depression with clinical variables, neuropsychological performance, and serotonergic/dopaminergic gene polymorphisms.

PubMed

Gudayol-Ferré, Esteve; Herrera-Guzmán, Ixchel; Camarena, Beatriz; Cortés-Penagos, Carlos; Herrera-Abarca, Jorge E; Martínez-Medina, Patricia; Asbun-Bojalil, Juan; Lira-Islas, Yuridia; Reyes-Ponce, Celia; Guàrdia-Olmos, Joan

2012-11-01

The aim of our work is to study the possible role of clinical variables, neuropsychological performance, and the 5HTTLPR, rs25531, and val108/58Met COMT polymorphisms on the prediction of depression remission after 12 weeks' treatment with fluoxetine. These variables have been studied as potential predictors of depression remission, but they present poor prognostic sensitivity and specificity by themselves. Seventy-two depressed patients were genotyped according to the aforementioned polymorphisms and were clinically and neuropsychologically assessed before a 12-week fluxetine treatment. Only the La allele of rs25531 polymorphism and the GG and AA forms of the val 108/158 Met polymorphism predict major depressive disorder remission after 12 weeks' treatment with fluoxetine. None of the clinical and neuropsychological variables studied predicted remission. Our results suggest that clinical and neuropsychological variables can initially predict early response to fluoxetine and mask the predictive role of genetic variables; but in remission, where clinical and neuropsychological symptoms associated with depression tend to disappear thanks to the treatment administered, the polymorphisms studied are the only variables in our model capable of predicting remission. However, placebo effects that are difficult to control require cautious interpretation of the results.

Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib

PubMed Central

Li, Bin; Shin, Hyunjin; Gulbekyan, Georgy; Pustovalova, Olga; Nikolsky, Yuri; Hope, Andrew; Bessarabova, Marina; Schu, Matthew; Kolpakova-Hart, Elona; Merberg, David; Dorner, Andrew; Trepicchio, William L.

2015-01-01

Development of drug responsive biomarkers from pre-clinical data is a critical step in drug discovery, as it enables patient stratification in clinical trial design. Such translational biomarkers can be validated in early clinical trial phases and utilized as a patient inclusion parameter in later stage trials. Here we present a study on building accurate and selective drug sensitivity models for Erlotinib or Sorafenib from pre-clinical in vitro data, followed by validation of individual models on corresponding treatment arms from patient data generated in the BATTLE clinical trial. A Partial Least Squares Regression (PLSR) based modeling framework was designed and implemented, using a special splitting strategy and canonical pathways to capture robust information for model building. Erlotinib and Sorafenib predictive models could be used to identify a sub-group of patients that respond better to the corresponding treatment, and these models are specific to the corresponding drugs. The model derived signature genes reflect each drug’s known mechanism of action. Also, the models predict each drug’s potential cancer indications consistent with clinical trial results from a selection of globally normalized GEO expression datasets. PMID:26107615

Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib.

PubMed

Li, Bin; Shin, Hyunjin; Gulbekyan, Georgy; Pustovalova, Olga; Nikolsky, Yuri; Hope, Andrew; Bessarabova, Marina; Schu, Matthew; Kolpakova-Hart, Elona; Merberg, David; Dorner, Andrew; Trepicchio, William L

2015-01-01

Development of drug responsive biomarkers from pre-clinical data is a critical step in drug discovery, as it enables patient stratification in clinical trial design. Such translational biomarkers can be validated in early clinical trial phases and utilized as a patient inclusion parameter in later stage trials. Here we present a study on building accurate and selective drug sensitivity models for Erlotinib or Sorafenib from pre-clinical in vitro data, followed by validation of individual models on corresponding treatment arms from patient data generated in the BATTLE clinical trial. A Partial Least Squares Regression (PLSR) based modeling framework was designed and implemented, using a special splitting strategy and canonical pathways to capture robust information for model building. Erlotinib and Sorafenib predictive models could be used to identify a sub-group of patients that respond better to the corresponding treatment, and these models are specific to the corresponding drugs. The model derived signature genes reflect each drug's known mechanism of action. Also, the models predict each drug's potential cancer indications consistent with clinical trial results from a selection of globally normalized GEO expression datasets.

Body mass index influences infliximab post-infusion levels and correlates with prospective loss of response to the drug in a cohort of inflammatory bowel disease patients under maintenance therapy with Infliximab.

PubMed

Scaldaferri, Franco; D'Ambrosio, Daria; Holleran, Grainne; Poscia, Andrea; Petito, Valentina; Lopetuso, Loris; Graziani, Cristina; Laterza, Lucrezia; Pistone, Maria Teresa; Pecere, Silvia; Currò, Diego; Gaetani, Eleonora; Armuzzi, Alessandro; Papa, Alfredo; Cammarota, Giovanni; Gasbarrini, Antonio

2017-01-01

Infliximab is an effective treatment for inflammatory bowel disease (IBD). Studies differ regarding the influence of body mass index (BMI) on the response to infliximab, with the majority of studies indicating that increased BMI may be associated with a poorer response to Infliximab. However, the pharmacokinetic mechanisms causing this have not yet been reported. Examine the correlation between BMI/immunosuppressant use with clinical response, trough and post-infusion levels of infliximab, tumour necrosis factor-α(TNF-α) and anti-drug antibodies(ATI), and determine if these factors can predict future response. We collected serum from 24 patients receiving Infliximab before and 30 minutes following infusion. Clinical parameters were collected retrospectively and prospectively. ELISA measurements of infliximab, TNF-α and ATI were performed. We confirmed that patients with higher infliximab trough levels have a better response rate and that patients with an elevated BMI display a higher rate of loss of response (20%). Patients with a higher BMI had elevated post-infusion levels of infliximab. Additionally, the ratio of IFX/TNF-α trough levels correlated with clinical response to the following infusion. This study confirms that an elevated BMI is associated with a poorer response to infliximab. For the first time, we describe that a higher BMI correlates with higher post-infusion levels, however this does not correlate with a higher rate of response to the drug, suggesting that circulating drug levels do not correlate with tissue levels. Furthermore, in our small cohort of patients, we identified a possible predictive marker of future response to treatment which may be used to guide dose escalation and predict non-response to infliximab.

Prospective assessment of a gene signature potentially predictive of clinical benefit in metastatic melanoma patients following MAGE-A3 immunotherapeutic (PREDICT).

PubMed

Saiag, P; Gutzmer, R; Ascierto, P A; Maio, M; Grob, J-J; Murawa, P; Dreno, B; Ross, M; Weber, J; Hauschild, A; Rutkowski, P; Testori, A; Levchenko, E; Enk, A; Misery, L; Vanden Abeele, C; Vojtek, I; Peeters, O; Brichard, V G; Therasse, P

2016-10-01

Genomic profiling of tumor tissue may aid in identifying predictive or prognostic gene signatures (GS) in some cancers. Retrospective gene expression profiling of melanoma and non-small-cell lung cancer led to the characterization of a GS associated with clinical benefit, including improved overall survival (OS), following immunization with the MAGE-A3 immunotherapeutic. The goal of the present study was to prospectively evaluate the predictive value of the previously characterized GS. An open-label prospective phase II trial ('PREDICT') in patients with MAGE-A3-positive unresectable stage IIIB-C/IV-M1a melanoma. Of 123 subjects who received the MAGE-A3 immunotherapeutic, 71 (58.7%) displayed the predictive GS (GS+). The 1-year OS rate was 83.1%/83.3% in the GS+/GS- populations. The rate of progression-free survival at 12 months was 5.8%/4.1% in GS+/GS- patients. The median time-to-treatment failure was 2.7/2.4 months (GS+/GS-). There was one complete response (GS-) and two partial responses (GS+). The MAGE-A3 immunotherapeutic was similarly immunogenic in both populations and had a clinically acceptable safety profile. Treatment of patients with MAGE-A3-positive unresectable stage IIIB-C/IV-M1a melanoma with the MAGE-A3 immunotherapeutic demonstrated an overall 1-year OS rate of 83.5%. GS- and GS+ patients had similar 1-year OS rates, indicating that in this study, GS was not predictive of outcome. Unexpectedly, the objective response rate was lower in this study than in other studies carried out in the same setting with the MAGE-A3 immunotherapeutic. Investigation of a GS to predict clinical benefit to adjuvant MAGE-A3 immunotherapeutic treatment is ongoing in another melanoma study.This study is registered at www.clinicatrials.gov NCT00942162. © The Author 2016. Published by Oxford University Press on behalf of the European Society for Medical Oncology.

Predicting outcome of Internet-based treatment for depressive symptoms.

PubMed

Warmerdam, Lisanne; Van Straten, Annemieke; Twisk, Jos; Cuijpers, Pim

2013-01-01

In this study we explored predictors and moderators of response to Internet-based cognitive behavioral therapy (CBT) and Internet-based problem-solving therapy (PST) for depressive symptoms. The sample consisted of 263 participants with moderate to severe depressive symptoms. Of those, 88 were randomized to CBT, 88 to PST and 87 to a waiting list control condition. Outcomes were improvement and clinically significant change in depressive symptoms after 8 weeks. Higher baseline depression and higher education predicted improvement, while higher education, less avoidance behavior and decreased rational problem-solving skills predicted clinically significant change across all groups. No variables were found that differentially predicted outcome between Internet-based CBT and Internet-based PST. More research is needed with sufficient power to investigate predictors and moderators of response to reveal for whom Internet-based therapy is best suited.

Multiple biomarkers in molecular oncology. II. Molecular diagnostics applications in breast cancer management.

PubMed

Malinowski, Douglas P

2007-05-01

In recent years, the application of genomic and proteomic technologies to the problem of breast cancer prognosis and the prediction of therapy response have begun to yield encouraging results. Independent studies employing transcriptional profiling of primary breast cancer specimens using DNA microarrays have identified gene expression profiles that correlate with clinical outcome in primary breast biopsy specimens. Recent advances in microarray technology have demonstrated reproducibility, making clinical applications more achievable. In this regard, one such DNA microarray device based upon a 70-gene expression signature was recently cleared by the US FDA for application to breast cancer prognosis. These DNA microarrays often employ at least 70 gene targets for transcriptional profiling and prognostic assessment in breast cancer. The use of PCR-based methods utilizing a small subset of genes has recently demonstrated the ability to predict the clinical outcome in early-stage breast cancer. Furthermore, protein-based immunohistochemistry methods have progressed from using gene clusters and gene expression profiling to smaller subsets of expressed proteins to predict prognosis in early-stage breast cancer. Beyond prognostic applications, DNA microarray-based transcriptional profiling has demonstrated the ability to predict response to chemotherapy in early-stage breast cancer patients. In this review, recent advances in the use of multiple markers for prognosis of disease recurrence in early-stage breast cancer and the prediction of therapy response will be discussed.

Bladder cancer treatment response assessment with radiomic, clinical, and radiologist semantic features

NASA Astrophysics Data System (ADS)

Gordon, Marshall N.; Cha, Kenny H.; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Cohan, Richard H.; Caoili, Elaine M.; Paramagul, Chintana; Alva, Ajjai; Weizer, Alon Z.

2018-02-01

We are developing a decision support system for assisting clinicians in assessment of response to neoadjuvant chemotherapy for bladder cancer. Accurate treatment response assessment is crucial for identifying responders and improving quality of life for non-responders. An objective machine learning decision support system may help reduce variability and inaccuracy in treatment response assessment. We developed a predictive model to assess the likelihood that a patient will respond based on image and clinical features. With IRB approval, we retrospectively collected a data set of pre- and post- treatment CT scans along with clinical information from surgical pathology from 98 patients. A linear discriminant analysis (LDA) classifier was used to predict the likelihood that a patient would respond to treatment based on radiomic features extracted from CT urography (CTU), a radiologist's semantic feature, and a clinical feature extracted from surgical and pathology reports. The classification accuracy was evaluated using the area under the ROC curve (AUC) with a leave-one-case-out cross validation. The classification accuracy was compared for the systems based on radiomic features, clinical feature, and radiologist's semantic feature. For the system based on only radiomic features the AUC was 0.75. With the addition of clinical information from examination under anesthesia (EUA) the AUC was improved to 0.78. Our study demonstrated the potential of designing a decision support system to assist in treatment response assessment. The combination of clinical features, radiologist semantic features and CTU radiomic features improved the performance of the classifier and the accuracy of treatment response assessment.

TU-CD-BRB-09: Prediction of Chemo-Radiation Outcome for Rectal Cancer Based On Radiomics of Tumor Clinical Characteristics and Multi-Parametric MRI

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nie, K; Yue, N; Shi, L

2015-06-15

Purpose: To evaluate the tumor clinical characteristics and quantitative multi-parametric MR imaging features for prediction of response to chemo-radiation treatment (CRT) in locally advanced rectal cancer (LARC). Methods: Forty-three consecutive patients (59.7±6.9 years, from 09/2013 – 06/2014) receiving neoadjuvant CRT followed by surgery were enrolled. All underwent MRI including anatomical T1/T2, Dynamic Contrast Enhanced (DCE)-MRI and Diffusion-Weighted MRI (DWI) prior to the treatment. A total of 151 quantitative features, including morphology/Gray Level Co-occurrence Matrix (GLCM) texture from T1/T2, enhancement kinetics and the voxelized distribution from DCE-MRI, apparent diffusion coefficient (ADC) from DWI, along with clinical information (carcinoembryonic antigen CEA level,more » TNM staging etc.), were extracted for each patient. Response groups were separated based on down-staging, good response and pathological complete response (pCR) status. Logistic regression analysis (LRA) was used to select the best predictors to classify different groups and the predictive performance were calculated using receiver operating characteristic (ROC) analysis. Results: Individual imaging category or clinical charateristics might yield certain level of power in assessing the response. However, the combined model outperformed than any category alone in prediction. With selected features as Volume, GLCM AutoCorrelation (T2), MaxEnhancementProbability (DCE-MRI), and MeanADC (DWI), the down-staging prediciton accuracy (area under the ROC curve, AUC) could be 0.95, better than individual tumor metrics with AUC from 0.53–0.85. While for the pCR prediction, the best set included CEA (clinical charateristics), Homogeneity (DCE-MRI) and MeanADC (DWI) with an AUC of 0.89, more favorable compared to conventional tumor metrics with an AUC ranging from 0.511–0.79. Conclusion: Through a systematic analysis of multi-parametric MR imaging features, we are able to build models with improved predictive value over conventional imaging or clinical metrics. This is encouraging, suggesting the wealth of imaging radiomics should be further explored to help tailor the treatment into the era of personalized medicine. This work is supported by the National Science Foundation of China (NSFC Grant No. 81201091), National High Technology Research and Development Program of China (863 program, Grant No. 2015AA020917), and Fund Project for Excellent Abroad Scholar Personnel in Science and Technology.« less

Proliferation kinetics and cyclic AMP as prognostic factors in adult acute leukemia.

PubMed

Paietta, E; Mittermayer, K; Schwarzmeier, J

1980-07-01

In 41 adult patients with acute leukemia (myeloblastic, lymphoblastic, and undifferentiated), proliferation kinetics (as determined by double-label autoradiography) and cyclic adenosine 3',5'-monophosphate (cAMP) concentration were studied for their significance in the prediction of responsiveness to cytostatic therapy. Patients with good clinical response had significantly shorter turnover times and higher labeling indices in the bone marrow than did those who failed to respond to treatment. Cases for which cell kinetics did not correlate with clinical response were explained by variance in the distribution of leukemic blasts between the proliferative cell cycle and the resting pool. Good clinical response was also found to be associated with low levels of cAMP in leukemic cells prior to therapy, whereas high cAMP contents predicted failure. Low cAMP concentrations, however, did not necessarily correlate with short turnover times and vice versa. This might be due to fluctuations of the cAMP concentrations during the cell cycle.

Crohn Disease: FDG PET/CT Before and After Initial Dose of Anti-Tumor Necrosis Factor Therapy to Predict Long-term Response.

PubMed

Epelboym, Yan; Shyn, Paul B; Chick, Jeffrey Forris Beecham; Hamilton, Matthew J; OʼConnor, Stacy D; Silverman, Stuart G; Kim, Chun K

2017-11-01

Clinical assessments of Crohn disease activity are limited in their capacity to assess treatment response to biologic therapy. The purpose of this study was to determine if changes in FDG activity between baseline PET and repeat PET performed prior to the second dose of induction anti-tumor necrosis factor (TNF) therapy correlate with clinical response. In this prospective, institutional review board-approved, Health Insurance Portability and Accountability Act-compliant pilot study of 8 adult patients with active Crohn disease, FDG activity before and 2 weeks after initiation of anti-TNF therapy was assessed using low-dose PET/CT. FDG activity was measured in the most inflamed bowel loop using an SUVratio (SUVmax/liver SUVmean). Changes in SUVratio were compared with a blinded gastroenterologist assessment of clinical response and steroid-free remission, as well as C-reactive protein (CRP), during a 12-month follow-up period. Of 8 patients, 7 showed FDG activity decline at 2 weeks, 5 of whom achieved a clinical response and steroid-free remission at 8, 26, and 52 weeks. The remaining 2 patients with FDG activity decline did not achieve a clinical response or steroid-free remission at these time points, but there were reductions in CRP. The 1 patient without FDG activity decline was a clinical nonresponder, had no reduction in CRP, and did not achieve steroid-free remission. A change in FDG activity at FDG PET/CT performed prior to the second induction dose of anti-TNF therapy has the potential to predict clinical response and steroid-free remission in patients with Crohn disease.

Do circulating long non-coding RNAs (lncRNAs) (LincRNA-p21, GAS 5, HOTAIR) predict the treatment response in patients with head and neck cancer treated with chemoradiotherapy?

PubMed

Fayda, Merdan; Isin, Mustafa; Tambas, Makbule; Guveli, Murat; Meral, Rasim; Altun, Musa; Sahin, Dilek; Ozkan, Gozde; Sanli, Yasemin; Isin, Husniye; Ozgur, Emre; Gezer, Ugur

2016-03-01

Long non-coding RNAs (lncRNAs) have been shown to be aberrantly expressed in head and neck cancer (HNC). The aim of the present study was to evaluate plasma levels of three lncRNA molecules (lincRNA-p21, GAS5, and HOTAIR) in the treatment response in HNC patients treated with radical chemoradiotherapy (CRT). Forty-one patients with HNC were enrolled in the study. Most of the patients had nasopharyngeal carcinoma (n = 27, 65.9 %) and locally advanced disease. Blood was drawn at baseline and treatment evaluation 4.5 months after therapy. lncRNAs in plasma were measured by semiquantitative PCR. Treatment response was evaluated according to clinical examination, RECIST and PERCIST criteria based on magnetic resonance imaging (MRI), and positron emission tomography with computed tomography (PET/CT) findings. Complete response (CR) rates were 73.2, 36.6, and 50 % for clinical investigation, PET/CT-, or MRI-based response evaluation, respectively. Predictive value of lncRNAs was investigated in patients with CR vs. those with partial response (PR)/progressive disease (PD). We found that post-treatment GAS5 levels in patients with PR/PD were significantly higher compared with patients with CR based on clinical investigation (p = 0.01). Receiver operator characteristic (ROC) analysis showed that at a cutoff value of 0.3 of GAS5, sensitivity and specificity for clinical tumor response were 82 and 77 %, respectively. Interestingly, pretreatment GAS5 levels were significantly increased in patients with PR/PD compared to those with CR upon MRI-based response evaluation (p = 0.042). In contrast to GAS5, pretreatment or post-treatment lincRNA-p21 and HOTAIR levels were not informative for treatment response. Our results suggest that circulating GAS5 could be a biomarker in predicting treatment response in HNC patients.

Clinical profile of responders to buprenorphine as a substitution treatment in heroin addicts: results of a multicenter study of 73 patients.

PubMed

Poirier, Marie-France; Laqueille, Xavier; Jalfre, Valérie; Willard, Dominique; Bourdel, Marie Chantal; Fermanian, Jacques; Olié, Jean Pierre

2004-03-01

In France, high-dosage buprenorphine (HDB) is the main substitution treatment for narcotic addiction. Few data have been published concerning clinical factors predicting a good response to this treatment in a daily practice. A hospital-based multicenter clinical research program (PHRC) was undertaken in heroin-addicted patients, diagnosed according to DSM-III-R, to detect clinical criteria susceptible of predicting a good response to HDB administered during a 3-month treatment period. At the inclusion time in the study, a diagnostic structured interview (DIGS) was performed, and the Addiction Severity Index (ASI), Zuckerman scale, depression scale from Jouvent, and CGI were scored. MMPI was also administered. Good response was defined as an ongoing participation in the study, with absence of opiate detected in 75% of urine collected during the last month of treatment. Only subjects treated for at least 1 month were eligible for analyses. One hundred fifteen patients were recruited and 73 were analyzed. Patients received 8.5+/-2.6 mg (m+/-S.D.) of buprenorphine for 1 to 3 months. A forward stepwise logistic regression showed that six clinical parameters may predict a good response to treatment: probability to respond to buprenorphine was higher in subjects having a high psychopathology (ASI) subscore, low disinhibition and boredom susceptibility factor scores (Zuckerman scale), no alcohol dependence, no family history of addiction or mood disorder, and duration of opiate dependence less than 10 years. Only the MMPI D subscale was a psychological pattern correlated to a good response to substitution treatment. These findings are important to consider when making the decision to prescribe HDB substitution treatment in opiate addiction.

The value of integrating pre-clinical data to predict nausea and vomiting risk in humans as illustrated by AZD3514, a novel androgen receptor modulator.

PubMed

Grant, Claire; Ewart, Lorna; Muthas, Daniel; Deavall, Damian; Smith, Simon A; Clack, Glen; Newham, Pete

2016-04-01

Nausea and vomiting are components of a complex mechanism that signals food avoidance and protection of the body against the absorption of ingested toxins. This response can also be triggered by pharmaceuticals. Predicting clinical nausea and vomiting liability for pharmaceutical agents based on pre-clinical data can be problematic as no single animal model is a universal predictor. Moreover, efforts to improve models are hampered by the lack of translational animal and human data in the public domain. AZD3514 is a novel, orally-administered compound that inhibits androgen receptor signaling and down-regulates androgen receptor expression. Here we have explored the utility of integrating data from several pre-clinical models to predict nausea and vomiting in the clinic. Single and repeat doses of AZD3514 resulted in emesis, salivation and gastrointestinal disturbances in the dog, and inhibited gastric emptying in rats after a single dose. AZD3514, at clinically relevant exposures, induced dose-responsive "pica" behaviour in rats after single and multiple daily doses, and induced retching and vomiting behaviour in ferrets after a single dose. We compare these data with the clinical manifestation of nausea and vomiting encountered in patients with castration-resistant prostate cancer receiving AZD3514. Our data reveal a striking relationship between the pre-clinical observations described and the experience of nausea and vomiting in the clinic. In conclusion, the emetic nature of AZD3514 was predicted across a range of pre-clinical models, and the approach presented provides a valuable framework for predicition of clinical nausea and vomiting. Copyright © 2016 Elsevier Inc. All rights reserved.

Prospective Molecular Profiling of Melanoma Metastases Suggests Classifiers of Immune Responsiveness

PubMed Central

Wang, Ena; Miller, Lance D.; Ohnmacht, Galen A.; Mocellin, Simone; Perez-Diez, Ainhoa; Petersen, David; Zhao, Yingdong; Simon, Richard; Powell, John I.; Asaki, Esther; Alexander, H. Richard; Duray, Paul H.; Herlyn, Meenhard; Restifo, Nicholas P.; Liu, Edison T.; Rosenberg, Steven A.; Marincola, Francesco M.

2008-01-01

We amplified RNAs from 63 fine needle aspiration (FNA) samples from 37 s.c. melanoma metastases from 25 patients undergoing immunotherapy for hybridization to a 6108-gene human cDNA chip. By prospectively following the history of the lesions, we could correlate transcript patterns with clinical outcome. Cluster analysis revealed a tight relationship among autologous synchronously sampled tumors compared with unrelated lesions (average Pearson's r = 0.83 and 0.7, respectively, P < 0.0003). As reported previously, two subgroups of metastatic melanoma lesions were identified that, however, had no predictive correlation with clinical outcome. Ranking of gene expression data from pretreatment samples identified ∼30 genes predictive of clinical response (P < 0.001). Analysis of their annotations denoted that approximately half of them were related to T-cell regulation, suggesting that immune responsiveness might be predetermined by a tumor microenvironment conducive to immune recognition. PMID:12097256

Validation of biomarkers to predict response to immunotherapy in cancer: Volume II - clinical validation and regulatory considerations.

PubMed

Dobbin, Kevin K; Cesano, Alessandra; Alvarez, John; Hawtin, Rachael; Janetzki, Sylvia; Kirsch, Ilan; Masucci, Giuseppe V; Robbins, Paul B; Selvan, Senthamil R; Streicher, Howard Z; Zhang, Jenny; Butterfield, Lisa H; Thurin, Magdalena

2016-01-01

There is growing recognition that immunotherapy is likely to significantly improve health outcomes for cancer patients in the coming years. Currently, while a subset of patients experience substantial clinical benefit in response to different immunotherapeutic approaches, the majority of patients do not but are still exposed to the significant drug toxicities. Therefore, a growing need for the development and clinical use of predictive biomarkers exists in the field of cancer immunotherapy. Predictive cancer biomarkers can be used to identify the patients who are or who are not likely to derive benefit from specific therapeutic approaches. In order to be applicable in a clinical setting, predictive biomarkers must be carefully shepherded through a step-wise, highly regulated developmental process. Volume I of this two-volume document focused on the pre-analytical and analytical phases of the biomarker development process, by providing background, examples and "good practice" recommendations. In the current Volume II, the focus is on the clinical validation, validation of clinical utility and regulatory considerations for biomarker development. Together, this two volume series is meant to provide guidance on the entire biomarker development process, with a particular focus on the unique aspects of developing immune-based biomarkers. Specifically, knowledge about the challenges to clinical validation of predictive biomarkers, which has been gained from numerous successes and failures in other contexts, will be reviewed together with statistical methodological issues related to bias and overfitting. The different trial designs used for the clinical validation of biomarkers will also be discussed, as the selection of clinical metrics and endpoints becomes critical to establish the clinical utility of the biomarker during the clinical validation phase of the biomarker development. Finally, the regulatory aspects of submission of biomarker assays to the U.S. Food and Drug Administration as well as regulatory considerations in the European Union will be covered.

Does a family history of RA influence the clinical presentation and treatment response in RA?

PubMed

Frisell, Thomas; Saevarsdottir, Saedis; Askling, Johan

2016-06-01

To assess whether family history of rheumatoid arthritis (RA), among the strongest risk factors for developing RA, also carries information on the clinical presentation and treatment response. The prospective Swedish Rheumatology register was linked to family history of RA, defined as diagnosed RA in any first-degree relative, ascertained through the Swedish Multi-Generation and Patient registers. Clinical presentation was examined among patients with early RA 2000-2011 (symptom onset <12 months before inclusion, N=6869), and response to methotrexate (MTX) monotherapy in the subset starting this treatment (N=4630). Response to tumour necrosis factor inhibitors (TNFi) was examined among all patients with RA starting a TNFi as the first biological disease-modifying antirheumatic drug 2000-2011 (N=9249). Association of family history with clinical characteristics, drug survival, European League Against Rheumatism (EULAR) response and change in disease activity at 3 and 6 months was estimated using linear and generalised logistic regression models. Correlation in relatives' response measures was also assessed. Patients with early RA with family history of RA were more often rheumatoid factor positive, but with no other clinically meaningful differences in their clinical presentation. Family history of RA did not predict response to MTX or TNFi, with the possible exception of no versus good EULAR response to TNFi at 6 months (OR=1.4, 95% CI 1.1 to 1.7). Having a relative who discontinued TNFi within a year increased the odds of doing the same (OR=3.7, 95% CI 1.8 to 7.5), although we found no significant familial correlations in change in disease activity measures. Family history of RA did not modify the clinical presentation of RA or predict response to standard treatment with MTX or TNFi. Treatment response, particularly drug survival, may itself be familial. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Use of reflectance spectrophotometry to predict the response of port wine stains to pulsed dye laser.

PubMed

Halachmi, Shlomit; Azaria, Ron; Inbar, Roy; Ad-El, Dean; Lapidoth, Moshe

2014-01-01

Reflectance spectroscopy can be used to quantitate subtle differences in color. We applied a portable reflectance spectrometer to determine its utility in the evaluation of pulsed dye laser treatment of port wine stains (PWS) and in prediction of clinical outcome, in a prospective study. Forty-eight patients with PWS underwent one to nine pulsed dye laser treatments. Patient age and skin color as well as PWS surface area, anatomic location, and color were recorded. Pretreatment spectrophotometric measurements were performed. The subjective clinical results of treatment and the quantitative spectrophotometry results were evaluated by two independent teams, and the findings were correlated. The impact of the clinical characteristics on the response to treatment was assessed as well. Patients with excellent to good clinical results of laser treatments had pretreatment spectrophotometric measurements which differed by more than 10%, whereas patients with fair to poor results had spectrophotometric measurements with a difference of of less than 10%. The correlation between the spectrophotometric results and the clinical outcome was 73% (p < 0.01). The impact of the other clinical variables on outcome agreed with the findings in the literature. Spectrophotometry has a higher correlation with clinical outcome and a better predictive value than other nonmeasurable, nonquantitative, dependent variables.

Refining success of cardiac resynchronization therapy using a simple score predicting the amount of reverse ventricular remodelling: results from the Markers and Response to CRT (MARC) study.

PubMed

Maass, Alexander H; Vernooy, Kevin; Wijers, Sofieke C; van 't Sant, Jetske; Cramer, Maarten J; Meine, Mathias; Allaart, Cornelis P; De Lange, Frederik J; Prinzen, Frits W; Gerritse, Bart; Erdtsieck, Erna; Scheerder, Coert O S; Hill, Michael R S; Scholten, Marcoen; Kloosterman, Mariëlle; Ter Horst, Iris A H; Voors, Adriaan A; Vos, Marc A; Rienstra, Michiel; Van Gelder, Isabelle C

2018-02-01

Cardiac resynchronization therapy (CRT) reduces morbidity and mortality in systolic heart failure patients with ventricular conduction delay. Variability of individual response to CRT warrants improved patient selection. The Markers and Response to CRT (MARC) study was designed to investigate markers related to response to CRT. We prospectively studied the ability of 11 clinical, 11 electrocardiographic, 4 echocardiographic, and 16 blood biomarkers to predict CRT response in 240 patients. Response was measured by the reduction of indexed left ventricular end-systolic volume (LVESVi) at 6 months follow-up. Biomarkers were related to LVESVi change using log-linear regression on continuous scale. Covariates that were significant univariately were included in a multivariable model. The final model was utilized to compose a response score. Age was 67 ± 10 years, 63% were male, 46% had ischaemic aetiology, LV ejection fraction was 26 ± 8%, LVESVi was 75 ± 31 mL/m2, and QRS was 178 ± 23 ms. At 6 months LVESVi was reduced to 58 ± 31 mL/m2 (relative reduction of 22 ± 24%), 130 patients (61%) showed ≥ 15% LVESVi reduction. In univariate analysis 17 parameters were significantly associated with LVESVi change. In the final model age, QRSAREA (using vectorcardiography) and two echocardiographic markers (interventricular mechanical delay and apical rocking) remained significantly associated with the amount of reverse ventricular remodelling. This CAVIAR (CRT-Age-Vectorcardiographic QRSAREA -Interventricular Mechanical delay-Apical Rocking) response score also predicted clinical outcome assessed by heart failure hospitalizations and all-cause mortality. The CAVIAR response score predicts the amount of reverse remodelling after CRT and may be used to improve patient selection. Clinical Trials: NCT01519908. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2017. For permissions, please email: journals.permissions@oup.com.

Motivation for change as a predictor of treatment response for dysthymia.

PubMed

Frías Ibáñez, Álvaro; González Vallespí, Laura; Palma Sevillano, Carol; Farriols Hernando, Núria

2016-05-01

Dysthymia constitutes a chronic, mild affective disorder characterized by heterogeneous treatment effects. Several predictors of clinical response and attendance have been postulated, although research on the role of the psychological variables involved in this mental disorder is still scarce. Fifty-four adult patients, who met criteria for dysthymia completed an ongoing naturalistic treatment based on the brief interpersonal psychotherapy (IPT-B), which was delivered bimonthly over 16 months. As potential predictor variables, the therapeutic alliance, coping strategies, perceived self-efficacy, and motivation for change were measured at baseline. Outcome variables were response to treatment (Clinical Global Impression and Beck’s Depression Inventory) and treatment attendance. Stepwise multiple linear regression analyses revealed that higher motivation for change predicted better response to treatment. Moreover, higher motivation for change also predicted treatment attendance. Therapeutic alliance was not a predictor variable of neither clinical response nor treatment attendance. These preliminary findings support the adjunctive use of motivational interviewing (MI) techniques in the treatment of dysthymia. Further research with larger sample size and follow-up assessment is warranted.

Inferences of drug responses in cancer cells from cancer genomic features and compound chemical and therapeutic properties

PubMed Central

Wang, Yongcui; Fang, Jianwen; Chen, Shilong

2016-01-01

Accurately predicting the response of a cancer patient to a therapeutic agent is a core goal of precision medicine. Existing approaches were mainly relied primarily on genomic alterations in cancer cells that have been treated with different drugs. Here we focus on predicting drug response based on integration of the heterogeneously pharmacogenomics data from both cell and drug sides. Through a systematical approach, named as PDRCC (Predict Drug Response in Cancer Cells), the cancer genomic alterations and compound chemical and therapeutic properties were incorporated to determine the chemotherapeutic response in cancer patients. Using the Cancer Cell Line Encyclopedia (CCLE) study as the benchmark dataset, all pharmacogenomics data exhibited their roles in inferring the relationships between cancer cells and drugs. When integrating both genomic resources and compound information, the prediction coverage was significantly increased. The validity of PDRCC was also supported by its effective in uncovering the unknown cell-drug associations with database and literature evidences. It set the stage for clinical testing of novel therapeutic strategies, such as the sensitive association between cancer cell ‘A549_LUNG’ and compound ‘Topotecan’. In conclusion, PDRCC offers the possibility for faster, safer, and cheaper the development of novel anti-cancer therapeutics in the early-stage clinical trails. PMID:27645580

Inferences of drug responses in cancer cells from cancer genomic features and compound chemical and therapeutic properties

NASA Astrophysics Data System (ADS)

Wang, Yongcui; Fang, Jianwen; Chen, Shilong

2016-09-01

Accurately predicting the response of a cancer patient to a therapeutic agent is a core goal of precision medicine. Existing approaches were mainly relied primarily on genomic alterations in cancer cells that have been treated with different drugs. Here we focus on predicting drug response based on integration of the heterogeneously pharmacogenomics data from both cell and drug sides. Through a systematical approach, named as PDRCC (Predict Drug Response in Cancer Cells), the cancer genomic alterations and compound chemical and therapeutic properties were incorporated to determine the chemotherapeutic response in cancer patients. Using the Cancer Cell Line Encyclopedia (CCLE) study as the benchmark dataset, all pharmacogenomics data exhibited their roles in inferring the relationships between cancer cells and drugs. When integrating both genomic resources and compound information, the prediction coverage was significantly increased. The validity of PDRCC was also supported by its effective in uncovering the unknown cell-drug associations with database and literature evidences. It set the stage for clinical testing of novel therapeutic strategies, such as the sensitive association between cancer cell ‘A549_LUNG’ and compound ‘Topotecan’. In conclusion, PDRCC offers the possibility for faster, safer, and cheaper the development of novel anti-cancer therapeutics in the early-stage clinical trails.

Inferences of drug responses in cancer cells from cancer genomic features and compound chemical and therapeutic properties.

PubMed

Wang, Yongcui; Fang, Jianwen; Chen, Shilong

2016-09-20

Accurately predicting the response of a cancer patient to a therapeutic agent is a core goal of precision medicine. Existing approaches were mainly relied primarily on genomic alterations in cancer cells that have been treated with different drugs. Here we focus on predicting drug response based on integration of the heterogeneously pharmacogenomics data from both cell and drug sides. Through a systematical approach, named as PDRCC (Predict Drug Response in Cancer Cells), the cancer genomic alterations and compound chemical and therapeutic properties were incorporated to determine the chemotherapeutic response in cancer patients. Using the Cancer Cell Line Encyclopedia (CCLE) study as the benchmark dataset, all pharmacogenomics data exhibited their roles in inferring the relationships between cancer cells and drugs. When integrating both genomic resources and compound information, the prediction coverage was significantly increased. The validity of PDRCC was also supported by its effective in uncovering the unknown cell-drug associations with database and literature evidences. It set the stage for clinical testing of novel therapeutic strategies, such as the sensitive association between cancer cell 'A549_LUNG' and compound 'Topotecan'. In conclusion, PDRCC offers the possibility for faster, safer, and cheaper the development of novel anti-cancer therapeutics in the early-stage clinical trails.

Longitudinal Risk and Resilience Factors Predicting Psychiatric Disruption, Mental Health Service Utilization & Military Retention in OIF National Guard Troops

DTIC Science & Technology

2009-04-01

completing in-person clinical assessments that include structured clinical interviews and psychological testing . As an introduction to the three...coordinator to opt out of the project. 2.2.2. Analyses of Response Bias. To test for response bias, we compared responders and non-responders to the...used to include these subjects without Wave 2 data in the final analyses. 2.3.2. Analyses of Response Bias. To test for response bias at Wave 3

Response monitoring of breast cancer patients receiving neoadjuvant chemotherapy using quantitative ultrasound, texture, and molecular features

PubMed Central

Gangeh, Mehrdad; Tadayyon, Hadi; Sadeghi-Naini, Ali; Gandhi, Sonal; Wright, Frances C.; Slodkowska, Elzbieta; Curpen, Belinda; Tran, William; Czarnota, Gregory J.

2018-01-01

Background Pathological response of breast cancer to chemotherapy is a prognostic indicator for long-term disease free and overall survival. Responses of locally advanced breast cancer in the neoadjuvant chemotherapy (NAC) settings are often variable, and the prediction of response is imperfect. The purpose of this study was to detect primary tumor responses early after the start of neoadjuvant chemotherapy using quantitative ultrasound (QUS), textural analysis and molecular features in patients with locally advanced breast cancer. Methods The study included ninety six patients treated with neoadjuvant chemotherapy. Breast tumors were scanned with a clinical ultrasound system prior to chemotherapy treatment, during the first, fourth and eighth week of treatment, and prior to surgery. Quantitative ultrasound parameters and scatterer-based features were calculated from ultrasound radio frequency (RF) data within tumor regions of interest. Additionally, texture features were extracted from QUS parametric maps. Prior to therapy, all patients underwent a core needle biopsy and histological subtypes and biomarker ER, PR, and HER2 status were determined. Patients were classified into three treatment response groups based on combination of clinical and pathological analyses: complete responders (CR), partial responders (PR), and non-responders (NR). Response classifications from QUS parameters, receptors status and pathological were compared. Discriminant analysis was performed on extracted parameters using a support vector machine classifier to categorize subjects into CR, PR, and NR groups at all scan times. Results Of the 96 patients, the number of CR, PR and NR patients were 21, 52, and 23, respectively. The best prediction of treatment response was achieved with the combination mean QUS values, texture and molecular features with accuracies of 78%, 86% and 83% at weeks 1, 4, and 8, after treatment respectively. Mean QUS parameters or clinical receptors status alone predicted the three response groups with accuracies less than 60% at all scan time points. Recurrence free survival (RFS) of response groups determined based on combined features followed similar trend as determined based on clinical and pathology. Conclusions This work demonstrates the potential of using QUS, texture and molecular features for predicting the response of primary breast tumors to chemotherapy early, and guiding the treatment planning of refractory patients. PMID:29298305

Rheumatoid arthritis patient perceptions on the value of predictive testing for treatments: a qualitative study.

PubMed

Kumar, Kanta; Peters, Sarah; Barton, Anne

2016-11-08

Rheumatoid arthritis (RA) is a long term condition that requires early treatment to control symptoms and improve long-term outcomes. Lack of response to RA treatments is not only a waste of healthcare resources, but also causes disability and distress to patients. Identifying biomarkers predictive of treatment response offers an opportunity to improve clinical decisions about which treatment to recommend in patients and could ultimately lead to better patient outcomes. The aim of this study was to explore the understanding of and factors affecting Rheumatoid Arthritis (RA) patients' decisions around predictive treatment testing. A qualitative study was conducted with a purposive sample of 16 patients with RA from three major UK cities. Four focus groups explored patient perceptions of the use of biomarker tests to predict response to treatments. Interviews were audio-recorded, transcribed verbatim and analysed using thematic analysis by three researchers. Data were organised within three interlinking themes: [1] Perceptions of predictive tests and patient preference of tests; [2] Utility of the test to manage expectations; [3] The influence of the disease duration on take up of predictive testing. During consultations for predictive testing, patients felt they would need, first, careful explanations detailing the consequences of untreated RA and delayed treatment response and, second, support to balance the risks of tests, which might be invasive and/or only moderately accurate, with the potential benefits of better management of symptoms. This study provides important insights into predictive testing. Besides supporting clinical decision making, the development of predictive testing in RA is largely supported by patients. Developing strategies which communicate risk information about predictive testing effectively while reducing the psychological burden associated with this information will be essential to maximise uptake.

5-Aminoimidazole-4-carboxamide ribonucleotide-transformylase and inosine-triphosphate-pyrophosphatase genes variants predict remission rate during methotrexate therapy in patients with juvenile idiopathic arthritis.

PubMed

Pastore, Serena; Stocco, Gabriele; Moressa, Valentina; Zandonà, Luigi; Favretto, Diego; Malusà, Noelia; Decorti, Giuliana; Lepore, Loredana; Ventura, Alessandro

2015-04-01

For children with juvenile idiopathic arthritis (JIA) who fail to respond to methotrexate, the delay in identifying the optimal treatment at an early stage of disease can lead to long-term joint damage. Recent studies indicate that relevant variants to predict methotrexate response in JIA are those in 5-aminoimidazole-4-carboxamide ribonucleotide-transformylase (ATIC), inosine-triphosphate-pyrophosphatase (ITPA) and solute-liquid-carrier-19A1 genes. The purpose of the study was, therefore, to explore the role of these candidate genetic factors on methotrexate response in an Italian cohort of children with JIA. Clinical response to methotrexate was evaluated as clinical remission stable for a 6-month period, as ACRPed score and as change in Juvenile Arthritis Disease score. The most relevant SNPs for each gene considered were assayed on patients' DNA. ITPA activity was measured in patients' erythrocytes. Sixty-nine patients with JIA were analyzed: 52.2 % responded to therapy (ACRPed70 score), while 37.7 % reached clinical remission stable for 6 months. ATIC rs2372536 GG genotype was associated with improved clinical remission (adjusted p value = 0.0090). For ITPA, rs1127354 A variant was associated with reduced clinical remission: (adjusted p value = 0.028); this association was present even for patients with wild-type ITPA and low ITPA activity. These preliminary results indicate that genotyping of ATIC rs2372536 and ITPA rs1127354 variants or measuring ITPA activity could be useful to predict methotrexate response in children with JIA after validation by further prospective studies on a larger patient cohort.

Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome.

PubMed

Davatzikos, Christos; Rathore, Saima; Bakas, Spyridon; Pati, Sarthak; Bergman, Mark; Kalarot, Ratheesh; Sridharan, Patmaa; Gastounioti, Aimilia; Jahani, Nariman; Cohen, Eric; Akbari, Hamed; Tunc, Birkan; Doshi, Jimit; Parker, Drew; Hsieh, Michael; Sotiras, Aristeidis; Li, Hongming; Ou, Yangming; Doot, Robert K; Bilello, Michel; Fan, Yong; Shinohara, Russell T; Yushkevich, Paul; Verma, Ragini; Kontos, Despina

2018-01-01

The growth of multiparametric imaging protocols has paved the way for quantitative imaging phenotypes that predict treatment response and clinical outcome, reflect underlying cancer molecular characteristics and spatiotemporal heterogeneity, and can guide personalized treatment planning. This growth has underlined the need for efficient quantitative analytics to derive high-dimensional imaging signatures of diagnostic and predictive value in this emerging era of integrated precision diagnostics. This paper presents cancer imaging phenomics toolkit (CaPTk), a new and dynamically growing software platform for analysis of radiographic images of cancer, currently focusing on brain, breast, and lung cancer. CaPTk leverages the value of quantitative imaging analytics along with machine learning to derive phenotypic imaging signatures, based on two-level functionality. First, image analysis algorithms are used to extract comprehensive panels of diverse and complementary features, such as multiparametric intensity histogram distributions, texture, shape, kinetics, connectomics, and spatial patterns. At the second level, these quantitative imaging signatures are fed into multivariate machine learning models to produce diagnostic, prognostic, and predictive biomarkers. Results from clinical studies in three areas are shown: (i) computational neuro-oncology of brain gliomas for precision diagnostics, prediction of outcome, and treatment planning; (ii) prediction of treatment response for breast and lung cancer, and (iii) risk assessment for breast cancer.

Vectra DA for the objective measurement of disease activity in patients with rheumatoid arthritis.

PubMed

Segurado, O G; Sasso, E H

2014-01-01

Quantitative and regular assessment of disease activity in rheumatoid arthritis (RA) is required to achieve treatment targets such as remission and to optimize clinical outcomes. To assess inflammation accurately, predict joint damage and monitor treatment response, a measure of disease activity in RA should reflect the pathological processes resulting in irreversible joint damage and functional disability. The Vectra DA blood test is an objective measure of disease activity for patients with RA. Vectra DA provides an accurate, reproducible score on a scale of 1 to 100 based on the concentrations of 12 biomarkers that reflect the pathophysiologic diversity of RA. The analytical validity, clinical validity, and clinical utility of Vectra DA have been evaluated for patients with RA in registries and prospective and retrospective clinical studies. As a biomarker-based instrument for assessing disease activity in RA, the Vectra DA test can help monitor therapeutic response to methotrexate and biologic agents and assess clinically challenging situations, such as when clinical measures are confounded by non-inflammatory pain from fibromyalgia. Vectra DA scores correlate with imaging of joint inflammation and are predictive for radiographic progression, with high Vectra DA scores being associated with more frequent and severe progression and low scores being predictive for non-progression. In summary, the Vectra DA score is an objective measure of RA disease activity that quantifies inflammatory status. By predicting risk for joint damage more effectively than conventional clinical and laboratory measures, it has the potential to complement these measures and optimise clinical decision making.

Modulation of Limbic and Prefrontal Connectivity by Electroconvulsive Therapy in Treatment-resistant Depression: A Preliminary Study.

PubMed

Cano, Marta; Cardoner, Narcís; Urretavizcaya, Mikel; Martínez-Zalacaín, Ignacio; Goldberg, Ximena; Via, Esther; Contreras-Rodríguez, Oren; Camprodon, Joan; de Arriba-Arnau, Aida; Hernández-Ribas, Rosa; Pujol, Jesús; Soriano-Mas, Carles; Menchón, José M

2016-01-01

Although current models of depression suggest that a sequential modulation of limbic and prefrontal connectivity is needed for illness recovery, neuroimaging studies of electroconvulsive therapy (ECT) have focused on assessing functional connectivity (FC) before and after an ECT course, without characterizing functional changes occurring at early treatment phases. To assess sequential changes in limbic and prefrontal FC during the course of ECT and their impact on clinical response. Longitudinal intralimbic and limbic-prefrontal networks connectivity study. We assessed 15 patients with treatment-resistant depression at four different time-points throughout the entire course of an ECT protocol and 10 healthy participants at two functional neuroimaging examinations. Furthermore, a path analysis to test direct and indirect predictive effects of limbic and prefrontal FC changes on clinical response measured with the Hamilton Rating Scale for Depression was also performed. An early significant intralimbic FC decrease significantly predicted a later increase in limbic-prefrontal FC, which in turn significantly predicted clinical improvement at the end of an ECT course. Our data support that treatment response involves sequential changes in FC within regions of the intralimbic and limbic-prefrontal networks. This approach may help in identifying potential early biomarkers of treatment response. Copyright © 2015 Elsevier Inc. All rights reserved.

Thymidilate synthase and p53 primary tumour expression as predictive factors for advanced colorectal cancer patients.

PubMed

Paradiso, A; Simone, G; Petroni, S; Leone, B; Vallejo, C; Lacava, J; Romero, A; Machiavelli, M; De Lena, M; Allegra, C J; Johnston, P G

2000-02-01

The purpose of this work was to analyse the ability of p53 and thymidilate synthase (TS) primary tumour expression to retrospectively predict clinical response to chemotherapy and long-term prognosis in patients with advanced colorectal cancers homogeneously treated by methotrexate (MTX)-modulated-5-fluorouracil (5-FU-FA). A total of 108 advanced colorectal cancer patients entered the present retrospective study. Immunohistochemical p53 (pAb 1801 mAb) and TS (TS106 mAb) expression on formalin-fixed paraffin-embedded primary tumour specimens was related to probability of clinical response to chemotherapy, time to progression and overall survival. p53 was expressed in 53/108 (49%) tumours, while 54/108 (50%) showed TS immunostaining. No relationship was demonstrated between p53 positivity and clinical response to chemotherapy (objective response (OR): 20% vs 23%, in p53+ and p53- cases respectively) or overall survival. Percent of OR was significantly higher in TS-negative with respect to TS-positive tumours (30% vs 15% respectively; P < 0.04); simultaneous analysis of TS and p53 indicated 7% OR for p53-positive/TS-positive tumours vs 46% for p53-positive/TS-negative tumours (P < 0.03). Logistic regression analysis confirmed a significant association between TS tumour status and clinical response to chemotherapy (hazard ratio (HR): 2.91; 95% confidence interval (CI) 8.34-1.01; two-sided P < 0.05). A multivariate analysis of overall survival showed that only a small number of metastatic sites was statistically relevant (HR 1.89; 95% CI 2.85-1.26; two-sided P < 0.03). Our study suggests that immunohistochemical expression of p53 and TS could assist the clinician in predicting response of colorectal cancer patients to modulated MTX-5-FU therapy.

Relation between N-terminal pro-brain natriuretic peptide levels and response to enhanced external counterpulsation in chronic angina pectoris.

PubMed

Sahlén, Anders; Wu, Eline; Rück, Andreas; Hagerman, Inger; Förstedt, Gunilla; Sylvén, Christer; Berglund, Margareta; Jernberg, Tomas

2014-01-01

Although enhanced external counterpulsation (EECP) provides symptom reduction in many patients with severe angina pectoris, one-quarter of patients fail to respond. Earlier reports have not clearly established whether and how EECP responders may be identified pre-hoc. We hypothesized that clinical and biochemical data may be used to predict EECP response. We explored a database of n=53 patients who had undergone clinically indicated EECP during 35 1-h sessions in our unit (65±7 years; 49 male), and sought to clarify which factors are predictive of response. Efficiency of counterpulsation was measured as the diastolic augmentation (DA) ratio, and was recorded both at beginning and end of the EECP treatment course. An increase in 6-min walk (6MW) distance of 5% was indicative of clinical response. Response occurred in 28 patients (53%; nonresponse in n=25, 47%). Responders had shorter baseline 6MW distance (377±81 vs. 445±62 m; P<0.01), lower left ventricular ejection fraction (48±9 vs. 54±8%; P<0.05), frequently had an increase in DA ratio during the EECP treatment course (23/28 vs. 5/28 with unchanged or decreased DA ratio; P<0.05), and higher levels of N-terminal pro-brain natriuretic peptide [NT-proBNP; 256 (123-547) vs. 62 (26-444) ng/l, P<0.01]. In multivariate logistic regression, response was independently predicted by baseline 6MW distance and baseline NT-proBNP levels (P<0.05 for both; model sensitivity: 82%, specificity: 72%, accuracy: 79%). There is larger clinical benefit of EECP in patients with greater functional impairment and higher levels of NT-proBNP.

Targeted Proteomics Predicts a Sustained Complete-Response after Transarterial Chemoembolization and Clinical Outcomes in Patients with Hepatocellular Carcinoma: A Prospective Cohort Study.

PubMed

Yu, Su Jong; Kim, Hyunsoo; Min, Hophil; Sohn, Areum; Cho, Young Youn; Yoo, Jeong-Ju; Lee, Dong Hyeon; Cho, Eun Ju; Lee, Jeong-Hoon; Gim, Jungsoo; Park, Taesung; Kim, Yoon Jun; Kim, Chung Yong; Yoon, Jung-Hwan; Kim, Youngsoo

2017-03-03

This study was aimed to identify blood-based biomarkers to predict a sustained complete response (CR) after transarterial chemoembolization (TACE) using targeted proteomics. Consecutive patients with HCC who had undergone TACE were prospectively enrolled (training (n = 100) and validation set (n = 80)). Serum samples were obtained before and 6 months after TACE. Treatment responses were evaluated using the modified Response Evaluation Criteria in Solid Tumors (mRECIST). In the training set, the MRM-MS assay identified five marker candidate proteins (LRG1, APCS, BCHE, C7, and FCN3). When this five-marker panel was combined with the best-performing clinical variables (tumor number, baseline PIVKA, and baseline AFP), the resulting ensemble model had the highest area under the receiver operating curve (AUROC) value in predicting a sustained CR after TACE in the training and validation sets (0.881 and 0.813, respectively). Furthermore, the ensemble model was an independent predictor of rapid progression (hazard ratio (HR), 2.889; 95% confidence interval (CI), 1.612-5.178; P value < 0.001) and overall an unfavorable survival rate (HR, 1.985; 95% CI, 1.024-3.848; P value = 0.042) in the entire population by multivariate analysis. Targeted proteomics-based ensemble model can predict clinical outcomes after TACE. Therefore, this model can aid in determining the best candidates for TACE and the need for adjuvant therapy.

Distinct responses to predictable and unpredictable threat in anxiety pathologies: effect of panic attack.

PubMed

Grillon, Christian; O'Connell, Katherine; Lieberman, Lynne; Alvarez, Gabriella; Geraci, Marilla; Pine, Daniel S; Ernst, Monique

2017-10-01

Delineating specific clinical phenotypes of anxiety disorders is a crucial step toward better classification and understanding of these conditions. The present study sought to identify differential aversive responses to predictable and unpredictable threat of shock in healthy comparisons and in non-medicated anxiety patients with and without a history of panic attacks (PAs). 143 adults (72 healthy controls; 71 patients with generalized anxiety disorder (GAD) or/and social anxiety disorder (SAD), 24 with and 47 without PAs) were exposed to three conditions: 1) predictable shocks signaled by a cue, 2) unpredictable shocks, and 3) no shock. Startle magnitude was used to assess aversive responses. Across disorders, a PA history was specifically associated with hypersensitivity to unpredictable threat. By disorder, SAD was associated with hypersensitivity to predictable threat, whereas GAD was associated with exaggerated baseline startle. These results identified three physiological patterns. The first is hypersensitivity to unpredictable threat in individuals with PAs. The second is hypersensitivity to predictable threat, which characterizes SAD. The third is enhanced baseline startle in GAD, which may reflect propensity for self-generated anxious thoughts in the absence of imminent danger. These results inform current thinking by linking specific clinical features to particular physiology profiles.

What is the benefit of treatment with multiple lines of chemotherapy for patients with metastatic breast cancer? A retrospective cohort study.

PubMed

Bakker, J L; Wever, K; van Waesberghe, J H; Beeker, A; Meijers-Heijboer, H; Konings, I R; Verheul, H M W

2015-12-01

Despite the extensive clinical experience, it is still under debate to what extent patients with metastatic breast cancer (MBC) benefit from multiple lines of chemotherapy beyond standard first or second line treatment. Selection of patients with MBC who will benefit from treatment is crucial to improve outcome and reduce unnecessary toxicity. In this retrospective study, systemic treatment outcome for patients with metastatic MBC is being evaluated. We evaluated to what extent the clinical benefit of prior chemotherapy can predict the success of a subsequent treatment line. Ninety-one patients treated with chemotherapy for MBC between January 2005 and January 2009 were included in this study. Clinical characteristics of patients, choices of chemotherapy and response at first evaluation of every treatment line was evaluated based on radiologic and clinical data. Patients received multiple systemic cytotoxic and biological (combination) therapies. 30% of these patients received more than five consecutive systemic (combination) treatments. First line chemotherapy was mostly anthracycline-based, followed by taxanes, capecitabine and vinorelbine. The response rate (RR, complete response plus partial response according to RECIST 1.1) decreased from 20% (95% CI 11-28%) upon first line of treatment to 0% upon the fourth line. The clinical benefit rate (combining RR and stable disease) decreased from 85% (95% CI 78-93%) in the first to 54% (95% CI 26-67) upon the fourth line. 24% of the patients with clinical benefit at first evaluation did not receive a subsequent line of treatment when progressive disease occurred, while sixty-one percent of the patients with progressive disease at first evaluation of a treatment did not receive a subsequent line of chemotherapy. When applied, the efficacy of a subsequent line of treatment was similar for patients independent of previous treatment benefit. The clinical benefit at first evaluation from systemic treatment in MBC does not predict for subsequent treatment benefit in this retrospective analysis. The fact that 61% of patients did not receive subsequent treatment after previous treatment failure suggests that either clinical judgement is of critical value in selection of patients to prevent them from unnecessary toxicity or, alternatively indicates that based on the assumption that prior treatment failure predicts for lack of benefit undertreatment of patients occurs. Therefore, a more adequate clinical judgement tool or predictive biomarkers for response are urgently needed to improve treatment outcome. Copyright © 2015. Published by Elsevier Ltd.

Prospective analysis of adoptive TIL therapy in patients with metastatic melanoma: response, impact of anti-CTLA4, and biomarkers to predict clinical outcome.

PubMed

Forget, Marie-Andrée; Haymaker, Cara; Hess, Kenneth R; Meng, Yuzhong Jeff; Creasy, Caitlin; Karpinets, Tatiana V; Fulbright, Orenthial J; Roszik, Jason; Woodman, Scott E; Kim, Young Uk; Sakellariou-Thompson, Donastas; Bhatta, Ankit; Wahl, Arely; Flores, Esteban; Thorsen, Shawne T; Tavera, Rene J; Ramachandran, Renjith; Gonzalez, Audrey M; Toth, Christopher; Wardell, Seth; Mansaray, Rahmatu; Patel, Vruti; Carpio, Destiny Joy; Vaughn, Carol S; Farinas, Chantell M; Velasquez, Portia G; Hwu, Wen-Jen; Patel, Sapna P; Davies, Michael A; Diab, Adi; Glitza, Isabella C; Tawbi, Hussein; Wong, Michael K K; Cain, Suzanne; Ross, Merrick I; Lee, Jeffrey E; Gershenwald, Jeffrey E; Lucci, Anthony; Royal, Richard; Cormier, J N; Wargo, Jennifer A; Radvanyi, Laszlo G; Torres Cabala, Carlos A; Beroukhim, Rameen; Hwu, Patrick; Amaria, Rodabe N; Bernatchez, Chantale

2018-05-30

Adoptive cell therapy (ACT) using tumor-infiltrating lymphocytes (TIL) has consistently demonstrated clinical efficacy in metastatic melanoma. Recent widespread use of checkpoint blockade has shifted the treatment landscape, raising questions regarding impact of these therapies on response to TIL and appropriate immunotherapy sequence. Seventy-four metastatic melanoma patients were treated with autologous TIL and evaluated for clinical response according to irRC, overall survival and progression free survival. Immunologic factors associated with response were also evaluated. Best overall response for the entire cohort was 42%; 47% in 43 checkpoint naïve patients, 38% when patients were exposed to anti-CTLA4 alone (21 patients) and 33% if also exposed to anti-PD1 (9 patients) prior to TIL ACT. Median overall survival was 17.3 months; 24.6 months in CTLA4 naïve patients and 8.6 months in patients with prior CTLA4 blockade. The latter patients were infused with fewer TIL and experienced a shorter duration of response. Infusion of higher numbers of TIL with CD8 predominance and expression of BTLA correlated with improved response in anti-CTLA-4 naive patients, but not in anti-CTLA-4 refractory patients. Baseline serum levels of IL-9 predicted response to TIL ACT, while TIL persistence, tumor recognition and mutation burden did not correlate with outcome. This study demonstrates the deleterious effects of prior exposure to anti-CTLA4 on TIL ACT response and shows that baseline IL-9 levels can potentially serve as a predictive tool to appropriately select sequence for immunotherapies. Copyright ©2018, American Association for Cancer Research.

Rapid response predicts 12-month post-treatment outcomes in binge-eating disorder: theoretical and clinical implications

PubMed Central

Grilo, C. M.; White, M. A.; Wilson, G. T.; Gueorguieva, R.; Masheb, R. M.

2011-01-01

Background We examined rapid response in obese patients with binge-eating disorder (BED) in a clinical trial testing cognitive behavioral therapy (CBT) and behavioral weight loss (BWL). Method Altogether, 90 participants were randomly assigned to CBT or BWL. Assessments were performed at baseline, throughout and post-treatment and at 6- and 12-month follow-ups. Rapid response, defined as ≥70% reduction in binge eating by week four, was determined by receiver operating characteristic curves and used to predict outcomes. Results Rapid response characterized 57% of participants (67% of CBT, 47% of BWL) and was unrelated to most baseline variables. Rapid response predicted greater improvements across outcomes but had different prognostic significance and distinct time courses for CBT versus BWL. Patients receiving CBT did comparably well regardless of rapid response in terms of reduced binge eating and eating disorder psychopathology but did not achieve weight loss. Among patients receiving BWL, those without rapid response failed to improve further. However, those with rapid response were significantly more likely to achieve binge-eating remission (62% v. 13%) and greater reductions in binge-eating frequency, eating disorder psychopathology and weight loss. Conclusions Rapid response to treatment in BED has prognostic significance through 12-month follow-up, provides evidence for treatment specificity and has clinical implications for stepped-care treatment models for BED. Rapid responders who receive BWL benefit in terms of both binge eating and short-term weight loss. Collectively, these findings suggest that BWL might be a candidate for initial intervention in stepped-care models with an evaluation of progress after 1 month to identify non-rapid responders who could be advised to consider a switch to a specialized treatment. PMID:21923964

[Clinical and biological predictors of ketamine response in treatment-resistant major depression: Review].

PubMed

Romeo, B; Choucha, W; Fossati, P; Rotge, J-Y

2017-08-01

The aim of this review was to determine the clinical and biological predictors of the ketamine response. A systematic research on PubMed and PsycINFO database was performed without limits on year of publication. The main predictive factors of ketamine response, which were found in different studies, were (i) a family history of alcohol dependence, (ii) unipolar depressive disorder, and (iii) neurocognitive impairments, especially a slower processing speed. Many other predictive factors were identified, but not replicated, such as personal history of alcohol dependence, no antecedent of suicide attempt, anxiety symptoms. Some biological factors were also found such as markers of neural plasticity (slow wave activity, brain-derived neurotrophic factor Val66Met polymorphism, expression of Shank 3 protein), other neurologic factors (anterior cingulate activity, concentration of glutamine/glutamate), inflammatory factors (IL-6 concentration) or metabolic factors (concentration of B12 vitamin, D- and L-serine, alterations in the mitochondrial β-oxidation of fatty acids). This review had several limits: (i) patients had exclusively resistant major depressive episodes which represent a sub-type of depression and not all depression, (ii) response criteria were more frequently assessed than remission criteria, it was therefore difficult to conclude that these predictors were similar, and finally (iii) many studies used a very small number of patients. In conclusion, this review found that some predictors of ketamine response, like basal activity of anterior cingulate or vitamin B12 concentration, were identical to other therapeutics used in major depressive episode. These factors could be more specific to the major depressive episode and not to the ketamine response. Others, like family history of alcohol dependence, body mass index, or D- and L-serine were different from the other therapeutics. Neurocognitive impairments like slower speed processing or alterations in attention tests were also predictive to a good response. These predictive factors could be more specific to ketamine. With these different predictor factors (clinical and biological), it could be interesting to develop clinical strategies to personalize ketamine's administration. Copyright © 2016 L'Encéphale, Paris. Published by Elsevier Masson SAS. All rights reserved.

Gene expression patterns in formalin-fixed, paraffin-embedded core biopsies predict docetaxel chemosensitivity in breast cancer patients.

PubMed

Chang, Jenny C; Makris, Andreas; Gutierrez, M Carolina; Hilsenbeck, Susan G; Hackett, James R; Jeong, Jennie; Liu, Mei-Lan; Baker, Joffre; Clark-Langone, Kim; Baehner, Frederick L; Sexton, Krsytal; Mohsin, Syed; Gray, Tara; Alvarez, Laura; Chamness, Gary C; Osborne, C Kent; Shak, Steven

2008-03-01

Previously, we had identified gene expression patterns that predicted response to neoadjuvant docetaxel. Other studies have validated that a high Recurrence Score (RS) by the 21-gene RT-PCR assay is predictive of worse prognosis but better response to chemotherapy. We investigated whether tumor expression of these 21 genes and other candidate genes can predict response to docetaxel. Core biopsies from 97 patients were obtained before treatment with neoadjuvant docetaxel (4 cycles, 100 mg/m2 q3 weeks). Three 10-microm FFPE sections were submitted for quantitative RT-PCR assays of 192 genes that were selected from our previous work and the literature. Of the 97 patients, 81 (84%) had sufficient invasive cancer, 80 (82%) had sufficient RNA for QRTPCR assay, and 72 (74%) had clinical response data. Mean age was 48.5 years, and the median tumor size was 6 cm. Clinical complete responses (CR) were observed in 12 (17%), partial responses in 41 (57%), stable disease in 17 (24%), and progressive disease in 2 patients (3%). A significant relationship (P<0.05) between gene expression and CR was observed for 14 genes, including CYBA. CR was associated with lower expression of the ER gene group and higher expression of the proliferation gene group from the 21 gene assay. Of note, CR was more likely with a high RS (P=0.008). We have established molecular profiles of sensitivity to docetaxel. RT-PCR technology provides a potential platform for a predictive test of docetaxel chemosensitivity using small amounts of routinely processed material.

Combining clinical variables to optimize prediction of antidepressant treatment outcomes.

PubMed

Iniesta, Raquel; Malki, Karim; Maier, Wolfgang; Rietschel, Marcella; Mors, Ole; Hauser, Joanna; Henigsberg, Neven; Dernovsek, Mojca Zvezdana; Souery, Daniel; Stahl, Daniel; Dobson, Richard; Aitchison, Katherine J; Farmer, Anne; Lewis, Cathryn M; McGuffin, Peter; Uher, Rudolf

2016-07-01

The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remission during treatment with escitalopram or nortriptyline and to identify contributing predictors from a range of demographic and clinical variables in 793 adults with major depressive disorder. A combination of demographic and clinical variables, with strong contributions from symptoms of depressed mood, reduced interest, decreased activity, indecisiveness, pessimism and anxiety significantly predicted treatment outcomes, explaining 5-10% of variance in symptom improvement with escitalopram. Similar combinations of variables predicted remission with area under the curve 0.72, explaining approximately 15% of variance (pseudo R(2)) in who achieves remission, with strong contributions from body mass index, appetite, interest-activity symptom dimension and anxious-somatizing depression subtype. Escitalopram-specific outcome prediction was more accurate than generic outcome prediction, and reached effect sizes that were near or above a previously established benchmark for clinical significance. Outcome prediction on the nortriptyline arm did not significantly differ from chance. These results suggest that easily obtained demographic and clinical variables can predict therapeutic response to escitalopram with clinically meaningful accuracy, suggesting a potential for individualized prescription of this antidepressant drug. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Applying Precision Medicine to Trial Design Using Physiology. Extracorporeal CO2 Removal for Acute Respiratory Distress Syndrome.

PubMed

Goligher, Ewan C; Amato, Marcelo B P; Slutsky, Arthur S

2017-09-01

In clinical trials of therapies for acute respiratory distress syndrome (ARDS), the average treatment effect in the study population may be attenuated because individual patient responses vary widely. This inflates sample size requirements and increases the cost and difficulty of conducting successful clinical trials. One solution is to enrich the study population with patients most likely to benefit, based on predicted patient response to treatment (predictive enrichment). In this perspective, we apply the precision medicine paradigm to the emerging use of extracorporeal CO 2 removal (ECCO 2 R) for ultraprotective ventilation in ARDS. ECCO 2 R enables reductions in tidal volume and driving pressure, key determinants of ventilator-induced lung injury. Using basic physiological concepts, we demonstrate that dead space and static compliance determine the effect of ECCO 2 R on driving pressure and mechanical power. This framework might enable prediction of individual treatment responses to ECCO 2 R. Enriching clinical trials by selectively enrolling patients with a significant predicted treatment response can increase treatment effect size and statistical power more efficiently than conventional enrichment strategies that restrict enrollment according to the baseline risk of death. To support this claim, we simulated the predicted effect of ECCO 2 R on driving pressure and mortality in a preexisting cohort of patients with ARDS. Our computations suggest that restricting enrollment to patients in whom ECCO 2 R allows driving pressure to be decreased by 5 cm H 2 O or more can reduce sample size requirement by more than 50% without increasing the total number of patients to be screened. We discuss potential implications for trial design based on this framework.

Driven by Mutations: The Predictive Value of Mutation Subtype in EGFR-Mutated Non-Small Cell Lung Cancer.

PubMed

Castellanos, Emily; Feld, Emily; Horn, Leora

2017-04-01

EGFR-mutated NSCLC is a genetically heterogeneous disease that includes more than 200 distinct mutations. The implications of mutational subtype for both prognostic and predictive value are being increasingly understood. Although the most common EGFR mutations-exon 19 deletions or L858R mutations-predict sensitivity to EGFR tyrosine kinase inhibitors (TKIs), it is now being recognized that outcomes may be improved in patients with exon 19 deletions. Additionally, 10% of patients will have an uncommon EGFR mutation, and response to EGFR TKI therapy is highly variable depending on the mutation. Given the growing recognition of the genetic and clinical variation seen in this disease, the development of comprehensive bioinformatics-driven tools to both analyze response in uncommon mutation subtypes and inform clinical decision making will be increasingly important. Clinical trials of novel EGFR TKIs should prospectively account for the presence of uncommon mutation subtypes in study design. Copyright © 2016 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

Clinical and biological predictors of response to electroconvulsive therapy (ECT): a review.

PubMed

Pinna, Martina; Manchia, Mirko; Oppo, Rossana; Scano, Filomena; Pillai, Gianluca; Loche, Anna Paola; Salis, Piergiorgio; Minnai, Gian Paolo

2018-03-16

Electroconvulsive therapy (ECT), developed in the 30's by Bini and Cerletti, remains a key element of the therapeutic armamentarium in psychiatry, particularly for severe and life-threatening psychiatric symptoms. However, despite its well-established clinical efficacy, the prescription of ECT has declined constantly over the years due to concerns over its safety (cognitive side effects) and an increasingly negative public perception. As for other treatments in the field of psychiatry, ECT is well suited to a personalized approach that would increment its efficacy, as well as reducing the impact of side effects. This should be based on the priori identification of sub-populations of patients sharing common clinical and biological features that predict a good response to ECT. In this review we have selectively reviewed the evidence on clinical and biological predictors of ECT response. Clinical features such as an older age, presence of psychotic and melancholic depression, a high severity of suicide behavior, and speed of response, appear to be shared by ECT good responders with depressive symptoms. In mania, a greater severity of the index episode, and a reduction of whole brain cortical blood flow are associated with ECT good response. Biological determinants of ECT response in depressive patients are the presence of pre-treatment hyperconnectivity between key areas of brain circuitry of depression, as well as of reduced glutamine/glutamate levels, particularly in the anterior cingulated cortex (ACC). Furthermore, pre ECT high plasma homovanillic acid (HVA) levels, as well as of tumor necrosis factor (TNF)-α, and low pre-ECT levels of S-100B protein, appear to predict ECT response. Finally, polymorphisms within the genes encoding for the brain-derived neurotrophic factor (BDNF), the dopamine 2 receptor gene (DRD2), the dopamine receptor 3 gene (DRD3), the cathechol-o-methyltransferase (COMT), the serotonin-transporter (5-HTT), the 5-hydroxytryptamine 2A receptor (5-HT 2A ), and the norepinephrine transporter (NET), appear to predict a good response to ECT. The integration of these data in specific treatment algorithm might facilitate a personalized approach in ECT. Copyright © 2016. Published by Elsevier B.V.

New Paradigm for Translational Modeling to Predict Long‐term Tuberculosis Treatment Response

PubMed Central

Bartelink, IH; Zhang, N; Keizer, RJ; Strydom, N; Converse, PJ; Dooley, KE; Nuermberger, EL

2017-01-01

Abstract Disappointing results of recent tuberculosis chemotherapy trials suggest that knowledge gained from preclinical investigations was not utilized to maximal effect. A mouse‐to‐human translational pharmacokinetics (PKs) – pharmacodynamics (PDs) model built on a rich mouse database may improve clinical trial outcome predictions. The model included Mycobacterium tuberculosis growth function in mice, adaptive immune response effect on bacterial growth, relationships among moxifloxacin, rifapentine, and rifampin concentrations accelerating bacterial death, clinical PK data, species‐specific protein binding, drug‐drug interactions, and patient‐specific pathology. Simulations of recent trials testing 4‐month regimens predicted 65% (95% confidence interval [CI], 55–74) relapse‐free patients vs. 80% observed in the REMox‐TB trial, and 79% (95% CI, 72–87) vs. 82% observed in the Rifaquin trial. Simulation of 6‐month regimens predicted 97% (95% CI, 93–99) vs. 92% and 95% observed in 2RHZE/4RH control arms, and 100% predicted and observed in the 35 mg/kg rifampin arm of PanACEA MAMS. These results suggest that the model can inform regimen optimization and predict outcomes of ongoing trials. PMID:28561946

GENOMIC PREDICTOR OF RESPONSE AND SURVIVAL FOLLOWING TAXANE-ANTHRACYCLINE CHEMOTHERAPY FOR INVASIVE BREAST CANCER

PubMed Central

Hatzis, Christos; Pusztai, Lajos; Valero, Vicente; Booser, Daniel J.; Esserman, Laura; Lluch, Ana; Vidaurre, Tatiana; Holmes, Frankie; Souchon, Eduardo; Martin, Miguel; Cotrina, José; Gomez, Henry; Hubbard, Rebekah; Chacón, J. Ignacio; Ferrer-Lozano, Jaime; Dyer, Richard; Buxton, Meredith; Gong, Yun; Wu, Yun; Ibrahim, Nuhad; Andreopoulou, Eleni; Ueno, Naoto T.; Hunt, Kelly; Yang, Wei; Nazario, Arlene; DeMichele, Angela; O’Shaughnessy, Joyce; Hortobagyi, Gabriel N.; Symmans, W. Fraser

2017-01-01

CONTEXT Accurate prediction of who will (or won’t) have high probability of survival benefit from standard treatments is fundamental for individualized cancer treatment strategies. OBJECTIVE To develop a predictor of response and survival from chemotherapy for newly diagnosed invasive breast cancer. DESIGN Development of different predictive signatures for resistance and response to neoadjuvant chemotherapy (stratified according to estrogen receptor (ER) status) from gene expression microarrays of newly diagnosed breast cancer (310 patients). Then prediction of breast cancer treatment-sensitivity using the combination of signatures for: 1) sensitivity to endocrine therapy, 2) chemo-resistance, and 3) chemo-sensitivity. Independent validation (198 patients) and comparison with other reported genomic predictors of chemotherapy response. SETTING Prospective multicenter study to develop and test genomic predictors for neoadjuvant chemotherapy. PATIENTS Newly diagnosed HER2-negative breast cancer treated with chemotherapy containing sequential taxane and anthracycline-based regimens then endocrine therapy (if hormone receptor-positive). MAIN OUTCOME MEASURES Distant relapse-free survival (DRFS) if predicted treatment-sensitive and absolute risk reduction (ARR, difference in DRFS of the two predicted groups) at median follow-up (3 years), and their 95% confidence intervals (CI). RESULTS Patients in the independent validation cohort (99% clinical Stage II–III) who were predicted to be treatment-sensitive (28% of total) had DRFS of 92% (CI 85–100) and survival benefit compared to others (absolute risk reduction (ARR) 18%; CI 6–28). Predictions were accurate if breast cancer was ER-positive (30% predicted sensitive, DRFS 97%, CI 91–100; ARR 11%, CI 0.1–21) or ER-negative (26% predicted sensitive, DRFS 83%, CI 68–100; ARR 26%, CI 4–28), and were significant in multivariate analysis after adjusting for relevant clinical-pathologic characteristics. Other genomic predictors showed paradoxically worse survival if predicted to be responsive to chemotherapy. CONCLUSION A genomic predictor combining ER status, predicted chemo-resistance, predicted chemo-sensitivity, and predicted endocrine sensitivity accurately identified patients with survival benefit following taxane-anthracycline chemotherapy. PMID:21558518

Characteristics of Placebo Responders in Pediatric Clinical Trials of Attention-Deficit/Hyperactivity Disorder

ERIC Educational Resources Information Center

Newcorn, Jeffrey H.; Sutton, Virginia K.; Zhang, Shuyu; Wilens, Timothy; Kratochvil, Christopher; Emslie, Graham J.; D'Souza, Deborah N.; Schuh, Leslie M.; Allen, Albert J.

2009-01-01

Objective: Understanding placebo response is a prerequisite to improving clinical trial methodology. Data from placebo-controlled trials of atomoxetine in the treatment of children and adolescents with attention-deficit/hyperactivity disorder (ADHD) were analyzed to identify demographic and clinical characteristics that might predict placebo…

Emerging biomarkers in breast cancer care.

PubMed

Napieralski, Rudolf; Brünner, Nils; Mengele, Karin; Schmitt, Manfred

2010-08-01

Currently, decision-making for breast cancer treatment in the clinical setting is mainly based on clinical data, histomorphological features of the tumor tissue and a few cancer biomarkers such as steroid hormone receptor status (estrogen and progesterone receptors) and oncoprotein HER2 status. Although various therapeutic options were introduced into the clinic in recent decades, with the objective of improving surgery, radiotherapy, biochemotherapy and chemotherapy, varying response of individual patients to certain types of therapy and therapy resistance is still a challenge in breast cancer care. Therefore, since breast cancer treatment should be based on individual features of the patient and her tumor, tailored therapy should be an option by integrating cancer biomarkers to define patients at risk and to reliably predict their course of the disease and/or response to cancer therapy. Recently, candidate-marker approaches and genome-wide transcriptomic and epigenetic screening of different breast cancer tissues and bodily fluids resulted in new promising biomarker panels, allowing breast cancer prognosis, prediction of therapy response and monitoring of therapy efficacy. These biomarkers are now subject of validation in prospective clinical trials.

A Bayesian predictive two-stage design for phase II clinical trials.

PubMed

Sambucini, Valeria

2008-04-15

In this paper, we propose a Bayesian two-stage design for phase II clinical trials, which represents a predictive version of the single threshold design (STD) recently introduced by Tan and Machin. The STD two-stage sample sizes are determined specifying a minimum threshold for the posterior probability that the true response rate exceeds a pre-specified target value and assuming that the observed response rate is slightly higher than the target. Unlike the STD, we do not refer to a fixed experimental outcome, but take into account the uncertainty about future data. In both stages, the design aims to control the probability of getting a large posterior probability that the true response rate exceeds the target value. Such a probability is expressed in terms of prior predictive distributions of the data. The performance of the design is based on the distinction between analysis and design priors, recently introduced in the literature. The properties of the method are studied when all the design parameters vary.

An investigation into the blood-flow characteristics of telangiectatic skin lesions in systemic sclerosis using dual-wavelength laser Doppler imaging.

PubMed

Murray, A K; Moore, T L; Griffiths, C E M; Herrick, A L

2009-07-01

Superficial telangiectases associated with systemic sclerosis may be more responsive to treatment than those deeper in the dermis. We investigated whether dual-wavelength laser Doppler imaging (LDI) is sufficiently sensitive to ascertain the distribution of blood flow within telangiectases and whether blood flow relates to telangiectatic diameter. The perfusion and diameter of 20 telangiectases were measured in superficial and deeper layers of the skin using dual-wavelength LDI. Of 20 telangiectases, 18 had higher blood flow in the red (representing deeper blood flow), rather than the green (representing superficial blood flow) wavelength images. Clinically apparent diameters correlated with those of the superficial (r = 0.61, P = 0.01), but not with the deeper blood flow images. Hence, the apparent size of telangiectases at the skin surface does not predict blood flow through the microvessel(s) at deeper levels, and thus clinically apparent size is unlikely to predict treatment response. Dual-wavelength LDI may help predict treatment response.

Clinical Relevance of Prognostic and Predictive Molecular Markers in Gliomas.

PubMed

Siegal, Tali

2016-01-01

Sorting and grading of glial tumors by the WHO classification provide clinicians with guidance as to the predicted course of the disease and choice of treatment. Nonetheless, histologically identical tumors may have very different outcome and response to treatment. Molecular markers that carry both diagnostic and prognostic information add useful tools to traditional classification by redefining tumor subtypes within each WHO category. Therefore, molecular markers have become an integral part of tumor assessment in modern neuro-oncology and biomarker status now guides clinical decisions in some subtypes of gliomas. The routine assessment of IDH status improves histological diagnostic accuracy by differentiating diffuse glioma from reactive gliosis. It carries a favorable prognostic implication for all glial tumors and it is predictive for chemotherapeutic response in anaplastic oligodendrogliomas with codeletion of 1p/19q chromosomes. Glial tumors that contain chromosomal codeletion of 1p/19q are defined as tumors of oligodendroglial lineage and have favorable prognosis. MGMT promoter methylation is a favorable prognostic marker in astrocytic high-grade gliomas and it is predictive for chemotherapeutic response in anaplastic gliomas with wild-type IDH1/2 and in glioblastoma of the elderly. The clinical implication of other molecular markers of gliomas like mutations of EGFR and ATRX genes and BRAF fusion or point mutation is highlighted. The potential of molecular biomarker-based classification to guide future therapeutic approach is discussed and accentuated.

Predicting meaningful outcomes to medication and self-help treatments for binge-eating disorder in primary care: The significance of early rapid response.

PubMed

Grilo, Carlos M; White, Marney A; Masheb, Robin M; Gueorguieva, Ralitza

2015-04-01

We examined rapid response among obese patients with binge-eating disorder (BED) in a randomized clinical trial testing antiobesity medication and self-help cognitive-behavioral therapy (shCBT), alone and in combination, in primary-care settings. One hundred four obese patients with BED were randomly assigned to 1 of 4 treatments: sibutramine, placebo, shCBT + sibutramine, or shCBT + placebo. Treatments were delivered by generalist primary-care physicians and the medications were given double-blind. Independent assessments were performed by trained and monitored doctoral research clinicians monthly throughout treatment, posttreatment (4 months), and at 6- and 12-month follow-ups (i.e., 16 months after randomization). Rapid response, defined as ≥65% reduction in binge eating by the fourth treatment week, was used to predict outcomes. Rapid response characterized 47% of patients, was unrelated to demographic and baseline clinical characteristics, and was significantly associated, prospectively, with remission from binge eating at posttreatment (51% vs. 9% for nonrapid responders), 6-month (53% vs. 23.6%), and 12-month (46.9% vs. 23.6%) follow-ups. Mixed-effects model analyses revealed that rapid response was significantly associated with greater decreases in binge-eating or eating-disorder psychopathology, depression, and percent weight loss. Our findings, based on a diverse obese patient group receiving medication and shCBT for BED in primary-care settings, indicate that patients who have a rapid response achieve good clinical outcomes through 12-month follow-ups after ending treatment. Rapid response represents a strong prognostic indicator of clinically meaningful outcomes, even in low-intensity medication and self-help interventions. Rapid response has important clinical implications for stepped-care treatment models for BED. clinicaltrials.gov: NCT00537810 (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Predictive factors and outcomes for ibrutinib therapy in relapsed/refractory mantle cell lymphoma-a "real world" study.

PubMed

Epperla, Narendranath; Hamadani, Mehdi; Cashen, Amanda F; Ahn, Kwang W; Oak, Eunhye; Kanate, Abraham S; Calzada, Oscar; Cohen, Jonathon B; Farmer, Luke; Ghosh, Nilanjan; Tallarico, Michael; Nabhan, Chadi; Costa, Luciano J; Kenkre, Vaishalee P; Hari, Parameswaran N; Fenske, Timothy S

2017-12-01

Ibrutinib has demonstrated significant activity in relapsed/refractory mantle cell lymphoma (MCL) in clinical trials. However, the impact of hematopoietic cell transplantation on the outcomes of ibrutinib and the predictive factors for ibrutinib response has not been well studied. Hence, we conducted a multicenter retrospective study of MCL patients who received ibrutinib to (1) determine the overall response rate (ORR), duration of response (DOR), progression-free survival (PFS), and overall survival (OS) of ibrutinib in routine clinical practice, (2) examine characteristics predictive of response to ibrutinib therapy, and (3) describe the outcomes of patients failing ibrutinib. Ninety-seven patients met the eligibility criteria. Overall response rate and median DOR to ibrutinib were 65% and 17 months, respectively. Only lack of primary refractory disease was predictive of ibrutinib response on multivariate analysis. Twenty-nine patients received postibrutinib therapies, with an ORR of 48% and a median DOR of 3 months. The median OS and PFS for the entire group (n = 97) was 22 and 15 months, respectively. On multivariate analysis, ibrutinib response, low MCL international prognostic index, and absence of primary refractory disease were predictors of better PFS, while ibrutinib response and Eastern Cooperative Oncology Group performance status ≤1 were predictors of better OS. The median OS postibrutinib failure was 2.5 months. Our results confirm the high ORR and DOR of ibrutinib in MCL and that prior hematopoietic cell transplantation does not negatively influence ibrutinib outcomes. Survival following ibrutinib failure is poor with no specific subsequent therapy showing superior activity in this setting. As a result, for select (transplant eligible) patients, allogeneic transplant should be strongly considered soon after ibrutinib response is documented to provide durable responses. Copyright © 2017 John Wiley & Sons, Ltd.

Prediction of the Efficacy of Antihistamines in Chronic Spontaneous Urticaria Based on Initial Suppression of the Histamine- Induced Wheal.

PubMed

Sánchez, J; Zakzuk, J; Cardona, R

2016-01-01

Antihistamines are the first line of treatment for chronic spontaneous urticaria. However, there is no effective method to predict whether an antihistamine will have a beneficial clinical effect or not. To assess whether the change in histamine-induced wheal and flare measurements 24 hours after administration of antihistamine can predict the efficacy of treatment. We performed a multicenter, triple-blind, randomized study. Patients received a daily oral dose of cetirizine, fexofenadine, bilastine, desloratadine, or ebastine over 8 weeks. After 4 weeks, a higher dose of antihistamine was administered to patients who did not experience a clinical response. A histamine skin prick test was carried out at baseline and 24 hours after the first dose of antihistamine. Disease severity (Urticaria Activity Score [UAS]), response to the histamine skin prick test, and impact on the patient's quality of life (Dermatology Life Quality Index [DLQI]) were determined every 2 weeks. The study population comprised 150 patients (30 per group) and 30 controls. Twenty-four hours after administration of antihistamine, inhibition of the histamine wheal by >75% was significantly associated with better UAS and DLQI scores. The safety and efficacy of the 5 antihistamines were similar. After updosing, rates of disease control (DLQI score <5) increased from 58.7% to 76.7%. Measurement of the histamine-induced wheal can predict which patients will have a strong clinical response to antihistamines but has limited utility for identifying nonresponders. The clinical significance of these data could be relevant in the search for new urticaria treatment regimens.

Noninvasive Subharmonic Pressure Estimation for Monitoring Breast Cancer Response to Neoadjuvant Therapy

DTIC Science & Technology

2011-09-01

predict disease free survival for cervix cancer (34% disease free survival (DFS) if IFP > 19 mmHg, 68% DFS if IFP < 19 mmHg (p = 0.002)) [11]. Thus, the...pressure predicts survival in patients with cervix cancer independent of clinical prognostic factors and tumor oxygen measurements. Cancer Res...Estimation for Monitoring Breast Cancer Response to Neoadjuvant Therapy PRINCIPAL INVESTIGATOR: Flemming Forsberg, Ph.D

Diffusion MRI in early cancer therapeutic response assessment

PubMed Central

Galbán, C. J.; Hoff, B. A.; Chenevert, T. L.; Ross, B. D.

2016-01-01

Imaging biomarkers for the predictive assessment of treatment response in patients with cancer earlier than standard tumor volumetric metrics would provide new opportunities to individualize therapy. Diffusion-weighted MRI (DW-MRI), highly sensitive to microenvironmental alterations at the cellular level, has been evaluated extensively as a technique for the generation of quantitative and early imaging biomarkers of therapeutic response and clinical outcome. First demonstrated in a rodent tumor model, subsequent studies have shown that DW-MRI can be applied to many different solid tumors for the detection of changes in cellularity as measured indirectly by an increase in the apparent diffusion coefficient (ADC) of water molecules within the lesion. The introduction of quantitative DW-MRI into the treatment management of patients with cancer may aid physicians to individualize therapy, thereby minimizing unnecessary systemic toxicity associated with ineffective therapies, saving valuable time, reducing patient care costs and ultimately improving clinical outcome. This review covers the theoretical basis behind the application of DW-MRI to monitor therapeutic response in cancer, the analytical techniques used and the results obtained from various clinical studies that have demonstrated the efficacy of DW-MRI for the prediction of cancer treatment response. PMID:26773848

Pharmacogenetically driven treatments for alcoholism: are we there yet?

PubMed

Arias, Albert J; Sewell, R Andrew

2012-06-01

Pharmacogenetic analyses of treatments for alcohol dependence attempt to predict treatment response and side-effect risk for specific medications. We review the literature on pharmacogenetics relevant to alcohol dependence treatment, and describe state-of-the-art methods of pharmacogenetic research in this area. Two main pharmacogenetic study designs predominate: challenge studies and treatment-trial analyses. Medications studied include US FDA-approved naltrexone and acamprosate, both indicated for treating alcohol dependence, as well as several investigational (and off-label) treatments such as sertraline, olanzapine and ondansetron. The best-studied functional genetic variant relevant to alcoholism treatment is rs1799971, a single-nucleotide polymorphism in exon 1 of the OPRM1 gene that encodes the μ-opioid receptor. Evidence from clinical trials suggests that the presence of the variant G allele of rs1799971 may predict better treatment response to opioid receptor antagonists such as naltrexone. Evidence from clinical trials also suggests that several medications interact pharmacogenetically with variation in genes that encode proteins involved in dopaminergic and serotonergic neurotransmission. Variation in the DRD4 gene, which encodes the dopamine D(4) receptor, may predict better response to naltrexone and olanzapine. A polymorphism in the serotonin transporter gene SLC6A4 promoter region appears related to differential treatment response to sertraline depending on the subject's age of onset of alcoholism. Genetic variation in SLC6A4 may also be associated with better treatment response to ondansetron. Initial pharmacogenetic efforts in alcohol research have identified functional variants with potential clinical utility, but more research is needed to further elucidate the mechanism of these pharmacogenetic interactions and their moderators in order to translate them into clinical practice.

Neural Reactivity to Angry Faces Predicts Treatment Response in Pediatric Anxiety.

PubMed

Bunford, Nora; Kujawa, Autumn; Fitzgerald, Kate D; Swain, James E; Hanna, Gregory L; Koschmann, Elizabeth; Simpson, David; Connolly, Sucheta; Monk, Christopher S; Phan, K Luan

2017-02-01

Although cognitive-behavioral psychotherapy (CBT) and pharmacotherapy are evidence-based treatments for pediatric anxiety, many youth with anxiety disorders fail to respond to these treatments. Given limitations of clinical measures in predicting treatment response, identifying neural predictors is timely. In this study, 35 anxious youth (ages 7-19 years) completed an emotional face-matching task during which the late positive potential (LPP), an event-related potential (ERP) component that indexes sustained attention towards emotional stimuli, was measured. Following the ERP measurement, youth received CBT or selective serotonin reuptake inhibitor (SSRI) treatment, and the LPP was examined as a predictor of treatment response. Findings indicated that, accounting for pre-treatment anxiety severity, neural reactivity to emotional faces predicted anxiety severity post- CBT and SSRI treatment such that enhanced electrocortical response to angry faces was associated with better treatment response. An enhanced LPP to angry faces may predict treatment response insofar as it may reflect greater emotion dysregulation or less avoidance and/or enhanced engagement with environmental stimuli in general, including with treatment.

Evaluation and comparison of predictive individual-level general surrogates.

PubMed

Gabriel, Erin E; Sachs, Michael C; Halloran, M Elizabeth

2018-07-01

An intermediate response measure that accurately predicts efficacy in a new setting at the individual level could be used both for prediction and personalized medical decisions. In this article, we define a predictive individual-level general surrogate (PIGS), which is an individual-level intermediate response that can be used to accurately predict individual efficacy in a new setting. While methods for evaluating trial-level general surrogates, which are predictors of trial-level efficacy, have been developed previously, few, if any, methods have been developed to evaluate individual-level general surrogates, and no methods have formalized the use of cross-validation to quantify the expected prediction error. Our proposed method uses existing methods of individual-level surrogate evaluation within a given clinical trial setting in combination with cross-validation over a set of clinical trials to evaluate surrogate quality and to estimate the absolute prediction error that is expected in a new trial setting when using a PIGS. Simulations show that our method performs well across a variety of scenarios. We use our method to evaluate and to compare candidate individual-level general surrogates over a set of multi-national trials of a pentavalent rotavirus vaccine.

KRAS Genomic Status Predicts the Sensitivity of Ovarian Cancer Cells to Decitabine | Office of Cancer Genomics

Cancer.gov

Decitabine, a cancer therapeutic that inhibits DNA methylation, produces variable antitumor response rates in patients with solid tumors that might be leveraged clinically with identification of a predictive biomarker. In this study, we profiled the response of human ovarian, melanoma, and breast cancer cells treated with decitabine, finding that RAS/MEK/ERK pathway activation and DNMT1 expression correlated with cytotoxic activity. Further, we showed that KRAS genomic status predicted decitabine sensitivity in low-grade and high-grade serous ovarian cancer cells.

Tracking medication changes to assess outcomes in comparative effectiveness research: A bipolar CHOICE study.

PubMed

Reilly-Harrington, Noreen A; Sylvia, Louisa G; Rabideau, Dustin J; Gold, Alexandra K; Deckersbach, Thilo; Bowden, Charles L; Bobo, William V; Singh, Vivek; Calabrese, Joseph R; Shelton, Richard C; Friedman, Edward S; Thase, Michael E; Kamali, Masoud; Tohen, Mauricio; McInnis, Melvin G; McElroy, Susan L; Ketter, Terence A; Kocsis, James H; Kinrys, Gustavo; Nierenberg, Andrew A

2016-11-15

Comparative effectiveness research uses multiple tools, but lacks outcome measures to assess large electronic medical records and claims data. Aggregate changes in medications in response to clinical need may serve as a surrogate outcome measure. We developed the Medication Recommendation Tracking Form (MRTF) to record the frequency, types, and reasons for medication adjustments in order to calculate Necessary Clinical Adjustments (NCAs), medication adjustments to reduce symptoms, maximize treatment response, or address problematic side effects. The MRTF was completed at every visit for 482 adult patients in Bipolar CHOICE, a 6-month randomized comparative effectiveness trial. Responders had significantly fewer NCAs compared to non-responders. NCAs predicted subsequent response status such that every additional NCA during the previous visit decreased a patient's odds of response by approximately 30%. Patients with more severe symptoms had a greater number of NCAs at the subsequent visit. Patients with a comorbid anxiety disorder demonstrated a significantly higher rate of NCAs per month than those without a comorbid anxiety disorder. Patients with greater frequency, intensity, and interference of side effects had higher rates of NCAs. Participants with fewer NCAs reported a higher quality of life and decreased functional impairment. The MRTF has not been examined in community clinic settings and did not predict response more efficiently than the Clinical Global Impression-Bipolar Version (CGI-BP). The MRTF is a feasible proxy of clinical outcome, with implications for clinical training and decision-making. Analyses of big data could use changes in medications as a surrogate outcome measure. Copyright © 2016 Elsevier B.V. All rights reserved.

Recent advances in understanding idiopathic pulmonary fibrosis

PubMed Central

Daccord, Cécile; Maher, Toby M.

2016-01-01

Despite major research efforts leading to the recent approval of pirfenidone and nintedanib, the dismal prognosis of idiopathic pulmonary fibrosis (IPF) remains unchanged. The elaboration of international diagnostic criteria and disease stratification models based on clinical, physiological, radiological, and histopathological features has improved the accuracy of IPF diagnosis and prediction of mortality risk. Nevertheless, given the marked heterogeneity in clinical phenotype and the considerable overlap of IPF with other fibrotic interstitial lung diseases (ILDs), about 10% of cases of pulmonary fibrosis remain unclassifiable. Moreover, currently available tools fail to detect early IPF, predict the highly variable course of the disease, and assess response to antifibrotic drugs. Recent advances in understanding the multiple interrelated pathogenic pathways underlying IPF have identified various molecular phenotypes resulting from complex interactions among genetic, epigenetic, transcriptional, post-transcriptional, metabolic, and environmental factors. These different disease endotypes appear to confer variable susceptibility to the condition, differing risks of rapid progression, and, possibly, altered responses to therapy. The development and validation of diagnostic and prognostic biomarkers are necessary to enable a more precise and earlier diagnosis of IPF and to improve prediction of future disease behaviour. The availability of approved antifibrotic therapies together with potential new drugs currently under evaluation also highlights the need for biomarkers able to predict and assess treatment responsiveness, thereby allowing individualised treatment based on risk of progression and drug response. This approach of disease stratification and personalised medicine is already used in the routine management of many cancers and provides a potential road map for guiding clinical care in IPF. PMID:27303645

Fourier and non-Fourier bio-heat transfer models to predict ex vivo temperature response to focused ultrasound heating

NASA Astrophysics Data System (ADS)

Li, Chenghai; Miao, Jiaming; Yang, Kexin; Guo, Xiasheng; Tu, Juan; Huang, Pintong; Zhang, Dong

2018-05-01

Although predicting temperature variation is important for designing treatment plans for thermal therapies, research in this area is yet to investigate the applicability of prevalent thermal conduction models, such as the Pennes equation, the thermal wave model of bio-heat transfer, and the dual phase lag (DPL) model. To address this shortcoming, we heated a tissue phantom and ex vivo bovine liver tissues with focused ultrasound (FU), measured the temperature response, and compared the results with those predicted by these models. The findings show that, for a homogeneous-tissue phantom, the initial temperature increase is accurately predicted by the Pennes equation at the onset of FU irradiation, although the prediction deviates from the measured temperature with increasing FU irradiation time. For heterogeneous liver tissues, the predicted response is closer to the measured temperature for the non-Fourier models, especially the DPL model. Furthermore, the DPL model accurately predicts the temperature response in biological tissues because it increases the phase lag, which characterizes microstructural thermal interactions. These findings should help to establish more precise clinical treatment plans for thermal therapies.

Dissociation predicts treatment response in eye-movement desensitization and reprocessing for posttraumatic stress disorder.

PubMed

Bae, Hwallip; Kim, Daeho; Park, Yong Chon

2016-01-01

Using clinical data from a specialized trauma clinic, this study investigated pretreatment clinical factors predicting response to eye-movement desensitization and reprocessing (EMDR) among adult patients diagnosed with posttraumatic stress disorder (PTSD). Participants were evaluated using the Clinician-Administered PTSD Scale (CAPS), the Symptom Checklist-90-Revised, the Beck Depression Inventory, and the Dissociative Experiences Scale before treatment and were reassessed using the CAPS after treatment and at 6-month follow-up. A total of 69 patients underwent an average of 4 sessions of EMDR, and 60 (87%) completed the posttreatment evaluation, including 8 participants who terminated treatment prematurely. Intent-to-treat analysis revealed that 39 (65%) of the 60 patients were classified as responders and 21 (35%) as nonresponders when response was defined as more than a 30% decrease in total CAPS score. The nonresponders had higher levels of dissociation (depersonalization and derealization) and numbing symptoms, but other PTSD symptoms, such as avoidance, hyperarousal, and intrusion, were not significantly different. The number of psychiatric comorbidities was also associated with treatment nonresponse. The final logistic regression model yielded 2 significant variables: dissociation (p < .001) and more than 2 comorbidities compared to none (p < .05). These results indicate that complex symptom patterns in PTSD may predict treatment response and support the inclusion of the dissociative subtype of PTSD in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition.

Circulating Inflammatory Mediators in Patients with Fever: Predicting Bloodstream Infection

PubMed Central

Groeneveld, A. B. Johan; Bossink, Ailko W. J.; van Mierlo, Gerard J.; Hack, C. Erik

2001-01-01

The systemic host response to microbial infection involves clinical signs and symptoms of infection, including fever and elevated white blood cell (WBC) counts. In addition, inflammatory mediators are released, including activated complement product C3a, interleukin 6 (IL-6), and the acute-phase reactant secretory phospholipase A2 (sPLA2). To compare the value of the latter with the former in predicting (the degree of) microbial infection at the bedside, we determined clinical variables and took blood samples daily for 3 consecutive days in 300 patients with a new fever (>38.0°C rectally or >38.3°C axillary). Microbiological culture results for 7 days after inclusion were collected. Patients were divided into clinical and microbial categories: those without and with a clinical focus of infection and those with negative cultures, with positive local cultures or specific stains for fungal (n = 13) or tuberculous infections (n = 1), and with positive blood cultures, including one patient with malaria parasitemia. The area under the curve (AUC) of the receiver operating characteristic (ROC) for prediction of positive cultures was 0.60 (P < 0.005) for peak temperature and 0.59 (P < 0.01) for peak WBC count, 0.60 (P < 0.005) for peak C3a, 0.63 (P < 0.001) for peak IL-6, and 0.61 (P < 0.001) for peak sPLA2. The AUC under the ROC curve for prediction of positive blood cultures was 0.68 (P < 0.001) for peak temperature and 0.56 for peak WBC count (P < 0.05). The AUC for peak C3a was 0.69, that for peak IL-6 was 0.70, and that for sPLA2 was 0.67 (for all, P < 0.001). The degree of microbial invasion is thus a major determinant of the clinical and inflammatory host response in patients with fever. Moreover, circulating inflammatory mediators such as C3a and IL-6 may help to predict positive blood cultures, together with clinical signs and symptoms of the host response to microbial infection, even before culture results are available. This may help in the designing of entry criteria for therapeutic intervention studies. PMID:11687462

Preclinical efficacy of immune-checkpoint monotherapy does not recapitulate corresponding biomarkers-based clinical predictions in glioblastoma

PubMed Central

Vandenberk, Lien; Van Woensel, Matthias; Schaaf, Marco; De Vleeschouwer, Steven; Agostinis, Patrizia

2017-01-01

ABSTRACT Glioblastoma (GBM) is resistant to most multimodal therapies. Clinical success of immune-checkpoint inhibitors (ICIs) has spurred interest in applying ICIs targeting CTLA4, PD1 or IDO1 against GBM. This amplifies the need to ascertain GBM's intrinsic susceptibility (or resistance) toward these ICIs, through clinical biomarkers that may also “guide and prioritize” preclinical testing. Here, we interrogated the TCGA and/or REMBRANDT human patient-cohorts to predict GBM's predisposition toward ICIs. We exploited various broad clinical biomarkers, including mutational or predicted-neoantigen burden, pre-existing or basal levels of tumor-infiltrating T lymphocytes (TILs), differential expression of immune-checkpoints within the tumor and their correlation with particular TILs/Treg-associated functional signature and prognostic impact of differential immune-checkpoint expression. Based on these analyses, we found that predictive biomarkers of ICI responsiveness exhibited inconsistent patterns in GBM patients, i.e., they either predicted ICI resistance (as compared with typical ICI-responsive cancer-types like melanoma, lung cancer or bladder cancer) or susceptibility to therapeutic targeting of CTLA4 or IDO1. On the other hand, our comprehensive literature meta-analysis and preclinical testing of ICIs using an orthotopic GL261-glioma mice model, indicated significant antitumor properties of anti-PD1 antibody, whereas blockade of IDO1 or CTLA4 either failed or provided very marginal advantage. These trends raise the need to better assess the applicability of ICIs and associated biomarkers for GBM. PMID:28507806

Predictive assessment in pharmacogenetics of XRCC1 gene on clinical outcomes of advanced lung cancer patients treated with platinum-based chemotherapy

PubMed Central

Yuan, Zhengrong; Li, Jiao; Hu, Ruiqi; Jiao, Yang; Han, Yingying; Weng, Qiang

2015-01-01

Published data have shown inconsistent results about the pharmacogenetics of XRCC1 gene on clinical outcomes of advanced lung cancer patients treated with platinum-based chemotherapy. This meta-analysis aimed to summarize published findings and provide more reliable association. A total of 53 eligible studies including 7433 patients were included. Patients bearing the favorable TrpTrp and TrpArg genotypes of Arg194Trp were more likely to better response rates to platinum-based chemotherapy compared to those with the unfavorable ArgArg genotype (TrpTrp+TrpArg vs. ArgArg: odds ratio (OR) = 2.02, 95% CI, 1.66–2.45). The GlnGln and GlnArg genotypes of Arg399Gln were significantly associated with the poorer response rates compared to those with the ArgArg genotype (GlnGln +GlnArg vs. ArgArg: OR = 0.68, 95% CI, 0.54–0.86). The GlnGln genotype might be more closely associated with shorter survival time and higher risks of death for patients (GlnGln vs. ArgArg: hazard ratio (HR) = 1.14, 95% CI, 0.75–1.75). Our cumulative meta-analyses indicated a distinct apparent trend toward a better response rate for Arg194Trp, but a poorer response rate in Arg399Gln. These findings indicate a predictive role of XRCC1 polymorphisms in clinical outcomes. The use of XRCC1 polymorphisms as predictive factor of clinical outcomes in personalized chemotherapy treatment requires further verification from large well-designed pharmacogenetics studies. PMID:26585370

Genetic predictors of response to serotonergic and noradrenergic antidepressants in major depressive disorder: a genome-wide analysis of individual-level data and a meta-analysis.

PubMed

Tansey, Katherine E; Guipponi, Michel; Perroud, Nader; Bondolfi, Guido; Domenici, Enrico; Evans, David; Hall, Stephanie K; Hauser, Joanna; Henigsberg, Neven; Hu, Xiaolan; Jerman, Borut; Maier, Wolfgang; Mors, Ole; O'Donovan, Michael; Peters, Tim J; Placentino, Anna; Rietschel, Marcella; Souery, Daniel; Aitchison, Katherine J; Craig, Ian; Farmer, Anne; Wendland, Jens R; Malafosse, Alain; Holmans, Peter; Lewis, Glyn; Lewis, Cathryn M; Stensbøl, Tine Bryan; Kapur, Shitij; McGuffin, Peter; Uher, Rudolf

2012-01-01

It has been suggested that outcomes of antidepressant treatment for major depressive disorder could be significantly improved if treatment choice is informed by genetic data. This study aims to test the hypothesis that common genetic variants can predict response to antidepressants in a clinically meaningful way. The NEWMEDS consortium, an academia-industry partnership, assembled a database of over 2,000 European-ancestry individuals with major depressive disorder, prospectively measured treatment outcomes with serotonin reuptake inhibiting or noradrenaline reuptake inhibiting antidepressants and available genetic samples from five studies (three randomized controlled trials, one part-randomized controlled trial, and one treatment cohort study). After quality control, a dataset of 1,790 individuals with high-quality genome-wide genotyping provided adequate power to test the hypotheses that antidepressant response or a clinically significant differential response to the two classes of antidepressants could be predicted from a single common genetic polymorphism. None of the more than half million genetic markers significantly predicted response to antidepressants overall, serotonin reuptake inhibitors, or noradrenaline reuptake inhibitors, or differential response to the two types of antidepressants (genome-wide significance p<5×10(-8)). No biological pathways were significantly overrepresented in the results. No significant associations (genome-wide significance p<5×10(-8)) were detected in a meta-analysis of NEWMEDS and another large sample (STAR*D), with 2,897 individuals in total. Polygenic scoring found no convergence among multiple associations in NEWMEDS and STAR*D. No single common genetic variant was associated with antidepressant response at a clinically relevant level in a European-ancestry cohort. Effects specific to particular antidepressant drugs could not be investigated in the current study. Please see later in the article for the Editors' Summary.

Diagnostic value of clinical tests for degenerative rotator cuff disease in medical practice.

PubMed

Lasbleiz, S; Quintero, N; Ea, K; Petrover, D; Aout, M; Laredo, J D; Vicaut, E; Bardin, T; Orcel, P; Beaudreuil, J

2014-06-01

To assess the diagnostic value of clinical tests for degenerative rotator cuff disease (DRCD) in medical practice. Patients with DRCD were prospectively included. Eleven clinical tests of the rotator cuff have been done. One radiologist performed ultrasonography (US) of the shoulder. Results of US were expressed as normal tendon, tendinopathy or full-thickness tear (the reference). For each clinical test and each US criteria, sensitivity, specificity, negative predictive value and positive predictive value, accuracy, negative likelihood ratio (NLR) and positive likelihood ratio (PLR) were calculated. Clinical relevance was defined as PLR ≥2 and NLR ≤0.5. For 35 patients (39 shoulders), Jobe (PLR: 2.08, NLR: 0.31) and full-can (2, 0.5) test results were relevant for diagnosis of supraspinatus tears and resisted lateral rotation (2.42, 0.5) for infraspinatus tears, with weakness as response criteria. The lift-off test (8.50, 0.27) was relevant for subscapularis tears with lag sign as response criteria. Yergason's test (3.7, 0.41) was relevant for tendinopathy of the long head of the biceps with pain as a response criterion. There was no relevant clinical test for diagnosis of tendinopathy of supraspinatus, infraspinatus or subscapularis. Five of 11 clinical tests were relevant for degenerative rotator cuff disease. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Baseline Factors Predicting Placebo Response to Treatment in Children and Adolescents With Autism Spectrum Disorders

PubMed Central

King, Bryan H.; Dukes, Kimberly; Donnelly, Craig L.; Sikich, Linmarie; McCracken, James T.; Scahill, Lawrence; Hollander, Eric; Bregman, Joel D.; Anagnostou, Evdokia; Robinson, Fay; Sullivan, Lisa; Hirtz, Deborah

2016-01-01

IMPORTANCE The finding of factors that differentially predict the likelihood of response to placebo over that of an active drug could have a significant impact on study design in this population. OBJECTIVE To identify possible nonspecific, baseline predictors of response to intervention in a large randomized clinical trial of children and adolescents with autism spectrum disorders. DESIGN, SETTING, AND PARTICIPANTS Randomized clinical trial of citalopram hydrobromide for children and adolescents with autism spectrum disorders and prominent repetitive behavior. Baseline data at study entry were examined with respect to final outcome to determine if response predictors could be identified. A total of 149 children and adolescents 5 to 17 years of age (mean [SD] age, 9.4 [3.1] years) from 6 academic centers were randomly assigned to citalopram (n = 73) or placebo (n = 76). Participants had autistic disorder, Asperger syndrome, or pervasive developmental disorder, not otherwise specified; had illness severity ratings that were moderate or more than moderate on the Clinical Global Impression–Severity scale; and scored moderate or more than moderate on compulsive behaviors measured with the modified Children’s Yale-Brown Obsessive-Compulsive Scale. INTERVENTIONS Twelve weeks of treatment with citalopram (10 mg/5 mL) or placebo. The mean (SD) maximum dose of citalopram was 16.5 (6.5) mg by mouth daily (maximum dose, 20 mg/d). MAIN OUTCOMES AND MEASURES A positive response was defined as having a score of at least much improved on the Clinical Global Impression–Improvement scale at week 12. Baseline measures included demographic (sex, age, weight, and pubertal status), clinical, and family measures. Clinical variables included baseline illness severity ratings (the Aberrant Behavior Checklist, the Child and Adolescent Symptom Inventory, the Vineland Adaptive Behavior Scales, the Repetitive Behavior Scale–Revised, and the Children’s Yale-Brown Obsessive-Compulsive Scale). Family measures included the Caregiver Strain Questionnaire. RESULTS Several baseline predictors of response were identified, and a principal component analysis yielded 3 composite measures (disruptive behavior, autism/mood, and caregiver strain) that significantly predicted response at week 12. Specifically, participants in the placebo group were significantly less likely than participants in the citalopram group to respond at week 12 if they entered the study more symptomatic on each of the 3 composite measures, and they were at least 2 times less likely to be responders. CONCLUSIONS AND RELEVANCE This analysis suggests strategies that may be useful in anticipating and potentially mitigating the nonspecific response in randomized clinical trials of children and adolescents with autism spectrum disorders. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT00086645 PMID:24061784

Baseline clinical predictors of antitumor response to the PARP inhibitor olaparib in germline BRCA1/2 mutated patients with advanced ovarian cancer.

PubMed

Rafii, Saeed; Gourley, Charlie; Kumar, Rajiv; Geuna, Elena; Ern Ang, Joo; Rye, Tzyvia; Chen, Lee-May; Shapira-Frommer, Ronnie; Friedlander, Michael; Matulonis, Ursula; De Greve, Jacques; Oza, Amit M; Banerjee, Susana; Molife, L Rhoda; Gore, Martin E; Kaye, Stan B; Yap, Timothy A

2017-07-18

The PARP inhibitor olaparib was recently granted Food and Drug Administration (FDA) accelerated approval in patients with advanced BRCA1/2 mutation ovarian cancer. However, antitumor responses are observed in only approximately 40% of patients and the impact of baseline clinical factors on response to treatment remains unclear. Although platinum sensitivity has been suggested as a marker of response to PARP inhibitors, patients with platinum-resistant disease still respond to olaparib. 108 patients with advanced BRCA1/2 mutation ovarian cancers were included. The interval between the end of the most recent platinum chemotherapy and PARPi (PTPI) was used to predict response to olaparib independent of conventional definition of platinum sensitivity. RECIST complete response (CR) and partial response (PR) rates were 35% in patients with platinum-sensitive versus 13% in platinum-resistant (p<0.005). Independent of platinum sensitivity status, the RECIST CR/PR rates were 42% in patients with PTPI greater than 52 weeks and 18% in patients with PTPI less than 52 weeks (p=0.016). No association was found between baseline clinical factors such as FIGO staging, debulking surgery, BRCA1 versus BRCA2 mutations, prior history of breast cancer and prior chemotherapy for breast cancer, and the response to olaparib. We conducted an international multicenter retrospective study to investigate the association between baseline clinical characteristics of patients with advanced BRCA1/2 mutation ovarian cancers from eight different cancer centers and their antitumor response to olaparib. PTPI may be used to refine the prediction of response to PARP inhibition based on the conventional categorization of platinum sensitivity.

Clinical and Vitamin Response to a Short-Term Multi-Micronutrient Intervention in Brazilian Children and Teens: From Population Data to Interindividual Responses.

PubMed

Mathias, Mariana Giaretta; Coelho-Landell, Carolina de Almeida; Scott-Boyer, Marie-Pier; Lacroix, Sébastien; Morine, Melissa J; Salomão, Roberta Garcia; Toffano, Roseli Borges Donegá; Almada, Maria Olímpia Ribeiro do Vale; Camarneiro, Joyce Moraes; Hillesheim, Elaine; de Barros, Tamiris Trevisan; Camelo-Junior, José Simon; Campos Giménez, Esther; Redeuil, Karine; Goyon, Alexandre; Bertschy, Emmanuelle; Lévêques, Antoine; Oberson, Jean-Marie; Giménez, Catherine; Carayol, Jerome; Kussmann, Martin; Descombes, Patrick; Métairon, Slyviane; Draper, Colleen Fogarty; Conus, Nelly; Mottaz, Sara Colombo; Corsini, Giovanna Zambianchi; Myoshi, Stephanie Kazu Brandão; Muniz, Mariana Mendes; Hernandes, Lívia Cristina; Venâncio, Vinícius Paula; Antunes, Lusania Maria Greggi; da Silva, Rosana Queiroz; Laurito, Taís Fontellas; Rossi, Isabela Ribeiro; Ricci, Raquel; Jorge, Jéssica Ré; Fagá, Mayara Leite; Quinhoneiro, Driele Cristina Gomes; Reche, Mariana Chinarelli; Silva, Paula Vitória Sozza; Falquetti, Letícia Lima; da Cunha, Thaís Helena Alves; Deminice, Thalia Manfrin Martins; Tambellini, Tâmara Hambúrguer; de Souza, Gabriela Cristina Arces; de Oliveira, Mariana Moraes; Nogueira-Pileggi, Vicky; Matsumoto, Marina Takemoto; Priami, Corrado; Kaput, Jim; Monteiro, Jacqueline Pontes

2018-03-01

Micronutrients are in small amounts in foods, act in concert, and require variable amounts of time to see changes in health and risk for disease. These first principles are incorporated into an intervention study designed to develop new experimental strategies for setting target recommendations for food bioactives for populations and individuals. A 6-week multivitamin/mineral intervention is conducted in 9-13 year olds. Participants (136) are (i) their own control (n-of-1); (ii) monitored for compliance; (iii) measured for 36 circulating vitamin forms, 30 clinical, anthropometric, and food intake parameters at baseline, post intervention, and following a 6-week washout; and (iv) had their ancestry accounted for as modifier of vitamin baseline or response. The same intervention is repeated the following year (135 participants). Most vitamins respond positively and many clinical parameters change in directions consistent with improved metabolic health to the intervention. Baseline levels of any metabolite predict its own response to the intervention. Elastic net penalized regression models are identified, and significantly predict response to intervention on the basis of multiple vitamin/clinical baseline measures. The study design, computational methods, and results are a step toward developing recommendations for optimizing vitamin levels and health parameters for individuals. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Clinical relevance of KRAS mutation detection in metastatic colorectal cancer treated by Cetuximab plus chemotherapy

PubMed Central

Di Fiore, F; Blanchard, F; Charbonnier, F; Le Pessot, F; Lamy, A; Galais, M P; Bastit, L; Killian, A; Sesboüé, R; Tuech, J J; Queuniet, A M; Paillot, B; Sabourin, J C; Michot, F; Michel, P; Frebourg, T

2007-01-01

The predictive value of KRAS mutation in metastatic colorectal cancer (MCRC) patients treated with cetuximab plus chemotherapy has recently been suggested. In our study, 59 patients with a chemotherapy-refractory MCRC treated with cetuximab plus chemotherapy were included and clinical response was evaluated according to response evaluation criteria in solid tumours (RECIST). Tumours were screened for KRAS mutations using first direct sequencing, then two sensitive methods based on SNaPshot and PCR-ligase chain reaction (LCR) assays. Clinical response was evaluated according to gene mutations using the Fisher exact test. Times to progression (TTP) were calculated using the Kaplan–Meier method and compared with log-rank test. A KRAS mutation was detected in 22 out of 59 tumours and, in six cases, was missed by sequencing analysis but detected using the SNaPshot and PCR-LCR assays. Remarkably, no KRAS mutation was found in the 12 patients with clinical response. KRAS mutation was associated with disease progression (P=0.0005) and TTP was significantly decreased in mutated KRAS patients (3 vs 5.5 months, P=0.015). Our study confirms that KRAS mutation is highly predictive of a non-response to cetuximab plus chemotherapy in MCRC and highlights the need to use sensitive molecular methods, such as SNaPshot or PCR-LCR assays, to ensure an efficient mutation detection. PMID:17375050

Intra- and interspecies gene expression models for predicting drug response in canine osteosarcoma.

PubMed

Fowles, Jared S; Brown, Kristen C; Hess, Ann M; Duval, Dawn L; Gustafson, Daniel L

2016-02-19

Genomics-based predictors of drug response have the potential to improve outcomes associated with cancer therapy. Osteosarcoma (OS), the most common primary bone cancer in dogs, is commonly treated with adjuvant doxorubicin or carboplatin following amputation of the affected limb. We evaluated the use of gene-expression based models built in an intra- or interspecies manner to predict chemosensitivity and treatment outcome in canine OS. Models were built and evaluated using microarray gene expression and drug sensitivity data from human and canine cancer cell lines, and canine OS tumor datasets. The "COXEN" method was utilized to filter gene signatures between human and dog datasets based on strong co-expression patterns. Models were built using linear discriminant analysis via the misclassification penalized posterior algorithm. The best doxorubicin model involved genes identified in human lines that were co-expressed and trained on canine OS tumor data, which accurately predicted clinical outcome in 73 % of dogs (p = 0.0262, binomial). The best carboplatin model utilized canine lines for gene identification and model training, with canine OS tumor data for co-expression. Dogs whose treatment matched our predictions had significantly better clinical outcomes than those that didn't (p = 0.0006, Log Rank), and this predictor significantly associated with longer disease free intervals in a Cox multivariate analysis (hazard ratio = 0.3102, p = 0.0124). Our data show that intra- and interspecies gene expression models can successfully predict response in canine OS, which may improve outcome in dogs and serve as pre-clinical validation for similar methods in human cancer research.

Age of depressed patient does not affect clinical outcome in collaborative care management.

PubMed

Angstman, Kurt B; MacLaughlin, Kathy L; Rasmussen, Norman H; DeJesus, Ramona S; Katzelnick, David J

2011-09-01

Clinical response and remission for the treatment of depression has been shown to be improved utilizing collaborative care management (CCM). Prior studies have indicated that the presence of mental health comorbidities noted by self-rated screening tools at the intake for CCM are associated with worsening outcomes; few have examined directly the impact of age on clinical response and remission. The hypothesis was that when controlling for other mental health and demographic variables, the age of the patient at implementation of CCM does not significantly impact clinical outcome, and that CCM shows consistent efficacy across the adult age spectrum. We performed a retrospective chart analysis of a cohort of 574 patients with a clinical diagnosis of major depression (not dysthymia) treated in CCM who had 6 months of follow-up data. Using the age group as a categorical variable in logistic regression models demonstrated that while maintaining control of all other variables, age grouping remained a nonsignificant predictor of clinical response (P ≥ 0.1842) and remission (P ≥ 0.1919) after 6 months of treatment. In both models, a lower Generalized Anxiety Disorder-7 score and a negative Mood Disorder Questionnaire score were predictive of clinical response and remission. However, the initial Patient Health Questionnaire-9 score was a statistically significant predictor only for clinical remission (P = 0.0094), not for response (P = 0.0645), at 6 months. In a subset (n = 295) of the study cohort, clinical remission at 12 months was also not associated with age grouping (P ≥ 0.3355). The variables that were predictive of remission at 12 months were the presence of clinical remission at 6 months (odds ratio [OR], 7.4820; confidence interval [CI], 3.9301-14.0389; P < 0.0001), clinical response (with persistent symptoms) (OR, 2.7722; CI, 1.1950-6.4313; P = 0.0176), and a lower initial Patient Health Questionnaire-9 score (OR, 0.9121; CI, 0.8475-0.9816; P = 0.0140). Our study suggests that using CCM for depression treatment may transcend age-related differences in depression and result in positive outcomes regardless of age.

Novel biomarker-based model for the prediction of sorafenib response and overall survival in advanced hepatocellular carcinoma: a prospective cohort study.

PubMed

Kim, Hwi Young; Lee, Dong Hyeon; Lee, Jeong-Hoon; Cho, Young Youn; Cho, Eun Ju; Yu, Su Jong; Kim, Yoon Jun; Yoon, Jung-Hwan

2018-03-20

Prediction of the outcome of sorafenib therapy using biomarkers is an unmet clinical need in patients with advanced hepatocellular carcinoma (HCC). The aim was to develop and validate a biomarker-based model for predicting sorafenib response and overall survival (OS). This prospective cohort study included 124 consecutive HCC patients (44 with disease control, 80 with progression) with Child-Pugh class A liver function, who received sorafenib. Potential serum biomarkers (namely, hepatocyte growth factor [HGF], fibroblast growth factor [FGF], vascular endothelial growth factor receptor-1, CD117, and angiopoietin-2) were tested. After identifying independent predictors of tumor response, a risk scoring system for predicting OS was developed and 3-fold internal validation was conducted. A risk scoring system was developed with six covariates: etiology, platelet count, Barcelona Clinic Liver Cancer stage, protein induced by vitamin K absence-II, HGF, and FGF. When patients were stratified into low-risk (score ≤ 5), intermediate-risk (score 6), and high-risk (score ≥ 7) groups, the model provided good discriminant functions on tumor response (concordance [c]-index, 0.884) and 12-month survival (area under the curve [AUC], 0.825). The median OS was 19.0, 11.2, and 6.1 months in the low-, intermediate-, and high-risk group, respectively (P < 0.001). In internal validation, the model maintained good discriminant functions on tumor response (c-index, 0.825) and 12-month survival (AUC, 0.803), and good calibration functions (all P > 0.05 between expected and observed values). This new model including serum FGF and HGF showed good performance in predicting the response to sorafenib and survival in patients with advanced HCC.

Surrogate clinical endpoints to predict overall survival in non-small cell lung cancer trials-are we in a new era?

PubMed

Clarke, Jeffrey M; Wang, Xiaofei; Ready, Neal E

2015-12-01

Surrogate endpoints for clinical trials in oncology offer an alternative metric for measuring clinical benefit, allowing for shorter trial duration, smaller patient cohorts, and single arm design. The correlation of surrogate endpoints with overall survival (OS) in therapeutic studies is a central consideration to their validity. The Food and Drug Administration (FDA) recently published an analysis of fourteen clinical trials in advanced non-small cell lung cancer (NSCLC), and discovered a strong association between response rate and progression free survival. Furthermore, a correlation between response rate and OS is demonstrated when analyzing the experimental treatment arm separately, minimizing bias from patient crossover. We also highlight multiple, important considerations when using response as an endpoint in clinical trials involving NSCLC patients.

Predictive factors for the regression of canine transmissible venereal tumor during vincristine therapy.

PubMed

Scarpelli, Karime C; Valladão, Maria L; Metze, Konradin

2010-03-01

Canine transmissible venereal tumor (CTVT) is a neoplasm transmitted by transplantation. Monochemotherapy with vincristine is considered to be effective, but treatment time until complete clinical remission may vary. The aim of this study was to determine which clinical data at diagnosis could predict the responsiveness of CTVT to vincristine chemotherapy. One hundred dogs with CTVT entered this prospective study. The animals were treated with vincristine sulfate (0.025 mg/kg) at weekly intervals until the tumor had macroscopically disappeared. The time to complete remission was recorded. A multivariate Cox regression model indicated that larger tumor mass, increased age and therapy during hot and rainy months were independent significant unfavorable predictive factors retarding remission, whereas sex, weight, status as owned dog or breed were of no predictive relevance. Further studies are necessary to investigate whether these results are due to changes in immunological response mechanisms in animals with a diminished immune surveillance, resulting in delays in tumor regression. 2008 Elsevier Ltd. All rights reserved.

Can we predict the blood pressure response to renal denervation?

PubMed Central

Fink, Gregory D.; Phelps, Jeremiah T.

2016-01-01

Renal denervation (RDN) is a new therapy used to treat drug-resistant hypertension in the clinical setting. Published human trials show substantial inter-individual variability in the blood pressure (BP) response to RDN, even when technical aspects of the treatment are standardized as much as possible between patients. Widespread acceptance of RDN for treating hypertension will require accurate identification of patients likely to respond to RDN with a fall in BP that is clinically significant in magnitude, well-maintained over time and does not cause adverse consequences. In this paper we review and evaluate clinical studies that address possible predictors of the BP response to RDN. We conclude that only one generally reliable predictor has been identified to date, namely pre-RDN BP level, although there is some evidence for a few other factors. Experimental interventions in laboratory animals provide the opportunity to explore potential predictors that are difficult to investigate in human patients. Therefore we also describe results (from our lab and others) with RDN in spontaneously hypertensive rats. Since virtually all patients receiving RDN are taking three or more antihypertensive drugs, a particular focus of our work was on how ongoing antihypertensive drug treatment might alter the BP response to RDN. We conclude that patient age (or duration of hypertension) and concomitant treatment with certain drugs can affect the blood pressure response to RDN and that this information could help predict a favorable clinical response. PMID:27530600

Drug-induced death signaling strategy rapidly predicts cancer response to chemotherapy.

PubMed

Montero, Joan; Sarosiek, Kristopher A; DeAngelo, Joseph D; Maertens, Ophélia; Ryan, Jeremy; Ercan, Dalia; Piao, Huiying; Horowitz, Neil S; Berkowitz, Ross S; Matulonis, Ursula; Jänne, Pasi A; Amrein, Philip C; Cichowski, Karen; Drapkin, Ronny; Letai, Anthony

2015-02-26

There is a lack of effective predictive biomarkers to precisely assign optimal therapy to cancer patients. While most efforts are directed at inferring drug response phenotype based on genotype, there is very focused and useful phenotypic information to be gained from directly perturbing the patient's living cancer cell with the drug(s) in question. To satisfy this unmet need, we developed the Dynamic BH3 Profiling technique to measure early changes in net pro-apoptotic signaling at the mitochondrion ("priming") induced by chemotherapeutic agents in cancer cells, not requiring prolonged ex vivo culture. We find in cell line and clinical experiments that early drug-induced death signaling measured by Dynamic BH3 Profiling predicts chemotherapy response across many cancer types and many agents, including combinations of chemotherapies. We propose that Dynamic BH3 Profiling can be used as a broadly applicable predictive biomarker to predict cytotoxic response of cancers to chemotherapeutics in vivo. Copyright © 2015 Elsevier Inc. All rights reserved.

The Application of Bone Marrow Transplantation to the Treatment of Genetic Diseases

NASA Astrophysics Data System (ADS)

Parkman, Robertson

1986-06-01

Genetic diseases can be treated by transplantation of either normal allogeneic bone marrow or, potentially, autologous bone marrow into which the normal gene has been inserted in vitro (gene therapy). Histocompatible allogeneic bone marrow transplantation is used for the treatment of genetic diseases whose clinical expression is restricted to lymphoid or hematopoietic cells. The therapeutic role of bone marrow transplantation in the treatment of generalized genetic diseases, especially those affecting the central nervous system, is under investigation. The response of a generalized genetic disease to allogeneic bone marrow transplantation may be predicted by experiments in vitro. Gene therapy can be used only when the gene responsible for the disease has been characterized. Success of gene therapy for a specific genetic disease may be predicted by its clinical response to allogeneic bone marrow transplantation.

Clinical value of serum anti-mullerian hormone and inhibin B in prediction of ovarian response in patients with polycystic ovary syndrome.

PubMed

Zhang, Fan; Liu, Xiao-Ling; Rong, Nan; Huang, Xiao-Wen

2017-02-01

The present study aimed to investigate the clinical value of serum anti-mullerian hormone (AMH) and inhibin B (INHB) in predicting the ovarian response of patients with polycystic ovary syndrome (PCOS). A total of 120 PCOS patients were enrolled and divided into three groups in terms of the ovarian response: a low-response group (n=36), a normal-response group (n=44), and a high-response group (n=40). The serum AMH and INHB levels were measured by enzyme-linked immunosorbent assay (ELISA). The follicle stimulating hormone (FSH), luteinizing hormone (LH), and estradiol (E2) levels were determined by chemiluminescence microparticle immunoassay. The correlation of the serum AMH and INHB levels with other indicators was analyzed. A receiver operating characteristic (ROC) curve was established to analyze the prediction of ovarian response by AMH and INHB. The results showed that there were significant differences in age, body mass index (BMI), FSH, total gonadotropin-releasing hormone (GnRH), LH, E2, and antral follicle counts (AFCs) between the groups (P<0.05). The serum AMH and INHB levels were increased significantly with the ovarian response of PCOS patients increasing (P<0.05). The serum AMH and INHB levels were negatively correlated with the age, BMI, FSH level, Gn, and E2 levels (P<0.05). They were positively correlated with the LH levels and AFCs (P<0.05). ROC curve analysis of serum AMH and INHB in prediction of a low ovarian response showed that the area under the ROC curve (AUC) value of the serum AMH level was 0.817, with a cut-off value of 1.29 ng/mL. The sensitivity and specificity were 71.2% and 79.6%, respectively. The AUC value of serum INHB was 0.674, with a cut-off value of 38.65 ng/mL, and the sensitivity and specificity were 50.7% and 74.5%, respectively. ROC curve analysis showed when the serum AMH and INHB levels were used to predict a high ovarian response, the AUC value of the serum AMH level was 0.742, with a cut-off value of 2.84 ng/mL, and the sensitivity and specificity were 72.7% and 65.9%, respectively; the AUC value of the serum INHB level was 0.551 with a cut-off of 45.76 ng/mL, and the sensitivity and specificity were 76.3% and 40.2%, respectively. It was suggested the serum AMH and INHB levels have high clinical value in predicting the ovarian response of PCOS patients.

Predicting breast cancer using an expression values weighted clinical classifier.

PubMed

Thomas, Minta; De Brabanter, Kris; Suykens, Johan A K; De Moor, Bart

2014-12-31

Clinical data, such as patient history, laboratory analysis, ultrasound parameters-which are the basis of day-to-day clinical decision support-are often used to guide the clinical management of cancer in the presence of microarray data. Several data fusion techniques are available to integrate genomics or proteomics data, but only a few studies have created a single prediction model using both gene expression and clinical data. These studies often remain inconclusive regarding an obtained improvement in prediction performance. To improve clinical management, these data should be fully exploited. This requires efficient algorithms to integrate these data sets and design a final classifier. LS-SVM classifiers and generalized eigenvalue/singular value decompositions are successfully used in many bioinformatics applications for prediction tasks. While bringing up the benefits of these two techniques, we propose a machine learning approach, a weighted LS-SVM classifier to integrate two data sources: microarray and clinical parameters. We compared and evaluated the proposed methods on five breast cancer case studies. Compared to LS-SVM classifier on individual data sets, generalized eigenvalue decomposition (GEVD) and kernel GEVD, the proposed weighted LS-SVM classifier offers good prediction performance, in terms of test area under ROC Curve (AUC), on all breast cancer case studies. Thus a clinical classifier weighted with microarray data set results in significantly improved diagnosis, prognosis and prediction responses to therapy. The proposed model has been shown as a promising mathematical framework in both data fusion and non-linear classification problems.

Serum Immune Responses Predict Rapid Disease Progression among Children with Crohn’s Disease: Immune Responses Predict Disease Progression

PubMed Central

Dubinsky, Marla C.; Lin, Ying-Chao; Dutridge, Debra; Picornell, Yoana; Landers, Carol J.; Farrior, Sharmayne; Wrobel, Iwona; Quiros, Antonio; Vasiliauskas, Eric A.; Grill, Bruce; Israel, David; Bahar, Ron; Christie, Dennis; Wahbeh, Ghassan; Silber, Gary; Dallazadeh, Saied; Shah, Praful; Thomas, Danny; Kelts, Drew; Hershberg, Robert M.; Elson, Charles O.; Targan, Stephan R.; Taylor, Kent D.; Rotter, Jerome I.; Yang, Huiying

2007-01-01

BACKGROUND AND AIM Crohn’s disease (CD) is a heterogeneous disorder characterized by diverse clinical phenotypes. Childhood-onset CD has been described as a more aggressive phenotype. Genetic and immune factors may influence disease phenotype and clinical course. We examined the association of immune responses to microbial antigens with disease behavior and prospectively determined the influence of immune reactivity on disease progression in pediatric CD patients. METHODS Sera were collected from 196 pediatric CD cases and tested for immune responses: anti-I2, anti-outer membrane protein C (anti-OmpC), anti-CBir1 flagellin (anti-CBir1), and anti-Saccharomyces-cerevisiae (ASCA) using ELISA. Associations between immune responses and clinical phenotype were evaluated. RESULTS Fifty-eight patients (28%) developed internal penetrating and/or stricturing (IP/S) disease after a median follow-up of 18 months. Both anti-OmpC (p < 0.0006) and anti-I2 (p < 0.003) were associated with IP/S disease. The frequency of IP/S disease increased with increasing number of immune responses (p trend = 0.002). The odds of developing IP/S disease were highest in patients positive for all four immune responses (OR (95% CI): 11 (1.5–80.4); p = 0.03). Pediatric CD patients positive for ≥1 immune response progressed to IP/S disease sooner after diagnosis as compared to those negative for all immune responses (p < 0.03). CONCLUSIONS The presence and magnitude of immune responses to microbial antigens are significantly associated with more aggressive disease phenotypes among children with CD. This is the first study to prospectively demonstrate that the time to develop a disease complication in children is significantly faster in the presence of immune reactivity, thereby predicting disease progression to more aggressive disease phenotypes among pediatric CD patients. PMID:16454844

Quantitative Stratification of Diffuse Parenchymal Lung Diseases

PubMed Central

Raghunath, Sushravya; Rajagopalan, Srinivasan; Karwoski, Ronald A.; Maldonado, Fabien; Peikert, Tobias; Moua, Teng; Ryu, Jay H.; Bartholmai, Brian J.; Robb, Richard A.

2014-01-01

Diffuse parenchymal lung diseases (DPLDs) are characterized by widespread pathological changes within the pulmonary tissue that impair the elasticity and gas exchange properties of the lungs. Clinical-radiological diagnosis of these diseases remains challenging and their clinical course is characterized by variable disease progression. These challenges have hindered the introduction of robust objective biomarkers for patient-specific prediction based on specific phenotypes in clinical practice for patients with DPLD. Therefore, strategies facilitating individualized clinical management, staging and identification of specific phenotypes linked to clinical disease outcomes or therapeutic responses are urgently needed. A classification schema consistently reflecting the radiological, clinical (lung function and clinical outcomes) and pathological features of a disease represents a critical need in modern pulmonary medicine. Herein, we report a quantitative stratification paradigm to identify subsets of DPLD patients with characteristic radiologic patterns in an unsupervised manner and demonstrate significant correlation of these self-organized disease groups with clinically accepted surrogate endpoints. The proposed consistent and reproducible technique could potentially transform diagnostic staging, clinical management and prognostication of DPLD patients as well as facilitate patient selection for clinical trials beyond the ability of current radiological tools. In addition, the sequential quantitative stratification of the type and extent of parenchymal process may allow standardized and objective monitoring of disease, early assessment of treatment response and mortality prediction for DPLD patients. PMID:24676019

Early improvement with pregabalin predicts endpoint response in patients with generalized anxiety disorder: an integrated and predictive data analysis.

PubMed

Montgomery, Stuart A; Lyndon, Gavin; Almas, Mary; Whalen, Ed; Prieto, Rita

2017-01-01

Generalized anxiety disorder (GAD), a common mental disorder, has several treatment options including pregabalin. Not all patients respond to treatment; quickly determining which patients will respond is an important treatment goal. Patient-level data were pooled from nine phase II and III randomized, double-blind, short-term, placebo-controlled trials of pregabalin for the treatment of GAD. Efficacy outcomes included the change from baseline in the Hamilton Anxiety Scale (HAM-A) total score and psychic and somatic subscales. Predictive modelling assessed baseline characteristics and early clinical responses to determine those predictive of clinical improvement at endpoint. A total of 2155 patients were included in the analysis (1447 pregabalin, 708 placebo). Pregabalin significantly improved the HAM-A total score compared with the placebo at endpoint, treatment difference (95% confidence interval), -2.61 (-3.21 to -2.01), P<0.0001. Pregabalin significantly improved HAM-A psychic and somatic scores compared with placebo, -1.52 (-1.85 to -1.18), P<0.0001, and -1.10 (-1.41 to -0.80), P<0.0001, respectively. Response to pregabalin in the first 1-2 weeks (≥20 or ≥30% improvement in HAM-A total, psychic or somatic score) was predictive of an endpoint greater than or equal to 50% improvement in the HAM-A total score. Pregabalin is an effective treatment option for patients with GAD. Patients with early response to pregabalin are more likely to respond significantly at endpoint.

Molecular markers in bladder cancer: Novel research frontiers.

PubMed

Sanguedolce, Francesca; Cormio, Antonella; Bufo, Pantaleo; Carrieri, Giuseppe; Cormio, Luigi

2015-01-01

Bladder cancer (BC) is a heterogeneous disease encompassing distinct biologic features that lead to extremely different clinical behaviors. In the last 20 years, great efforts have been made to predict disease outcome and response to treatment by developing risk assessment calculators based on multiple standard clinical-pathological factors, as well as by testing several molecular markers. Unfortunately, risk assessment calculators alone fail to accurately assess a single patient's prognosis and response to different treatment options. Several molecular markers easily assessable by routine immunohistochemical techniques hold promise for becoming widely available and cost-effective tools for a more reliable risk assessment, but none have yet entered routine clinical practice. Current research is therefore moving towards (i) identifying novel molecular markers; (ii) testing old and new markers in homogeneous patients' populations receiving homogeneous treatments; (iii) generating a multimarker panel that could be easily, and thus routinely, used in clinical practice; (iv) developing novel risk assessment tools, possibly combining standard clinical-pathological factors with molecular markers. This review analyses the emerging body of literature concerning novel biomarkers, ranging from genetic changes to altered expression of a huge variety of molecules, potentially involved in BC outcome and response to treatment. Findings suggest that some of these indicators, such as serum circulating tumor cells and tissue mitochondrial DNA, seem to be easily assessable and provide reliable information. Other markers, such as the phosphoinositide-3-kinase (PI3K)/AKT (serine-threonine kinase)/mTOR (mammalian target of rapamycin) pathway and epigenetic changes in DNA methylation seem to not only have prognostic/predictive value but also, most importantly, represent valuable therapeutic targets. Finally, there is increasing evidence that the development of novel risk assessment tools combining standard clinical-pathological factors with molecular markers represents a major quest in managing this poorly predictable disease.

Prediction of HR/BP response to the spontaneous breathing trial by fluctuation dissipation theory

NASA Astrophysics Data System (ADS)

Chen, Man

2014-03-01

We applied the non-equilibrium fluctuation dissipation theorem to predict how critically-ill patients respond to treatment, based on both heart rate data and blood pressure data collected by standard hospital monitoring devices. The non-equilibrium fluctuation dissipation theorem relates the response of a system to a perturbation to the fluctuations in the stationary state of the system. It is shown that the response of patients to a standard procedure performed on patients, the spontaneous breathing trial (SBT), can be predicted by the non-equilibrium fluctuation dissipation approach. We classify patients into different groups according to the patients' characteristics. For each patient group, we extend the fluctuation dissipation theorem to predict interactions between blood pressure and beat-to-beat dynamics of heart rate in response to a perturbation (SBT), We also extract the form of the perturbation function directly from the physiological data, which may help to reduce the prediction error. We note this method is not limited to the analysis of the heart rate dynamics, but also can be applied to analyze the response of other physiological signals to other clinical interventions.

Post-Exercise Heart Rate Recovery Independently Predicts Clinical Outcome in Patients with Acute Decompensated Heart Failure.

PubMed

Youn, Jong-Chan; Lee, Hye Sun; Choi, Suk-Won; Han, Seong-Woo; Ryu, Kyu-Hyung; Shin, Eui-Cheol; Kang, Seok-Min

2016-01-01

Post-exercise heart rate recovery (HRR) is an index of parasympathetic function associated with clinical outcome in patients with chronic heart failure. However, its relationship with the pro-inflammatory response and prognostic value in consecutive patients with acute decompensated heart failure (ADHF) has not been investigated. We measured HRR and pro-inflammatory markers in 107 prospectively and consecutively enrolled, recovered ADHF patients (71 male, 59 ± 15 years, mean ejection fraction 28.9 ± 14.2%) during the pre-discharge period. The primary endpoint included cardiovascular (CV) events defined as CV mortality, cardiac transplantation, or rehospitalization due to HF aggravation. The CV events occurred in 30 (28.0%) patients (5 cardiovascular deaths and 7 cardiac transplantations) during the follow-up period (median 214 days, 11-812 days). When the patients with ADHF were grouped by HRR according to the Contal and O'Quigley's method, low HRR was shown to be associated with significantly higher levels of serum monokine-induced by gamma interferon (MIG) and poor clinical outcome. Multivariate Cox regression analysis revealed that low HRR was an independent predictor of CV events in both enter method and stepwise method. The addition of HRR to a model significantly increased predictability for CV events across the entire follow-up period. Impaired post-exercise HRR is associated with a pro-inflammatory response and independently predicts clinical outcome in patients with ADHF. These findings may explain the relationship between autonomic dysfunction and clinical outcome in terms of the inflammatory response in these patients.

Reward Prediction Errors in Drug Addiction and Parkinson's Disease: from Neurophysiology to Neuroimaging.

PubMed

García-García, Isabel; Zeighami, Yashar; Dagher, Alain

2017-06-01

Surprises are important sources of learning. Cognitive scientists often refer to surprises as "reward prediction errors," a parameter that captures discrepancies between expectations and actual outcomes. Here, we integrate neurophysiological and functional magnetic resonance imaging (fMRI) results addressing the processing of reward prediction errors and how they might be altered in drug addiction and Parkinson's disease. By increasing phasic dopamine responses, drugs might accentuate prediction error signals, causing increases in fMRI activity in mesolimbic areas in response to drugs. Chronic substance dependence, by contrast, has been linked with compromised dopaminergic function, which might be associated with blunted fMRI responses to pleasant non-drug stimuli in mesocorticolimbic areas. In Parkinson's disease, dopamine replacement therapies seem to induce impairments in learning from negative outcomes. The present review provides a holistic overview of reward prediction errors across different pathologies and might inform future clinical strategies targeting impulsive/compulsive disorders.

Towards personalized therapy for multiple sclerosis: prediction of individual treatment response.

PubMed

Kalincik, Tomas; Manouchehrinia, Ali; Sobisek, Lukas; Jokubaitis, Vilija; Spelman, Tim; Horakova, Dana; Havrdova, Eva; Trojano, Maria; Izquierdo, Guillermo; Lugaresi, Alessandra; Girard, Marc; Prat, Alexandre; Duquette, Pierre; Grammond, Pierre; Sola, Patrizia; Hupperts, Raymond; Grand'Maison, Francois; Pucci, Eugenio; Boz, Cavit; Alroughani, Raed; Van Pesch, Vincent; Lechner-Scott, Jeannette; Terzi, Murat; Bergamaschi, Roberto; Iuliano, Gerardo; Granella, Franco; Spitaleri, Daniele; Shaygannejad, Vahid; Oreja-Guevara, Celia; Slee, Mark; Ampapa, Radek; Verheul, Freek; McCombe, Pamela; Olascoaga, Javier; Amato, Maria Pia; Vucic, Steve; Hodgkinson, Suzanne; Ramo-Tello, Cristina; Flechter, Shlomo; Cristiano, Edgardo; Rozsa, Csilla; Moore, Fraser; Luis Sanchez-Menoyo, Jose; Laura Saladino, Maria; Barnett, Michael; Hillert, Jan; Butzkueven, Helmut

2017-09-01

Timely initiation of effective therapy is crucial for preventing disability in multiple sclerosis; however, treatment response varies greatly among patients. Comprehensive predictive models of individual treatment response are lacking. Our aims were: (i) to develop predictive algorithms for individual treatment response using demographic, clinical and paraclinical predictors in patients with multiple sclerosis; and (ii) to evaluate accuracy, and internal and external validity of these algorithms. This study evaluated 27 demographic, clinical and paraclinical predictors of individual response to seven disease-modifying therapies in MSBase, a large global cohort study. Treatment response was analysed separately for disability progression, disability regression, relapse frequency, conversion to secondary progressive disease, change in the cumulative disease burden, and the probability of treatment discontinuation. Multivariable survival and generalized linear models were used, together with the principal component analysis to reduce model dimensionality and prevent overparameterization. Accuracy of the individual prediction was tested and its internal validity was evaluated in a separate, non-overlapping cohort. External validity was evaluated in a geographically distinct cohort, the Swedish Multiple Sclerosis Registry. In the training cohort (n = 8513), the most prominent modifiers of treatment response comprised age, disease duration, disease course, previous relapse activity, disability, predominant relapse phenotype and previous therapy. Importantly, the magnitude and direction of the associations varied among therapies and disease outcomes. Higher probability of disability progression during treatment with injectable therapies was predominantly associated with a greater disability at treatment start and the previous therapy. For fingolimod, natalizumab or mitoxantrone, it was mainly associated with lower pretreatment relapse activity. The probability of disability regression was predominantly associated with pre-baseline disability, therapy and relapse activity. Relapse incidence was associated with pretreatment relapse activity, age and relapsing disease course, with the strength of these associations varying among therapies. Accuracy and internal validity (n = 1196) of the resulting predictive models was high (>80%) for relapse incidence during the first year and for disability outcomes, moderate for relapse incidence in Years 2-4 and for the change in the cumulative disease burden, and low for conversion to secondary progressive disease and treatment discontinuation. External validation showed similar results, demonstrating high external validity for disability and relapse outcomes, moderate external validity for cumulative disease burden and low external validity for conversion to secondary progressive disease and treatment discontinuation. We conclude that demographic, clinical and paraclinical information helps predict individual response to disease-modifying therapies at the time of their commencement. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Identifying a predictive model for response to atypical antipsychotic monotherapy treatment in south Indian schizophrenia patients.

PubMed

Gupta, Meenal; Moily, Nagaraj S; Kaur, Harpreet; Jajodia, Ajay; Jain, Sanjeev; Kukreti, Ritushree

2013-08-01

Atypical antipsychotic (AAP) drugs are the preferred choice of treatment for schizophrenia patients. Patients who do not show favorable response to AAP monotherapy are subjected to random prolonged therapeutic treatment with AAP multitherapy, typical antipsychotics or a combination of both. Therefore, prior identification of patients' response to drugs can be an important step in providing efficacious and safe therapeutic treatment. We thus attempted to elucidate a genetic signature which could predict patients' response to AAP monotherapy. Our logistic regression analyses indicated the probability that 76% patients carrying combination of four SNPs will not show favorable response to AAP therapy. The robustness of this prediction model was assessed using repeated 10-fold cross validation method, and the results across n-fold cross-validations (mean accuracy=71.91%; 95%CI=71.47-72.35) suggest high accuracy and reliability of the prediction model. Further validations of these results in large sample sets are likely to establish their clinical applicability. Copyright © 2013 Elsevier Inc. All rights reserved.

Personalizing oncology treatments by predicting drug efficacy, side-effects, and improved therapy: mathematics, statistics, and their integration.

PubMed

Agur, Zvia; Elishmereni, Moran; Kheifetz, Yuri

2014-01-01

Despite its great promise, personalized oncology still faces many hurdles, and it is increasingly clear that targeted drugs and molecular biomarkers alone yield only modest clinical benefit. One reason is the complex relationships between biomarkers and the patient's response to drugs, obscuring the true weight of the biomarkers in the overall patient's response. This complexity can be disentangled by computational models that integrate the effects of personal biomarkers into a simulator of drug-patient dynamic interactions, for predicting the clinical outcomes. Several computational tools have been developed for personalized oncology, notably evidence-based tools for simulating pharmacokinetics, Bayesian-estimated tools for predicting survival, etc. We describe representative statistical and mathematical tools, and discuss their merits, shortcomings and preliminary clinical validation attesting to their potential. Yet, the individualization power of mathematical models alone, or statistical models alone, is limited. More accurate and versatile personalization tools can be constructed by a new application of the statistical/mathematical nonlinear mixed effects modeling (NLMEM) approach, which until recently has been used only in drug development. Using these advanced tools, clinical data from patient populations can be integrated with mechanistic models of disease and physiology, for generating personal mathematical models. Upon a more substantial validation in the clinic, this approach will hopefully be applied in personalized clinical trials, P-trials, hence aiding the establishment of personalized medicine within the main stream of clinical oncology. © 2014 Wiley Periodicals, Inc.

(99m)Tc-Annexin A5 quantification of apoptotic tumor response: a systematic review and meta-analysis of clinical imaging trials.

PubMed

Belhocine, Tarik Z; Blankenberg, Francis G; Kartachova, Marina S; Stitt, Larry W; Vanderheyden, Jean-Luc; Hoebers, Frank J P; Van de Wiele, Christophe

2015-12-01

(99m)Tc-Annexin A5 has been used as a molecular imaging probe for the visualization, characterization and measurement of apoptosis. In an effort to define the quantitative (99m)Tc-annexin A5 uptake criteria that best predict tumor response to treatment, we performed a systematic review and meta-analysis of the results of all clinical imaging trials found in the literature or publicly available databases. Included in this review were 17 clinical trials investigating quantitative (99m)Tc-annexin A5 (qAnx5) imaging using different parameters in cancer patients before and after the first course of chemotherapy and/or radiation therapy. Qualitative assessment of the clinical studies for diagnostic accuracy was performed using the QUADAS-2 criteria. Of these studies, five prospective single-center clinical trials (92 patients in total) were included in the meta-analysis after exclusion of one multicenter clinical trial due to heterogeneity. Pooled positive predictive values (PPV) and pooled negative predictive values (NPV) (with 95% CI) were calculated using Meta-Disc software version 1.4. Absolute quantification and/or relative quantification of (99m)Tc-annexin A5 uptake were performed at baseline and after the start of treatment. Various quantitative parameters have been used for the calculation of (99m)Tc-annexin A5 tumor uptake and delta (Δ) tumor changes post-treatment compared to baseline including: tumor-to-background ratio (TBR), ΔTBR, tumor-to-noise ratio, relative tumor ratio (TR), ΔTR, standardized tumor uptake ratio (STU), ΔSTU, maximum count per pixel within the tumor volume (Cmax), Cmax%, absolute ΔU and percentage (ΔU%), maximum ΔU counts, semiquantitative visual scoring, percent injected dose (%ID) and %ID/cm(3). Clinical trials investigating qAnx5 imaging have included patients with lung cancer, lymphoma, breast cancer, head and neck cancer and other less common tumor types. In two phase I/II single-center clinical trials, an increase of ≥25% in uptake following treatment was considered a significant threshold for an apoptotic tumor response (partial response, complete response). In three other phase I/II clinical trials, increases of ≥28%, ≥42% and ≥47% in uptake following treatment were found to be the mean cut-off levels in responders. In a phase II/III multicenter clinical trial, an increase of ≥23% in uptake following treatment was found to be the minimum cut-off level for a tumor response. In one clinical trial, no significant difference in (99m)Tc-annexin A5 uptake in terms of %ID was found in healthy tissues after chemotherapy compared to baseline. In two other clinical trials, intraobserver and interobserver measurements of (99m)Tc-annexin A5 tumor uptake were found to be reproducible (mean difference <5%, kappa =  0.90 and 0.82, respectively) and to be highly correlated with treatment outcome (Spearman r = 0.99, p < 0.0001). The meta-analysis demonstrated a pooled positive PPV of 100% (95% CI 92 - 100%) and a pooled NPV of 70% (95% CI 55 - 82%) for prediction of a tumor response after the first course of chemotherapy and/or radiotherapy in terms of ΔU%. In a symmetric sROC analysis, the AUC was 0.919 and the Q* index was 85.21 %. Quantitative (99m)Tc-annexin A5 imaging has been investigated in clinical trials for the assessment of apoptotic tumor responses. This meta-analysis showed a high pooled PPV and a moderate pooled NPV with ΔU cut-off values ranging between 20% and 30%. Standardization of quantification and harmonization of results are required for high-quality clinical research. A standardized uptake value score (SUV, ΔSUV) using quantitative SPECT/CT imaging may be a promising approach to the simple, reproducible and semiquantitative assessment of apoptotic tumor changes.

Thymidilate synthase and p53 primary tumour expression as predictive factors for advanced colorectal cancer patients

PubMed Central

Paradiso, A; Simone, G; Petroni, S; Leone, B; Vallejo, C; Lacava, J; Romero, A; Machiavelli, M; Lena, M De; Allegra, C J; Johnston, P G

2000-01-01

The purpose of this work was to analyse the ability of p53 and thymidilate synthase (TS) primary tumour expression to retrospectively predict clinical response to chemotherapy and long-term prognosis in patients with advanced colorectal cancers homogeneously treated by methotrexate (MTX)-modulated–5-fluorouracil (5-FU-FA). A total of 108 advanced colorectal cancer patients entered the present retrospective study. Immunohistochemical p53 (pAb 1801 mAb) and TS (TS106 mAb) expression on formalin-fixed paraffin-embedded primary tumour specimens was related to probability of clinical response to chemotherapy, time to progression and overall survival. p53 was expressed in 53/108 (49%) tumours, while 54/108 (50%) showed TS immunostaining. No relationship was demonstrated between p53 positivity and clinical response to chemotherapy (objective response (OR): 20% vs 23%, in p53+ and p53– cases respectively) or overall survival. Percent of OR was significantly higher in TS-negative with respect to TS-positive tumours (30% vs 15% respectively;P< 0.04); simultaneous analysis of TS and p53 indicated 7% OR for p53-positive/TS-positive tumours vs 46% for p53-positive/TS-negative tumours (P< 0.03). Logistic regression analysis confirmed a significant association between TS tumour status and clinical response to chemotherapy (hazard ratio (HR): 2.91; 95% confidence interval (CI) 8.34–1.01; two-sided P< 0.05). A multivariate analysis of overall survival showed that only a small number of metastatic sites was statistically relevant (HR 1.89; 95% CI 2.85–1.26; two-sided P< 0.03). Our study suggests that immunohistochemical expression of p53 and TS could assist the clinician in predicting response of colorectal cancer patients to modulated MTX-5-FU therapy. © 2000 Cancer Research Campaign PMID:10682666

Discovering Pediatric Asthma Phenotypes on the Basis of Response to Controller Medication Using Machine Learning.

PubMed

Ross, Mindy K; Yoon, Jinsung; van der Schaar, Auke; van der Schaar, Mihaela

2018-01-01

Pediatric asthma has variable underlying inflammation and symptom control. Approaches to addressing this heterogeneity, such as clustering methods to find phenotypes and predict outcomes, have been investigated. However, clustering based on the relationship between treatment and clinical outcome has not been performed, and machine learning approaches for long-term outcome prediction in pediatric asthma have not been studied in depth. Our objectives were to use our novel machine learning algorithm, predictor pursuit (PP), to discover pediatric asthma phenotypes on the basis of asthma control in response to controller medications, to predict longitudinal asthma control among children with asthma, and to identify features associated with asthma control within each discovered pediatric phenotype. We applied PP to the Childhood Asthma Management Program study data (n = 1,019) to discover phenotypes on the basis of asthma control between assigned controller therapy groups (budesonide vs. nedocromil). We confirmed PP's ability to discover phenotypes using the Asthma Clinical Research Network/Childhood Asthma Research and Education network data. We next predicted children's asthma control over time and compared PP's performance with that of traditional prediction methods. Last, we identified clinical features most correlated with asthma control in the discovered phenotypes. Four phenotypes were discovered in both datasets: allergic not obese (A + /O - ), obese not allergic (A - /O + ), allergic and obese (A + /O + ), and not allergic not obese (A - /O - ). Of the children with well-controlled asthma in the Childhood Asthma Management Program dataset, we found more nonobese children treated with budesonide than with nedocromil (P = 0.015) and more obese children treated with nedocromil than with budesonide (P = 0.008). Within the obese group, more A + /O + children's asthma was well controlled with nedocromil than with budesonide (P = 0.022) or with placebo (P = 0.011). The PP algorithm performed significantly better (P < 0.001) than traditional machine learning algorithms for both short- and long-term asthma control prediction. Asthma control and bronchodilator response were the features most predictive of short-term asthma control, regardless of type of controller medication or phenotype. Bronchodilator response and serum eosinophils were the most predictive features of asthma control, regardless of type of controller medication or phenotype. Advanced statistical machine learning approaches can be powerful tools for discovery of phenotypes based on treatment response and can aid in asthma control prediction in complex medical conditions such as asthma.

Long-Term Post-CABG Survival: Performance of Clinical Risk Models Versus Actuarial Predictions.

PubMed

Carr, Brendan M; Romeiser, Jamie; Ruan, Joyce; Gupta, Sandeep; Seifert, Frank C; Zhu, Wei; Shroyer, A Laurie

2016-01-01

Clinical risk models are commonly used to predict short-term coronary artery bypass grafting (CABG) mortality but are less commonly used to predict long-term mortality. The added value of long-term mortality clinical risk models over traditional actuarial models has not been evaluated. To address this, the predictive performance of a long-term clinical risk model was compared with that of an actuarial model to identify the clinical variable(s) most responsible for any differences observed. Long-term mortality for 1028 CABG patients was estimated using the Hannan New York State clinical risk model and an actuarial model (based on age, gender, and race/ethnicity). Vital status was assessed using the Social Security Death Index. Observed/expected (O/E) ratios were calculated, and the models' predictive performances were compared using a nested c-index approach. Linear regression analyses identified the subgroup of risk factors driving the differences observed. Mortality rates were 3%, 9%, and 17% at one-, three-, and five years, respectively (median follow-up: five years). The clinical risk model provided more accurate predictions. Greater divergence between model estimates occurred with increasing long-term mortality risk, with baseline renal dysfunction identified as a particularly important driver of these differences. Long-term mortality clinical risk models provide enhanced predictive power compared to actuarial models. Using the Hannan risk model, a patient's long-term mortality risk can be accurately assessed and subgroups of higher-risk patients can be identified for enhanced follow-up care. More research appears warranted to refine long-term CABG clinical risk models. © 2015 The Authors. Journal of Cardiac Surgery Published by Wiley Periodicals, Inc.

Long‐Term Post‐CABG Survival: Performance of Clinical Risk Models Versus Actuarial Predictions

PubMed Central

Carr, Brendan M.; Romeiser, Jamie; Ruan, Joyce; Gupta, Sandeep; Seifert, Frank C.; Zhu, Wei

2015-01-01

Abstract Background/aim Clinical risk models are commonly used to predict short‐term coronary artery bypass grafting (CABG) mortality but are less commonly used to predict long‐term mortality. The added value of long‐term mortality clinical risk models over traditional actuarial models has not been evaluated. To address this, the predictive performance of a long‐term clinical risk model was compared with that of an actuarial model to identify the clinical variable(s) most responsible for any differences observed. Methods Long‐term mortality for 1028 CABG patients was estimated using the Hannan New York State clinical risk model and an actuarial model (based on age, gender, and race/ethnicity). Vital status was assessed using the Social Security Death Index. Observed/expected (O/E) ratios were calculated, and the models' predictive performances were compared using a nested c‐index approach. Linear regression analyses identified the subgroup of risk factors driving the differences observed. Results Mortality rates were 3%, 9%, and 17% at one‐, three‐, and five years, respectively (median follow‐up: five years). The clinical risk model provided more accurate predictions. Greater divergence between model estimates occurred with increasing long‐term mortality risk, with baseline renal dysfunction identified as a particularly important driver of these differences. Conclusions Long‐term mortality clinical risk models provide enhanced predictive power compared to actuarial models. Using the Hannan risk model, a patient's long‐term mortality risk can be accurately assessed and subgroups of higher‐risk patients can be identified for enhanced follow‐up care. More research appears warranted to refine long‐term CABG clinical risk models. doi: 10.1111/jocs.12665 (J Card Surg 2016;31:23–30) PMID:26543019

Physical examination variables predict response to conservative treatment of non-chronic plantar fasciitis: Secondary analysis of a randomized placebo controlled footwear study

PubMed Central

Matzkin-Bridger, Jonathon; Fascione, Jeanna; Crews, Ryan; Bruning, Nicholas; Jarrett, Beth

2016-01-01

Background Plantar fasciitis is a common disabling condition and the prognosis of conservative treatment is difficult to predict. Objective To determine whether initial clinical findings could help predict patient response to conservative treatment primarily consisting of supportive footwear and stretching. Setting Patients were recruited and seen at two outpatient podiatric clinics in the Chicago, Illinois metropolitan area. Patients Seventy-seven patients with non-chronic plantar fasciitis were recruited. Patients were excluded if they had a heel injection in the previous six months or were currently utilizing custom foot orthoses at the time of screening. Sixty-nine patients completed the final follow-up visit three months after receiving the footwear intervention. Methods Treatment failure was considered a <50% reduction in heel pain at three month follow23 Logistic regression models evaluated the possible association between over thirty clinical and physical exam findings prospectively assessed at enrollment, and treatment response. Results Inability to dorsiflex the ankle past −5° (OR 27 3.9, p=.024), non-severe (≤ 7 on ordinal scale) first-step pain (OR 3.8, p=.021), and heel valgus in relaxed stance (OR 4.0, p=.014) each predicted treatment failure in multivariable analysis (Receiver operating characteristic area under the curve=.769). Limited ankle dorsiflexion also correlated with higher heel pain severity at initial presentation (r = −.312, p =.006). Conclusions Patients with severe ankle equinus were nearly four times more likely to experience a favorable response to treatment centered on home Achilles tendon stretching and supportive therapy. Thus earlier use of more advanced therapies may be most appropriate in those presenting without severe ankle equinus or without severe first step pain. The findings from our study may not be clinically intuitive as patients with less severe equinus and less severe pain at presentation did worse with conservative care. PMID:26409199

Thrombotic thrombocytopenic purpura-hemolytic uremic syndrome (TTP-HUS): a 24-year clinical experience with 178 patients

PubMed Central

Levandovsky, Mark; Harvey, Danielle; Lara, Primo; Wun, Ted

2008-01-01

Background Thrombotic thrombocytopenic purpura and the hemolytic uremic syndrome (TTP-HUS) are related and uncommon disorders with a high fatality and complication rate if untreated. Plasma exchange therapy has been shown to produce high response rates and improve survival in patients with many forms of TTP-HUS. We performed a retrospective cohort study of 178 consecutively treated patients with TTP-HUS and analyzed whether clinical or laboratory characteristics could predict for important short- and long-term outcome measures. Results Overall 30-day mortality was 16% (n = 27). 171 patients (96%) received plasma exchange as the principal treatment, with a mean of 8 exchanges and a mean cumulative infused volume of 42 ± 71 L of fresh frozen plasma. The rate of complete response was 65% or 55% depending on whether this was defined by a platelet count of 100,000/μl or 150,000/μl, respectively. The rate of relapse was 18%. The Clinical Severity Score did not predict for 30-day mortality or relapse. The time to complete response did not predict for relapse. Renal insufficiency at presentation was associated with a decreased risk of relapse, with each unit increase in serum creatinine associated with a 40% decreased odds of relapse. 72% of our cohort had an idiopathic TTP-sporadic HUS, while 17% had an underlying cancer, received a solid organ transplant or were treated with a mitomycin-based therapy. The estimated overall 5-year survival was 55% and was significantly better in those without serious underlying conditions. Conclusion Plasma exchange therapy produced both high response and survival rates in this large cohort of patients with TTP-HUS. The Clinical Severity Score did not predict for 30-day mortality or relapse, contrary to our previous findings. Interestingly, the presence of renal insufficiency was associated with a decreased risk of relapse. The most important predictor of mortality was the presence or absence of a serious underlying disorder. PMID:19046460

Physical Examination Variables Predict Response to Conservative Treatment of Nonchronic Plantar Fasciitis: Secondary Analysis of a Randomized, Placebo-Controlled Footwear Study.

PubMed

Wrobel, James S; Fleischer, Adam E; Matzkin-Bridger, Jonathon; Fascione, Jeanna; Crews, Ryan T; Bruning, Nicholas; Jarrett, Beth

2016-05-01

Plantar fasciitis is a common, disabling condition, and the prognosis of conservative treatment is difficult to predict. To determine whether initial clinical findings could help predict patient response to conservative treatment that primarily consisted of supportive footwear and stretching. Patients were recruited and seen at 2 outpatient podiatric clinics in the Chicago, Illinois, metropolitan area. Seventy-seven patients with nonchronic plantar fasciitis were recruited. Patients were excluded if they had a heel injection in the previous 6 months or were currently using custom foot orthoses at the time of screening. Sixty-nine patients completed the final follow-up visit 3 months after receiving the footwear intervention. Treatment failure was considered a <50% reduction in heel pain at 3 month follow-up. Logistic regression models evaluated the possible association between more than 30 clinical and physical examination findings prospectively assessed at enrollment, and treatment response. Inability to dorsiflex the ankle past -5° (odds ratio [OR] 3.9, P = .024), nonsevere (≤7 on ordinal scale) first-step pain (OR 3.8, P = .021), and heel valgus in relaxed stance (OR 4.0, P = .014) each predicted treatment failure in multivariable analysis (receiver operating characteristic area under the curve = .769). Limited ankle dorsiflexion also correlated with greater heel pain severity at initial presentation (r = - 0.312, P = .006). Patients with severe ankle equinus were nearly 4 times more likely to experience a favorable response to treatment centered on home Achilles tendon stretching and supportive therapy. Thus, earlier use of more advanced therapies may be most appropriate in those presenting without severe ankle equinus or without severe first step pain. The findings from our study may not be clinically intuitive because patients with less severe equinus and less severe pain at presentation did worse with conservative care. Copyright © 2016 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

[Current Possibilities for Predicting Responses to EGFR Blockade in Metastatic Colorectal Cancer].

PubMed

Němeček, R; Svoboda, M; Slabý, O

2016-01-01

The combination of modern systemic chemotherapy and anti-EGFR monoclonal antibodies improves overall survival and quality of life for patients with metastatic colorecal cancer. By contrast, the addition of anti-EGFR therapy to the treatment regime of resistant patients may lead to worse progression-free survival and overall survival. Therefore, identifying sensitive and resistant patients prior to targeted therapy of metastatic colorecal cancer is a key point during the initial decision making process. Previous research shows that primary resistance to EGFR blockade is in most cases caused by constitutive activation of signaling pathways downstream of EGFR. Of all relevant factors (mutation of KRAS, NRAS, BRAF, and PIK3CA oncogenes, inactivation of tumor suppressors PTEN and TP53, amplification of EGFR and HER2, and expression of epiregulin and amphiregulin, mikroRNA miR-31-3p, and miR-31-5p), only evaluation of KRAS and NRAS mutations has entered routine clinical practice. The role of the other markers still needs to be validated. The ongoing benefit of anti-EGFR therapy could be indicated by specific clinical parameters measured after the initiation of targeted therapy, including early tumor shrinkage, the deepness of the response, or hypomagnesemia. The accuracy of predictive dia-gnostic tools could be also increased by examining a combination of predictive markers using next generation sequencing methods. However, unjustified investigation of many molecular markers should be resisted as this may complicate interpretation of the results, particularly in terms of their specific clinical relevance. The aim of this review is to describe current possibilities with respect to predicting responses to EGFR blockade in the context of the EGFR pathway, and the utilization of such results in routine clinical practice.

Childhood trauma predicts antidepressant response in adults with major depression: data from the randomized international study to predict optimized treatment for depression.

PubMed

Williams, L M; Debattista, C; Duchemin, A-M; Schatzberg, A F; Nemeroff, C B

2016-05-03

Few reliable predictors indicate which depressed individuals respond to antidepressants. Several studies suggest that a history of early-life trauma predicts poorer response to antidepressant therapy but results are variable and limited in adults. The major goal of the present study was to evaluate the role of early-life trauma in predicting acute response outcomes to antidepressants in a large sample of well-characterized patients with major depressive disorder (MDD). The international Study to Predict Optimized Treatment for Depression (iSPOT-D) is a randomized clinical trial with enrollment from December 2008 to January 2012 at eight academic and nine private clinical settings in five countries. Patients (n=1008) meeting DSM-IV criteria for MDD and 336 matched healthy controls comprised the study sample. Six participants withdrew due to serious adverse events. Randomization was to 8 weeks of treatment with escitalopram, sertraline or venlafaxine with dosage adjusted by the participant's treating clinician per routine clinical practice. Exposure to 18 types of traumatic events before the age of 18 was assessed using the Early-Life Stress Questionnaire. Impact of early-life stressors-overall trauma 'load' and specific type of abuse-on treatment outcomes measures: response: (⩾50% improvement on the 17-item Hamilton Rating Scale for Depression, HRSD17 or on the 16-item Quick Inventory of Depressive Symptomatology-Self-Rated, QIDS_SR16) and remission (score ⩽7 on the HRSD17 and ⩽5 on the QIDS_SR16). Trauma prevalence in MDD was compared with controls. Depressed participants were significantly more likely to report early-life stress than controls; 62.5% of MDD participants reported more than two traumatic events compared with 28.4% of controls. The higher rate of early-life trauma was most apparent for experiences of interpersonal violation (emotional, sexual and physical abuses). Abuse and notably abuse occurring at ⩽7 years of age predicted poorer outcomes after 8 weeks of antidepressants, across the three treatment arms. In addition, the abuses occurring between ages 4 and 7 years differentially predicted the poorest outcome following the treatment with sertraline. Specific types of early-life trauma, particularly physical, emotional and sexual abuse, especially when occurring at ⩽7 years of age are important moderators of subsequent response to antidepressant therapy for MDD.

Childhood trauma predicts antidepressant response in adults with major depression: data from the randomized international study to predict optimized treatment for depression

PubMed Central

Williams, L M; Debattista, C; Duchemin, A-M; Schatzberg, A F; Nemeroff, C B

2016-01-01

Few reliable predictors indicate which depressed individuals respond to antidepressants. Several studies suggest that a history of early-life trauma predicts poorer response to antidepressant therapy but results are variable and limited in adults. The major goal of the present study was to evaluate the role of early-life trauma in predicting acute response outcomes to antidepressants in a large sample of well-characterized patients with major depressive disorder (MDD). The international Study to Predict Optimized Treatment for Depression (iSPOT-D) is a randomized clinical trial with enrollment from December 2008 to January 2012 at eight academic and nine private clinical settings in five countries. Patients (n=1008) meeting DSM-IV criteria for MDD and 336 matched healthy controls comprised the study sample. Six participants withdrew due to serious adverse events. Randomization was to 8 weeks of treatment with escitalopram, sertraline or venlafaxine with dosage adjusted by the participant's treating clinician per routine clinical practice. Exposure to 18 types of traumatic events before the age of 18 was assessed using the Early-Life Stress Questionnaire. Impact of early-life stressors—overall trauma ‘load' and specific type of abuse—on treatment outcomes measures: response: (⩾50% improvement on the 17-item Hamilton Rating Scale for Depression, HRSD17 or on the 16-item Quick Inventory of Depressive Symptomatology—Self-Rated, QIDS_SR16) and remission (score ⩽7 on the HRSD17 and ⩽5 on the QIDS_SR16). Trauma prevalence in MDD was compared with controls. Depressed participants were significantly more likely to report early-life stress than controls; 62.5% of MDD participants reported more than two traumatic events compared with 28.4% of controls. The higher rate of early-life trauma was most apparent for experiences of interpersonal violation (emotional, sexual and physical abuses). Abuse and notably abuse occurring at ⩽7 years of age predicted poorer outcomes after 8 weeks of antidepressants, across the three treatment arms. In addition, the abuses occurring between ages 4 and 7 years differentially predicted the poorest outcome following the treatment with sertraline. Specific types of early-life trauma, particularly physical, emotional and sexual abuse, especially when occurring at ⩽7 years of age are important moderators of subsequent response to antidepressant therapy for MDD. PMID:27138798

Clinical Predictors of Response to Cognitive-Behavioral Therapy in Pediatric Anxiety Disorders: The Genes for Treatment (GxT) Study

PubMed Central

Hudson, Jennifer L.; Keers, Robert; Roberts, Susanna; Coleman, Jonathan R.I.; Breen, Gerome; Arendt, Kristian; Bögels, Susan; Cooper, Peter; Creswell, Cathy; Hartman, Catharina; Heiervang, Einar R.; Hötzel, Katrin; In-Albon, Tina; Lavallee, Kristen; Lyneham, Heidi J.; Marin, Carla E.; McKinnon, Anna; Meiser-Stedman, Richard; Morris, Talia; Nauta, Maaike; Rapee, Ronald M.; Schneider, Silvia; Schneider, Sophie C.; Silverman, Wendy K.; Thastum, Mikael; Thirlwall, Kerstin; Waite, Polly; Wergeland, Gro Janne; Lester, Kathryn J.; Eley, Thalia C.

2015-01-01

Objective The Genes for Treatment study is an international, multisite collaboration exploring the role of genetic, demographic, and clinical predictors in response to cognitive-behavioral therapy (CBT) in pediatric anxiety disorders. The current article, the first from the study, examined demographic and clinical predictors of response to CBT. We hypothesized that the child’s gender, type of anxiety disorder, initial severity and comorbidity, and parents’ psychopathology would significantly predict outcome. Method A sample of 1,519 children 5 to 18 years of age with a primary anxiety diagnosis received CBT across 11 sites. Outcome was defined as response (change in diagnostic severity) and remission (absence of the primary diagnosis) at each time point (posttreatment, 3-, 6-, and/or 12-month follow-up) and analyzed using linear and logistic mixed models. Separate analyses were conducted using data from posttreatment and follow-up assessments to explore the relative importance of predictors at these time points. Results Individuals with social anxiety disorder (SoAD) had significantly poorer outcomes (poorer response and lower rates of remission) than those with generalized anxiety disorder (GAD). Although individuals with specific phobia (SP) also had poorer outcomes than those with GAD at posttreatment, these differences were not maintained at follow-up. Both comorbid mood and externalizing disorders significantly predicted poorer outcomes at posttreatment and follow-up, whereas self-reported parental psychopathology had little effect on posttreatment outcomes but significantly predicted response (although not remission) at follow-up. Conclusion SoAD, nonanxiety comorbidity, and parental psychopathology were associated with poorer outcomes after CBT. The results highlight the need for enhanced treatments for children at risk for poorer outcomes. PMID:26004660

Clinical Predictors of Response to Cognitive-Behavioral Therapy in Pediatric Anxiety Disorders: The Genes for Treatment (GxT) Study.

PubMed

Hudson, Jennifer L; Keers, Robert; Roberts, Susanna; Coleman, Jonathan R I; Breen, Gerome; Arendt, Kristian; Bögels, Susan; Cooper, Peter; Creswell, Cathy; Hartman, Catharina; Heiervang, Einar R; Hötzel, Katrin; In-Albon, Tina; Lavallee, Kristen; Lyneham, Heidi J; Marin, Carla E; McKinnon, Anna; Meiser-Stedman, Richard; Morris, Talia; Nauta, Maaike; Rapee, Ronald M; Schneider, Silvia; Schneider, Sophie C; Silverman, Wendy K; Thastum, Mikael; Thirlwall, Kerstin; Waite, Polly; Wergeland, Gro Janne; Lester, Kathryn J; Eley, Thalia C

2015-06-01

The Genes for Treatment study is an international, multisite collaboration exploring the role of genetic, demographic, and clinical predictors in response to cognitive-behavioral therapy (CBT) in pediatric anxiety disorders. The current article, the first from the study, examined demographic and clinical predictors of response to CBT. We hypothesized that the child's gender, type of anxiety disorder, initial severity and comorbidity, and parents' psychopathology would significantly predict outcome. A sample of 1,519 children 5 to 18 years of age with a primary anxiety diagnosis received CBT across 11 sites. Outcome was defined as response (change in diagnostic severity) and remission (absence of the primary diagnosis) at each time point (posttreatment, 3-, 6-, and/or 12-month follow-up) and analyzed using linear and logistic mixed models. Separate analyses were conducted using data from posttreatment and follow-up assessments to explore the relative importance of predictors at these time points. Individuals with social anxiety disorder (SoAD) had significantly poorer outcomes (poorer response and lower rates of remission) than those with generalized anxiety disorder (GAD). Although individuals with specific phobia (SP) also had poorer outcomes than those with GAD at posttreatment, these differences were not maintained at follow-up. Both comorbid mood and externalizing disorders significantly predicted poorer outcomes at posttreatment and follow-up, whereas self-reported parental psychopathology had little effect on posttreatment outcomes but significantly predicted response (although not remission) at follow-up. SoAD, nonanxiety comorbidity, and parental psychopathology were associated with poorer outcomes after CBT. The results highlight the need for enhanced treatments for children at risk for poorer outcomes. Copyright © 2015 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

Prediction of treatment refractoriness in ulcerative colitis and Crohn's disease--do we have reliable markers?

PubMed

Gelbmann, C M

2000-05-01

Treatment refractoriness is a severe problem in the management of patients with ulcerative colitis and Crohn's disease. Despite some promising new therapeutic approaches, corticosteroids are still the preferential primary treatment for moderate to severe Crohn's disease and of severe ulcerative colitis. However, clinical response to corticosteroids varies, and many patients are resistant to such treatment. Since corticosteroids have frequent and even severe side effects, and toxicity increases with chronic steroid intake, factors predictive of response to such treatment would be very helpful for decisions on further management of these patients. At least in severe attacks of ulcerative colitis, the consensus seems to be that a high frequency of bowel movements as well as a high C-reactive protein and low serum albumin recorded after a few days of intensive medical treatment are important signs for early prediction of treatment failure in the majority of the patients. In Crohn's disease thus far, data on predictive factors are conflicting. No reliable marker with sufficient predictive value for treatment refractoriness could be identified. This might be due to the tremendous heterogeneity of Crohn's disease with many clinical phenotypes, which requires subgroup analysis with sufficient numbers of patients. Corticosteroids as well as other immunomodulating and immunosuppressive medications interfere with the immune system, which plays a central role in the mediation of intestinal inflammation. Treatment refractoriness might have its origin in specific immunological peculiarities eventually reflected in abnormal immunological, biochemical, and clinical parameters. Further exploration of those parameters to predict treatment refractoriness in patients with ulcerative colitis or Crohn's disease is of great clinical importance for safe and efficient management of patients.

From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system

PubMed Central

Gultepe, Eren; Green, Jeffrey P; Nguyen, Hien; Adams, Jason; Albertson, Timothy; Tagkopoulos, Ilias

2014-01-01

Objective To develop a decision support system to identify patients at high risk for hyperlactatemia based upon routinely measured vital signs and laboratory studies. Materials and methods Electronic health records of 741 adult patients at the University of California Davis Health System who met at least two systemic inflammatory response syndrome criteria were used to associate patients’ vital signs, white blood cell count (WBC), with sepsis occurrence and mortality. Generative and discriminative classification (naïve Bayes, support vector machines, Gaussian mixture models, hidden Markov models) were used to integrate heterogeneous patient data and form a predictive tool for the inference of lactate level and mortality risk. Results An accuracy of 0.99 and discriminability of 1.00 area under the receiver operating characteristic curve (AUC) for lactate level prediction was obtained when the vital signs and WBC measurements were analysed in a 24 h time bin. An accuracy of 0.73 and discriminability of 0.73 AUC for mortality prediction in patients with sepsis was achieved with only three features: median of lactate levels, mean arterial pressure, and median absolute deviation of the respiratory rate. Discussion This study introduces a new scheme for the prediction of lactate levels and mortality risk from patient vital signs and WBC. Accurate prediction of both these variables can drive the appropriate response by clinical staff and thus may have important implications for patient health and treatment outcome. Conclusions Effective predictions of lactate levels and mortality risk can be provided with a few clinical variables when the temporal aspect and variability of patient data are considered. PMID:23959843

Predicting fear of heights, snakes, and public speaking from multimodal classical conditioning events.

PubMed

Wu, Ning Ying; Conger, Anthony J; Dygdon, Judith A

2006-04-01

Two hundred fifty one men and women participated in a study of the prediction of fear of heights, snakes, and public speaking by providing retrospective accounts of multimodal classical conditioning events involving those stimuli. The fears selected for study represent those believed by some to be innate (i.e., heights), prepared (i.e., snakes), and purely experientially learned (i.e., public speaking). This study evaluated the extent to which classical conditioning experiences in direct, observational, and verbal modes contributed to the prediction of the current level of fear severity. Subjects were asked to describe their current level of fear and to estimate their experience with fear response-augmenting events (first- and higher-order aversive pairings) and fear response-moderating events (first- and higher-order appetitive pairings, and pre- and post-conditioning neutral presentations) in direct, observational, and verbal modes. For each stimulus, fear was predictable from direct response-augmenting events and prediction was enhanced by the inclusion of response-moderating events. Furthermore, for each fear, maximum prediction was attained by the addition of variables tapping experiences in the observational and/or verbal modes. Conclusions are offered regarding the importance of including response-augmenting and response-moderating events in all three modes in both research and clinical applications of classical conditioning.

Old drugs, old problems: where do we stand in prediction of rheumatoid arthritis responsiveness to methotrexate and other synthetic DMARDs?

PubMed Central

2013-01-01

Methotrexate (MTX) is the central drug in the management of rheumatoid arthritis (RA) and other immune mediated inflammatory diseases. It is widely used either in monotherapy or in association with other synthetic and biologic disease modifying anti-rheumatic drugs (DMARDs). Although comprehensive clinical experience exists for MTX and synthetic DMARDs, to date it has not been possible to preview correctly whether or not a patient will respond to treatment with these drugs. Predicting response to MTX and other DMARDs would allow the selection of patients based on their likelihood of response, thus enabling individualized therapy and avoiding unnecessary adverse effects and elevated costs. However, studies analyzing this issue have struggled to obtain consistent, replicable results and no factor has yet been recognized to individually distinguish responders from nonresponders at treatment start. Variables possibly influencing drug effectiveness may be disease-, patient- or treatment-related, clinical or biological (genetic and nongenetic). In this review we summarize current evidence on predictors of response to MTX and other synthetic DMARDs, discuss possible causes for the heterogeneity observed and address its translation into daily clinical practice. PMID:23343013

Modelling Predictors of Molecular Response to Frontline Imatinib for Patients with Chronic Myeloid Leukaemia

PubMed Central

Brown, Fred; Adelson, David; White, Deborah; Hughes, Timothy; Chaudhri, Naeem

2017-01-01

Background Treatment of patients with chronic myeloid leukaemia (CML) has become increasingly difficult in recent years due to the variety of treatment options available and challenge deciding on the most appropriate treatment strategy for an individual patient. To facilitate the treatment strategy decision, disease assessment should involve molecular response to initial treatment for an individual patient. Patients predicted not to achieve major molecular response (MMR) at 24 months to frontline imatinib may be better treated with alternative frontline therapies, such as nilotinib or dasatinib. The aims of this study were to i) understand the clinical prediction ‘rules’ for predicting MMR at 24 months for CML patients treated with imatinib using clinical, molecular, and cell count observations (predictive factors collected at diagnosis and categorised based on available knowledge) and ii) develop a predictive model for CML treatment management. This predictive model was developed, based on CML patients undergoing imatinib therapy enrolled in the TIDEL II clinical trial with an experimentally identified achieving MMR group and non-achieving MMR group, by addressing the challenge as a machine learning problem. The recommended model was validated externally using an independent data set from King Faisal Specialist Hospital and Research Centre, Saudi Arabia. Principle Findings The common prognostic scores yielded similar sensitivity performance in testing and validation datasets and are therefore good predictors of the positive group. The G-mean and F-score values in our models outperformed the common prognostic scores in testing and validation datasets and are therefore good predictors for both the positive and negative groups. Furthermore, a high PPV above 65% indicated that our models are appropriate for making decisions at diagnosis and pre-therapy. Study limitations include that prior knowledge may change based on varying expert opinions; hence, representing the category boundaries of each predictive factor could dramatically change performance of the models. PMID:28045960

Toward a science of tumor forecasting for clinical oncology

DOE PAGES

Yankeelov, Thomas E.; Quaranta, Vito; Evans, Katherine J.; ...

2015-03-15

We propose that the quantitative cancer biology community makes a concerted effort to apply lessons from weather forecasting to develop an analogous methodology for predicting and evaluating tumor growth and treatment response. Currently, the time course of tumor response is not predicted; instead, response is only assessed post hoc by physical examination or imaging methods. This fundamental practice within clinical oncology limits optimization of a treatment regimen for an individual patient, as well as to determine in real time whether the choice was in fact appropriate. This is especially frustrating at a time when a panoply of molecularly targeted therapiesmore » is available, and precision genetic or proteomic analyses of tumors are an established reality. By learning from the methods of weather and climate modeling, we submit that the forecasting power of biophysical and biomathematical modeling can be harnessed to hasten the arrival of a field of predictive oncology. Furthermore, with a successful methodology toward tumor forecasting, it should be possible to integrate large tumor-specific datasets of varied types and effectively defeat one cancer patient at a time.« less

Towards a Science of Tumor Forecasting for Clinical Oncology

PubMed Central

Yankeelov, Thomas E.; Quaranta, Vito; Evans, Katherine J.; Rericha, Erin C.

2015-01-01

We propose that the quantitative cancer biology community make a concerted effort to apply lessons from weather forecasting to develop an analogous methodology for predicting and evaluating tumor growth and treatment response. Currently, the time course of tumor response is not predicted; instead, response is- only assessed post hoc by physical exam or imaging methods. This fundamental practice within clinical oncology limits optimization of atreatment regimen for an individual patient, as well as to determine in real time whether the choice was in fact appropriate. This is especially frustrating at a time when a panoply of molecularly targeted therapies is available, and precision genetic or proteomic analyses of tumors are an established reality. By learning from the methods of weather and climate modeling, we submit that the forecasting power of biophysical and biomathematical modeling can be harnessed to hasten the arrival of a field of predictive oncology. With a successful methodology towards tumor forecasting, it should be possible to integrate large tumor specific datasets of varied types, and effectively defeat cancer one patient at a time. PMID:25592148

Toward a science of tumor forecasting for clinical oncology.

PubMed

Yankeelov, Thomas E; Quaranta, Vito; Evans, Katherine J; Rericha, Erin C

2015-03-15

We propose that the quantitative cancer biology community makes a concerted effort to apply lessons from weather forecasting to develop an analogous methodology for predicting and evaluating tumor growth and treatment response. Currently, the time course of tumor response is not predicted; instead, response is only assessed post hoc by physical examination or imaging methods. This fundamental practice within clinical oncology limits optimization of a treatment regimen for an individual patient, as well as to determine in real time whether the choice was in fact appropriate. This is especially frustrating at a time when a panoply of molecularly targeted therapies is available, and precision genetic or proteomic analyses of tumors are an established reality. By learning from the methods of weather and climate modeling, we submit that the forecasting power of biophysical and biomathematical modeling can be harnessed to hasten the arrival of a field of predictive oncology. With a successful methodology toward tumor forecasting, it should be possible to integrate large tumor-specific datasets of varied types and effectively defeat one cancer patient at a time. ©2015 American Association for Cancer Research.

International Study to Predict Optimized Treatment for Depression (iSPOT-D), a randomized clinical trial: rationale and protocol.

PubMed

Williams, Leanne M; Rush, A John; Koslow, Stephen H; Wisniewski, Stephen R; Cooper, Nicholas J; Nemeroff, Charles B; Schatzberg, Alan F; Gordon, Evian

2011-01-05

Clinically useful treatment moderators of Major Depressive Disorder (MDD) have not yet been identified, though some baseline predictors of treatment outcome have been proposed. The aim of iSPOT-D is to identify pretreatment measures that predict or moderate MDD treatment response or remission to escitalopram, sertraline or venlafaxine; and develop a model that incorporates multiple predictors and moderators. The International Study to Predict Optimized Treatment - in Depression (iSPOT-D) is a multi-centre, international, randomized, prospective, open-label trial. It is enrolling 2016 MDD outpatients (ages 18-65) from primary or specialty care practices (672 per treatment arm; 672 age-, sex- and education-matched healthy controls). Study-eligible patients are antidepressant medication (ADM) naïve or willing to undergo a one-week wash-out of any non-protocol ADM, and cannot have had an inadequate response to protocol ADM. Baseline assessments include symptoms; distress; daily function; cognitive performance; electroencephalogram and event-related potentials; heart rate and genetic measures. A subset of these baseline assessments are repeated after eight weeks of treatment. Outcomes include the 17-item Hamilton Rating Scale for Depression (primary) and self-reported depressive symptoms, social functioning, quality of life, emotional regulation, and side-effect burden (secondary). Participants may then enter a naturalistic telephone follow-up at weeks 12, 16, 24 and 52. The first half of the sample will be used to identify potential predictors and moderators, and the second half to replicate and confirm. First enrolment was in December 2008, and is ongoing. iSPOT-D evaluates clinical and biological predictors of treatment response in the largest known sample of MDD collected worldwide. International Study to Predict Optimised Treatment - in Depression (iSPOT-D) ClinicalTrials.gov Identifier: NCT00693849. URL: http://clinicaltrials.gov/ct2/show/NCT00693849?term=International+Study+to+Predict+Optimized+Treatment+for+Depression&rank=1

Timed non-transferrin bound iron determinations probe the origin of chelatable iron pools during deferiprone regimens and predict chelation response

PubMed Central

Aydinok, Yesim; Evans, Patricia; Manz, Chantal Y.; Porter, John B.

2012-01-01

Background Plasma non-transferrin bound iron refers to heterogeneous plasma iron species, not bound to transferrin, which appear in conditions of iron overload and ineffective erythropoiesis. The clinical utility of non-transferrin bound iron in predicting complications from iron overload, or response to chelation therapy remains unproven. We undertook carefully timed measurements of non-transferrin bound iron to explore the origin of chelatable iron and to predict clinical response to deferiprone. Design and Methods Non-transferrin bound iron levels were determined at baseline and after 1 week of chelation in 32 patients with thalassemia major receiving deferiprone alone, desferrioxamine alone, or a combination of the two chelators. Samples were taken at baseline, following a 2-week washout without chelation, and after 1 week of chelation, this last sample being taken 10 hours after the previous evening dose of deferiprone and, in those receiving desferrioxamine, 24 hours after cessation of the overnight subcutaneous infusion. Absolute or relative non-transferrin bound iron levels were related to transfusional iron loading rates, liver iron concentration, 24-hour urine iron and response to chelation therapy over the subsequent year. Results Changes in non-transferrin bound iron at week 1 were correlated positively with baseline liver iron, and inversely with transfusional iron loading rates, with deferiprone-containing regimens but not with desferrioxamine monotherapy. Changes in week 1 non-transferrin bound iron were also directly proportional to the plasma concentration of deferiprone-iron complexes and correlated significantly with urine iron excretion and with changes in liver iron concentration over the next 12 months. Conclusions The widely used assay chosen for this study detects both endogenous non-transferrin bound iron and the iron complexes of deferiprone. The week 1 increments reflect chelatable iron derived both from liver stores and from red cell catabolism. These increments correlate with urinary iron excretion and the change in liver iron concentration over the subsequent year thus predicting response to deferiprone-containing chelation regimes. This clinical study was registered at clinical.trials.gov with the number NCT00350662. PMID:22180427

Modeling Pathologic Response of Esophageal Cancer to Chemoradiation Therapy Using Spatial-Temporal {sup 18}F-FDG PET Features, Clinical Parameters, and Demographics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Hao; Tan, Shan; Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan

2014-01-01

Purpose: To construct predictive models using comprehensive tumor features for the evaluation of tumor response to neoadjuvant chemoradiation therapy (CRT) in patients with esophageal cancer. Methods and Materials: This study included 20 patients who underwent trimodality therapy (CRT + surgery) and underwent {sup 18}F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) both before and after CRT. Four groups of tumor features were examined: (1) conventional PET/CT response measures (eg, standardized uptake value [SUV]{sub max}, tumor diameter); (2) clinical parameters (eg, TNM stage, histology) and demographics; (3) spatial-temporal PET features, which characterize tumor SUV intensity distribution, spatial patterns, geometry, and associated changesmore » resulting from CRT; and (4) all features combined. An optimal feature set was identified with recursive feature selection and cross-validations. Support vector machine (SVM) and logistic regression (LR) models were constructed for prediction of pathologic tumor response to CRT, cross-validations being used to avoid model overfitting. Prediction accuracy was assessed by area under the receiver operating characteristic curve (AUC), and precision was evaluated by confidence intervals (CIs) of AUC. Results: When applied to the 4 groups of tumor features, the LR model achieved AUCs (95% CI) of 0.57 (0.10), 0.73 (0.07), 0.90 (0.06), and 0.90 (0.06). The SVM model achieved AUCs (95% CI) of 0.56 (0.07), 0.60 (0.06), 0.94 (0.02), and 1.00 (no misclassifications). With the use of spatial-temporal PET features combined with conventional PET/CT measures and clinical parameters, the SVM model achieved very high accuracy (AUC 1.00) and precision (no misclassifications)—results that were significantly better than when conventional PET/CT measures or clinical parameters and demographics alone were used. For groups with many tumor features (groups 3 and 4), the SVM model achieved significantly higher accuracy than did the LR model. Conclusions: The SVM model that used all features including spatial-temporal PET features accurately and precisely predicted pathologic tumor response to CRT in esophageal cancer.« less

Quality of life measured with EuroQol-five dimensions questionnaire predicts long-term mortality, response, and reverse remodelling in cardiac resynchronization therapy patients.

PubMed

Nagy, Klaudia Vivien; Széplaki, Gábor; Perge, Péter; Boros, András Mihály; Kosztin, Annamária; Apor, Astrid; Molnár, Levente; Szilágyi, Szabolcs; Tahin, Tamás; Zima, Endre; Kutyifa, Valentina; Gellér, László; Merkely, Béla

2017-11-22

There are previous studies on quality of life (QoL) in cardiac resynchronization therapy (CRT) patients; however, there are no data with the short EuroQol-five dimensions (EQ-5D) questionnaire predicting outcomes. We aimed to assess the predictive role of baseline QoL and QoL change at 6 months after CRT with EQ-5D on 5-year mortality and response. In our prospective follow-up study, 130 heart failure (HF) patients undergoing CRT were enrolled. Clinical evaluation, echocardiography, and EQ-5D were performed at baseline and at 6 months of follow-up, continued to 5 years. Primary endpoint was all-cause mortality at 5 years. Secondary endpoints were (i) clinical response with at least one class improvement in New York Heart Association without HF hospitalization and (ii) reverse remodelling with 15% reduction in left ventricular end-systolic volume at 6 months. Fifty-four (41.5%) patients died during 5 years, 85 (65.3%) clinical responders were identified, and 63 patients (48.5%) had reverse remodelling. Baseline issues with mobility were associated with lower response [odds ratio (OR) 0.36, 95% confidence interval (CI) 0.16-0.84; P = 0.018]. Lack of reverse remodelling correlated with self-care issues at baseline (OR 0.10, 95% CI 0.01-0.94; P = 0.04). Furthermore, self-care difficulties [hazard ratio (HR) 2.39, 95% CI 1.17-4.86; P = 0.01) or more anxiety (HR 1.51, 95% CI 1.00-2.26; P = 0.04) predicted worse long-term survival. At 6 months, mobility (HR 3.95, 95% CI 1.89-8.20; P < 0.001), self-care (HR 7.69, 95% CI 2.23-25.9; P = 0.001), or ≥ 10% visual analogue scale (VAS) (HR 2.24, 95% CI 1.27-3.94; P = 0.005) improvement anticipated better survival at 5 years. EuroQol-five dimension is a simple method assessing QoL in CRT population. Mobility issues at baseline are associated with lower clinical response, whereas self-care issues predict lack of reverse remodelling. Problems with mobility or anxiety before CRT and persistent issues with mobility, self-care, and VAS scale at 6 months predict adverse outcome. © The Author 2017. Published by Oxford University Press on behalf of the European Society of Cardiology

Neural Reactivity to Angry Faces Predicts Treatment Response in Pediatric Anxiety

PubMed Central

Kujawa, Autumn; Fitzgerald, Kate D.; Swain, James E.; Hanna, Gregory L.; Koschmann, Elizabeth; Simpson, David; Connolly, Sucheta; Monk, Christopher S.; Phan, K. Luan

2018-01-01

Although cognitive-behavioral psychotherapy (CBT) and pharmacotherapy are evidence-based treatments for pediatric anxiety, many youth with anxiety disorders fail to respond to these treatments. Given limitations of clinical measures in predicting treatment response, identifying neural predictors is timely. In this study, 35 anxious youth (ages 7–19 years) completed an emotional face-matching task during which the late positive potential (LPP), an event-related potential (ERP) component that indexes sustained attention towards emotional stimuli, was measured. Following the ERP measurement, youth received CBT or selective serotonin reuptake inhibitor (SSRI) treatment, and the LPP was examined as a predictor of treatment response. Findings indicated that, accounting for pre-treatment anxiety severity, neural reactivity to emotional faces predicted anxiety severity post-CBT and SSRI treatment such that enhanced electrocortical response to angry faces was associated with better treatment response. An enhanced LPP to angry faces may predict treatment response insofar as it may reflect greater emotion dysregulation or less avoidance and/or enhanced engagement with environmental stimuli in general, including with treatment. PMID:27255517

Pareidolias in REM Sleep Behavior Disorder: A Possible Predictive Marker of Lewy Body Diseases?

PubMed

Sasai-Sakuma, Taeko; Nishio, Yoshiyuki; Yokoi, Kayoko; Mori, Etsuro; Inoue, Yuichi

2017-02-01

To investigate conditions and clinical significance of pareidolias in patients with idiopathic rapid eyemovent (REM) sleep behavior disorder (iRBD). This cross-sectional study examined 202 patients with iRBD (66.8 ± 8.0 yr, 58 female) and 46 healthy control subjects (64.7 ± 5.8 years, 14 females). They underwent the Pareidolia test, a newly developed instrument for evoking pareidolias, video polysomnography, olfactory tests, and Addenbrooke's cognitive examination-revised. Results show that 53.5% of iRBD patients exhibited one or more pareidolic responses: The rate was higher than control subjects showed (21.7%). The pictures evoking pareidolic responses were more numerous for iRBD patients than for control subjects (1.2 ± 1.8 vs. 0.4 ± 0.8, p < .001). Subgroup analyses revealed that iRBD patients with pareidolic responses had higher amounts of REM sleep without atonia (RWA), with lower sleep efficiency, lower cognitive function, and older age than subjects without pareidolic responses. Results of multivariate analyses show the number of pareidolic responses as a factor associated with decreased cognitive function in iRBD patients with better predictive accuracy. Morbidity length and severity of iRBD, olfactory function, and the amount of RWA were not factors associated with better predictive accuracy. Half or more of the iRBD patients showed pareidolic responses. The responses were proven to be associated more intimately with their cognitive decline than clinical or physiological variables related to RBD. Pareidolias in iRBD are useful as a predictive marker of future development of Lewy body diseases. © Sleep Research Society 2017. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

Penalized Ordinal Regression Methods for Predicting Stage of Cancer in High-Dimensional Covariate Spaces.

PubMed

Gentry, Amanda Elswick; Jackson-Cook, Colleen K; Lyon, Debra E; Archer, Kellie J

2015-01-01

The pathological description of the stage of a tumor is an important clinical designation and is considered, like many other forms of biomedical data, an ordinal outcome. Currently, statistical methods for predicting an ordinal outcome using clinical, demographic, and high-dimensional correlated features are lacking. In this paper, we propose a method that fits an ordinal response model to predict an ordinal outcome for high-dimensional covariate spaces. Our method penalizes some covariates (high-throughput genomic features) without penalizing others (such as demographic and/or clinical covariates). We demonstrate the application of our method to predict the stage of breast cancer. In our model, breast cancer subtype is a nonpenalized predictor, and CpG site methylation values from the Illumina Human Methylation 450K assay are penalized predictors. The method has been made available in the ordinalgmifs package in the R programming environment.

Data quality assurance: an analysis of patient non-response.

PubMed

Derby, Dustin C; Haan, Andrea; Wood, Kurt

2011-01-01

Patient satisfaction is paramount to maintaining high clinical quality assurance. This study seeks to compare response rates, response bias, and the completeness of data between paper and electronic collection modes of a chiropractic patient satisfaction survey. A convenience sample of 206 patients presenting to a chiropractic college clinic were surveyed concerning satisfaction with their chiropractic care. Paper (in-clinic and postal) and electronic modes of survey administration were compared for response rates and non-response bias. The online data collection mode resulted in fewer non-responses and a higher response rate, and did not evince response bias when compared to paper modes. The postal paper mode predicted non-response rates over the in-clinic paper and online modalities and exhibited a gender bias. This current study was a single clinic study; future studies should consider multi-clinic data collections. Busy clinic operations and available staff resources restricted the ability to conduct a random sampling of patients or to invite all eligible patients, therefore limiting the generalizability of collected survey data. Results of this study will provide data to aid development of survey protocols that efficiently, account for available human resources, and are convenient for patients while allowing for the most complete and accurate data collection possible in an educational clinic setting. Understanding patient responses across survey modes is critical for the cultivation of quality business intelligence within college teaching clinic settings. This study bridges measurement evidence from three popular data collection modalities and offers support for higher levels of quality for web-based data collection.

Mid-Treatment Sleep Duration Predicts Clinically Significant Knee Osteoarthritis Pain reduction at 6 months: Effects From a Behavioral Sleep Medicine Clinical Trial.

PubMed

Salwen, Jessica K; Smith, Michael T; Finan, Patrick H

2017-02-01

To determine the relative influence of sleep continuity (sleep efficiency, sleep onset latency, total sleep time [TST], and wake after sleep onset) on clinical pain outcomes within a trial of cognitive behavioral therapy for insomnia (CBT-I) for patients with comorbid knee osteoarthritis and insomnia. Secondary analyses were performed on data from 74 patients with comorbid insomnia and knee osteoarthritis who completed a randomized clinical trial of 8-session multicomponent CBT-I versus an active behavioral desensitization control condition (BD), including a 6-month follow-up assessment. Data used herein include daily diaries of sleep parameters, actigraphy data, and self-report questionnaires administered at specific time points. Patients who reported at least 30% improvement in self-reported pain from baseline to 6-month follow-up were considered responders (N = 31). Pain responders and nonresponders did not differ significantly at baseline across any sleep continuity measures. At mid-treatment, only TST predicted pain response via t tests and logistic regression, whereas other measures of sleep continuity were nonsignificant. Recursive partitioning analyses identified a minimum cut-point of 382 min of TST achieved at mid-treatment in order to best predict pain improvements 6-month posttreatment. Actigraphy results followed the same pattern as daily diary-based results. Clinically significant pain reductions in response to both CBT-I and BD were optimally predicted by achieving approximately 6.5 hr sleep duration by mid-treatment. Thus, tailoring interventions to increase TST early in treatment may be an effective strategy to promote long-term pain reductions. More comprehensive research on components of behavioral sleep medicine treatments that contribute to pain response is warranted. © Sleep Research Society 2016. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

Prefrontal mediation of the reading network predicts intervention response in dyslexia.

PubMed

Aboud, Katherine S; Barquero, Laura A; Cutting, Laurie E

2018-04-01

A primary challenge facing the development of interventions for dyslexia is identifying effective predictors of intervention response. While behavioral literature has identified core cognitive characteristics of response, the distinction of reading versus executive cognitive contributions to response profiles remains unclear, due in part to the difficulty of segregating these constructs using behavioral outputs. In the current study we used functional neuroimaging to piece apart the mechanisms of how/whether executive and reading network relationships are predictive of intervention response. We found that readers who are responsive to intervention have more typical pre-intervention functional interactions between executive and reading systems compared to nonresponsive readers. These findings suggest that intervention response in dyslexia is influenced not only by domain-specific reading regions, but also by contributions from intervening domain-general networks. Our results make a significant gain in identifying predictive bio-markers of outcomes in dyslexia, and have important implications for the development of personalized clinical interventions. Copyright © 2018 Elsevier Ltd. All rights reserved.

Implications of genome-wide association studies in cancer therapeutics.

PubMed

Patel, Jai N; McLeod, Howard L; Innocenti, Federico

2013-09-01

Genome wide association studies (GWAS) provide an agnostic approach to identifying potential genetic variants associated with disease susceptibility, prognosis of survival and/or predictive of drug response. Although these techniques are costly and interpretation of study results is challenging, they do allow for a more unbiased interrogation of the entire genome, resulting in the discovery of novel genes and understanding of novel biological associations. This review will focus on the implications of GWAS in cancer therapy, in particular germ-line mutations, including findings from major GWAS which have identified predictive genetic loci for clinical outcome and/or toxicity. Lessons and challenges in cancer GWAS are also discussed, including the need for functional analysis and replication, as well as future perspectives for biological and clinical utility. Given the large heterogeneity in response to cancer therapeutics, novel methods of identifying mechanisms and biology of variable drug response and ultimately treatment individualization will be indispensable. © 2013 The British Pharmacological Society.

Multimodal imaging-based therapeutic fingerprints for optimizing personalized interventions: Application to neurodegeneration.

PubMed

Iturria-Medina, Yasser; Carbonell, Félix M; Evans, Alan C

2018-06-14

Personalized Medicine (PM) seeks to assist the patients according to their specific treatment needs and potential intervention responses. However, in the neurological context, this approach is limited by crucial methodological challenges, such as the requirement for an understanding of the causal disease mechanisms and the inability to predict the brain's response to therapeutic interventions. Here, we introduce and validate the concept of the personalized Therapeutic Intervention Fingerprint (pTIF), which predicts the effectiveness of potential interventions for controlling a patient's disease evolution. Each subject's pTIF can be inferred from multimodal longitudinal imaging (e.g. amyloid-β, metabolic and tau PET; vascular, functional and structural MRI). We studied an aging population (N = 331) comprising cognitively normal and neurodegenerative patients, longitudinally scanned using six different neuroimaging modalities. We found that the resulting pTIF vastly outperforms cognitive and clinical evaluations on predicting individual variability in gene expression (GE) profiles. Furthermore, after regrouping the patients according to their predicted primary single-target interventions, we observed that these pTIF-based subgroups present distinctively altered molecular pathway signatures, supporting the across-population identification of dissimilar pathological stages, in active correspondence with different therapeutic needs. The results further evidence the imprecision of using broad clinical categories for understanding individual molecular alterations and selecting appropriate therapeutic needs. To our knowledge, this is the first study highlighting the direct link between multifactorial brain dynamics, predicted treatment responses, and molecular alterations at the patient level. Inspired by the principles of PM, the proposed pTIF framework is a promising step towards biomarker-driven assisted therapeutic interventions, with additional important implications for selective enrollment of patients in clinical trials. Copyright © 2018 Elsevier Inc. All rights reserved.

Infants, Mothers, and Dyadic Contributions to Stability and Prediction of Social Stress Response at 6 Months

ERIC Educational Resources Information Center

Provenzi, Livio; Olson, Karen L.; Montirosso, Rosario; Tronick, Ed

2016-01-01

The study of infants' interactive style and social stress response to repeated stress exposures is of great interest for developmental and clinical psychologists. Stable maternal and dyadic behavior is critical to sustain infants' development of an adaptive social stress response, but the association between infants' interactive style and social…

Rapid Response Predicts Treatment Outcomes in Binge Eating Disorder: Implications for Stepped Care

ERIC Educational Resources Information Center

Masheb, Robin M.; Grilo, Carlos M.

2007-01-01

The authors examined rapid response in 75 overweight patients with binge eating disorder (BED) who participated in a randomized clinical trial of guided self-help treatments (cognitive-behavioral therapy [CBTgsh] and behavioral weight loss [BWLgsh]). Rapid response, defined as a 65% or greater reduction in binge eating by the 4th treatment week,…

Evaluation of chemotherapy response in ovarian cancer treatment using quantitative CT image biomarkers: a preliminary study

NASA Astrophysics Data System (ADS)

Qiu, Yuchen; Tan, Maxine; McMeekin, Scott; Thai, Theresa; Moore, Kathleen; Ding, Kai; Liu, Hong; Zheng, Bin

2015-03-01

The purpose of this study is to identify and apply quantitative image biomarkers for early prediction of the tumor response to the chemotherapy among the ovarian cancer patients participated in the clinical trials of testing new drugs. In the experiment, we retrospectively selected 30 cases from the patients who participated in Phase I clinical trials of new drug or drug agents for ovarian cancer treatment. Each case is composed of two sets of CT images acquired pre- and post-treatment (4-6 weeks after starting treatment). A computer-aided detection (CAD) scheme was developed to extract and analyze the quantitative image features of the metastatic tumors previously tracked by the radiologists using the standard Response Evaluation Criteria in Solid Tumors (RECIST) guideline. The CAD scheme first segmented 3-D tumor volumes from the background using a hybrid tumor segmentation scheme. Then, for each segmented tumor, CAD computed three quantitative image features including the change of tumor volume, tumor CT number (density) and density variance. The feature changes were calculated between the matched tumors tracked on the CT images acquired pre- and post-treatments. Finally, CAD predicted patient's 6-month progression-free survival (PFS) using a decision-tree based classifier. The performance of the CAD scheme was compared with the RECIST category. The result shows that the CAD scheme achieved a prediction accuracy of 76.7% (23/30 cases) with a Kappa coefficient of 0.493, which is significantly higher than the performance of RECIST prediction with a prediction accuracy and Kappa coefficient of 60% (17/30) and 0.062, respectively. This study demonstrated the feasibility of analyzing quantitative image features to improve the early predicting accuracy of the tumor response to the new testing drugs or therapeutic methods for the ovarian cancer patients.

Clinical relevance of total HCV core antigen testing for hepatitis C monitoring and for predicting patients' response to therapy.

PubMed

Maynard, M; Pradat, P; Berthillon, P; Picchio, G; Voirin, N; Martinot, M; Marcellin, P; Trepo, C

2003-07-01

To study the correlation between total Hepatitis C virus (HCV) Core antigen (Ag) and HCV-RNA, and to assess the proficiency of HCV Core Ag testing in monitoring and predicting virologic response during and after pegylated interferon (PEG-IFN) and ribavirin combination therapy. A total of 307 samples from treated and untreated patients were used to assess the correlation between the total HCV Core Ag test and quantitative HCV-RNA assays (Superquant, and Quantiplex branched DNA 2.0 assay). Twenty-four patients received combination therapy for 48 weeks. Blood samples were collected at day 0, and week 2, 4, 12, 24, 48 and 72 for virologic evaluation. A linear relation exists between total HCV Core Ag and HCV-RNA levels. At 3 months the positive predictive value (PPV) of response to therapy was 100% with either HCV Core Ag or HCV-RNA. For HCV Core Ag the negative predictive value (NPV) was 100% whereas for HCV-RNA the NPV was 80% (P > 0.05). At month 1, the PPV was 95% and 100% when determined by HCV Core Ag and HCV-RNA, respectively. The NPV value was 100% for HCV Core Ag and 33% for HCV-RNA (P = 0.005). HCV Core Ag quantification could be useful in clinical practice to predict a sustained virological response early during therapy (4 weeks), reaching an optimal performance at month 3. The determination of total HCV Core Ag levels in serum, constitutes an accurate and reliable alternative to HCV-RNA for monitoring and predicting treatment outcome in patients receiving PEG-IFN/Ribavirin combination therapy.

Understanding the tumor immune microenvironment (TIME) for effective therapy

PubMed Central

Binnewies, Mikhail; Roberts, Edward W.; Kersten, Kelly; Chan, Vincent; Fearon, Douglas F.; Merad, Miriam; Coussens, Lisa M.; Gabrilovich, Dmitry I.; Ostrand-Rosenberg, Suzanne; Hedrick, Catherine C.; Vonderheide, Robert H.; Pittet, Mikael J.; Jain, Rakesh K.; Zou, Weiping; Howcroft, T. Kevin; Woodhouse, Elisa C.; Weinberg, Robert A.; Krummel, Matthew F.

2018-01-01

The clinical successes in immunotherapy have been both astounding and at the same time unsatisfactory. Countless patients with varied tumor types have seen pronounced clinical response with immunotherapeutic intervention; however, many more patients have experienced minimal or no clinical benefit when provided the same treatment. As technology has advanced, so has the understanding of the complexity and diversity of the immune context of the tumor microenvironment and its influence on response to therapy. It has been possible to identify different subclasses of immune environment that have an influence on tumor initiation and response and therapy; by parsing the unique classes and subclasses of tumor immune microenvironment (TIME) that exist within a patient’s tumor, the ability to predict and guide immunotherapeutic responsiveness will improve, and new therapeutic targets will be revealed. PMID:29686425

Emerging biomarkers for cancer immunotherapy in melanoma.

PubMed

Axelrod, Margaret L; Johnson, Douglas B; Balko, Justin M

2017-09-14

The treatment and prognosis of metastatic melanoma has changed substantially since the advent of novel immune checkpoint inhibitors (ICI), agents that enhance the anti-tumor immune response. Despite the success of these agents, clinically actionable biomarkers to aid patient and regimen selection are lacking. Herein, we summarize and review the evidence for candidate biomarkers of response to ICIs in melanoma. Many of these candidates can be examined as parts of a known molecular pathway of immune response, while others are clinical in nature. Due to the ability of ICIs to illicit dramatic and durable responses, well-validated biomarkers that can be effectively implemented in the clinic will require strong negative predictive values that do not limit patients with who may benefit from ICI therapy. Copyright © 2017 Elsevier Ltd. All rights reserved.

RNA sequencing to predict response to TNF-α inhibitors reveals possible mechanism for nonresponse in smokers.

PubMed

Cuppen, Bart V J; Rossato, Marzia; Fritsch-Stork, Ruth D E; Concepcion, Arno N; Linn-Rasker, Suzanne P; Bijlsma, Johannes W J; van Laar, Jacob M; Lafeber, Floris P J G; Radstake, Timothy R

2018-06-06

Several studies have employed microarray-based profiling to predict response to tumor necrosis factor-alpha inhibitors (TNFi) in rheumatoid arthritis (RA); yet efforts to validate these targets have failed to show predictive abilities acceptable for clinical practice. The eighty most extreme responders and nonresponders to TNFi therapy were selected from the observational BiOCURA cohort. RNA sequencing was performed on mRNA from peripheral blood mononuclear cells (PBMCs) collected before initiation of treatment. The expression of pathways as well as individual gene transcripts between responders and nonresponders was investigated. Promising targets were technically replicated and validated in n = 40 new patients using qPCR assays. Before therapy initiation, nonresponders had lower expression of pathways related to interferon and cytokine signaling, while also showing higher levels of two genes, GPR15 and SEMA6B (p = 0.02). The two targets could be validated, however, additional analyses revealed that GPR15 and SEMA6B did not independently predict response, but were rather dose-dependent markers of smoking (p < 0.0001). The study did not identify new transcripts ready to use in clinical practice, yet GPR15 and SEMA6B were recognized as candidate explanatory markers for the reduced treatment success in RA smokers.

Call for Applications – Clinical Assay Development Program | Office of Cancer Clinical Proteomics Research

Cancer.gov

The NCI Clinical Assay Development Program (CADP) is requesting project applications from investigators seeking clinical assay validation resources.  These resources are designed to assist with the development of assays that may predict therapy response or prognostic behavior of a diagnosed cancer, primarily for use in clinical trials. Approved projects for the NCI CADP will be provided access to the Institute’s assay development and validation resources, including project management support.

Identifying patients likely to have atopic dermatitis: development of a pilot algorithm.

PubMed

Farage, Miranda A; Bowtell, Philip; Katsarou, Alexandra

2010-01-01

A quick method to distinguish people who are predisposed to skin complaints would be useful in a variety of fields. Certain subgroups, such as people with atopic dermatitis, might be more susceptible to skin irritation than the typical consumer and may be more likely to report product-related complaints. To develop a rapid, questionnaire-based algorithm to predict whether or not individuals who report skin complaints have atopic dermatitis. A 9-item questionnaire on self-perceived skin sensitivity and product categories reportedly associated with skin reactions was administered to two groups of patients from a dermatology clinic: one with clinically diagnosed, active atopic dermatitis (n = 25) and a control group of patients with dermatologic complaints unrelated to atopic dermatitis (n = 25). Questionnaire responses were correlated with the patients' clinical diagnoses in order to derive the minimum number of questions needed to best predict the patients' original diagnoses. We demonstrated that responses to a sequence of three targeted questions related to self-perceived skin sensitivity, preference for hypoallergenic products, and reactions to or avoidance of alpha-hydroxy acids were highly predictive of atopic dermatitis among a population of dermatology clinic patients. The predictive algorithm concept may be useful in postmarketing surveillance programs to rapidly assess the possible status of consumers who report frequent or persistent product-related complaints. Further refinement and validation of this concept is planned with samples drawn from the general population and from consumers who report skin complaints associated with personal products.

Does the MCAT predict medical school and PGY-1 performance?

PubMed

Saguil, Aaron; Dong, Ting; Gingerich, Robert J; Swygert, Kimberly; LaRochelle, Jeffrey S; Artino, Anthony R; Cruess, David F; Durning, Steven J

2015-04-01

The Medical College Admissions Test (MCAT) is a high-stakes test required for entry to most U. S. medical schools; admissions committees use this test to predict future accomplishment. Although there is evidence that the MCAT predicts success on multiple choice-based assessments, there is little information on whether the MCAT predicts clinical-based assessments of undergraduate and graduate medical education performance. This study looked at associations between the MCAT and medical school grade point average (GPA), Medical Licensing Examination (USMLE) scores, observed patient care encounters, and residency performance assessments. This study used data collected as part of the Long-Term Career Outcome Study to determine associations between MCAT scores, USMLE Step 1, Step 2 clinical knowledge and clinical skill, and Step 3 scores, Objective Structured Clinical Examination performance, medical school GPA, and PGY-1 program director (PD) assessment of physician performance for students graduating 2010 and 2011. MCAT data were available for all students, and the PGY PD evaluation response rate was 86.2% (N = 340). All permutations of MCAT scores (first, last, highest, average) were weakly associated with GPA, Step 2 clinical knowledge scores, and Step 3 scores. MCAT scores were weakly to moderately associated with Step 1 scores. MCAT scores were not significantly associated with Step 2 clinical skills Integrated Clinical Encounter and Communication and Interpersonal Skills subscores, Objective Structured Clinical Examination performance or PGY-1 PD evaluations. MCAT scores were weakly to moderately associated with assessments that rely on multiple choice testing. The association is somewhat stronger for assessments occurring earlier in medical school, such as USMLE Step 1. The MCAT was not able to predict assessments relying on direct clinical observation, nor was it able to predict PD assessment of PGY-1 performance. Reprint & Copyright © 2015 Association of Military Surgeons of the U.S.

Depression and anxiety predict sex-specific cortisol responses to interpersonal stress.

PubMed

Powers, Sally I; Laurent, Heidemarie K; Gunlicks-Stoessel, Meredith; Balaban, Susan; Bent, Eileen

2016-07-01

Clinical theories posit interpersonal stress as an important factor in the emergence and exacerbation of depression and anxiety, while neuroendocrine research confirms the association of these syndromes with dysregulation in a major stress response system, the hypothalamic-pituitary-adrenal (HPA) axis. However, the proposal that depression and anxiety symptoms and diagnoses are associated with problematic HPA responses to close relationship stress has not been directly tested. We examined 196 heterosexual dating couples' depression and anxiety symptoms and diagnoses, assessed with questionnaires and diagnostic interviews, in relation to cortisol responses to discussion of an unresolved relationship conflict. Participants provided seven salivary samples in anticipation of and directly following the discussion, and throughout an hour-long recovery period, which were assayed for cortisol. Multilevel models of the HPA response predicted by symptoms or diagnoses showed that women's depressive symptoms predicted attenuated cortisol levels, with a flatter response curve. In contrast, men's depression symptoms and women's anxiety symptoms and diagnoses predicted higher cortisol levels. These findings highlight the importance of examining sex differences in responses to interpersonal stressors for understanding HPA dysregulation in internalizing psychopathology. Copyright © 2016 Elsevier Ltd. All rights reserved.

Parent Prediction of Child Mood and Emotional Resilience: The Role of Parental Responsiveness and Psychological Control

PubMed Central

Boughton, Kristy L.; Lumley, Margaret N.

2011-01-01

Research consistently shows low to moderate agreement between parent and child reports of child mood, suggesting that parents are not always the best predictors of child emotional functioning. This study examines parental responsiveness and psychological control for improving prediction of early adolescent mood and emotional resilience beyond parent report of child emotional functioning. Participants were 268 early adolescents administered measures of depression symptoms, emotional resilience, and perceptions of parenting. Parents of participating youth completed measures of youth emotional functioning. Parental responsiveness and psychological control each emerged as family variables that may be of value for predicting child emotional functioning beyond parent reports. Specifically, responsiveness explained significant variance in child depression and resilience after accounting for parent reports, while parental psychological control increased prediction of child mood alone. Results generally suggest that parenting behaviours may be an important consideration when children and parents provide discrepant reports of child emotional well-being. Conceptual and clinical implications of these results are discussed. PMID:22110912

Prediction of Response to Preoperative Chemoradiotherapy in Rectal Cancer by Multiplex Kinase Activity Profiling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Folkvord, Sigurd; Flatmark, Kjersti; Department of Cancer and Surgery, Norwegian Radium Hospital, Oslo University Hospital

2010-10-01

Purpose: Tumor response of rectal cancer to preoperative chemoradiotherapy (CRT) varies considerably. In experimental tumor models and clinical radiotherapy, activity of particular subsets of kinase signaling pathways seems to predict radiation response. This study aimed to determine whether tumor kinase activity profiles might predict tumor response to preoperative CRT in locally advanced rectal cancer (LARC). Methods and Materials: Sixty-seven LARC patients were treated with a CRT regimen consisting of radiotherapy, fluorouracil, and, where possible, oxaliplatin. Pretreatment tumor biopsy specimens were analyzed using microarrays with kinase substrates, and the resulting substrate phosphorylation patterns were correlated with tumor response to preoperative treatmentmore » as assessed by histomorphologic tumor regression grade (TRG). A predictive model for TRG scores from phosphosubstrate signatures was obtained by partial-least-squares discriminant analysis. Prediction performance was evaluated by leave-one-out cross-validation and use of an independent test set. Results: In the patient population, 73% and 15% were scored as good responders (TRG 1-2) or intermediate responders (TRG 3), whereas 12% were assessed as poor responders (TRG 4-5). In a subset of 7 poor responders and 12 good responders, treatment outcome was correctly predicted for 95%. Application of the prediction model on the remaining patient samples resulted in correct prediction for 85%. Phosphosubstrate signatures generated by poor-responding tumors indicated high kinase activity, which was inhibited by the kinase inhibitor sunitinib, and several discriminating phosphosubstrates represented proteins derived from signaling pathways implicated in radioresistance. Conclusions: Multiplex kinase activity profiling may identify functional biomarkers predictive of tumor response to preoperative CRT in LARC.« less

Identification of predictive markers of cytarabine response in AML by integrative analysis of gene-expression profiles with multiple phenotypes

PubMed Central

Lamba, Jatinder K; Crews, Kristine R; Pounds, Stanley B; Cao, Xueyuan; Gandhi, Varsha; Plunkett, William; Razzouk, Bassem I; Lamba, Vishal; Baker, Sharyn D; Raimondi, Susana C; Campana, Dario; Pui, Ching-Hon; Downing, James R; Rubnitz, Jeffrey E; Ribeiro, Raul C

2011-01-01

Aim To identify gene-expression signatures predicting cytarabine response by an integrative analysis of multiple clinical and pharmacological end points in acute myeloid leukemia (AML) patients. Materials & methods We performed an integrated analysis to associate the gene expression of diagnostic bone marrow blasts from acute myeloid leukemia (AML) patients treated in the discovery set (AML97; n = 42) and in the independent validation set (AML02; n = 46) with multiple clinical and pharmacological end points. Based on prior biological knowledge, we defined a gene to show a therapeutically beneficial (detrimental) pattern of association of its expression positively (negatively) correlated with favorable phenotypes such as intracellular cytarabine 5´-triphosphate levels, morphological response and event-free survival, and negatively (positively) correlated with unfavorable end points such as post-cytarabine DNA synthesis levels, minimal residual disease and cytarabine LC50. Results We identified 240 probe sets predicting a therapeutically beneficial pattern and 97 predicting detrimental pattern (p ≤ 0.005) in the discovery set. Of these, 60 were confirmed in the independent validation set. The validated probe sets correspond to genes involved in PIK3/PTEN/AKT/mTOR signaling, G-protein-coupled receptor signaling and leukemogenesis. This suggests that targeting these pathways as potential pharmacogenomic and therapeutic candidates could be useful for improving treatment outcomes in AML. Conclusion This study illustrates the power of integrated data analysis of genomic data as well as multiple clinical and pharmacologic end points in the identification of genes and pathways of biological relevance. PMID:21449673

Predicting pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review.

PubMed

Ryan, J E; Warrier, S K; Lynch, A C; Ramsay, R G; Phillips, W A; Heriot, A G

2016-03-01

Approximately 20% of patients treated with neoadjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer achieve a pathological complete response (pCR) while the remainder derive the benefit of improved local control and downstaging and a small proportion show a minimal response. The ability to predict which patients will benefit would allow for improved patient stratification directing therapy to those who are likely to achieve a good response, thereby avoiding ineffective treatment in those unlikely to benefit. A systematic review of the English language literature was conducted to identify pathological factors, imaging modalities and molecular factors that predict pCR following chemoradiotherapy. PubMed, MEDLINE and Cochrane Database searches were conducted with the following keywords and MeSH search terms: 'rectal neoplasm', 'response', 'neoadjuvant', 'preoperative chemoradiation', 'tumor response'. After review of title and abstracts, 85 articles addressing the prediction of pCR were selected. Clear methods to predict pCR before chemoradiotherapy have not been defined. Clinical and radiological features of the primary cancer have limited ability to predict response. Molecular profiling holds the greatest potential to predict pCR but adoption of this technology will require greater concordance between cohorts for the biomarkers currently under investigation. At present no robust markers of the prediction of pCR have been identified and the topic remains an area for future research. This review critically evaluates existing literature providing an overview of the methods currently available to predict pCR to nCRT for locally advanced rectal cancer. The review also provides a comprehensive comparison of the accuracy of each modality. Colorectal Disease © 2015 The Association of Coloproctology of Great Britain and Ireland.

Markers predicting response to bacillus Calmette-Guérin immunotherapy in high-risk bladder cancer patients: a systematic review.

PubMed

Zuiverloon, Tahlita C M; Nieuweboer, Annemieke J M; Vékony, Hedvig; Kirkels, Wim J; Bangma, Chris H; Zwarthoff, Ellen C

2012-01-01

Currently, bacillus Calmette-Guérin (BCG) intravesical instillations are standard treatment for patients with high-grade non-muscle-invasive bladder cancer; however, no markers are available to predict BCG response. To review the contemporary literature on markers predicting BCG response, to discuss the key issues concerning the identification of predictive markers, and to provide recommendations for further research studies. We performed a systematic review of the literature using PubMed and Embase databases in the period 1996-2010. The free-text search was extended by adding the following keywords: recurrence, progression, survival, molecular marker, prognosis, TP53, Ki-67, RB, fibronectin, immunotherapy, cytokine, interleukin, natural killer, macrophage, PMN, polymorphism, SNP, single nucleotide polymorphism, and gene signature. If thresholds for the detection of urinary interleukin (IL)-8, IL-18, and tumour necrosis factor apoptosis-inducing ligand levels are standardised, measurement of these cytokines holds promise in the assessment of BCG therapy outcome. Studies on immunohistochemical markers (ie, TP53, Ki-67, and retinoblastoma) display contradictory results, probably because of the small patient groups that were used and seem unsuitable to predict BCG response. Exploring combinations of protein levels might prove to be more helpful to establish the effect of BCG therapy. Single nucleotide polymorphisms, either in cytokines or in genes involved in DNA repair, need to be investigated in different ethnicities before their clinical relevance can be determined. Measurement of urinary IL-2 levels seems to be the most potent marker of all the clinical parameters reviewed. IL-2 levels are currently the most promising predictive markers of BCG response. For future studies focusing on new biomarkers, it is essential to make more use of new biomedical techniques such as microRNA profiling and genomewide sequencing. Copyright © 2011 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Predicting the Best Fit: A Comparison of Response Surface Models for Midazolam and Alfentanil Sedation in Procedures With Varying Stimulation.

PubMed

Liou, Jing-Yang; Ting, Chien-Kun; Mandell, M Susan; Chang, Kuang-Yi; Teng, Wei-Nung; Huang, Yu-Yin; Tsou, Mei-Yung

2016-08-01

Selecting an effective dose of sedative drugs in combined upper and lower gastrointestinal endoscopy is complicated by varying degrees of pain stimulation. We tested the ability of 5 response surface models to predict depth of sedation after administration of midazolam and alfentanil in this complex model. The procedure was divided into 3 phases: esophagogastroduodenoscopy (EGD), colonoscopy, and the time interval between the 2 (intersession). The depth of sedation in 33 adult patients was monitored by Observer Assessment of Alertness/Scores. A total of 218 combinations of midazolam and alfentanil effect-site concentrations derived from pharmacokinetic models were used to test 5 response surface models in each of the 3 phases of endoscopy. Model fit was evaluated with objective function value, corrected Akaike Information Criterion (AICc), and Spearman ranked correlation. A model was arbitrarily defined as accurate if the predicted probability is <0.5 from the observed response. The effect-site concentrations tested ranged from 1 to 76 ng/mL and from 5 to 80 ng/mL for midazolam and alfentanil, respectively. Midazolam and alfentanil had synergistic effects in colonoscopy and EGD, but additivity was observed in the intersession group. Adequate prediction rates were 84% to 85% in the intersession group, 84% to 88% during colonoscopy, and 82% to 87% during EGD. The reduced Greco and Fixed alfentanil concentration required for 50% of the patients to achieve targeted response Hierarchy models performed better with comparable predictive strength. The reduced Greco model had the lowest AICc with strong correlation in all 3 phases of endoscopy. Dynamic, rather than fixed, γ and γalf in the Hierarchy model improved model fit. The reduced Greco model had the lowest objective function value and AICc and thus the best fit. This model was reliable with acceptable predictive ability based on adequate clinical correlation. We suggest that this model has practical clinical value for patients undergoing procedures with varying degrees of stimulation.

Emerging biomarkers in anaplastic oligodendroglioma: implications for clinical investigation and patient management.

PubMed

Sahebjam, Solmaz; McNamara, Mairéad G; Mason, Warren P

2013-07-01

Oligodendrogliomas are heterogeneous tumors with a variable response to treatment. This clinical variability underlines the urgent need for markers that can reliably aid diagnosis and guide clinical decision-making. Long-term follow-up data from the EORTC 26951 and RTOG 9402 clinical trials in newly diagnosed anaplastic oligodendroglioma have established chromosome 1p19q codeletion as a predictive marker of response to procarbazine, lomustine and vincristine chemotherapy in anaplastic oligodendrogliomas. In addition, MGMT promoter hypermethylation has been strongly associated with glioma CpG island hypermethylation phenotype (G-CIMP+) status, this has been suggested as an epiphenomenon of genome-wide methylation, conferring a more favorable prognosis. Molecular profiling of these tumors has identified several other markers with potential clinical significance: mutations of IDH, CIC, FUBP1 and CDKN2A require further validation before they can be implemented as clinical decision-making tools. Additionally, recent data on the clinical significance of intrinsic glioma subtyping appears promising. Indeed, existing evidence suggests that comprehensive analyses such as intrinsic glioma subtyping or G-CIMP status are superior to single molecular markers. Clearly, with evolving treatment strategies and in the era of individualized therapy, broader omics-based molecular evaluations are required to improve outcome prediction and to identify patients who will benefit from specific treatment strategies.

Do we have biomarkers to predict response to neoadjuvant and adjuvant chemotherapy and immunotherapy in bladder cancer?

PubMed Central

Wezel, Felix; Vallo, Stefan

2017-01-01

Radical cystectomy (RC) is the standard of care treatment of localized muscle-invasive bladder cancer (BC). However, about 50% of patients develop metastases within 2 years after cystectomy. Neoadjuvant cisplatin-based chemotherapy before cystectomy improves the overall survival (OS) in patients with muscle-invasive BC. Pathological response to neoadjuvant treatment is a strong predictor of better disease-specific survival. Nevertheless, some patients do not benefit from chemotherapy. The identification of reliable biomarkers enabling clinicians to identify patients who might benefit from chemotherapy is a very important clinical task. An identification tool could lead to individualized therapy, optimizing response rates. In addition, unnecessary treatment with chemotherapy which potentially leads to a loss of quality of life and which might also might cause a delay of cystectomy in a neoadjuvant setting could be avoided. The present review aims to summarize and discuss the current literature on biomarkers for the prediction of response to systemic therapy in muscle-invasive BC. Tremendous efforts in genetic and molecular characterization have led to the identification of predictive candidate biomarkers in urothelial carcinoma (UC), although prospective validation is pending. Ongoing clinical trials examining the benefit of individual therapies in UC of the bladder (UCB) by molecular patient selection hold promise to shed light on this question. PMID:29354494

Pharmacokinetic optimization of class-selective histone deacetylase inhibitors and identification of associated candidate predictive biomarkers of hepatocellular carcinoma tumor response.

PubMed

Wong, Jason C; Tang, Guozhi; Wu, Xihan; Liang, Chungen; Zhang, Zhenshan; Guo, Lei; Peng, Zhenghong; Zhang, Weixing; Lin, Xianfeng; Wang, Zhanguo; Mei, Jianghua; Chen, Junli; Pan, Song; Zhang, Nan; Liu, Yongfu; Zhou, Mingwei; Feng, Lichun; Zhao, Weili; Li, Shijie; Zhang, Chao; Zhang, Meifang; Rong, Yiping; Jin, Tai-Guang; Zhang, Xiongwen; Ren, Shuang; Ji, Ying; Zhao, Rong; She, Jin; Ren, Yi; Xu, Chunping; Chen, Dawei; Cai, Jie; Shan, Song; Pan, Desi; Ning, Zhiqiang; Lu, Xianping; Chen, Taiping; He, Yun; Chen, Li

2012-10-25

Herein, we describe the pharmacokinetic optimization of a series of class-selective histone deacetylase (HDAC) inhibitors and the subsequent identification of candidate predictive biomarkers of hepatocellular carcinoma (HCC) tumor response for our clinical lead using patient-derived HCC tumor xenograft models. Through a combination of conformational constraint and scaffold hopping, we lowered the in vivo clearance (CL) and significantly improved the bioavailability (F) and exposure (AUC) of our HDAC inhibitors while maintaining selectivity toward the class I HDAC family with particular potency against HDAC1, resulting in clinical lead 5 (HDAC1 IC₅₀ = 60 nM, mouse CL = 39 mL/min/kg, mouse F = 100%, mouse AUC after single oral dose at 10 mg/kg = 6316 h·ng/mL). We then evaluated 5 in a biomarker discovery pilot study using patient-derived tumor xenograft models, wherein two out of the three models responded to treatment. By comparing tumor response status to compound tumor exposure, induction of acetylated histone H3, candidate gene expression changes, and promoter DNA methylation status from all three models at various time points, we identified preliminary candidate response prediction biomarkers that warrant further validation in a larger cohort of patient-derived tumor models and through confirmatory functional studies.

Cytologic anaplasia is a prognostic factor in osteosarcoma biopsies, but mitotic rate or extent of spontaneous tumor necrosis are not: a critique of the College of American Pathologists Bone Biopsy template.

PubMed

Cates, Justin Mm; Dupont, William D

2017-01-01

The current College of American Pathologists cancer template for reporting biopsies of bone tumors recommends including information that is of unproven prognostic significance for osteosarcoma, such as the presence of spontaneous tumor necrosis and mitotic rate. Conversely, the degree of cytologic anaplasia (degree of differentiation) is not reported in this template. This retrospective cohort study of 125 patients with high-grade osteosarcoma was performed to evaluate the prognostic impact of these factors in diagnostic biopsy specimens in predicting the clinical outcome and response to neoadjuvant chemotherapy. Multivariate Cox regression was performed to adjust survival analyses for well-established prognostic factors. Multivariate logistic regression was used to determine odds ratios for good chemotherapy response (≥90% tumor necrosis). Osteosarcomas with severe anaplasia were independently associated with increased overall and disease-free survival, but mitotic rate and spontaneous necrosis had no prognostic impact after controlling for other confounding factors. Mitotic rate showed a trend towards increased odds of a good histologic response, but this effect was diminished after controlling for other predictive factors. Neither spontaneous necrosis nor the degree of cytologic anaplasia observed in biopsy specimens was predictive of a good response to chemotherapy. Mitotic rate and spontaneous tumor necrosis observed in pretreatment biopsy specimens of high-grade osteosarcoma are not strong independent prognostic factors for clinical outcome or predictors of response to neoadjuvant chemotherapy. Therefore, reporting these parameters for osteosarcoma, as recommended in the College of American Pathologists Bone Biopsy template, does not appear to have clinical utility. In contrast, histologic grading schemes for osteosarcoma based on the degree of cytologic anaplasia may have independent prognostic value and should continue to be evaluated.

DNA Repair Biomarkers Predict Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alexander, Brian M., E-mail: bmalexander@lroc.harvard.edu; Wang Xiaozhe; Niemierko, Andrzej

2012-05-01

Purpose: The addition of neoadjuvant chemoradiotherapy prior to surgical resection for esophageal cancer has improved clinical outcomes in some trials. Pathologic complete response (pCR) following neoadjuvant therapy is associated with better clinical outcome in these patients, but only 22% to 40% of patients achieve pCR. Because both chemotherapy and radiotherapy act by inducing DNA damage, we analyzed proteins selected from multiple DNA repair pathways, using quantitative immunohistochemistry coupled with a digital pathology platform, as possible biomarkers of treatment response and clinical outcome. Methods and Materials: We identified 79 patients diagnosed with esophageal cancer between October 1994 and September 2002, withmore » biopsy tissue available, who underwent neoadjuvant chemoradiotherapy prior to surgery at the Massachusetts General Hospital and used their archived, formalin-fixed, paraffin-embedded biopsy samples to create tissue microarrays (TMA). TMA sections were stained using antibodies against proteins in various DNA repair pathways including XPF, FANCD2, PAR, MLH1, PARP1, and phosphorylated MAPKAP kinase 2 (pMK2). Stained TMA slides were evaluated using machine-based image analysis, and scoring incorporated both the intensity and the quantity of positive tumor nuclei. Biomarker scores and clinical data were assessed for correlations with clinical outcome. Results: Higher scores for MLH1 (p = 0.018) and lower scores for FANCD2 (p = 0.037) were associated with pathologic response to neoadjuvant chemoradiation on multivariable analysis. Staining of MLH1, PARP1, XPF, and PAR was associated with recurrence-free survival, and staining of PARP1 and FANCD2 was associated with overall survival on multivariable analysis. Conclusions: DNA repair proteins analyzed by immunohistochemistry may be useful as predictive markers for response to neoadjuvant chemoradiotherapy in patients with esophageal cancer. These results are hypothesis generating and need confirmation in an independent data set.« less

The relationship between obsessive beliefs and symptom dimensions in obsessive-compulsive disorder.

PubMed

Wheaton, Michael G; Abramowitz, Jonathan S; Berman, Noah C; Riemann, Bradley C; Hale, Lisa R

2010-10-01

Research findings on the specific relationships between beliefs and OCD symptoms have been inconsistent, yet the existing studies vary in their approach to measuring the highly heterogeneous symptoms of this disorder. The Dimensional Obsessive-Compulsive Scale (DOCS) is a new measure that allows for the assessment of OCD symptom dimensions, rather than types of obsessions and compulsions per se. The present study examined the relationship between OCD symptom dimensions and dysfunctional (obsessive) beliefs believed to underlie these symptom dimensions using a large clinical sample of treatment-seeking adults with OCD. Results revealed that certain obsessive beliefs predicted certain OCD symptom dimensions in a manner consistent with cognitive-behavioral conceptual models. Specifically, contamination symptoms were predicted by responsibility/threat estimation beliefs, symmetry symptoms were predicted by perfectionism/certainty beliefs, unacceptable thoughts were predicted by importance/control of thoughts beliefs and symptoms related to being responsible for harm were predicted by responsibility/threat estimation beliefs. Implications for cognitive conceptualizations of OCD symptom dimensions are discussed. Copyright 2010 Elsevier Ltd. All rights reserved.

Solitary Alcohol Use in Teens Is Associated With Drinking in Response to Negative Affect and Predicts Alcohol Problems in Young Adulthood

PubMed Central

Creswell, Kasey G.; Chung, Tammy; Clark, Duncan B.; Martin, Christopher S.

2015-01-01

Adolescent solitary drinking may represent an informative divergence from normative behavior, with important implications for understanding risk for alcohol-use disorders later in life. Within a self-medication framework, we hypothesized that solitary alcohol use would be associated with drinking in response to negative affect and that such a pattern of drinking would predict alcohol problems in young adulthood. We tested these predictions in a longitudinal study in which we examined whether solitary drinking in adolescence (ages 12–18) predicted alcohol-use disorders in young adulthood (age 25) in 466 alcohol-using teens recruited from clinical programs and 243 alcohol-using teens recruited from the community. Findings showed that solitary drinking was associated with drinking in response to negative affect during adolescence and predicted alcohol problems in young adulthood. Results indicate that drinking alone is an important type of alcohol-use behavior that increases risk for the escalation of alcohol use and the development of alcohol problems. PMID:25977842

Solitary Alcohol Use in Teens Is Associated With Drinking in Response to Negative Affect and Predicts Alcohol Problems in Young Adulthood.

PubMed

Creswell, Kasey G; Chung, Tammy; Clark, Duncan B; Martin, Christopher S

2014-09-01

Adolescent solitary drinking may represent an informative divergence from normative behavior, with important implications for understanding risk for alcohol-use disorders later in life. Within a self-medication framework, we hypothesized that solitary alcohol use would be associated with drinking in response to negative affect and that such a pattern of drinking would predict alcohol problems in young adulthood. We tested these predictions in a longitudinal study in which we examined whether solitary drinking in adolescence (ages 12-18) predicted alcohol-use disorders in young adulthood (age 25) in 466 alcohol-using teens recruited from clinical programs and 243 alcohol-using teens recruited from the community. Findings showed that solitary drinking was associated with drinking in response to negative affect during adolescence and predicted alcohol problems in young adulthood. Results indicate that drinking alone is an important type of alcohol-use behavior that increases risk for the escalation of alcohol use and the development of alcohol problems.

TnSeq of Mycobacterium tuberculosis clinical isolates reveals strain-specific antibiotic liabilities

PubMed Central

Carey, Allison F.; Rock, Jeremy M.; Krieger, Inna V.; Gagneux, Sebastien; Sacchettini, James C.; Fortune, Sarah M.

2018-01-01

Once considered a phenotypically monomorphic bacterium, there is a growing body of work demonstrating heterogeneity among Mycobacterium tuberculosis (Mtb) strains in clinically relevant characteristics, including virulence and response to antibiotics. However, the genetic and molecular basis for most phenotypic differences among Mtb strains remains unknown. To investigate the basis of strain variation in Mtb, we performed genome-wide transposon mutagenesis coupled with next-generation sequencing (TnSeq) for a panel of Mtb clinical isolates and the reference strain H37Rv to compare genetic requirements for in vitro growth across these strains. We developed an analytic approach to identify quantitative differences in genetic requirements between these genetically diverse strains, which vary in genomic structure and gene content. Using this methodology, we found differences between strains in their requirements for genes involved in fundamental cellular processes, including redox homeostasis and central carbon metabolism. Among the genes with differential requirements were katG, which encodes the activator of the first-line antitubercular agent isoniazid, and glcB, which encodes malate synthase, the target of a novel small-molecule inhibitor. Differences among strains in their requirement for katG and glcB predicted differences in their response to these antimicrobial agents. Importantly, these strain-specific differences in antibiotic response could not be predicted by genetic variants identified through whole genome sequencing or by gene expression analysis. Our results provide novel insight into the basis of variation among Mtb strains and demonstrate that TnSeq is a scalable method to predict clinically important phenotypic differences among Mtb strains. PMID:29505613

Supraphysiologic Testosterone Therapy in the Treatment of Prostate Cancer: Models, Mechanisms and Questions

PubMed Central

Nyquist, Michael D.; Schweizer, Michael T.; Balk, Stephen P.; Corey, Eva; Plymate, Stephen; Nelson, Peter S.; Mostaghel, Elahe A.

2017-01-01

Since Huggins defined the androgen-sensitive nature of prostate cancer (PCa), suppression of systemic testosterone (T) has remained the most effective initial therapy for advanced disease although progression inevitably occurs. From the inception of clinical efforts to suppress androgen receptor (AR) signaling by reducing AR ligands, it was also recognized that administration of T in men with castration-resistant prostate cancer (CRPC) could result in substantial clinical responses. Data from preclinical models have reproducibly shown biphasic responses to T administration, with proliferation at low androgen concentrations and growth inhibition at supraphysiological T concentrations. Many questions regarding the biphasic response of PCa to androgen treatment remain, primarily regarding the mechanisms driving these responses and how best to exploit the biphasic phenomenon clinically. Here we review the preclinical and clinical data on high dose androgen growth repression and discuss cellular pathways and mechanisms likely to be involved in mediating this response. Although meaningful clinical responses have now been observed in men with PCa treated with high dose T, not all men respond, leading to questions regarding which tumor characteristics promote response or resistance, and highlighting the need for studies designed to determine the molecular mechanism(s) driving these responses and identify predictive biomarkers. PMID:29210989

[Application of decision curve on evaluation of MRI predictive model for early assessing pathological complete response to neoadjuvant therapy in breast cancer].

PubMed

He, Y J; Li, X T; Fan, Z Q; Li, Y L; Cao, K; Sun, Y S; Ouyang, T

2018-01-23

Objective: To construct a dynamic enhanced MR based predictive model for early assessing pathological complete response (pCR) to neoadjuvant therapy in breast cancer, and to evaluate the clinical benefit of the model by using decision curve. Methods: From December 2005 to December 2007, 170 patients with breast cancer treated with neoadjuvant therapy were identified and their MR images before neoadjuvant therapy and at the end of the first cycle of neoadjuvant therapy were collected. Logistic regression model was used to detect independent factors for predicting pCR and construct the predictive model accordingly, then receiver operating characteristic (ROC) curve and decision curve were used to evaluate the predictive model. Results: ΔArea(max) and Δslope(max) were independent predictive factors for pCR, OR =0.942 (95% CI : 0.918-0.967) and 0.961 (95% CI : 0.940-0.987), respectively. The area under ROC curve (AUC) for the constructed model was 0.886 (95% CI : 0.820-0.951). Decision curve showed that in the range of the threshold probability above 0.4, the predictive model presented increased net benefit as the threshold probability increased. Conclusions: The constructed predictive model for pCR is of potential clinical value, with an AUC>0.85. Meanwhile, decision curve analysis indicates the constructed predictive model has net benefit from 3 to 8 percent in the likely range of probability threshold from 80% to 90%.

Physiological responses to dyadic interactions are influenced by neurotypical adults' levels of autistic and empathy traits.

PubMed

Truzzi, Anna; Setoh, Peipei; Shinohara, Kazuyuki; Esposito, Gianluca

2016-10-15

Autistic traits are distributed on a continuum that ranges from non-clinical to clinical condition. Atypical responses to social situations represent a core feature of the Autism Spectrum Disorders phenotype. Here, we hypothesize that atypical physiological responses to social stimuli may predict non-clinical autistic and empathy traits levels. We measured physiological responses (heart rate, facial temperature) of 40 adults (20F) while showing them 24 movies representing dyadic interactions. Autistic traits were assessed through Autism Quotient questionnaire (AQ), while empathy traits were measured using the Empathy Quotient questionnaire (EQ). Opposite correlations between AQ and EQ scores and physiological responses were found. Analysis of physiological responses revealed that individuals with better social abilities, low AQ and high EQ, show opposite activation patterns compared to people with high AQ and low EQ. Findings show that physiological responses could be biomarkers for people's autistic traits and social abilities. Copyright © 2016 Elsevier Inc. All rights reserved.

Neural responses to negative outcomes predict success in community-based substance use treatment

PubMed Central

Forster, Sarah E.; Finn, Peter R.; Brown, Joshua W.

2017-01-01

Background and aims Activation in some specific brain regions has demonstrated promise as prognostic indicators in substance dependent individuals (SDIs) but this issue has not yet been explored in SDIs attending typical of community-based treatment. We used a data-driven, exploratory approach to identify brain-based predictors of treatment outcome in a representative community sample of SDIs. The predictive utility of brain-based measures was evaluated against clinical indicators, cognitive-behavioral performance, and self-report assessments. Design Prospective clinical outcome design, evaluating baseline functional magnetic resonance imaging data from the Balloon Analogue Risk Task (BART) as a predictor of 3-month substance use treatment outcomes. Setting Community-based substance use programs in Bloomington, Indiana, USA. Participants Twenty-three SDIs (17 male, ages 18–43) in an intensive outpatient or residential treatment program; abstinent 1–4 weeks at baseline. Measurements Event-related brain response, BART performance, and self-report scores at treatment onset, substance use outcome measure (based on days of use) Findings Using voxel-level predictive modeling and leave-one-out cross-validation, an elevated response to unexpected negative feedback in bilateral amygdala and anterior hippocampus (Amyg/aHipp) at baseline successfully predicted greater substance use over the 3-month study interval (p ≤ 0.006, cluster-corrected). This effect was robust to inclusion of significant non-brain-based covariates. A larger response to negative feedback in bilateral Amyg/aHipp was also associated with faster reward-seeking responses after negative feedback (r(23) = −0.544, p = 0.007; r(23) = −0.588, p = 0.003). A model including Amyg/aHipp activation, faster reward-seeking after negative feedback, and significant self-report scores accounted for 45% of the variance in substance use outcomes in our sample. Conclusions An elevated response to unexpected negative feedback in bilateral amygdala and anterior hippocampus (Amyg/aHipp) appears to predict relapse to substance use in people attending community-based treatment. PMID:28029198

Predictors of the patency of self-expandable metallic stents in malignant gastroduodenal obstruction.

PubMed

Kim, Seung Han; Chun, Hoon Jai; Yoo, In Kyung; Lee, Jae Min; Nam, Seung Joo; Choi, Hyuk Soon; Kim, Eun Sun; Keum, Bora; Seo, Yeon Seok; Jeen, Yoon Tae; Lee, Hong Sik; Um, Soon Ho; Kim, Chang Duck

2015-08-14

To investigate the predictive factors of self-expandable metallic stent patency after stent placement in patients with inoperable malignant gastroduodenal obstruction. A total of 116 patients underwent stent placements for inoperable malignant gastroduodenal obstruction at a tertiary academic center. Clinical success was defined as acceptable decompression of the obstructive lesion within the malignant gastroduodenal neoplasm. We evaluated patient comorbidities and clinical statuses using the World Health Organization's scoring system and categorized patient responses to chemotherapy using the Response Evaluation Criteria in Solid Tumors criteria. We analyzed the relationships between possible predictive factors and stent patency. Self-expandable metallic stent placement was technically successful in all patients (100%), and the clinical success rate was 84.2%. In a multivariate Cox proportional hazards model, carcinoembryonic antigen (CEA) levels were correlated with a reduction in stent patency [P = 0.006; adjusted hazard ratio (aHR) = 2.92, 95%CI: 1.36-6.25]. Palliative chemotherapy was statistically associated with an increase in stent patency (P = 0.009; aHR = 0.27, 95%CI: 0.10-0.72). CEA levels can easily be measured at the time of stent placement and may help clinicians to predict stent patency and determine the appropriate stent procedure.

Time to clinical response: an outcome of antibiotic therapy of febrile neutropenia with implications for quality and cost of care.

PubMed

Elting, L S; Rubenstein, E B; Rolston, K; Cantor, S B; Martin, C G; Kurtin, D; Rodriguez, S; Lam, T; Kanesan, K; Bodey, G

2000-11-01

To determine whether antibiotic regimens with similar rates of response differ significantly in the speed of response and to estimate the impact of this difference on the cost of febrile neutropenia. The time point of clinical response was defined by comparing the sensitivity, specificity, and predictive values of alternative objective and subjective definitions. Data from 488 episodes of febrile neutropenia, treated with either of two commonly used antibiotics (coded A or B) during six clinical trials, were pooled to compare the median time to clinical response, days of antibiotic therapy and hospitalization, and estimated costs. Response rates were similar; however, the median time to clinical response was significantly shorter with A-based regimens (5 days) compared with B-based regimens (7 days; P =.003). After 72 hours of therapy, 33% of patients who received A but only 18% of those who received B had responded (P =.01). These differences resulted in fewer days of antibiotic therapy and hospitalization with A-based regimens (7 and 9 days) compared with B-based regimens (9 and 12 days, respectively; P <.04) and in significantly lower estimated median costs ($8,491 v $11,133 per episode; P =.03). Early discharge at the time of clinical response should reduce the median cost from $10,752 to $8,162 (P <.001). Despite virtually identical rates of response, time to clinical response and estimated cost of care varied significantly among regimens. An early discharge strategy based on our definition of the time point of clinical response may further reduce the cost of treating non-low-risk patients with febrile neutropenia.

Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies.

PubMed

Geeleher, Paul; Zhang, Zhenyu; Wang, Fan; Gruener, Robert F; Nath, Aritro; Morrison, Gladys; Bhutra, Steven; Grossman, Robert L; Huang, R Stephanie

2017-10-01

Obtaining accurate drug response data in large cohorts of cancer patients is very challenging; thus, most cancer pharmacogenomics discovery is conducted in preclinical studies, typically using cell lines and mouse models. However, these platforms suffer from serious limitations, including small sample sizes. Here, we have developed a novel computational method that allows us to impute drug response in very large clinical cancer genomics data sets, such as The Cancer Genome Atlas (TCGA). The approach works by creating statistical models relating gene expression to drug response in large panels of cancer cell lines and applying these models to tumor gene expression data in the clinical data sets (e.g., TCGA). This yields an imputed drug response for every drug in each patient. These imputed drug response data are then associated with somatic genetic variants measured in the clinical cohort, such as copy number changes or mutations in protein coding genes. These analyses recapitulated drug associations for known clinically actionable somatic genetic alterations and identified new predictive biomarkers for existing drugs. © 2017 Geeleher et al.; Published by Cold Spring Harbor Laboratory Press.

Revisiting the Biomedicalization of Aging: Clinical Trends and Ethical Challenges

ERIC Educational Resources Information Center

Kaufman, Sharon R.; Shim, Janet K.; Russ, Ann J.

2004-01-01

Developments in the realms of medical innovation and geriatric clinical intervention impact our understanding of the nature of late life, the possibilities for health in advanced age, medical decision making, and family responsibility in ways that could not have been predicted 15 years ago. This essay begins to map new forms of biomedicalization…

Cultural Responsivity in Clinical Psychology Graduate Students: A Developmental Approach to the Prediction of Learning

ERIC Educational Resources Information Center

Berrin, Sebastian Everett

2010-01-01

This study used a mixed-method approach to examine students' experiences in multicultural training and their opinions about various aspects of their course(s). A developmental model of learning was employed to analyze results. More specifically, this study explored the relationship between clinical psychology doctoral students' self-reported…

Evaluating an Objective Structured Clinical Examination (OSCE) Adapted for Social Work

ERIC Educational Resources Information Center

Bogo, Marion; Regehr, Cheryl; Katz, Ellen; Logie, Carmen; Tufford, Lea; Litvack, Andrea

2012-01-01

Objectives: To evaluate an objective structured clinical examination (OSCE) adapted for social work in a lab course and examine the degree to which it predicts competence in the practicum. Method: 125 Masters students participated in a one-scenario OSCE and wrote responses to standardized reflection questions. OSCE performance and reflections were…

The place of targeted therapies in the management of non-small cell bronchial carcinoma. Molecular markers as predictors of tumor response and survival in lung cancer.

PubMed

Rosell, R; Moran, T; Fernanda Salazar, M; Mendez, P; De Aguirre, I; Ramirez, J-L; Isla, D; Cobo, M; Camps, C; Lopez-Vivanco, G; Alberola, V; Taron, M

2006-11-01

This review highlights the numerous molecular biology findings in the field of lung cancer with potential therapeutic impact in both the near and distant future. Abundant pre-clinical and clinical data indicate that BRCA1 mRNA expression is a differential modulator of chemotherapy sensitivity. Low levels predict cisplatin sensitivity and antimicrotubule drug resistance, and the opposite occurs with high levels. The main core of recent research has centered on epidermal growth factor receptor (EGFR) mutations and gene copy numbers. For the first time, EGFR mutations have been shown to predict dramatic responses in metastatic lung adenocarcinomas, with a threefold increase in time to progression and survival in patients receiving EGFR tyrosine-kinase inhibitors. Evidence has also been accumulated on the crosstalk between estrogen and EGFR receptor pathways, paving the way for clinical trials of EGFR tyrosine-kinase inhibitors plus aromatase inhibitors. Understanding the relevance of these findings can help to change the clinical practice in oncology towards customizing chemotherapy and targeted therapies, leading to improvement both in survival and in cost-effectiveness.

Sci-Sat AM: Radiation Dosimetry and Practical Therapy Solutions - 12: Suitability of plan class specific reference fields for estimating dosimeter correction factors for small clinical CyberKnife fields

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vandervoort, Eric; Christiansen, Eric; Belec, Jaso

Purpose: The purpose of this work is to investigate the utility of plan class specific reference (PCSR) fields for predicting dosimeter response within isocentric and non-isocentric composite clinical fields using the smallest fields employed by the CyberKnife radiosurgery system. Methods: Monte Carlo dosimeter response correction factors (CFs) were calculated for a plastic scintillator and microchamber dosimeter in 21 clinical fields and 9 candidate plan-class PCSR fields which employ the 5, 7.5 and 10 mm diameter collimators. Measurements were performed in 5 PCSR fields to confirm the predicted relative response of detectors in the same field. Results: Ratios of corrected measuredmore » dose in the PCSR fields agree to within 1% of unity. Calculated CFs for isocentric fields agree within 1.5% of those for PCSR fields. Large and variable microchamber CFs are required for non-isocentric fields, with differences as high as 5% between different clinical fields in the same plan class and 4% within the same field depending on the point of measurement. Non-isocentric PCSR fields constructed to have relatively homogenous dose over a region larger than the detector have very different ion chamber CFs from clinical fields. The plastic scintillator detector has much more consistent response within each plan class but still require 3–4% corrections in some fields. Conclusions: While the PCSR field concept is useful for small isocentric fields, this approach may not be appropriate for non-isocentric clinical fields which exhibit large and variable ion chamber CFs which differ significantly from CFs for homogenous field PCSRs.« less

A comparison of scoring weights for the EuroQol derived from patients and the general public.

PubMed

Polsky, D; Willke, R J; Scott, K; Schulman, K A; Glick, H A

2001-01-01

General health state classification systems, such as the EuroQol instrument, have been developed to improve the systematic measurement and comparability of health state preferences. In this paper we generate valuations for EuroQol health states using responses to this instrument's visual analogue scale made by patients enrolled in a randomized clinical trial evaluating tirilazad mesylate, a new drug used to treat subarachnoid haemorrhage. We then compare these valuations derived from patients with published valuations derived from responses made by a sample from the general public. The data were derived from two sources: (1) responses to the EuroQol instrument from 649 patients 3 months after enrollment in the clinical trial, and (2) from a published study reporting a scoring rule for the EuroQol instrument that was based upon responses made by the general public. We used a linear regression model to develop an additive scoring rule. This rule enables direct valuation of all 243 EuroQol health states using patients' scores for their own health states elicited using a visual analogue scale. We then compared predicted scores generated using our scoring rule with predicted scores derived from a sample from the general public. The predicted scores derived using the additive scoring rules met convergent validity criteria and explained a substantial amount of the variation in visual analogue scale scores (R(2)=0.57). In the pairwise comparison of the predicted scores derived from the study sample with those derived from the general public, we found that the former set of scores were higher for 223 of the 243 states. Despite the low level of correspondence in the pairwise comparison, the overall correlation between the two sets of scores was 87%. The model presented in this paper demonstrated that scoring weights for the EuroQol instrument can be derived directly from patient responses from a clinical trial and that these weights can explain a substantial amount of variation in health valuations. Scoring weights based on patient responses are significantly higher than those derived from the general public. Further research is required to understand the source of these differences. Copyright 2001 John Wiley & Sons, Ltd.

Predictors of response to Systems Training for Emotional Predictability and Problem Solving (STEPPS) for borderline personality disorder: an exploratory study.

PubMed

Black, D W; Allen, J; St John, D; Pfohl, B; McCormick, B; Blum, N

2009-07-01

Few predictors of treatment outcome or early discontinuation have been identified in persons with borderline personality disorder (BPD). The aim of the study was to examine the relationship between baseline clinical variables and treatment response and early discontinuation in a randomized controlled trial of System Training for Emotional Predictability and Problem Solving, a new cognitive group treatment. Improvement was rated using the Zanarini Rating Scale for BPD, the Clinical Global Impression Scale, the Global Assessment Scale and the Beck Depression Inventory. Subjects were assessed during the 20 week trial and a 1-year follow-up. Higher baseline severity was associated with greater improvement in global functioning and BPD-related symptoms. Higher impulsivity was predictive of early discontinuation. Optimal improvement was associated with attending > or = 15 sessions. Subjects likely to improve have the more severe BPD symptoms at baseline, while high levels of impulsivity are associated with early discontinuation.

Poor early response to methotrexate portends inadequate long-term outcomes in patients with moderate-to-severe psoriasis: Evidence from 2 phase 3 clinical trials.

PubMed

Gordon, Kenneth B; Betts, Keith A; Sundaram, Murali; Signorovitch, James E; Li, Junlong; Xie, Meng; Wu, Eric Q; Okun, Martin M

2017-12-01

Most methotrexate-treated psoriasis patients do not achieve a long-term PASI75 (75% reduction from baseline Psoriasis Area and Severity Index score) response. Indications of nonresponse can be apparent after only 4 weeks of treatment. To develop a prediction rule to identify patients unlikely to respond adequately to methotrexate. Patient-level data from CHAMPION (NCT00235820, N = 110) was used to construct a prediction model for week 16 PASI75 by using patient baseline characteristics and week 4 PASI25. A prediction rule was determined on the basis of the sensitivity and specificity and validated in terms of week 16 PASI75 response in an independent validation sample from trial M10-255 (NCT00679731, N = 163). PASI25 achievement at week 4 (odds ratio = 8.917) was highly predictive of response with methotrexate at week 16. Patients with a predicted response probability <30% were recommended to discontinue methotrexate. The rates of week 16 PASI75 response were 65.8% and 21.1% (P < .001) for patients recommended to continue and discontinue methotrexate, respectively. The CHAMPION trial excluded patients previously treated with biologics, and the M10-255 trial had no restrictions. A prediction rule was developed and validated to identify patients unlikely to respond adequately to methotrexate. The rule indicates that 4 weeks of methotrexate might be sufficient to predict long-term response with limited safety risk. Copyright © 2017 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

Magnetic Resonance Imaging Assessment of Squamous Cell Carcinoma of the Anal Canal Before and After Chemoradiation: Can MRI Predict for Eventual Clinical Outcome?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Goh, Vicky, E-mail: vicky.goh@stricklandscanner.org.u; Gollub, Frank K.; Liaw, Jonathan

2010-11-01

Purpose: To describe the MRI appearances of squamous cell carcinoma of the anal canal before and after chemoradiation and to assess whether MRI features predict for clinical outcome. Methods and Materials: Thirty-five patients (15 male, 20 female; mean age 60.8 years) with histologically proven squamous cell cancer of the anal canal underwent MRI before and 6-8 weeks after definitive chemoradiation. Images were reviewed retrospectively by two radiologists in consensus blinded to clinical outcome: tumor size, signal intensity, extent, and TNM stage were recorded. Following treatment, patients were defined as responders by T and N downstaging and Response Evaluation Criteria inmore » Solid Tumors (RECIST). Final clinical outcome was determined by imaging and case note review: patients were divided into (1) disease-free and (2) with relapse and compared using appropriate univariate methods to identify imaging predictors; statistical significance was at 5%. Results: The majority of tumors were {<=}T2 (23/35; 65.7%) and N0 (21/35; 60%), mean size 3.75cm, and hyperintense (++ to +++, 24/35 patients; 68%). Following chemoradiation, there was a size reduction in all cases (mean 73.3%) and a reduction in signal intensity in 26/35 patients (74.2%). The majority of patients were classified as responders (26/35 (74.2%) patients by T and N downstaging; and 30/35 (85.7%) patients by RECIST). At a median follow-up of 33.5 months, 25 patients (71.4%) remained disease-free; 10 patients (28.6%) had locoregional or metastatic disease. Univariate analysis showed that no individual MRI features were predictive of eventual outcome. Conclusion: Early assessment of response by MRI at 6-8 weeks is unhelpful in predicting future clinical outcome.« less

HTR2A A-1438G/T102C polymorphisms predict negative symptoms performance upon aripiprazole treatment in schizophrenic patients.

PubMed

Chen, Shih-Fen; Shen, Yu-Chih; Chen, Chia-Hsiang

2009-08-01

Aripiprazole acts as a partial agonist at dopamine D2 and D3 and serotonin 1A receptors and as an antagonist at serotonin 2A receptors (HTR2A). Since aripiprazole acts as an antagonist at HTR2A, genetic variants of HTR2A may be important in explaining variability in response to aripiprazole. This study investigated whether the efficacy of aripiprazole can be predicted by functional HTR2A A-1438G/T102C polymorphisms (rs63311/rs6313) as modified by clinical factors in Han Chinese hospitalized patients with acutely exacerbated schizophrenia. After hospitalization, the patients (n = 128) were given a 4-week course of aripiprazole. Patients were genotyped for HTR2A A-1438G/T102C polymorphisms via the restriction fragment length polymorphism method. Clinical factors such as gender, age, duration of illness, education level, diagnostic subtype, and medication dosage were noted as well. The researchers measured psychopathology biweekly, using the Positive and Negative Syndrome Scale (PANSS). A mixed model regression approach (SAS Proc MIXED) was used to analyze the effects of genetic and clinical factors on PANSS performance after aripiprazole treatment. We found that the GG/CC genotype group of HTR2A A-1438G/T102C polymorphisms predicts poor aripiprazole response specifically for negative symptoms. In addition, the clinical factors, including dosage of aripiprazole, age, duration of illness, and diagnostic subtype, were found to influence PANSS performance after aripiprazole treatment. The data suggest HTR2A A-1438G/T102C polymorphisms may predict negative symptoms performance upon aripiprazole treatment in schizophrenic patients as modified by clinical factors.

Implementations of clinical functional magnetic resonance imaging using character-based paradigms for the prediction of Chinese language dominance.

PubMed

Liu, Ho-Ling; Wu, Chien-Te; Chen, Jian-Chuan; Hsu, Yuan-Yu; Wai, Yau-Yau; Wan, Yung-Liang

2003-01-01

Recently, functional MRI (fMRI) using word generation (WG) tasks has been shown to be effective for mapping the Chinese language-related brain areas. In clinical applications, however, patients' performance cannot be easily monitored during WG tasks. In this study, we evaluated the feasibility of a word choice (WC) paradigm in the clinical setting and compared the results with those from WG tasks. Intrasubject comparisons of fMRI with both WG and WC paradigms were performed on six normal human subjects and two tumor patients. Subject responses in the WC paradigm, based on semantic judgments, were recorded. Activation strength, extent, and laterality were evaluated and compared. Our results showed that fMRI with the WC paradigm evoked weaker neuronal activation than that with the WG paradigm in Chinese language-related brain areas. It was sufficient to reveal language laterality for clinical use, however. In addition, it resulted in less nonlanguage-specific brain activation. Results from the patient data demonstrated strong evidence for the necessity of incorporating response monitoring during fMRI studies, which suggested that fMRI with the WC paradigm is more appropriate to be implemented for the prediction of Chinese language dominance in clinical environments.

Detecting a Clinically Meaningful Change in Tic Severity in Tourette Syndrome: A Comparison of Three Methods

PubMed Central

Jeon, Sangchoon; Walkup, John T; Woods, Douglas W.; Peterson, Alan; Piacentini, John; Wilhelm, Sabine; Katsovich, Lily; McGuire, Joseph F.; Dziura, James; Scahill, Lawrence

2014-01-01

Objective To compare three statistical strategies for classifying positive treatment response based on a dimensional measure (Yale Global Tic Severity Scale [YGTSS]) and a categorical measure (Clinical Global Impression-Improvement [CGI-I]). Method Subjects (N=232; 69.4% male; ages 9-69 years) with Tourette syndrome or chronic tic disorder participated in one of two 10-week, randomized controlled trials comparing behavioral treatment to supportive therapy. The YGTSS and CGI-I were rated by clinicians blind to treatment assignment. We examined the percent reduction in the YGTSS-Total Tic Score (TTS) against Much Improved or Very Much Improved on the CGI-I, computed a signal detection analysis (SDA) and built a mixture model to classify dimensional response based on the change in the YGTSS-TTS. Results A 25% decrease on the YGTSS-TTS predicted positive response on the CGI-I during the trial. The SDA showed that a 25% reduction in the YGTSS-TTS provided optimal sensitivity (87%) and specificity (84%) for predicting positive response. Using a mixture model without consideration of the CGI-I, the dimensional response was defined by 23% (or greater) reduction on the YGTSS-TTS. The odds ratio (OR) of positive response (OR=5.68, 95% CI=[2.99, 10.78]) on the CGI-I for behavioral intervention was greater than the dimensional response (OR=2.86, 95% CI=[1.65, 4.99]). Conclusion A twenty five percent reduction on the YGTSS-TTS is highly predictive of positive response by all three analytic methods. For trained raters, however, tic severity alone does not drive the classification of positive response. PMID:24001701

Observational study on the efficacy of adalimumab for the treatment of ulcerative colitis and predictors of outcome.

PubMed

García-Bosch, Orlando; Gisbert, Javier P; Cañas-Ventura, Alex; Merino, Olga; Cabriada, José L; García-Sánchez, Valle; Gutiérrez, Ana; Nos, Pilar; Peñalva, Mireia; Hinojosa, Joaquin; García-Planella, Esther; Muñoz, Fernando; Calvet, Xavier; Panés, Julián

2013-10-01

Information on efficacy and predictors of response to adalimumab in ulcerative colitis (UC) clinical practice is limited. Assessment of response to adalimumab and its predictors in an observational cohort study. Retrospective cohort study based on data obtained from ENEIDA registry. All patients diagnosed with UC treated with adalimumab were included. Response to adalimumab was evaluated at weeks 12, 28, and 54 according to the partial Mayo score, and requirement of colectomy until end of follow-up. 48 patients with UC treated with adalimumab were included; 39 (81.3%) had previously received infliximab. Response rates at weeks 12, 28 and 54 were 70.8%, 43.2% and 35% respectively. Response to prior treatment with infliximab was the only predictive factor of response to adalimumab at week 12, which was obtained in 90% of infliximab remitters, 53.8% of responders and 33.3% of primary non-responders (p=0.01). Colectomy was required in 11 patients (22.9%), after a mean time of 205 days. The only clinical independent predictor of colectomy was non-response to adalimumab at week 12: colectomy rates were 5/34 (14.7%) in responders and 6/14 (42.9%) in non-responders (p=0.035), time free of colectomy was significantly reduced in non-responders (p=0.01). Adalimumab withdrawal due to adverse events occurred in 4.2% of patients. This study shows that adalimumab is an effective treatment in patients with UC. If used as a second anti-TNF, previous achievement of remission with the first anti-TNF predicts response, and failure to achieve response at week 12 predicts colectomy. Copyright © 2012 European Crohn's and Colitis Organisation. Published by Elsevier B.V. All rights reserved.

Food allergy animal models: an overview.

PubMed

Helm, Ricki M

2002-05-01

Specific food allergy is characterized by sensitization to innocuous food proteins with production of allergen-specific IgE that binds to receptors on basophils and mast cells. Upon recurrent exposure to the same allergen, an allergic response is induced by mediator release following cross-linking of cell-bound allergen-specific IgE. The determination of what makes an innocuous food protein an allergen in predisposed individuals is unknown; however, mechanistic and protein allergen predictive models are being actively investigated in a number of animal models. Currently, there is no animal model that will actively profile known food allergens, predict the allergic potential of novel food proteins, or demonstrate clinically the human food allergic sensitization/allergic response. Animal models under investigation include mice, rats, the guinea pig, atopic dog, and neonatal swine. These models are being assessed for production of IgE, clinical responses to re-exposure, and a ranking of food allergens (based on potency) including a nonfood allergen protein source. A selection of animal models actively being investigated that will contribute to our understanding of what makes a protein an allergen and future predictive models for assessing the allergenicity of novel proteins is presented in this review.

Response Expectancy and the Placebo Effect.

PubMed

Kirsch, Irving

2018-01-01

In this chapter, I review basic tenets of response expectancy theory (Kirsch, 1985), beginning with the important distinction between response expectancies and stimulus expectancies. Although both can affect experience, the effects of response expectancies are stronger and more resistant to extinction than those of stimulus expectancies. Further, response expectancies are especially important to understanding placebo effects. The response expectancy framework is consistent with and has been amplified by the Bayesian model of predictive coding. Clinical implications of these phenomena are exemplified. © 2018 Elsevier Inc. All rights reserved.

Early prediction of blonanserin response in Japanese patients with schizophrenia.

PubMed

Kishi, Taro; Matsuda, Yuki; Fujita, Kiyoshi; Iwata, Nakao

2014-01-01

Blonanserin is a second-generation antipsychotic used for the treatment of schizophrenia in Japan and Korea. The present study aimed to examine early prediction of blonanserin in patients with schizophrenia. An 8-week, prospective, single-arm, flexible-dose clinical trial of blonanserin in patients with schizophrenia was conducted under real-world conditions. The inclusion criteria were antipsychotic naïve, and first-episode schizophrenia patients or schizophrenia patients with no consumption of any antipsychotic medication for more than 4 weeks before enrollment in this study. The positive predictive value, negative predictive value, sensitivity, specificity, and predictive power were calculated for the response status at week 4 to predict the subsequent response at week 8. Thirty-seven patients were recruited (56.8% of them had first-episode schizophrenia), and 28 (75.7%) completed the trial. At week 8, blonanserin was associated with a significant improvement in the Positive and Negative Syndrome Scale (PANSS) total score (P<0.0001) and in positive (P<0.0001), negative (P<0.0001), and general subscale scores (P<0.0001). In terms of percentage improvement of PANSS total scores from baseline to week 8, 64.9% of patients showed a ≥20% reduction in the PANSS total score and 48.6% showed a ≥30% reduction. However, 8.1% of patients experienced at least one adverse event. Using the 20% reduction in the PANSS total score at week 4 as a definition of an early response, the negative predictive values for later responses (ie, reductions of ≥30 and ≥40 in the PANSS total scores) were 88.9% and 94.1%, respectively. The specificities were 80.0% and 51.6%, respectively. Our results suggest that the blonanserin response at week 4 could predict the later response at week 8.

Early prediction of blonanserin response in Japanese patients with schizophrenia

PubMed Central

Kishi, Taro; Matsuda, Yuki; Fujita, Kiyoshi; Iwata, Nakao

2014-01-01

Background Blonanserin is a second-generation antipsychotic used for the treatment of schizophrenia in Japan and Korea. The present study aimed to examine early prediction of blonanserin in patients with schizophrenia. Methods An 8-week, prospective, single-arm, flexible-dose clinical trial of blonanserin in patients with schizophrenia was conducted under real-world conditions. The inclusion criteria were antipsychotic naïve, and first-episode schizophrenia patients or schizophrenia patients with no consumption of any antipsychotic medication for more than 4 weeks before enrollment in this study. The positive predictive value, negative predictive value, sensitivity, specificity, and predictive power were calculated for the response status at week 4 to predict the subsequent response at week 8. Results Thirty-seven patients were recruited (56.8% of them had first-episode schizophrenia), and 28 (75.7%) completed the trial. At week 8, blonanserin was associated with a significant improvement in the Positive and Negative Syndrome Scale (PANSS) total score (P<0.0001) and in positive (P<0.0001), negative (P<0.0001), and general subscale scores (P<0.0001). In terms of percentage improvement of PANSS total scores from baseline to week 8, 64.9% of patients showed a ≥20% reduction in the PANSS total score and 48.6% showed a ≥30% reduction. However, 8.1% of patients experienced at least one adverse event. Using the 20% reduction in the PANSS total score at week 4 as a definition of an early response, the negative predictive values for later responses (ie, reductions of ≥30 and ≥40 in the PANSS total scores) were 88.9% and 94.1%, respectively. The specificities were 80.0% and 51.6%, respectively. Conclusion Our results suggest that the blonanserin response at week 4 could predict the later response at week 8. PMID:25285009

Prediction of individual response to anticancer therapy: historical and future perspectives.

PubMed

Unger, Florian T; Witte, Irene; David, Kerstin A

2015-02-01

Since the introduction of chemotherapy for cancer treatment in the early 20th century considerable efforts have been made to maximize drug efficiency and at the same time minimize side effects. As there is a great interpatient variability in response to chemotherapy, the development of predictive biomarkers is an ambitious aim for the rapidly growing research area of personalized molecular medicine. The individual prediction of response will improve treatment and thus increase survival and life quality of patients. In the past, cell cultures were used as in vitro models to predict in vivo response to chemotherapy. Several in vitro chemosensitivity assays served as tools to measure miscellaneous endpoints such as DNA damage, apoptosis and cytotoxicity or growth inhibition. Twenty years ago, the development of high-throughput technologies, e.g. cDNA microarrays enabled a more detailed analysis of drug responses. Thousands of genes were screened and expression levels were correlated to drug responses. In addition, mutation analysis became more and more important for the prediction of therapeutic success. Today, as research enters the area of -omics technologies, identification of signaling pathways is a tool to understand molecular mechanism underlying drug resistance. Combining new tissue models, e.g. 3D organoid cultures with modern technologies for biomarker discovery will offer new opportunities to identify new drug targets and in parallel predict individual responses to anticancer therapy. In this review, we present different currently used chemosensitivity assays including 2D and 3D cell culture models and several -omics approaches for the discovery of predictive biomarkers. Furthermore, we discuss the potential of these assays and biomarkers to predict the clinical outcome of individual patients and future perspectives.

The nonlinear, complex sequential organization of behavior in schizophrenic patients: neurocognitive strategies and clinical correlations.

PubMed

Paulus, M P; Perry, W; Braff, D L

1999-09-01

Thought disorder is a hallmark of schizophrenia and can be inferred from disorganized behavior. Measures of the sequential organization of behavior are important because they reflect the cognitive processes of the selection and sequencing of behavioral elements, which generate observable and analyzable behavioral patterns. In this context, sequences of choices generated by schizophrenic patients in a two-choice guessing task fluctuate significantly, which reflects an "oscillating dysregulation" between highly predictable and highly unpredictable subsequences within a single test session. In this study, we aimed to clarify the significance of dysregulation by seeing whether demographic, clinical, neuropsychological, and psychological measures predict the degree of dysregulation observed on this two-choice task. Thirty schizophrenic patients repeatedly performed a LEFT or RIGHT key press that was followed by a stimulus, which occurred randomly on the left or right side of the computer screen. Thus, the stimulus location had nothing to do with the key press behavior. The range of key press sequence predictabilities as measured by the dynamical entropy was used to quantify the dysregulation of response sequences and reflects the range of fixity and randomness of the responses. A factor analysis was performed and step-wise multiple regression analyses were used to relate the factor scores to demographic, clinical, symptomatic, Wisconsin Card Sorting Test (WCST), and Rorschach variables. The LEFT/RIGHT key press sequences were determined by three factors: 1) the degree of win-stay/lose-shift strategy; 2) the degree of contextual influence on the current choice; and 3) the degree of dysregulation on the choice task. Demographic and clinical variables did not predict any of the three response patterns on the choice task. In contrast, the WCST and Rorschach test predicted performance on various factors of choice task response patterns. Schizophrenic patients employ several rules, i.e., "win-stay/lose-shift" and "decide according to the previous choice," that fluctuate significantly when generating sequences on this task, confirming that a basic behavioral dysregulation occurs in a single schizophrenic subject across a single test session. The organization or the "temporal architecture" of the behavioral sequences is not related to symptoms per se, but is related to deficits in executive functioning, problem solving, and perceptual organizational abilities.

Oxygen uptake on-kinetics during six-minute walk test predicts short-term outcomes after off-pump coronary artery bypass surgery.

PubMed

Rocco, Isadora Salvador; Viceconte, Marcela; Pauletti, Hayanne Osiro; Matos-Garcia, Bruna Caroline; Marcondi, Natasha Oliveira; Bublitz, Caroline; Bolzan, Douglas William; Moreira, Rita Simone Lopes; Reis, Michel Silva; Hossne, Nelson Américo; Gomes, Walter José; Arena, Ross; Guizilini, Solange

2017-12-26

We aimed to investigate the ability of oxygen uptake kinetics to predict short-term outcomes after off-pump coronary artery bypass grafting. Fifty-two patients aged 60.9 ± 7.8 years waiting for off-pump coronary artery bypass surgery were evaluated. The 6-min walk test distance was performed pre-operatively, while simultaneously using a portable cardiopulmonary testing device. The transition of oxygen uptake kinetics from rest to exercise was recorded to calculate oxygen uptake kinetics fitting a monoexponential regression model. Oxygen uptake at steady state, constant time, and mean response time corrected by work rate were analysed. Short-term clinical outcomes were evaluated during the early post-operative of off-pump coronary artery bypass surgery. Multivariate analysis showed body mass index, surgery time, and mean response time corrected by work rate as independent predictors for short-term outcomes. The optimal mean response time corrected by work rate cut-off to estimate short-term clinical outcomes was 1.51 × 10 -3  min 2 /ml. Patients with slower mean response time corrected by work rate demonstrated higher rates of hypertension, diabetes, EuroSCOREII, left ventricular dysfunction, and impaired 6-min walk test parameters. The per cent-predicted distance threshold of 66% in the pre-operative was associated with delayed oxygen uptake kinetics. Pre-operative oxygen uptake kinetics during 6-min walk test predicts short-term clinical outcomes after off-pump coronary artery bypass surgery. From a clinically applicable perspective, a threshold of 66% of pre-operative predicted 6-min walk test distance indicated slower kinetics, which leads to longer intensive care unit and post-surgery hospital length of stay. Implications for rehabilitation Coronary artery bypass grafting is a treatment aimed to improve expectancy of life and prevent disability due to the disease progression; The use of pre-operative submaximal functional capacity test enabled the identification of patients with high risk of complications, where patients with delayed oxygen uptake kinetics exhibited worse short-term outcomes; Our findings suggest the importance of the rehabilitation in the pre-operative in order to "pre-habilitate" the patients to the surgical procedure; Faster oxygen uptake on-kinetics could be achieved by improving the oxidative capacity of muscles and cardiovascular conditioning through rehabilitation, adding better results following cardiac surgery.

A quick behavioral dichotic word test is prognostic for clinical response to cognitive therapy for depression: A replication study.

PubMed

Bruder, Gerard E; Haggerty, Agnes; Siegle, Greg J

2017-02-01

There are no commonly used clinical indicators of whether an individual will benefit from cognitive therapy (CT) for depression. A prior study found right ear (left hemisphere) advantage for perceiving dichotic words predicted CT response. This study replicates this finding at a different research center in clinical trials that included clinically representative samples and community therapists. Right-handed individuals with unipolar major depressive disorder who subsequently received 12-14 weeks of CT at the University of Pittsburgh were tested on dichotic fused words and complex tones tests. Responders to CT showed twice the mean right ear advantage in dichotic fused words performance than non-responders. Patients with a right ear advantage greater than the mean for healthy controls had an 81% response rate to CT, whereas those with performance lower than the mean for controls had a 46% response rate. Individuals with a right ear advantage, indicative of strong left hemisphere language dominance, may be better at utilizing cognitive processes and left frontotemporal cortical regions critical for success of CT for depression. Findings at two clinical research centers suggest that verbal dichotic listening may be a clinically disseminative brief, inexpensive and easily automated test prognostic for response to CT across diverse clinical settings. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Predicting clinical outcomes in chordoma patients receiving immunotherapy: a comparison between volumetric segmentation and RECIST.

PubMed

Fenerty, Kathleen E; Folio, Les R; Patronas, Nicholas J; Marté, Jennifer L; Gulley, James L; Heery, Christopher R

2016-08-23

The Response Evaluation Criteria in Solid Tumors (RECIST) are the current standard for evaluating disease progression or therapy response in patients with solid tumors. RECIST 1.1 calls for axial, longest-diameter (or perpendicular short axis of lymph nodes) measurements of a maximum of five tumors, which limits clinicians' ability to adequately measure disease burden, especially in patients with irregularly shaped tumors. This is especially problematic in chordoma, a disease for which RECIST does not always adequately capture disease burden because chordoma tumors are typically irregularly shaped and slow-growing. Furthermore, primary chordoma tumors tend to be adjacent to vital structures in the skull or sacrum that, when compressed, lead to significant clinical consequences. Volumetric segmentation is a newer technology that allows tumor burden to be measured in three dimensions on either MR or CT. Here, we compared the ability of RECIST measurements and tumor volumes to predict clinical outcomes in a cohort of 21 chordoma patients receiving immunotherapy. There was a significant difference in radiologic time to progression Kaplan-Meier curves between clinical outcome groups using volumetric segmentation (P = 0.012) but not RECIST (P = 0.38). In several cases, changes in volume were earlier and more sensitive reflections of clinical status. RECIST is a useful evaluation method when obvious changes are occurring in patients with chordoma. However, in many cases, RECIST does not detect small changes, and volumetric assessment was capable of detecting changes and predicting clinical outcome earlier than RECIST. Although this study was small and retrospective, we believe our results warrant further research in this area.

Radiomics in radiooncology - Challenging the medical physicist.

PubMed

Peeken, Jan C; Bernhofer, Michael; Wiestler, Benedikt; Goldberg, Tatyana; Cremers, Daniel; Rost, Burkhard; Wilkens, Jan J; Combs, Stephanie E; Nüsslin, Fridtjof

2018-04-01

Noticing the fast growing translation of artificial intelligence (AI) technologies to medical image analysis this paper emphasizes the future role of the medical physicist in this evolving field. Specific challenges are addressed when implementing big data concepts with high-throughput image data processing like radiomics and machine learning in a radiooncology environment to support clinical decisions. Based on the experience of our interdisciplinary radiomics working group, techniques for processing minable data, extracting radiomics features and associating this information with clinical, physical and biological data for the development of prediction models are described. A special emphasis was placed on the potential clinical significance of such an approach. Clinical studies demonstrate the role of radiomics analysis as an additional independent source of information with the potential to influence the radiooncology practice, i.e. to predict patient prognosis, treatment response and underlying genetic changes. Extending the radiomics approach to integrate imaging, clinical, genetic and dosimetric data ('panomics') challenges the medical physicist as member of the radiooncology team. The new field of big data processing in radiooncology offers opportunities to support clinical decisions, to improve predicting treatment outcome and to stimulate fundamental research on radiation response both of tumor and normal tissue. The integration of physical data (e.g. treatment planning, dosimetric, image guidance data) demands an involvement of the medical physicist in the radiomics approach of radiooncology. To cope with this challenge national and international organizations for medical physics should organize more training opportunities in artificial intelligence technologies in radiooncology. Copyright © 2018 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Population pharmacodynamic modelling of midazolam induced sedation in terminally ill adult patients

PubMed Central

de Winter, Brenda C. M.; Masman, Anniek D.; van Dijk, Monique; Baar, Frans P. M.; Tibboel, Dick; Koch, Birgit C. P.; van Gelder, Teun; Mathot, Ron A. A.

2017-01-01

Aims Midazolam is the drug of choice for palliative sedation and is titrated to achieve the desired level of sedation. A previous pharmacokinetic (PK) study showed that variability between patients could be partly explained by renal function and inflammatory status. The goal of this study was to combine this PK information with pharmacodynamic (PD) data, to evaluate the variability in response to midazolam and to find clinically relevant covariates that may predict PD response. Method A population PD analysis using nonlinear mixed effect models was performed with data from 43 terminally ill patients. PK profiles were predicted by a previously described PK model and depth of sedation was measured using the Ramsay sedation score. Patient and disease characteristics were evaluated as possible covariates. The final model was evaluated using a visual predictive check. Results The effect of midazolam on the sedation level was best described by a differential odds model including a baseline probability, Emax model and interindividual variability on the overall effect. The EC50 value was 68.7 μg l–1 for a Ramsay score of 3–5 and 117.1 μg l–1 for a Ramsay score of 6. Comedication with haloperidol was the only significant covariate. The visual predictive check of the final model showed good model predictability. Conclusion We were able to describe the clinical response to midazolam accurately. As expected, there was large variability in response to midazolam. The use of haloperidol was associated with a lower probability of sedation. This may be a result of confounding by indication, as haloperidol was used to treat delirium, and deliria has been linked to a more difficult sedation procedure. PMID:28960387

A TCP model for external beam treatment of intermediate-risk prostate cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Walsh, Sean; Putten, Wil van der

2013-03-15

Purpose: Biological models offer the ability to predict clinical outcomes. The authors describe a model to predict the clinical response of intermediate-risk prostate cancer to external beam radiotherapy for a variety of fractionation regimes. Methods: A fully heterogeneous population averaged tumor control probability model was fit to clinical outcome data for hyper, standard, and hypofractionated treatments. The tumor control probability model was then employed to predict the clinical outcome of extreme hypofractionation regimes, as utilized in stereotactic body radiotherapy. Results: The tumor control probability model achieves an excellent level of fit, R{sup 2} value of 0.93 and a root meanmore » squared error of 1.31%, to the clinical outcome data for hyper, standard, and hypofractionated treatments using realistic values for biological input parameters. Residuals Less-Than-Or-Slanted-Equal-To 1.0% are produced by the tumor control probability model when compared to clinical outcome data for stereotactic body radiotherapy. Conclusions: The authors conclude that this tumor control probability model, used with the optimized radiosensitivity values obtained from the fit, is an appropriate mechanistic model for the analysis and evaluation of external beam RT plans with regard to tumor control for these clinical conditions.« less

Evaluating Candidate Principal Surrogate Endpoints

PubMed Central

Gilbert, Peter B.; Hudgens, Michael G.

2009-01-01

Summary Frangakis and Rubin (2002, Biometrics 58, 21–29) proposed a new definition of a surrogate endpoint (a “principal” surrogate) based on causal effects. We introduce an estimand for evaluating a principal surrogate, the causal effect predictiveness (CEP) surface, which quantifies how well causal treatment effects on the biomarker predict causal treatment effects on the clinical endpoint. Although the CEP surface is not identifiable due to missing potential outcomes, it can be identified by incorporating a baseline covariate(s) that predicts the biomarker. Given case–cohort sampling of such a baseline predictor and the biomarker in a large blinded randomized clinical trial, we develop an estimated likelihood method for estimating the CEP surface. This estimation assesses the “surrogate value” of the biomarker for reliably predicting clinical treatment effects for the same or similar setting as the trial. A CEP surface plot provides a way to compare the surrogate value of multiple biomarkers. The approach is illustrated by the problem of assessing an immune response to a vaccine as a surrogate endpoint for infection. PMID:18363776

Incorporating drug delivery into an imaging-driven, mechanics-coupled reaction diffusion model for predicting the response of breast cancer to neoadjuvant chemotherapy: theory and preliminary clinical results

NASA Astrophysics Data System (ADS)

Jarrett, Angela M.; Hormuth, David A.; Barnes, Stephanie L.; Feng, Xinzeng; Huang, Wei; Yankeelov, Thomas E.

2018-05-01

Clinical methods for assessing tumor response to therapy are largely rudimentary, monitoring only temporal changes in tumor size. Our goal is to predict the response of breast tumors to therapy using a mathematical model that utilizes magnetic resonance imaging (MRI) data obtained non-invasively from individual patients. We extended a previously established, mechanically coupled, reaction-diffusion model for predicting tumor response initialized with patient-specific diffusion weighted MRI (DW-MRI) data by including the effects of chemotherapy drug delivery, which is estimated using dynamic contrast-enhanced (DCE-) MRI data. The extended, drug incorporated, model is initialized using patient-specific DW-MRI and DCE-MRI data. Data sets from five breast cancer patients were used—obtained before, after one cycle, and at mid-point of neoadjuvant chemotherapy. The DCE-MRI data was used to estimate spatiotemporal variations in tumor perfusion with the extended Kety–Tofts model. The physiological parameters derived from DCE-MRI were used to model changes in delivery of therapy drugs within the tumor for incorporation in the extended model. We simulated the original model and the extended model in both 2D and 3D and compare the results for this five-patient cohort. Preliminary results show reductions in the error of model predicted tumor cellularity and size compared to the experimentally-measured results for the third MRI scan when therapy was incorporated. Comparing the two models for agreement between the predicted total cellularity and the calculated total cellularity (from the DW-MRI data) reveals an increased concordance correlation coefficient from 0.81 to 0.98 for the 2D analysis and 0.85 to 0.99 for the 3D analysis (p  <  0.01 for each) when the extended model was used in place of the original model. This study demonstrates the plausibility of using DCE-MRI data as a means to estimate drug delivery on a patient-specific basis in predictive models and represents a step toward the goal of achieving individualized prediction of tumor response to therapy.

Intolerance of uncertainty and transdiagnostic group cognitive behavioral therapy for anxiety.

PubMed

Talkovsky, Alexander M; Norton, Peter J

2016-06-01

Recent evidence suggests intolerance of uncertainty (IU) is a transdiagnostic variable elevated across anxiety disorders. No studies have investigated IU's response to transdiagnostic group CBT for anxiety (TGCBT). This study evaluated IU outcomes following TGCBT across anxiety disorders. 151 treatment-seekers with primary diagnoses of social anxiety disorder, panic disorder, or GAD were evaluated before and after 12 weeks of TGCBT and completed self-report questionnaires at pre-, mid-, and post-treatment. IU decreased significantly following treatment. Decreases in IU predicted improvements in clinical presentation across diagnoses. IU interacted with time to predict improvement in clinical presentation irrespective of primary diagnosis. IU also interacted with time to predict improvement in clinical presentation although interactions of time with diagnosis-specific measures did not. IUS interacted with time to predict reduction in anxiety and fear symptoms, and inhibitory IU interacted with time to predicted reductions in anxiety symptoms but prospective IU did not. IU appears to be an important transdiagnostic variable in CBT implicated in both initial presentation and treatment change. Further implications are discussed. Published by Elsevier Ltd.

Scintigraphic scoring system for grading severity of gastro-esophageal reflux on 99mTc sulfur colloid gastro-esophageal reflux scintigraphy: A prospective study of 39 cases with pre and post treatment assessment.

PubMed

Puranik, Ameya D; Nair, Gopinathan; Aggarwal, Rajiv; Bandyopadhyay, Abhijit; Shinto, Ajit; Zade, Anand

2013-04-01

The study aimed at developing a scoring system for scintigraphic grading of gastro-esophageal reflux (GER), on gastro-esophageal reflux scintigraphy (GERS) and comparison of clinical and scintigraphic scores, pre- and post-treatment. A total of 39 cases with clinically symptomatic GER underwent 99mTc sulfur colloid GERS; scores were assigned based on the clinical and scintigraphic parameters. Post domperidone GERS was performed after completion of treatment. Follow up GERS was performed and clinical and scintigraphic parameters were compared with baseline parameters. Paired t-test on pre and post domperidone treatment clinical scores showed that the decline in post-treatment scores was highly significant, with P value < 0.001. The scintigraphic scoring system had a sensitivity of 93.9% in assessing treatment response to domperidone, specificity of 83.3% i.e., 83.3% of children with no decline in scintigraphic scores show no clinical response to Domperidone. The scintigraphic scoring system had a positive predictive value of 96.9% and a negative predictive value of 71.4%. GERS with its quantitative parameters is a good investigation for assessing the severity of reflux and also for following children post-treatment.

An Integrative Model of Physiological Traits Can be Used to Predict Obstructive Sleep Apnea and Response to Non Positive Airway Pressure Therapy.

PubMed

Owens, Robert L; Edwards, Bradley A; Eckert, Danny J; Jordan, Amy S; Sands, Scott A; Malhotra, Atul; White, David P; Loring, Stephen H; Butler, James P; Wellman, Andrew

2015-06-01

Both anatomical and nonanatomical traits are important in obstructive sleep apnea (OSA) pathogenesis. We have previously described a model combining these traits, but have not determined its diagnostic accuracy to predict OSA. A valid model, and knowledge of the published effect sizes of trait manipulation, would also allow us to predict the number of patients with OSA who might be effectively treated without using positive airway pressure (PAP). Fifty-seven subjects with and without OSA underwent standard clinical and research sleep studies to measure OSA severity and the physiological traits important for OSA pathogenesis, respectively. The traits were incorporated into a physiological model to predict OSA. The model validity was determined by comparing the model prediction of OSA to the clinical diagnosis of OSA. The effect of various trait manipulations was then simulated to predict the proportion of patients treated by each intervention. The model had good sensitivity (80%) and specificity (100%) for predicting OSA. A single intervention on one trait would be predicted to treat OSA in approximately one quarter of all patients. Combination therapy with two interventions was predicted to treat OSA in ∼50% of patients. An integrative model of physiological traits can be used to predict population-wide and individual responses to non-PAP therapy. Many patients with OSA would be expected to be treated based on known trait manipulations, making a strong case for the importance of non-anatomical traits in OSA pathogenesis and the effectiveness of non-PAP therapies. © 2015 Associated Professional Sleep Societies, LLC.

Appropriate clinical use of human leukocyte antigen typing for coeliac disease: an Australasian perspective

PubMed Central

Tye-Din, J A; Cameron, D J S; Daveson, A J; Day, A S; Dellsperger, P; Hogan, C; Newnham, E D; Shepherd, S J; Steele, R H; Wienholt, L; Varney, M D

2015-01-01

The past decade has seen human leukocyte antigen (HLA) typing emerge as a remarkably popular test for the diagnostic work-up of coeliac disease with high patient acceptance. Although limited in its positive predictive value for coeliac disease, the strong disease association with specific HLA genes imparts exceptional negative predictive value to HLA typing, enabling a negative result to exclude coeliac disease confidently. In response to mounting evidence that the clinical use and interpretation of HLA typing often deviates from best practice, this article outlines an evidence-based approach to guide clinically appropriate use of HLA typing, and establishes a reporting template for pathology providers to improve communication of results. PMID:25827511

Photosensitizer fluorescence and singlet oxygen luminescence as dosimetric predictors of topical 5-aminolevulinic acid photodynamic therapy induced clinical erythema.

PubMed

Mallidi, Srivalleesha; Anbil, Sriram; Lee, Seonkyung; Manstein, Dieter; Elrington, Stefan; Kositratna, Garuna; Schoenfeld, David; Pogue, Brian; Davis, Steven J; Hasan, Tayyaba

2014-02-01

The need for patient-specific photodynamic therapy (PDT) in dermatologic and oncologic applications has triggered several studies that explore the utility of surrogate parameters as predictive reporters of treatment outcome. Although photosensitizer (PS) fluorescence, a widely used parameter, can be viewed as emission from several fluorescent states of the PS (e.g., minimally aggregated and monomeric), we suggest that singlet oxygen luminescence (SOL) indicates only the active PS component responsible for the PDT. Here, the ability of discrete PS fluorescence-based metrics (absolute and percent PS photobleaching and PS re-accumulation post-PDT) to predict the clinical phototoxic response (erythema) resulting from 5-aminolevulinic acid PDT was compared with discrete SOL (DSOL)-based metrics (DSOL counts pre-PDT and change in DSOL counts pre/post-PDT) in healthy human skin. Receiver operating characteristic curve (ROC) analyses demonstrated that absolute fluorescence photobleaching metric (AFPM) exhibited the highest area under the curve (AUC) of all tested parameters, including DSOL based metrics. The combination of dose-metrics did not yield better AUC than AFPM alone. Although sophisticated real-time SOL measurements may improve the clinical utility of SOL-based dosimetry, discrete PS fluorescence-based metrics are easy to implement, and our results suggest that AFPM may sufficiently predict the PDT outcomes and identify treatment nonresponders with high specificity in clinical contexts.

Comprehensive analysis of MGMT promoter methylation: correlation with MGMT expression and clinical response in GBM.

PubMed

Shah, Nameeta; Lin, Biaoyang; Sibenaller, Zita; Ryken, Timothy; Lee, Hwahyung; Yoon, Jae-Geun; Rostad, Steven; Foltz, Greg

2011-01-07

O⁶-methylguanine DNA-methyltransferase (MGMT) promoter methylation has been identified as a potential prognostic marker for glioblastoma patients. The relationship between the exact site of promoter methylation and its effect on gene silencing, and the patient's subsequent response to therapy, is still being defined. The aim of this study was to comprehensively characterize cytosine-guanine (CpG) dinucleotide methylation across the entire MGMT promoter and to correlate individual CpG site methylation patterns to mRNA expression, protein expression, and progression-free survival. To best identify the specific MGMT promoter region most predictive of gene silencing and response to therapy, we determined the methylation status of all 97 CpG sites in the MGMT promoter in tumor samples from 70 GBM patients using quantitative bisulfite sequencing. We next identified the CpG site specific and regional methylation patterns most predictive of gene silencing and improved progression-free survival. Using this data, we propose a new classification scheme utilizing methylation data from across the entire promoter and show that an analysis based on this approach, which we call 3R classification, is predictive of progression-free survival (HR  = 5.23, 95% CI [2.089-13.097], p<0.0001). To adapt this approach to the clinical setting, we used a methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA) test based on the 3R classification and show that this test is both feasible in the clinical setting and predictive of progression free survival (HR  = 3.076, 95% CI [1.301-7.27], p = 0.007). We discuss the potential advantages of a test based on this promoter-wide analysis and compare it to the commonly used methylation-specific PCR test. Further prospective validation of these two methods in a large independent patient cohort will be needed to confirm the added value of promoter wide analysis of MGMT methylation in the clinical setting.

Comprehensive Analysis of MGMT Promoter Methylation: Correlation with MGMT Expression and Clinical Response in GBM

PubMed Central

Shah, Nameeta; Lin, Biaoyang; Sibenaller, Zita; Ryken, Timothy; Lee, Hwahyung; Yoon, Jae-Geun; Rostad, Steven; Foltz, Greg

2011-01-01

O6-methylguanine DNA-methyltransferase (MGMT) promoter methylation has been identified as a potential prognostic marker for glioblastoma patients. The relationship between the exact site of promoter methylation and its effect on gene silencing, and the patient's subsequent response to therapy, is still being defined. The aim of this study was to comprehensively characterize cytosine-guanine (CpG) dinucleotide methylation across the entire MGMT promoter and to correlate individual CpG site methylation patterns to mRNA expression, protein expression, and progression-free survival. To best identify the specific MGMT promoter region most predictive of gene silencing and response to therapy, we determined the methylation status of all 97 CpG sites in the MGMT promoter in tumor samples from 70 GBM patients using quantitative bisulfite sequencing. We next identified the CpG site specific and regional methylation patterns most predictive of gene silencing and improved progression-free survival. Using this data, we propose a new classification scheme utilizing methylation data from across the entire promoter and show that an analysis based on this approach, which we call 3R classification, is predictive of progression-free survival (HR  = 5.23, 95% CI [2.089–13.097], p<0.0001). To adapt this approach to the clinical setting, we used a methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA) test based on the 3R classification and show that this test is both feasible in the clinical setting and predictive of progression free survival (HR  = 3.076, 95% CI [1.301–7.27], p = 0.007). We discuss the potential advantages of a test based on this promoter-wide analysis and compare it to the commonly used methylation-specific PCR test. Further prospective validation of these two methods in a large independent patient cohort will be needed to confirm the added value of promoter wide analysis of MGMT methylation in the clinical setting. PMID:21249131

Chemosensitivity and Endocrine Sensitivity in Clinical Luminal Breast Cancer Patients in the Prospective Neoadjuvant Breast Registry Symphony Trial (NBRST) Predicted by Molecular Subtyping.

PubMed

Whitworth, Pat; Beitsch, Peter; Mislowsky, Angela; Pellicane, James V; Nash, Charles; Murray, Mary; Lee, Laura A; Dul, Carrie L; Rotkis, Michael; Baron, Paul; Stork-Sloots, Lisette; de Snoo, Femke A; Beatty, Jennifer

2017-03-01

Hormone receptor-positive (HR+) tumors have heterogeneous biology and present a challenge for determining optimal treatment. In the Neoadjuvant Breast Registry Symphony Trial (NBRST) patients were classified according to MammaPrint/BluePrint subtyping to provide insight into the response to neoadjuvant endocrine therapy (NET) or neoadjuvant chemotherapy (NCT). The purpose of this predefined substudy was to compare MammaPrint/BluePrint with conventional 'clinical' immunohistochemistry/fluorescence in situ hybridization (IHC/FISH) subtyping in 'clinical luminal' [HR+/human epidermal growth factor receptor 2-negative (HER2-)] breast cancer patients to predict treatment sensitivity. NBRST IHC/FISH HR+/HER2- breast cancer patients (n = 474) were classified into four molecular subgroups by MammaPrint/BluePrint subtyping: Luminal A, Luminal B, HER2, and Basal type. Pathological complete response (pCR) rates were compared with conventional IHC/FISH subtype. The overall pCR rate for 'clinical luminal' patients to NCT was 11 %; however, 87 of these 474 patients were reclassified as Basal type by BluePrint, with a high pCR rate of 32 %. The MammaPrint index was highly associated with the likelihood of pCR (p < 0.001). Fifty-three patients with BluePrint Luminal tumors received NET with an aromatase inhibitor and 36 (68 %) had a clinical response. With BluePrint subtyping, 18 % of clinical 'luminal' patients are classified in a different subgroup, compared with conventional assessment, and these patients have a significantly higher response rate to NCT compared with BluePrint Luminal patients. MammaPrint/BluePrint subtyping can help allocate effective treatment to appropriate patients. In addition, accurate identification of subtype biology is important in the interpretation of neoadjuvant treatment response since lack of pCR in luminal patients does not portend the worse prognosis associated with residual disease in Basal and HER2 subtypes.

Clinical course, costs and predictive factors for response to treatment in carpal tunnel syndrome: the PALMS study protocol.

PubMed

Jerosch-Herold, Christina; Shepstone, Lee; Wilson, Edward C F; Dyer, Tony; Blake, Julian

2014-02-07

Carpal tunnel syndrome (CTS) is the most common neuropathy of the upper limb and a significant contributor to hand functional impairment and disability. Effective treatment options include conservative and surgical interventions, however it is not possible at present to predict the outcome of treatment. The primary aim of this study is to identify which baseline clinical factors predict a good outcome from conservative treatment (by injection) or surgery in patients diagnosed with carpal tunnel syndrome. Secondary aims are to describe the clinical course and progression of CTS, and to describe and predict the UK cost of CTS to the individual, National Health Service (NHS) and society over a two year period. In this prospective observational cohort study patients presenting with clinical signs and symptoms typical of CTS and in whom the diagnosis is confirmed by nerve conduction studies are invited to participate. Data on putative predictive factors are collected at baseline and follow-up through patient questionnaires and include standardised measures of symptom severity, hand function, psychological and physical health, comorbidity and quality of life. Resource use and cost over the 2 year period such as prescribed medications, NHS and private healthcare contacts are also collected through patient self-report at 6, 12, 18 and 24 months. The primary outcome used to classify treatment success or failures will be a 5-point global assessment of change. Secondary outcomes include changes in clinical symptoms, functioning, psychological health, quality of life and resource use. A multivariable model of factors which predict outcome and cost will be developed. This prospective cohort study will provide important data on the clinical course and UK costs of CTS over a two-year period and begin to identify predictive factors for treatment success from conservative and surgical interventions.

Race, Genetic Ancestry and Response to Antidepressant Treatment for Major Depression

PubMed Central

Murphy, Eleanor; Hou, Liping; Maher, Brion S; Woldehawariat, Girma; Kassem, Layla; Akula, Nirmala; Laje, Gonzalo; McMahon, Francis J

2013-01-01

The Sequenced Treatment Alternatives to Relieve Depression (STAR*D) Study revealed poorer antidepressant treatment response among black compared with white participants. This racial disparity persisted even after socioeconomic and baseline clinical factors were taken into account. Some studies have suggested genetic contributions to this disparity, but none have attempted to disentangle race and genetic ancestry. Here we used genome-wide single-nucleotide polymorphism (SNP) data to examine independent contributions of race and genetic ancestry to citalopram response. Secondary data analyses included 1877 STAR*D participants who completed an average of 10 weeks of citalopram treatment and provided DNA samples. Participants reported their race as White (n=1464), black (n=299) or other/mixed (n=114). Genetic ancestry was estimated by multidimensional scaling (MDS) analyses of about 500 000 SNPs. Ancestry proportions were estimated by STRUCTURE. Structural equation modeling was used to examine the direct and indirect effects of observed and latent predictors of response, defined as change in the Quick Inventory of Depressive Symptomatology (QIDS) score from baseline to exit. Socioeconomic and baseline clinical factors, race, and anxiety significantly predicted response, as previously reported. However, direct effects of race disappeared in all models that included genetic ancestry. Genetic African ancestry predicted lower treatment response in all models. Although socioeconomic and baseline clinical factors drive racial differences in antidepressant response, genetic ancestry, rather than self-reported race, explains a significant fraction of the residual differences. Larger samples would be needed to identify the specific genetic mechanisms that may be involved, but these findings underscore the importance of including more African-American patients in drug trials. PMID:23827886

Clinical presentation and outcome prediction of clinical, serological, and histopathological classification schemes in ANCA-associated vasculitis with renal involvement.

PubMed

Córdova-Sánchez, Bertha M; Mejía-Vilet, Juan M; Morales-Buenrostro, Luis E; Loyola-Rodríguez, Georgina; Uribe-Uribe, Norma O; Correa-Rotter, Ricardo

2016-07-01

Several classification schemes have been developed for anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV), with actual debate focusing on their clinical and prognostic performance. Sixty-two patients with renal biopsy-proven AAV from a single center in Mexico City diagnosed between 2004 and 2013 were analyzed and classified under clinical (granulomatosis with polyangiitis [GPA], microscopic polyangiitis [MPA], renal limited vasculitis [RLV]), serological (proteinase 3 anti-neutrophil cytoplasmic antibodies [PR3-ANCA], myeloperoxidase anti-neutrophil cytoplasmic antibodies [MPO-ANCA], ANCA negative), and histopathological (focal, crescenteric, mixed-type, sclerosing) categories. Clinical presentation parameters were compared at baseline between classification groups, and the predictive value of different classification categories for disease and renal remission, relapse, renal, and patient survival was analyzed. Serological classification predicted relapse rate (PR3-ANCA hazard ratio for relapse 2.93, 1.20-7.17, p = 0.019). There were no differences in disease or renal remission, renal, or patient survival between clinical and serological categories. Histopathological classification predicted response to therapy, with a poorer renal remission rate for sclerosing group and those with less than 25 % normal glomeruli; in addition, it adequately delimited 24-month glomerular filtration rate (eGFR) evolution, but it did not predict renal nor patient survival. On multivariate models, renal replacement therapy (RRT) requirement (HR 8.07, CI 1.75-37.4, p = 0.008) and proteinuria (HR 1.49, CI 1.03-2.14, p = 0.034) at presentation predicted renal survival, while age (HR 1.10, CI 1.01-1.21, p = 0.041) and infective events during the induction phase (HR 4.72, 1.01-22.1, p = 0.049) negatively influenced patient survival. At present, ANCA-based serological classification may predict AAV relapses, but neither clinical nor serological categories predict renal or patient survival. Age, renal function and proteinuria at presentation, histopathology, and infectious complications constitute the main outcome predictors and should be considered for individualized management.

Computational biomechanics of bone's responses to dental prostheses - osseointegration, remodeling and resorption

NASA Astrophysics Data System (ADS)

Li, Wei; Rungsiyakull, Chaiy; Field, Clarice; Lin, Daniel; Zhang, Leo; Li, Qing; Swain, Michael

2010-06-01

Clinical and experimental studies showed that human bone has the ability to remodel itself to better adapt to its biomechanical environment by changing both its material properties and geometry. As a consequence of the rapid development and extensive applications of major dental restorations such as implantation and fixed partial denture (FPD), the effect of bone remodeling on the success of a dental restorative surgery is becoming critical for prosthetic design and pre-surgical assessment. This paper aims to provide a computational biomechanics framework to address dental bone's responses as a result of dental restoration. It explored three important issues of resorption, apposition and osseointegration in terms of remodeling simulation. The published remodeling data in long bones were regulated to drive the computational remodeling prediction for the dental bones by correlating the results to clinical data. It is anticipated that the study will provide a more predictive model of dental bone response and help develop a new design methodology for patient-specific dental prosthetic restoration.

Gut Microbiome Function Predicts Response to Anti-integrin Biologic Therapy in Inflammatory Bowel Diseases.

PubMed

Ananthakrishnan, Ashwin N; Luo, Chengwei; Yajnik, Vijay; Khalili, Hamed; Garber, John J; Stevens, Betsy W; Cleland, Thomas; Xavier, Ramnik J

2017-05-10

The gut microbiome plays a central role in inflammatory bowel diseases (IBDs) pathogenesis and propagation. To determine whether the gut microbiome may predict responses to IBD therapy, we conducted a prospective study with Crohn's disease (CD) or ulcerative colitis (UC) patients initiating anti-integrin therapy (vedolizumab). Disease activity and stool metagenomes at baseline, and weeks 14, 30, and 54 after therapy initiation were assessed. Community α-diversity was significantly higher, and Roseburia inulinivorans and a Burkholderiales species were more abundant at baseline among CD patients achieving week 14 remission. Several significant associations were identified with microbial function; 13 pathways including branched chain amino acid synthesis were significantly enriched in baseline samples from CD patients achieving remission. A neural network algorithm, vedoNet, incorporating microbiome and clinical data, provided highest classifying power for clinical remission. We hypothesize that the trajectory of early microbiome changes may be a marker of response to IBD treatment. Copyright © 2017 Elsevier Inc. All rights reserved.

Measuring and predicting prostate cancer related quality of life changes using EPIC for clinical practice.

PubMed

Chipman, Jonathan J; Sanda, Martin G; Dunn, Rodney L; Wei, John T; Litwin, Mark S; Crociani, Catrina M; Regan, Meredith M; Chang, Peter

2014-03-01

We expanded the clinical usefulness of EPIC-CP (Expanded Prostate Cancer Index Composite for Clinical Practice) by evaluating its responsiveness to health related quality of life changes, defining the minimally important differences for an individual patient change in each domain and applying it to a sexual outcome prediction model. In 1,201 subjects from a previously described multicenter longitudinal cohort we modeled the EPIC-CP domain scores of each treatment group before treatment, and at short-term and long-term followup. We considered a posttreatment domain score change from pretreatment of 0.5 SD or greater clinically significant and p ≤ 0.01 statistically significant. We determined the domain minimally important differences using the pooled 0.5 SD of the 2, 6, 12 and 24-month posttreatment changes from pretreatment values. We then recalibrated an EPIC-CP based nomogram model predicting 2-year post-prostatectomy functional erection from that developed using EPIC-26. For each health related quality of life domain EPIC-CP was sensitive to similar posttreatment health related quality of life changes with time, as was observed using EPIC-26. The EPIC-CP minimally important differences in changes in the urinary incontinence, urinary irritation/obstruction, bowel, sexual and vitality/hormonal domains were 1.0, 1.3, 1.2, 1.6 and 1.0, respectively. The EPIC-CP based sexual prediction model performed well (AUC 0.76). It showed robust agreement with its EPIC-26 based counterpart with 10% or less predicted probability differences between models in 95% of individuals and a mean ± SD difference of 0.0 ± 0.05 across all individuals. EPIC-CP is responsive to health related quality of life changes during convalescence and it can be used to predict 2-year post-prostatectomy sexual outcomes. It can facilitate shared medical decision making and patient centered care. Copyright © 2014 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Exposure–response model for sibutramine and placebo: suggestion for application to long-term weight-control drug development

PubMed Central

Han, Seunghoon; Jeon, Sangil; Hong, Taegon; Lee, Jongtae; Bae, Soo Hyeon; Park, Wan-su; Park, Gab-jin; Youn, Sunil; Jang, Doo Yeon; Kim, Kyung-Soo; Yim, Dong-Seok

2015-01-01

No wholly successful weight-control drugs have been developed to date, despite the tremendous demand. We present an exposure–response model of sibutramine mesylate that can be applied during clinical development of other weight-control drugs. Additionally, we provide a model-based evaluation of sibutramine efficacy. Data from a double-blind, randomized, placebo-controlled, multicenter study were used (N=120). Subjects in the treatment arm were initially given 8.37 mg sibutramine base daily, and those who lost <2 kg after 4 weeks’ treatment were escalated to 12.55 mg. The duration of treatment was 24 weeks. Drug concentration and body weight were measured predose and at 4 weeks, 8 weeks, and 24 weeks after treatment initiation. Exposure and response to sibutramine, including the placebo effect, were modeled using NONMEM 7.2. An asymptotic model approaching the final body weight was chosen to describe the time course of weight loss. Extent of weight loss was described successfully using a sigmoidal exposure–response relationship of the drug with a constant placebo effect in each individual. The placebo effect was influenced by subjects’ sex and baseline body mass index. Maximal weight loss was predicted to occur around 1 year after treatment initiation. The difference in mean weight loss between the sibutramine (daily 12.55 mg) and placebo groups was predicted to be 4.5% in a simulation of 1 year of treatment, with considerable overlap of prediction intervals. Our exposure–response model, which included the placebo effect, is the first example of a quantitative model that can be used to predict the efficacy of weight-control drugs. Similar approaches can help decision-making during clinical development of novel weight-loss drugs. PMID:26392753

Exposure-response model for sibutramine and placebo: suggestion for application to long-term weight-control drug development.

PubMed

Han, Seunghoon; Jeon, Sangil; Hong, Taegon; Lee, Jongtae; Bae, Soo Hyeon; Park, Wan-su; Park, Gab-jin; Youn, Sunil; Jang, Doo Yeon; Kim, Kyung-Soo; Yim, Dong-Seok

2015-01-01

No wholly successful weight-control drugs have been developed to date, despite the tremendous demand. We present an exposure-response model of sibutramine mesylate that can be applied during clinical development of other weight-control drugs. Additionally, we provide a model-based evaluation of sibutramine efficacy. Data from a double-blind, randomized, placebo-controlled, multicenter study were used (N=120). Subjects in the treatment arm were initially given 8.37 mg sibutramine base daily, and those who lost <2 kg after 4 weeks' treatment were escalated to 12.55 mg. The duration of treatment was 24 weeks. Drug concentration and body weight were measured predose and at 4 weeks, 8 weeks, and 24 weeks after treatment initiation. Exposure and response to sibutramine, including the placebo effect, were modeled using NONMEM 7.2. An asymptotic model approaching the final body weight was chosen to describe the time course of weight loss. Extent of weight loss was described successfully using a sigmoidal exposure-response relationship of the drug with a constant placebo effect in each individual. The placebo effect was influenced by subjects' sex and baseline body mass index. Maximal weight loss was predicted to occur around 1 year after treatment initiation. The difference in mean weight loss between the sibutramine (daily 12.55 mg) and placebo groups was predicted to be 4.5% in a simulation of 1 year of treatment, with considerable overlap of prediction intervals. Our exposure-response model, which included the placebo effect, is the first example of a quantitative model that can be used to predict the efficacy of weight-control drugs. Similar approaches can help decision-making during clinical development of novel weight-loss drugs.

Prediction of clinical response to drugs in ovarian cancer using the chemotherapy resistance test (CTR-test).

PubMed

Kischkel, Frank Christian; Meyer, Carina; Eich, Julia; Nassir, Mani; Mentze, Monika; Braicu, Ioana; Kopp-Schneider, Annette; Sehouli, Jalid

2017-10-27

In order to validate if the test result of the Chemotherapy Resistance Test (CTR-Test) is able to predict the resistances or sensitivities of tumors in ovarian cancer patients to drugs, the CTR-Test result and the corresponding clinical response of individual patients were correlated retrospectively. Results were compared to previous recorded correlations. The CTR-Test was performed on tumor samples from 52 ovarian cancer patients for specific chemotherapeutic drugs. Patients were treated with monotherapies or drug combinations. Resistances were classified as extreme (ER), medium (MR) or slight (SR) resistance in the CTR-Test. Combination treatment resistances were transformed by a scoring system into these classifications. Accurate sensitivity prediction was accomplished in 79% of the cases and accurate prediction of resistance in 100% of the cases in the total data set. The data set of single agent treatment and drug combination treatment were analyzed individually. Single agent treatment lead to an accurate sensitivity in 44% of the cases and the drug combination to 95% accuracy. The detection of resistances was in both cases to 100% correct. ROC curve analysis indicates that the CTR-Test result correlates with the clinical response, at least for the combination chemotherapy. Those values are similar or better than the values from a publication from 1990. Chemotherapy resistance testing in vitro via the CTR-Test is able to accurately detect resistances in ovarian cancer patients. These numbers confirm and even exceed results published in 1990. Better sensitivity detection might be caused by a higher percentage of drug combinations tested in 2012 compared to 1990. Our study confirms the functionality of the CTR-Test to plan an efficient chemotherapeutic treatment for ovarian cancer patients.

Expression of tolerance associated gene-1, a mitochondrial protein inhibiting T cell activation, can be used to predict response to immune modulating therapies.

PubMed

Keeren, Kathrin; Friedrich, Markus; Gebuhr, Inga; Philipp, Sandra; Sabat, Robert; Sterry, Wolfram; Brandt, Christine; Meisel, Christian; Grütz, Gerald; Volk, Hans-Dieter; Sawitzki, Birgit

2009-09-15

Immune modulating therapies gain increasing importance in treatment of patients with autoimmune diseases such as psoriasis. None of the currently applied biologics achieves significant clinical improvement in all treated patients. Because the therapy with biologics is cost intensive and sometimes associated with side effects, noninvasive diagnostic tools for early prediction of responders are of major interest. We studied the effects of Alefacept (LFA3Ig), an approved drug for treatment of psoriasis, on leukocytes in vitro and in vivo to identify gene markers predictive for treatment response and to further investigate its molecular mechanisms of action. In an open-label study, 20 psoriasis patients were treated weekly with 15 mg Alefacept over 12 wk. We demonstrate that transcription of the tolerance-associated gene (TOAG-1) is significantly up-regulated whereas receptor for hyaluronic acid mediated migration (RHAMM) transcription is down-regulated in PBMCs of responding patients before clinical improvement. TOAG-1 is exclusively localized within mitochondria. Overexpression of TOAG-1 in murine T cells leads to increased susceptibility to apoptosis. Addition of Alefacept to stimulated human T cells in vitro resulted in reduced frequencies of activated CD137(+) cells, increased TOAG-1 but reduced RHAMM expression. This was accompanied by reduced proliferation and enhanced apoptosis. Inhibition of proliferation was dependent on enhanced PDL1 expression of APCs. Thus, peripheral changes of TOAG-1 and RHAMM expression can be used to predict clinical response to Alefacept treatment in psoriasis patients. In the presence of APCs Alefacept can inhibit T cell activation and survival by increasing expression of TOAG-1 on T cells and PDL1 on APCs.

Bayesian Decision Support for Adaptive Lung Treatments

NASA Astrophysics Data System (ADS)

McShan, Daniel; Luo, Yi; Schipper, Matt; TenHaken, Randall

2014-03-01

Purpose: A Bayesian Decision Network will be demonstrated to provide clinical decision support for adaptive lung response-driven treatment management based on evidence that physiologic metrics may correlate better with individual patient response than traditional (population-based) dose and volume-based metrics. Further, there is evidence that information obtained during the course of radiation therapy may further improve response predictions. Methods: Clinical factors were gathered for 58 patients including planned mean lung dose, and the bio-markers IL-8 and TGF-β1 obtained prior to treatment and two weeks into treatment along with complication outcomes for these patients. A Bayesian Decision Network was constructed using Netica 5.0.2 from Norsys linking these clinical factors to obtain a prediction of radiation induced lung disese (RILD) complication. A decision node was added to the network to provide a plan adaption recommendation based on the trade-off between the RILD prediction and complexity of replanning. A utility node provides the weighting cost between the competing factors. Results: The decision node predictions were optimized against the data for the 58 cases. With this decision network solution, one can consider the decision result for a new patient with specific findings to obtain a recommendation to adaptively modify the originally planned treatment course. Conclusions: A Bayesian approach allows handling and propagating probabilistic data in a logical and principled manner. Decision networks provide the further ability to provide utility-based trade-offs, reflecting non-medical but practical cost/benefit analysis. The network demonstrated illustrates the basic concept, but many other factors may affect these decisions and work on building better models are being designed and tested. Acknowledgement: Supported by NIH-P01-CA59827

Sepsis patients in the emergency department: stratification using the Clinical Impression Score, Predisposition, Infection, Response and Organ dysfunction score or quick Sequential Organ Failure Assessment score?

PubMed

Quinten, Vincent M; van Meurs, Matijs; Wolffensperger, Anna E; Ter Maaten, Jan C; Ligtenberg, Jack J M

2017-05-08

The aim of this study was to compare the stratification of sepsis patients in the emergency department (ED) for ICU admission and mortality using the Predisposition, Infection, Response and Organ dysfunction (PIRO) and quick Sequential Organ Failure Assessment (qSOFA) scores with clinical judgement assessed by the ED staff. This was a prospective observational study in the ED of a tertiary care teaching hospital. Adult nontrauma patients with suspected infection and at least two Systemic Inflammatory Response Syndrome criteria were included. The primary outcome was direct ED to ICU admission. The secondary outcomes were in-hospital, 28-day and 6-month mortality, indirect ICU admission and length of stay. Clinical judgement was recorded using the Clinical Impression Scores (CIS), appraised by a nurse and the attending physician. The PIRO and qSOFA scores were calculated from medical records. We included 193 patients: 103 presented with sepsis, 81 with severe sepsis and nine with septic shock. Fifteen patients required direct ICU admission. The CIS scores of nurse [area under the curve (AUC)=0.896] and the attending physician (AUC=0.861), in conjunction with PIRO (AUC=0.876) and qSOFA scores (AUC=0.849), predicted direct ICU admission. The CIS scores did not predict any of the mortality endpoints. The PIRO predicted in-hospital (AUC=0.764), 28-day (AUC=0.784) and 6-month mortality (AUC=0.695). The qSOFA score also predicted in-hospital (AUC=0.823), 28-day (AUC=0.848) and 6-month mortality (AUC=0.620). Clinical judgement is a fast and reliable method to stratify between ICU and general ward admission in ED patients with sepsis. The PIRO and qSOFA scores do not add value to this stratification, but perform better on the prediction of mortality. In sepsis patients, therefore, the principle of 'treat first what kills first' can be supplemented with 'judge first and calculate later'.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0/.

Decorin in human oral cancer: A promising predictive biomarker of S-1 neoadjuvant chemosensitivity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kasamatsu, Atsushi, E-mail: kasamatsua@faculty.chiba-u.jp; Department of Dentistry and Oral–Maxillofacial Surgery, Chiba University Hospital, Chiba 260-8670; Uzawa, Katsuhiro, E-mail: uzawak@faculty.chiba-u.jp

Highlights: • DCN is significantly up-regulated in chemoresistant cancer cell lines. • DCN is a key regulator for chemoresistant mechanisms in vitro and in vivo. • DCN predicts the clinical responses to S-1 NAC for patients with oral cancer. - Abstract: We reported previously that decorin (DCN) is significantly up-regulated in chemoresistant cancer cell lines. DCN is a small leucine-rich proteoglycan that exists and functions in stromal and epithelial cells. Accumulating evidence suggests that DCN affects the biology of several types of cancer by directly/indirectly targeting the signaling molecules involved in cell growth, survival, metastasis, and angiogenesis, however, the molecularmore » mechanisms of DCN in chemoresistance and its clinical relevance are still unknown. Here we assumed that DCN silencing cells increase chemosusceptibility to S-1, consisted of tegafur, prodrug of 5-fluorouracil. We first established DCN knockdown transfectants derived from oral cancer cells for following experiments including chemosusceptibility assay to S-1. In addition to the in vitro data, DCN knockdown zenografting tumors in nude mice demonstrate decreasing cell proliferation and increasing apoptosis with dephosphorylation of AKT after S-1 chemotherapy. We also investigated whether DCN expression predicts the clinical responses of neoadjuvant chemotherapy (NAC) using S-1 (S-1 NAC) for oral cancer patients. Immunohistochemistry data in the preoperative biopsy samples was analyzed to determine the cut-off point for status of DCN expression by receiver operating curve analysis. Interestingly, low DCN expression was observed in five (83%) of six cases with complete responses to S-1 NAC, and in one (10%) case of 10 cases with stable/progressive disease, indicating that S-1 chemosensitivity is dramatically effective in oral cancer patients with low DCN expression compared with high DCN expression. Our findings suggest that DCN is a key regulator for chemoresistant mechanisms, and is a predictive immunomarker of the response to S-1 NAC and patient prognosis.« less

Radiomic biomarkers from PET/CT multi-modality fusion images for the prediction of immunotherapy response in advanced non-small cell lung cancer patients

NASA Astrophysics Data System (ADS)

Mu, Wei; Qi, Jin; Lu, Hong; Schabath, Matthew; Balagurunathan, Yoganand; Tunali, Ilke; Gillies, Robert James

2018-02-01

Purpose: Investigate the ability of using complementary information provided by the fusion of PET/CT images to predict immunotherapy response in non-small cell lung cancer (NSCLC) patients. Materials and methods: We collected 64 patients diagnosed with primary NSCLC treated with anti PD-1 checkpoint blockade. Using PET/CT images, fused images were created following multiple methodologies, resulting in up to 7 different images for the tumor region. Quantitative image features were extracted from the primary image (PET/CT) and the fused images, which included 195 from primary images and 1235 features from the fusion images. Three clinical characteristics were also analyzed. We then used support vector machine (SVM) classification models to identify discriminant features that predict immunotherapy response at baseline. Results: A SVM built with 87 fusion features and 13 primary PET/CT features on validation dataset had an accuracy and area under the ROC curve (AUROC) of 87.5% and 0.82, respectively, compared to a model built with 113 original PET/CT features on validation dataset 78.12% and 0.68. Conclusion: The fusion features shows better ability to predict immunotherapy response prediction compared to individual image features.

Predicting corticosteroid-free endoscopic remission with vedolizumab in ulcerative colitis.

PubMed

Waljee, A K; Liu, B; Sauder, K; Zhu, J; Govani, S M; Stidham, R W; Higgins, P D R

2018-03-01

Vedolizumab is an effective therapy for ulcerative colitis (UC), but costly and slow to work. New clinical responses occur after 30 weeks of therapy. To enable physicians, patients, and insurers to predict whether a patient with UC will respond to vedolizumab at an early time point after starting therapy. The clinical study data request website provided the phase 3 clinical trial data for vedolizumab. Random forest models were trained on 70% and tested on 30% of the data to predict corticosteroid-free endoscopic remission at week 52. Models were constructed using baseline data, or data through week 6 of vedolizumab therapy from 491 subjects. The AuROC for prediction of corticosteroid-free endoscopic remission at week 52 using baseline data was only 0.62 (95% CI: 0.53-0.72), but was 0.73 (95% CI: 0.65-0.82) when using data through week 6. A total of 47% of subjects were predicted to be remitters, and 59% of these subjects achieved corticosteroid-free endoscopic remission, in contrast to 21% of the predicted non-remitters. A week 6 prediction using FCP ≤234 μg/g was nearly as accurate. A machine learning algorithm using laboratory data through week 6 of vedolizumab therapy was able to accurately identify which UC patients would achieve corticosteroid-free endoscopic remission on vedolizumab at week 52. Application of this algorithm could have significant implications for clinical decisions on whom to continue on this costly medication when the benefits of the vedolizumab are not clinically apparent in the first 6 weeks of therapy. © 2018 John Wiley & Sons Ltd.

Neuroscience of inhibition for addiction medicine: From prediction of initiation to prediction of relapse

PubMed Central

Moeller, Scott J.; Bederson, Lucia; Alia-Klein, Nelly; Goldstein, Rita Z.

2017-01-01

A core deficit in drug addiction is the inability to inhibit maladaptive drug-seeking behavior. Consistent with this deficit, drug-addicted individuals show reliable cross-sectional differences from healthy non-addicted controls during tasks of response inhibition accompanied by brain activation abnormalities as revealed by functional neuroimaging. However, it is less clear whether inhibition-related deficits predate the transition to problematic use, and, in turn, whether these deficits predict the transition out of problematic substance use. Here, we review longitudinal studies of response inhibition in children/adolescents with little substance experience and longitudinal studies of already-addicted individuals attempting to sustain abstinence. Results show that response inhibition, and its underlying neural correlates, predict both substance use outcomes (onset and abstinence). Neurally, key roles were observed for multiple regions of the frontal cortex (e.g., inferior frontal gyrus, dorsal anterior cingulate cortex, and dorsolateral prefrontal cortex). In general, less activation of these regions during response inhibition predicted not only the onset of substance use, but interestingly, also better abstinence-related outcomes among individuals already addicted. The role of subcortical areas, although potentially important, is less clear because of inconsistent results and because these regions are less classically reported in studies of healthy response inhibition. Overall, this review indicates that response inhibition is not simply a manifestation of current drug addiction, but rather a core neurocognitive dimension that predicts key substance use outcomes. Early intervention in inhibitory deficits could have high clinical and public health relevance. PMID:26806776

Molecular classification and molecular forecasting of breast cancer: ready for clinical application?

PubMed

Brenton, James D; Carey, Lisa A; Ahmed, Ahmed Ashour; Caldas, Carlos

2005-10-10

Profiling breast cancer with expression arrays has become common, and it has been suggested that the results from early studies will lead to understanding of the molecular differences between clinical cases and allow individualization of care. We critically review two main applications of expression profiling; studies unraveling novel breast cancer classifications and those that aim to identify novel markers for prediction of clinical outcome. Breast cancer may now be subclassified into luminal, basal, and HER2 subtypes with distinct differences in prognosis and response to therapy. However, profiling studies to identify predictive markers have suffered from methodologic problems that prevent general application of their results. Future work will need to reanalyze existing microarray data sets to identify more representative sets of candidate genes for use as prognostic signatures and will need to take into account the new knowledge of molecular subtypes of breast cancer when assessing predictive effects.

Responsiveness and predictive validity of the tablet-based symbol digit modalities test in patients with stroke.

PubMed

Hsiao, Pei-Chi; Yu, Wan-Hui; Lee, Shih-Chieh; Chen, Mei-Hsiang; Hsieh, Ching-Lin

2018-06-14

The responsiveness and predictive validity of the Tablet-based Symbol Digit Modalities Test (T-SDMT) are unknown, which limits the utility of the T-SDMT in both clinical and research settings. The purpose of this study was to examine the responsiveness and predictive validity of the T-SDMT in inpatients with stroke. A follow-up, repeated-assessments design. One rehabilitation unit at a local medical center. A total of 50 inpatients receiving rehabilitation completed T-SDMT assessments at admission to and discharge from a rehabilitation ward. The median follow-up period was 14 days. The Barthel index (BI) was assessed at discharge and was used as the criterion of the predictive validity. The mean changes in the T-SDMT scores between admission and discharge were statistically significant (paired t-test = 3.46, p = 0.001). The T-SDMT scores showed a nearly moderate standardized response mean (0.49). A moderate association (Pearson's r = 0.47) was found between the scores of the T-SDMT at admission and those of the BI at discharge, indicating good predictive validity of the T-SDMT. Our results support the responsiveness and predictive validity of the T-SDMT in patients with stroke receiving rehabilitation in hospitals. This study provides empirical evidence supporting the use of the T-SDMT as an outcome measure for assessing processingspeed in inpatients with stroke. The scores of the T-SDMT could be used to predict basic activities of daily living function in inpatients with stroke.

Immunological tumor status may predict response to neoadjuvant chemotherapy and outcome after radical cystectomy in bladder cancer.

PubMed

Tervahartiala, Minna; Taimen, Pekka; Mirtti, Tuomas; Koskinen, Ilmari; Ecke, Thorsten; Jalkanen, Sirpa; Boström, Peter J

2017-10-04

Bladder cancer (BC) is the ninth most common cancer worldwide. Radical cystectomy (RC) with neoadjuvant chemotherapy (NAC) is recommended for muscle-invasive BC. The challenge of the neoadjuvant approach relates to challenges in selection of patients to chemotherapy that are likely to respond to the treatment. To date, there are no validated molecular markers or baseline clinical characteristics to identify these patients. Different inflammatory markers, including tumor associated macrophages with their plastic pro-tumorigenic and anti-tumorigenic functions, have extensively been under interests as potential prognostic and predictive biomarkers in different cancer types. In this immunohistochemical study we evaluated the predictive roles of three immunological markers, CD68, MAC387, and CLEVER-1, in response to NAC and outcome of BC. 41% of the patients had a complete response (pT0N0) to NAC. Basic clinicopathological variables did not predict response to NAC. In contrast, MAC387 + cells and CLEVER-1 + macrophages associated with poor NAC response, while CLEVER-1 + vessels associated with more favourable response to NAC. Higher counts of CLEVER-1 + macrophages associated with poorer overall survival and CD68 + macrophages seem to have an independent prognostic value in BC patients treated with NAC. Our findings point out that CD68, MAC387, and CLEVER-1 may be useful prognostic and predictive markers in BC.

Applying metabolomics to cardiometabolic intervention studies and trials: past experiences and a roadmap for the future.

PubMed

Rankin, Naomi J; Preiss, David; Welsh, Paul; Sattar, Naveed

2016-10-01

Metabolomics and lipidomics are emerging methods for detailed phenotyping of small molecules in samples. It is hoped that such data will: (i) enhance baseline prediction of patient response to pharmacotherapies (beneficial or adverse); (ii) reveal changes in metabolites shortly after initiation of therapy that may predict patient response, including adverse effects, before routine biomarkers are altered; and( iii) give new insights into mechanisms of drug action, particularly where the results of a trial of a new agent were unexpected, and thus help future drug development. In these ways, metabolomics could enhance research findings from intervention studies. This narrative review provides an overview of metabolomics and lipidomics in early clinical intervention studies for investigation of mechanisms of drug action and prediction of drug response (both desired and undesired). We highlight early examples from drug intervention studies associated with cardiometabolic disease. Despite the strengths of such studies, particularly the use of state-of-the-art technologies and advanced statistical methods, currently published studies in the metabolomics arena are largely underpowered and should be considered as hypothesis-generating. In order for metabolomics to meaningfully improve stratified medicine approaches to patient treatment, there is a need for higher quality studies, with better exploitation of biobanks from randomized clinical trials i.e. with large sample size, adjudicated outcomes, standardized procedures, validation cohorts, comparison witth routine biochemistry and both active and control/placebo arms. On the basis of this review, and based on our research experience using clinically established biomarkers, we propose steps to more speedily advance this area of research towards potential clinical impact. © The Author 2016; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.

Prediction of clinical outcome in patients treated with cardiac resynchronization therapy - the role of NT-ProBNP and a combined response score.

PubMed

Bakos, Z; Chatterjee, N C; Reitan, C; Singh, J P; Borgquist, R

2018-04-24

Cardiac resynchronization therapy (CRT) is an established therapy for appropriately selected patients with heart failure. Response to CRT has been heterogeneously defined using both clinical and echocardiographic measures, with poor correlation between the two. The study cohort was comprised of 202 CRT-treated patients and CRT response was defined at 6 months post-implant. Echocardiographic response (E+) was defined as a reduction in LVESV ≥ 15%, clinical response as an improvement of ≥ 1 NYHA class (C+), and biomarker response as a ≥ 25% reduction in NT-proBNP(B+). The association of response measures (E+, B+, C+; response score range 0-3) and clinical endpoints at 3 years was assessed in landmarked Cox models. Echo and clinical responders demonstrated greater declines in NT-proBNP than non-responders (median [E+/B+]: -52%, [E+]: -27%, [C+]: -39% and [E-/C-]: -13%; p = 0.01 for trend). Biomarker (HR 0.43 [95% CI: 0.22-0.86], p = 0.02) and clinical (HR 0.40 [0.23-0.70] p = 0.001) response were associated with a significantly reduced risk of the primary endpoint. When integrating each response measure into a composite score, each 1 point increase was associated with a 31% decreased risk for a composite endpoint of mortality, LVAD, transplant and HF hospitalization (HR 0.69 [95% CI: 0.50-0.96], p = 0.03), and a 52% decreased risk of all-cause mortality (HR 0.48 [95% CI: 0.26-0.89], p = 0.02). Serial changes in NT-proBNP are associated with clinical outcomes following CRT implant. Integration of biomarker, clinical, and echocardiographic response may discriminate CRT responders versus non-responders in a clinically meaningful way, and with higher accuracy. The cohort was combined from study NCT01949246 and the study based on local review board approval 2011/550 in Lund, Sweden.

Emergency Physician Attitudes, Preferences, and Risk Tolerance for Stroke as a Potential Cause of Dizziness Symptoms.

PubMed

Kene, Mamata V; Ballard, Dustin W; Vinson, David R; Rauchwerger, Adina S; Iskin, Hilary R; Kim, Anthony S

2015-09-01

We evaluated emergency physicians' (EP) current perceptions, practice, and attitudes towards evaluating stroke as a cause of dizziness among emergency department patients. We administered a survey to all EPs in a large integrated healthcare delivery system. The survey included clinical vignettes, perceived utility of historical and exam elements, attitudes about the value of and requisite post-test probability of a clinical prediction rule for dizziness. We calculated descriptive statistics and post-test probabilities for such a clinical prediction rule. The response rate was 68% (366/535). Respondents' median practice tenure was eight years (37% female, 92% emergency medicine board certified). Symptom quality and typical vascular risk factors increased suspicion for stroke as a cause of dizziness. Most respondents reported obtaining head computed tomography (CT) (74%). Nearly all respondents used and felt confident using cranial nerve and limb strength testing. A substantial minority of EPs used the Epley maneuver (49%) and HINTS (head-thrust test, gaze-evoked nystagmus, and skew deviation) testing (30%); however, few EPs reported confidence in these tests' bedside application (35% and 16%, respectively). Respondents favorably viewed applying a properly validated clinical prediction rule for assessment of immediate and 30-day stroke risk, but indicated it would have to reduce stroke risk to <0.5% to be clinically useful. EPs report relying on symptom quality, vascular risk factors, simple physical exam elements, and head CT to diagnose stroke as the cause of dizziness, but would find a validated clinical prediction rule for dizziness helpful. A clinical prediction rule would have to achieve a 0.5% post-test stroke probability for acceptability.

Apply radiomics approach for early stage prognostic evaluation of ovarian cancer patients: a preliminary study

NASA Astrophysics Data System (ADS)

Danala, Gopichandh; Wang, Yunzhi; Thai, Theresa; Gunderson, Camille; Moxley, Katherine; Moore, Kathleen; Mannel, Robert; Liu, Hong; Zheng, Bin; Qiu, Yuchen

2017-03-01

Predicting metastatic tumor response to chemotherapy at early stage is critically important for improving efficacy of clinical trials of testing new chemotherapy drugs. However, using current response evaluation criteria in solid tumors (RECIST) guidelines only yields a limited accuracy to predict tumor response. In order to address this clinical challenge, we applied Radiomics approach to develop a new quantitative image analysis scheme, aiming to accurately assess the tumor response to new chemotherapy treatment, for the advanced ovarian cancer patients. During the experiment, a retrospective dataset containing 57 patients was assembled, each of which has two sets of CT images: pre-therapy and 4-6 week follow up CT images. A Radiomics based image analysis scheme was then applied on these images, which is composed of three steps. First, the tumors depicted on the CT images were segmented by a hybrid tumor segmentation scheme. Then, a total of 115 features were computed from the segmented tumors, which can be grouped as 1) volume based features; 2) density based features; and 3) wavelet features. Finally, an optimal feature cluster was selected based on the single feature performance and an equal-weighed fusion rule was applied to generate the final predicting score. The results demonstrated that the single feature achieved an area under the receiver operating characteristic curve (AUC) of 0.838+/-0.053. This investigation demonstrates that the Radiomic approach may have the potential in the development of high accuracy predicting model for early stage prognostic assessment of ovarian cancer patients.

Predicting the sensitivity to ion therapy based on the response to photon irradiation--experimental evidence and mathematical modelling.

PubMed

Mohanty, Chitralekha; Zielinska-Chomej, Katarzyna; Edgren, Margareta; Hirayama, Ryoichi; Murakami, Takeshi; Lind, Bengt; Toma-Dasu, Iuliana

2014-06-01

The use of ion radiation therapy is growing due to the continuously increasing positive clinical experience obtained. Therefore, there is a high interest in radio-biological experiments comparing the relative efficiency in cell killing of ions and photons as photons are currently the main radiation modality used for cancer treatment. This comparison is particularly important since the treatment planning systems (TPSs) used at the main ion therapy Centers make use of parameters describing the cellular response to photons, respectively ions, determined in vitro. It was, therefore, the aim of this article to compare the effects of high linear energy transfer (LET) ion radiation with low LET photons and determine whether the cellular response to low LET could predict the response to high LET irradiation. Clonogenic cell survival data of five tumor cell lines irradiated with different ion beams of similar, clinically-relevant, LET were studied in relation to response to low LET photons. Two mathematical models were used to fit the data, the repairable-conditionally repairable damage (RCR) model and the linear quadratic (LQ) model. The results indicate that the relative biological efficiency of the high LET radiation assessed with the RCR model could be predicted based only on the response to the low LET irradiation. The particular features of the RCR model indicate that tumor cells showing a large capacity for repairing the damage will have the larger benefit from radiation therapy with ion beams. Copyright© 2014 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Systemic Corticosteroid Responses in Children with Severe Asthma: Phenotypic and Endotypic Features.

PubMed

Fitzpatrick, Anne M; Stephenson, Susan T; Brown, Milton R; Nguyen, Khristopher; Douglas, Shaneka; Brown, Lou Ann S

Severe asthma in children is a heterogeneous disorder associated with variable responses to corticosteroid treatment. Criterion standards for corticosteroid responsiveness assessment in children are lacking. This study sought to characterize systemic corticosteroid responses in children with severe asthma after treatment with intramuscular triamcinolone and to identify phenotypic and molecular predictors of an intramuscular triamcinolone response. Asthma-related quality of life, exhaled nitric oxide, blood eosinophils, lung function, and inflammatory cytokine and chemokine mRNA gene expression in peripheral blood mononuclear cells were assessed in 56 children with severe asthma at baseline and 14 days after intramuscular triamcinolone injection. The Asthma Control Questionnaire was used to classify children with severe asthma into corticosteroid response groups. Three groups of children with severe asthma were identified: controlled severe asthma, children who achieved control after triamcinolone, and children who did not achieve control. At baseline, these groups were phenotypically similar. After triamcinolone, discordance between symptoms, lung function, exhaled nitric oxide, and blood eosinophils was noted. Clinical phenotypic predictors were of limited utility in predicting the triamcinolone response, whereas systemic mRNA expression of inflammatory cytokines and chemokines related to IL-2, IL-10, and TNF signaling pathways, namely, AIMP1, CCR2, IL10RB, and IL5, strongly differentiated children who failed to achieve control with triamcinolone administration. Systemic corticosteroid responsiveness in children with severe asthma is heterogeneous. Alternative prediction models that include molecular endotypic as well as clinical phenotypic features are needed to identify which children derive the most clinical benefit from systemic corticosteroid step-up therapy given the potential side effects. Copyright © 2016 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

Modeling Efficacy of Bevacizumab Treatment for Metastatic Colon Cancer

PubMed Central

Islam, Rezwan; Chyou, Po-Huang; Burmester, James K

2013-01-01

Purpose: Bevacizumab, an FDA-approved adjuvant treatment for metastatic colon cancer, has extended survival for many patients. However, factors predicting response to treatment remain undefined. Patients and Methods: Relevant clinical and environmental data were abstracted from medical records of 149 evaluable patients treated with bevacizumab for metastatic colon cancer at a multi-specialty clinic. Tumor response was calculated from radiologic reports using Response Evaluation Criteria in Solid Tumors (RECIST) criteria and verified by oncologist review. Patients with at least one occurrence of complete or partial response or stable disease were classified as responders; those exhibiting progressive disease were classified as non-responders. Results: Univariate analysis demonstrated that blood in stool (P<0.05), unexplained weight loss (P<0.05), primary colon cancer site (P<0.05), chemotherapy treatment of primary tumor site (P<0.05), and adenocarcinoma versus adenoma subtype (P<0.05) was associated with tumor responsiveness. Factors remaining statistically significant following multivariate modeling included adenocarcinoma as tumor cell type versus other adenocarcinoma subtypes (OR=6.35, 95% CI: 1.08-37.18), chemotherapy treatment applied to primary tumor (OR= 0.07, 95% CI: 0.0-0.76,), tumor localization to cecal/ascending colon (OR=0.061, 95% CI: 0.006-0.588,), and unexplained weight loss (OR=0.1, 95% CI: 0.02-0.56,). Chemotherapy treatment of primary tumor, unexplained weight loss, and cecal/ascending localization of the tumor were associated with poorer outcomes. Adenocarcinoma as cell type compared to other adenocarcinoma subtypes was associated with better response to bevacizumab treatment. Conclusion: Results suggest that response to bevacizumab therapy may be predicted by modeling clinical factors including symptomology on presentation, tumor location and type, and initial response to chemotherapy. PMID:23678369

Delayed risk stratification, to include the response to initial treatment (surgery and radioiodine ablation), has better outcome predictivity in differentiated thyroid cancer patients.

PubMed

Castagna, Maria Grazia; Maino, Fabio; Cipri, Claudia; Belardini, Valentina; Theodoropoulou, Alexandra; Cevenini, Gabriele; Pacini, Furio

2011-09-01

After initial treatment, differentiated thyroid cancer (DTC) patients are stratified as low and high risk based on clinical/pathological features. Recently, a risk stratification based on additional clinical data accumulated during follow-up has been proposed. To evaluate the predictive value of delayed risk stratification (DRS) obtained at the time of the first diagnostic control (8-12 months after initial treatment). We reviewed 512 patients with DTC whose risk assessment was initially defined according to the American (ATA) and European Thyroid Association (ETA) guidelines. At the time of the first control, 8-12 months after initial treatment, patients were re-stratified according to their clinical status: DRS. Using DRS, about 50% of ATA/ETA intermediate/high-risk patients moved to DRS low-risk category, while about 10% of ATA/ETA low-risk patients moved to DRS high-risk category. The ability of the DRS to predict the final outcome was superior to that of ATA and ETA. Positive and negative predictive values for both ATA (39.2 and 90.6% respectively) and ETA (38.4 and 91.3% respectively) were significantly lower than that observed with the DRS (72.8 and 96.3% respectively, P<0.05). The observed variance in predicting final outcome was 25.4% for ATA, 19.1% for ETA, and 62.1% for DRS. Delaying the risk stratification of DTC patients at a time when the response to surgery and radioiodine ablation is evident allows to better define individual risk and to better modulate the subsequent follow-up.

SU-E-J-04: A Data-Driven, Response-Based, Multi-Criteria Decision Support System for Personalized Lung Radiation Treatment Planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Y; McShan, D; Schipper, M

2014-06-01

Purpose: To develop a decision support tool to predict a patient's potential overall survival (OS) and radiation induced toxicity (RIT) based on clinical factors and responses during the course of radiotherapy, and suggest appropriate radiation dose adjustments to improve therapeutic effect. Methods: Important relationships between a patient's basic information and their clinical features before and during the radiation treatment are identified from historical clinical data by using statistical learning and data mining approaches. During each treatment period, a data analysis (DA) module predicts radiotherapy features such as time to local progression (TTLP), time to distant metastases (TTDM), radiation toxicity tomore » different organs, etc., under possible future treatment plans based on patient specifics or responses. An information fusion (IF) module estimates intervals for a patient's OS and the probabilities of RIT from a treatment plan by integrating the outcomes of module DA. A decision making (DM) module calculates “satisfaction” with the predicted radiation outcome based on trade-offs between OS and RIT, and finds the best treatment plan for the next time period via multi-criteria optimization. Results: Using physical and biological data from 130 lung cancer patients as our test bed, we were able to train and implement the 3 modules of our decision support tool. Examples demonstrate how it can help predict a new patient's potential OS and RIT with different radiation dose plans along with how these combinations change with dose, thus presenting a range of satisfaction/utility for use in individualized decision support. Conclusion: Although the decision support tool is currently developed from a small patient sample size, it shows the potential for the improvement of each patient's satisfaction in personalized radiation therapy. The radiation treatment outcome prediction and decision making model needs to be evaluated with more patients and demonstrated for use in radiation treatments for other cancers. P01-CA59827;R01CA142840.« less

Circulating basal anti-Müllerian hormone levels as predictor of ovarian response in women undergoing ovarian stimulation for in vitro fertilization.

PubMed

Nardo, Luciano G; Gelbaya, Tarek A; Wilkinson, Hannah; Roberts, Stephen A; Yates, Allen; Pemberton, Phil; Laing, Ian

2009-11-01

To evaluate the clinical value of basal anti-Müllerian hormone (AMH) measurements compared with other available determinants, apart from chronologic age, in the prediction of ovarian response to gonadotrophin stimulation. Prospective cohort study. Tertiary referral center for reproductive medicine and an IVF unit. Women undergoing their first cycle of controlled ovarian hyperstimulation (COH) for in vitro fertilization (IVF). Basal levels of FSH and AMH as well as antral follicle count (AFC) were measured in 165 subjects. All patients were followed prospectively and their cycle outcomes recorded. Predictive value of FSH, AMH, and AFC for extremes of ovarian response to stimulation. Out of the 165 women, 134 were defined as normal responders, 15 as poor responders, and 16 as high responders. Subjects in the poor response group were significantly older then those in the other two groups. Anti-Müllerian hormone levels and AFC were markedly raised in the high responders and decreased in the poor responders. Compared with FSH and AFC, AMH performed better in the prediction of excessive response to ovarian stimulation-AMH area under receiver operating characteristic curve (ROC(AUC)) 0.81, FSH ROC(AUC) 0.66, AFC ROC(AUC) 0.69. For poor response, AMH (ROC(AUC) 0.88) was a significantly better predictor than FSH (ROC(AUC) 0.63) but not AFC (ROC(AUC) 0.81). AMH prediction of ovarian response was independent of age and PCOS. Anti-Müllerian hormone cutoffs of >3.75 ng/mL and <1.0 ng/mL would have modest sensitivity and specificity in predicting the extremes of response. Circulating AMH has the ability to predict excessive and poor response to stimulation with exogenous gonadotrophins. Overall, this biomarker is superior to basal FSH and AFC, and has the potential to be incorporated in to work-up protocols to predict patient's ovarian response to treatment and to individualize strategies aiming at reducing the cancellation rate and the iatrogenic complications of COH.

Coupling of EIT with computational lung modeling for predicting patient-specific ventilatory responses.

PubMed

Roth, Christian J; Becher, Tobias; Frerichs, Inéz; Weiler, Norbert; Wall, Wolfgang A

2017-04-01

Providing optimal personalized mechanical ventilation for patients with acute or chronic respiratory failure is still a challenge within a clinical setting for each case anew. In this article, we integrate electrical impedance tomography (EIT) monitoring into a powerful patient-specific computational lung model to create an approach for personalizing protective ventilatory treatment. The underlying computational lung model is based on a single computed tomography scan and able to predict global airflow quantities, as well as local tissue aeration and strains for any ventilation maneuver. For validation, a novel "virtual EIT" module is added to our computational lung model, allowing to simulate EIT images based on the patient's thorax geometry and the results of our numerically predicted tissue aeration. Clinically measured EIT images are not used to calibrate the computational model. Thus they provide an independent method to validate the computational predictions at high temporal resolution. The performance of this coupling approach has been tested in an example patient with acute respiratory distress syndrome. The method shows good agreement between computationally predicted and clinically measured airflow data and EIT images. These results imply that the proposed framework can be used for numerical prediction of patient-specific responses to certain therapeutic measures before applying them to an actual patient. In the long run, definition of patient-specific optimal ventilation protocols might be assisted by computational modeling. NEW & NOTEWORTHY In this work, we present a patient-specific computational lung model that is able to predict global and local ventilatory quantities for a given patient and any selected ventilation protocol. For the first time, such a predictive lung model is equipped with a virtual electrical impedance tomography module allowing real-time validation of the computed results with the patient measurements. First promising results obtained in an acute respiratory distress syndrome patient show the potential of this approach for personalized computationally guided optimization of mechanical ventilation in future. Copyright © 2017 the American Physiological Society.

[Faecal calprotectin as an aid to the diagnosis of non-IgE mediated cow's milk protein allergy].

PubMed

Trillo Belizón, Carlos; Ortega Páez, Eduardo; Medina Claros, Antonio F; Rodríguez Sánchez, Isabel; Reina González, Ana; Vera Medialdea, Rafael; Ramón Salguero, José Manuel

2016-06-01

The aim of the study was to assess the use of faecal calprotectin (FCP) in infants with signs and symptoms of non-IgE-mediated cow's milk protein allergy (CMA) for both diagnosis and prediction of clinical response at the time of withdrawal of milk proteins. A one year prospective study was conducted on 82 infants between 1 and 12 months of age in the Eastern area of Málaga-Axarquía, of whom 40 of them had been diagnosed with non-IgE-mediated CMA (with suggestive symptoms and positive response to milk withdrawal), 12 not diagnosed with CMA, and 30 of them were the control group. FCP was measured at three different times: time of diagnosis, and one and three months later. ANOVA for repeated measures, nominal logistic regression and ROC curves were prepared using the SPSS.20 package and Medcalc. Differences between diagnostic and control groups were assessed: there was a statistically significant relationship (p<.0001) between high FCP levels and infants suffering CMA, as well as the levels at time of diagnosis, 1 and 3 months (p <.001). A ROC curve was constructed between FCP levels and diagnosis of CMA, with 138 ug/g, with the best cut-off being with an area under the curve of 0.89. However, it is only 0.68 to predict a clinical response. FCP levels lower than 138ug/g could be useful to rule out non-IgE-mediated CMA diagnosis. Calprotectin is not a good test to predict clinical response to milk withdrawal. Copyright © 2015 Asociación Española de Pediatría. Published by Elsevier España, S.L.U. All rights reserved.

Application of PET/CT in treatment response evaluation and recurrence prediction in patients with newly-diagnosed multiple myeloma

PubMed Central

Li, Ying; Liu, Junru; Huang, Beihui; Chen, Meilan; Diao, Xiangwen; Li, Juan

2017-01-01

Multiple myeloma (MM) causes osteolytic lesions which can be detected by 18F-fluorodeoxyglucose positron emission tomography/Computed tomography (18F-FDG PET/CT). We prospectively involve 96 Newly diagnosed MM to take PET/CT scan at scheduled treatment time (figure 1), and 18F-FDG uptake of lesion was measured by SUVmax and T/Mmax. All MM patients took bortezomib based chemotherapy as induction and received ASCT and maintenance. All clinical features were analyzed with the PET/CT image changes, and some relationships between treatment response and FDG uptakes changes were found: Osteolytic lesions of MM uptakes higher FDG than healthy volunteers, and this trend is more obvious in extramedullary lesions. Compared to X-ray, PET/CT was more sensitive both in discoering bone as well as extramedullary lesions. In newly diagnosed MM, several adverse clinical factors were related to high FDG uptakes of bone lesions. Bone lesion FDG uptakes of MM with P53 mutation or with hypodiploidy and complex karyotype were also higher than those without such changes. In treatment response, PET/CT showed higher sensitivity in detecting tumor residual disease than immunofixation electrophoresis. But in relapse prediction, it might show false positive disease recurrences and the imaging changes might be influenced by infections and hemoglobulin levels. Conclusion: PET/CT is sensitive in discovering meduallary and extrameduallary lesions of MM, and the 18F-FDG uptake of lesions are related with clinical indictors and biological features of plasma cells. In evaluating treatment response and survival, PET/CT showed its superiority. But in predicting relapse or refractory, it may show false positive results. PMID:27556189

Model-Based Individualized Treatment of Chemotherapeutics: Bayesian Population Modeling and Dose Optimization

PubMed Central

Jayachandran, Devaraj; Laínez-Aguirre, José; Rundell, Ann; Vik, Terry; Hannemann, Robert; Reklaitis, Gintaras; Ramkrishna, Doraiswami

2015-01-01

6-Mercaptopurine (6-MP) is one of the key drugs in the treatment of many pediatric cancers, auto immune diseases and inflammatory bowel disease. 6-MP is a prodrug, converted to an active metabolite 6-thioguanine nucleotide (6-TGN) through enzymatic reaction involving thiopurine methyltransferase (TPMT). Pharmacogenomic variation observed in the TPMT enzyme produces a significant variation in drug response among the patient population. Despite 6-MP’s widespread use and observed variation in treatment response, efforts at quantitative optimization of dose regimens for individual patients are limited. In addition, research efforts devoted on pharmacogenomics to predict clinical responses are proving far from ideal. In this work, we present a Bayesian population modeling approach to develop a pharmacological model for 6-MP metabolism in humans. In the face of scarcity of data in clinical settings, a global sensitivity analysis based model reduction approach is used to minimize the parameter space. For accurate estimation of sensitive parameters, robust optimal experimental design based on D-optimality criteria was exploited. With the patient-specific model, a model predictive control algorithm is used to optimize the dose scheduling with the objective of maintaining the 6-TGN concentration within its therapeutic window. More importantly, for the first time, we show how the incorporation of information from different levels of biological chain-of response (i.e. gene expression-enzyme phenotype-drug phenotype) plays a critical role in determining the uncertainty in predicting therapeutic target. The model and the control approach can be utilized in the clinical setting to individualize 6-MP dosing based on the patient’s ability to metabolize the drug instead of the traditional standard-dose-for-all approach. PMID:26226448

Predicting Response of ADHD Symptoms to Methylphenidate Treatment Based on Comorbid Anxiety

ERIC Educational Resources Information Center

Blouin, Brittany; Maddeaux, Cindy; Stanley Firestone, Jill; van Stralen, Judy

2010-01-01

Objective: In this small pilot study, the association of comorbid anxiety with the treatment of ADHD is studied. Methods: Eighteen volunteers from a pediatric clinic are tested for ADHD and anxiety and assessed for behavioral and cognitive ADHD symptomology. Response to methylphenidate as treatment for ADHD symptoms is measured 2 to 3 weeks, and…

Predicting Clinical Outcome Using Brain Activation Associated with Set-Shifting and Central Coherence Skills in Anorexia Nervosa

PubMed Central

Garrett, Amy; Lock, James; Datta, Nandini; Beenhaker, Judy; Kesler, Shelli R.; Reiss, Allan L.

2014-01-01

Background Patients with Anorexia Nervosa (AN) have neuropsychological deficits in set shifting (SS) and central coherence (CC) consistent with an inflexible thinking style and overly detailed processing style, respectively. This study investigates brain activation during SS and CC tasks in patients with AN and tests whether this activation is a biomarker that predicts response to treatment. Methods : FMRI data were collected from 21 females with AN while performing a SS task (the Wisconsin Card Sort) and a CC task (embedded figures), and used to predict outcome following 16 weeks of treatment (either 16 weeks of cognitive behavioral therapy or 8 weeks cognitive remediation training followed by 8 weeks of cognitive behavioral therapy). Results Significant activation during the SS task included bilateral dorsolateral and ventrolateral prefrontal cortex and left anterior middle frontal gyrus. Higher scores on the neuropsychological test of SS (measured outside the scanner at baseline) were correlated with greater DLPFC and VLPFC activation. Improvements in SS following treatment were significantly predicted by a combination of low VLPFC and high anterior middle frontal activation (R squared = .68, p=.001). For the CC task, the visual and parietal areas were activated, but were not significantly correlated with neuropsychological measures of CC and did not predict outcome. Conclusion Cognitive flexibility requires the support of several prefrontal cortex resources. As previous studies suggest that the VLPFC is important for selecting responses, patients who demonstrate that deficit may benefit the most from cognitive therapy with or without cognitive remediation training. The ability to sustain inhibition of an unwanted response, subserved by the anterior middle frontal gyrus, is a cognitive feature that predicts favorable outcome to cognitive treatment. CC deficits may not be an effective predictor of clinical outcome. PMID:25027478

Sensitivity, Specificity, Predictive Values, and Accuracy of Three Diagnostic Tests to Predict Inferior Alveolar Nerve Blockade Failure in Symptomatic Irreversible Pulpitis

PubMed Central

Rodríguez-Wong, Laura; Noguera-González, Danny; Esparza-Villalpando, Vicente; Montero-Aguilar, Mauricio

2017-01-01

Introduction The inferior alveolar nerve block (IANB) is the most common anesthetic technique used on mandibular teeth during root canal treatment. Its success in the presence of preoperative inflammation is still controversial. The aim of this study was to evaluate the sensitivity, specificity, predictive values, and accuracy of three diagnostic tests used to predict IANB failure in symptomatic irreversible pulpitis (SIP). Methodology A cross-sectional study was carried out on the mandibular molars of 53 patients with SIP. All patients received a single cartridge of mepivacaine 2% with 1 : 100000 epinephrine using the IANB technique. Three diagnostic clinical tests were performed to detect anesthetic failure. Anesthetic failure was defined as a positive painful response to any of the three tests. Sensitivity, specificity, predictive values, accuracy, and ROC curves were calculated and compared and significant differences were analyzed. Results IANB failure was determined in 71.7% of the patients. The sensitivity scores for the three tests (lip numbness, the cold stimuli test, and responsiveness during endodontic access) were 0.03, 0.35, and 0.55, respectively, and the specificity score was determined as 1 for all of the tests. Clinically, none of the evaluated tests demonstrated a high enough accuracy (0.30, 0.53, and 0.68 for lip numbness, the cold stimuli test, and responsiveness during endodontic access, resp.). A comparison of the areas under the curve in the ROC analyses showed statistically significant differences between the three tests (p < 0.05). Conclusion None of the analyzed tests demonstrated a high enough accuracy to be considered a reliable diagnostic tool for the prediction of anesthetic failure. PMID:28694714

Use of sequential endorectal US to predict the tumor response of preoperative chemoradiotherapy in rectal cancer.

PubMed

Li, Ning; Dou, Lizhou; Zhang, Yueming; Jin, Jing; Wang, Guiqi; Xiao, Qin; Li, Yexiong; Wang, Xin; Ren, Hua; Fang, Hui; Wang, Weihu; Wang, Shulian; Liu, Yueping; Song, Yongwen

2017-03-01

Accurate prediction of the response to preoperative chemoradiotherapy (CRT) potentially assists in the individualized selection of treatment. Endorectal US (ERUS) is widely used for the pretreatment staging of rectal cancer, but its use for preoperatively predicting the effects of CRT is not well evaluated because of the inflammation, necrosis, and fibrosis induced by CRT. This study assessed the value of sequential ERUS in predicting the efficacy of preoperative CRT for locally advanced rectal cancer. Forty-one patients with clinical stage II/III rectal adenocarcinoma were enrolled prospectively. Radiotherapy was delivered to the pelvis with concurrent chemotherapy of capecitabine and oxaliplatin. Total mesorectal excision was performed 6 to 8 weeks later. EUS measurements of primary tumor maximum diameter were performed before (ERUS1), during (ERUS2), and 6 to 8 weeks after (ERUS3) CRT, and the ratios of these were calculated. Correlations between ERUS values, tumor regression grade (TRG), T down-staging rate, and pathologic complete response (pCR) rate were assessed, and survival was analyzed. There was no significant correlation between ERUS2/ERUS1 and TRG. The value of ERUS3/ERUS1 correlated with pCR rate and TRG but not T down-staging rate. An ERUS3 value of 6.3 mm and ERUS3/ERUS1 of 52% were used as the cut-off for predicting pCR, and patients were divided into good and poor prognosis groups. Although not statistically significant, 3-year recurrence and survival rates of the good prognosis group were better than those of the poor prognosis group. Sequential ERUS may predict therapeutic efficacy of preoperative CRT for locally advanced rectal cancer. (Clinical trial registration number: NCT01582750.). Copyright © 2017 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.

CT contrast predicts pancreatic cancer treatment response to verteporfin-based photodynamic therapy

NASA Astrophysics Data System (ADS)

Jermyn, Michael; Davis, Scott C.; Dehghani, Hamid; Huggett, Matthew T.; Hasan, Tayyaba; Pereira, Stephen P.; Bown, Stephen G.; Pogue, Brian W.

2014-04-01

The goal of this study was to determine dominant factors affecting treatment response in pancreatic cancer photodynamic therapy (PDT), based on clinically available information in the VERTPAC-01 trial. This trial investigated the safety and efficacy of verteporfin PDT in 15 patients with locally advanced pancreatic adenocarcinoma. CT scans before and after contrast enhancement from the 15 patients in the VERTPAC-01 trial were used to determine venous-phase blood contrast enhancement and this was correlated with necrotic volume determined from post-treatment CT scans, along with estimation of optical absorption in the pancreas for use in light modeling of the PDT treatment. Energy threshold contours yielded estimates for necrotic volume based on this light modeling. Both contrast-derived venous blood content and necrotic volume from light modeling yielded strong correlations with observed necrotic volume (R2 = 0.85 and 0.91, respectively). These correlations were much stronger than those obtained by correlating energy delivered versus necrotic volume in the VERTPAC-01 study and in retrospective analysis from a prior clinical study. This demonstrates that contrast CT can provide key surrogate dosimetry information to assess treatment response. It also implies that light attenuation is likely the dominant factor in the VERTPAC treatment response, as opposed to other factors such as drug distribution. This study is the first to show that contrast CT provides needed surrogate dosimetry information to predict treatment response in a manner which uses standard-of-care clinical images, rather than invasive dosimetry methods.

Drug Intervention Response Predictions with PARADIGM (DIRPP) identifies drug resistant cancer cell lines and pathway mechanisms of resistance.

PubMed

Brubaker, Douglas; Difeo, Analisa; Chen, Yanwen; Pearl, Taylor; Zhai, Kaide; Bebek, Gurkan; Chance, Mark; Barnholtz-Sloan, Jill

2014-01-01

The revolution in sequencing techniques in the past decade has provided an extensive picture of the molecular mechanisms behind complex diseases such as cancer. The Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Project (CGP) have provided an unprecedented opportunity to examine copy number, gene expression, and mutational information for over 1000 cell lines of multiple tumor types alongside IC50 values for over 150 different drugs and drug related compounds. We present a novel pipeline called DIRPP, Drug Intervention Response Predictions with PARADIGM7, which predicts a cell line's response to a drug intervention from molecular data. PARADIGM (Pathway Recognition Algorithm using Data Integration on Genomic Models) is a probabilistic graphical model used to infer patient specific genetic activity by integrating copy number and gene expression data into a factor graph model of a cellular network. We evaluated the performance of DIRPP on endometrial, ovarian, and breast cancer related cell lines from the CCLE and CGP for nine drugs. The pipeline is sensitive enough to predict the response of a cell line with accuracy and precision across datasets as high as 80 and 88% respectively. We then classify drugs by the specific pathway mechanisms governing drug response. This classification allows us to compare drugs by cellular response mechanisms rather than simply by their specific gene targets. This pipeline represents a novel approach for predicting clinical drug response and generating novel candidates for drug repurposing and repositioning.

The prognostic value of CT radiomic features for patients with pulmonary adenocarcinoma treated with EGFR tyrosine kinase inhibitors

PubMed Central

Kim, Hyungjin; Park, Sang Joon; Kim, Miso; Kim, Tae Min; Kim, Dong-Wan; Heo, Dae Seog; Goo, Jin Mo

2017-01-01

Purpose To determine if the radiomic features on CT can predict progression-free survival (PFS) in epidermal growth factor receptor (EGFR) mutant adenocarcinoma patients treated with first-line EGFR tyrosine kinase inhibitors (TKIs) and to identify the incremental value of radiomic features over conventional clinical factors in PFS prediction. Methods In this institutional review board–approved retrospective study, pretreatment contrast-enhanced CT and first follow-up CT after initiation of TKIs were analyzed in 48 patients (M:F = 23:25; median age: 61 years). Radiomic features at baseline, at 1st first follow-up, and the percentage change between the two were determined. A Cox regression model was used to predict PFS with nonredundant radiomic features and clinical factors, respectively. The incremental value of radiomic features over the clinical factors in PFS prediction was also assessed by way of a concordance index. Results Roundness (HR: 3.91; 95% CI: 1.72, 8.90; P = 0.001) and grey-level nonuniformity (HR: 3.60; 95% CI: 1.80, 7.18; P<0.001) were independent predictors of PFS. For clinical factors, patient age (HR: 2.11; 95% CI: 1.01, 4.39; P = 0.046), baseline tumor diameter (HR: 1.03; 95% CI: 1.01, 1.05; P = 0.002), and treatment response (HR: 0.46; 95% CI: 0.24, 0.87; P = 0.017) were independent predictors. The addition of radiomic features to clinical factors significantly improved predictive performance (concordance index; combined model = 0.77, clinical-only model = 0.69, P<0.001). Conclusions Radiomic features enable PFS estimation in EGFR mutant adenocarcinoma patients treated with first-line EGFR TKIs. Radiomic features combined with clinical factors provide significant improvement in prognostic performance compared with using only clinical factors. PMID:29099855

Predictors of the patency of self-expandable metallic stents in malignant gastroduodenal obstruction

PubMed Central

Kim, Seung Han; Chun, Hoon Jai; Yoo, In Kyung; Lee, Jae Min; Nam, Seung Joo; Choi, Hyuk Soon; Kim, Eun Sun; Keum, Bora; Seo, Yeon Seok; Jeen, Yoon Tae; Lee, Hong Sik; Um, Soon Ho; Kim, Chang Duck

2015-01-01

AIM: To investigate the predictive factors of self-expandable metallic stent patency after stent placement in patients with inoperable malignant gastroduodenal obstruction. METHODS: A total of 116 patients underwent stent placements for inoperable malignant gastroduodenal obstruction at a tertiary academic center. Clinical success was defined as acceptable decompression of the obstructive lesion within the malignant gastroduodenal neoplasm. We evaluated patient comorbidities and clinical statuses using the World Health Organization’s scoring system and categorized patient responses to chemotherapy using the Response Evaluation Criteria in Solid Tumors criteria. We analyzed the relationships between possible predictive factors and stent patency. RESULTS: Self-expandable metallic stent placement was technically successful in all patients (100%), and the clinical success rate was 84.2%. In a multivariate Cox proportional hazards model, carcinoembryonic antigen (CEA) levels were correlated with a reduction in stent patency [P = 0.006; adjusted hazard ratio (aHR) = 2.92, 95%CI: 1.36-6.25]. Palliative chemotherapy was statistically associated with an increase in stent patency (P = 0.009; aHR = 0.27, 95%CI: 0.10-0.72). CONCLUSION: CEA levels can easily be measured at the time of stent placement and may help clinicians to predict stent patency and determine the appropriate stent procedure. PMID:26290640

The validation of pharmacogenetics for the identification of Fabry patients to be treated with migalastat.

PubMed

Benjamin, Elfrida R; Della Valle, Maria Cecilia; Wu, Xiaoyang; Katz, Evan; Pruthi, Farhana; Bond, Sarah; Bronfin, Benjamin; Williams, Hadis; Yu, Julie; Bichet, Daniel G; Germain, Dominique P; Giugliani, Roberto; Hughes, Derralynn; Schiffmann, Raphael; Wilcox, William R; Desnick, Robert J; Kirk, John; Barth, Jay; Barlow, Carrolee; Valenzano, Kenneth J; Castelli, Jeff; Lockhart, David J

2017-04-01

Fabry disease is an X-linked lysosomal storage disorder caused by mutations in the α-galactosidase A gene. Migalastat, a pharmacological chaperone, binds to specific mutant forms of α-galactosidase A to restore lysosomal activity. A pharmacogenetic assay was used to identify the α-galactosidase A mutant forms amenable to migalastat. Six hundred Fabry disease-causing mutations were expressed in HEK-293 (HEK) cells; increases in α-galactosidase A activity were measured by a good laboratory practice (GLP)-validated assay (GLP HEK/Migalastat Amenability Assay). The predictive value of the assay was assessed based on pharmacodynamic responses to migalastat in phase II and III clinical studies. Comparison of the GLP HEK assay results in in vivo white blood cell α-galactosidase A responses to migalastat in male patients showed high sensitivity, specificity, and positive and negative predictive values (≥0.875). GLP HEK assay results were also predictive of decreases in kidney globotriaosylceramide in males and plasma globotriaosylsphingosine in males and females. The clinical study subset of amenable mutations (n = 51) was representative of all 268 amenable mutations identified by the GLP HEK assay. The GLP HEK assay is a clinically validated method of identifying male and female Fabry patients for treatment with migalastat.Genet Med 19 4, 430-438.

The Global ECT-MRI Research Collaboration (GEMRIC): Establishing a multi-site investigation of the neural mechanisms underlying response to electroconvulsive therapy.

PubMed

Oltedal, Leif; Bartsch, Hauke; Sørhaug, Ole Johan Evjenth; Kessler, Ute; Abbott, Christopher; Dols, Annemieke; Stek, Max L; Ersland, Lars; Emsell, Louise; van Eijndhoven, Philip; Argyelan, Miklos; Tendolkar, Indira; Nordanskog, Pia; Hamilton, Paul; Jorgensen, Martin Balslev; Sommer, Iris E; Heringa, Sophie M; Draganski, Bogdan; Redlich, Ronny; Dannlowski, Udo; Kugel, Harald; Bouckaert, Filip; Sienaert, Pascal; Anand, Amit; Espinoza, Randall; Narr, Katherine L; Holland, Dominic; Dale, Anders M; Oedegaard, Ketil J

2017-01-01

Major depression, currently the world's primary cause of disability, leads to profound personal suffering and increased risk of suicide. Unfortunately, the success of antidepressant treatment varies amongst individuals and can take weeks to months in those who respond. Electroconvulsive therapy (ECT), generally prescribed for the most severely depressed and when standard treatments fail, produces a more rapid response and remains the most effective intervention for severe depression. Exploring the neurobiological effects of ECT is thus an ideal approach to better understand the mechanisms of successful therapeutic response. Though several recent neuroimaging studies show structural and functional changes associated with ECT, not all brain changes associate with clinical outcome. Larger studies that can address individual differences in clinical and treatment parameters may better target biological factors relating to or predictive of ECT-related therapeutic response. We have thus formed the Global ECT-MRI Research Collaboration (GEMRIC) that aims to combine longitudinal neuroimaging as well as clinical, behavioral and other physiological data across multiple independent sites. Here, we summarize the ECT sample characteristics from currently participating sites, and the common data-repository and standardized image analysis pipeline developed for this initiative. This includes data harmonization across sites and MRI platforms, and a method for obtaining unbiased estimates of structural change based on longitudinal measurements with serial MRI scans. The optimized analysis pipeline, together with the large and heterogeneous combined GEMRIC dataset, will provide new opportunities to elucidate the mechanisms of ECT response and the factors mediating and predictive of clinical outcomes, which may ultimately lead to more effective personalized treatment approaches.

Sleep-stage sequencing of sleep-onset REM periods in MSLT predicts treatment response in patients with narcolepsy.

PubMed

Drakatos, Panagis; Patel, Kishankumar; Thakrar, Chiraag; Williams, Adrian J; Kent, Brian D; Leschziner, Guy D

2016-04-01

Current treatment recommendations for narcolepsy suggest that modafinil should be used as a first-line treatment ahead of conventional stimulants or sodium oxybate. In this study, performed in a tertiary sleep disorders centre, treatment responses were examined following these recommendations, and the ability of sleep-stage sequencing of sleep-onset rapid eye movement periods in the multiple sleep latency test to predict treatment response. Over a 3.5-year period, 255 patients were retrospectively identified in the authors' database as patients diagnosed with narcolepsy, type 1 (with cataplexy) or type 2 (without) using clinical and polysomnographic criteria. Eligible patients were examined in detail, sleep study data were abstracted and sleep-stage sequencing of sleep-onset rapid eye movement periods were analysed. Response to treatment was graded utilizing an internally developed scale. Seventy-five patients were included (39% males). Forty (53%) were diagnosed with type 1 narcolepsy with a mean follow-up of 2.37 ± 1.35 years. Ninety-seven percent of the patients were initially started on modafinil, and overall 59% reported complete response on the last follow-up. Twenty-nine patients (39%) had the sequence of sleep stage 1 or wake to rapid eye movement in all of their sleep-onset rapid eye movement periods, with most of these diagnosed as narcolepsy type 1 (72%). The presence of this specific sleep-stage sequence in all sleep-onset rapid eye movement periods was associated with worse treatment response (P = 0.0023). Sleep-stage sequence analysis of sleep-onset rapid eye movement periods in the multiple sleep latency test may aid the prediction of treatment response in narcoleptics and provide a useful prognostic tool in clinical practice, above and beyond their classification as narcolepsy type 1 or 2. © 2015 European Sleep Research Society.

Pharmacogenomics of Breast Cancer Therapy: An Update

PubMed Central

Westbrook, Kelly

2013-01-01

Clinical and histopathologic characteristics of breast cancer have long played an important role in treatment decision-making. Well-recognized prognostic factors include tumor size, node status, presence or absence of metastases, tumor grade, and hormone receptor expression. High tumor grade, presence of hormone receptors, and HER2-positivity are a few predictive markers of response to chemotherapy, endocrine manipulations, and anti-HER2 agents, respectively. However, there is much heterogeneity of outcomes in patients with similar clinical and pathologic features despite equivalent treatment regimens. Some of the difference in response to specific therapies can be attributed to somatic tumor characteristics, such as degree of estrogen receptor expression and HER2 status. In recent years, there has been great interest in evaluating the role that pharmacogenetics/pharmacogenomics, or variations in germline DNA, play in alteration of drug metabolism and activity, thus leading to disparate outcomes among patients with similar tumor characteristics. The utility of these variations in treatment decision-making remains debated. Here we review the data available to date on genomic variants that may influence response to drugs commonly used to treat breast cancer. While none of the variants reported to date have demonstrated clinical utility, ongoing prospective studies and increasing understanding of pharmacogenetics will allow us to better predict risk of toxicity or likelihood of response to specific treatments and to provide a more personalized therapy. PMID:23500718

Genome-wide data reveal novel genes for methotrexate response in a large cohort of juvenile idiopathic arthritis cases.

PubMed

Cobb, J; Cule, E; Moncrieffe, H; Hinks, A; Ursu, S; Patrick, F; Kassoumeri, L; Flynn, E; Bulatović, M; Wulffraat, N; van Zelst, B; de Jonge, R; Bohm, M; Dolezalova, P; Hirani, S; Newman, S; Whitworth, P; Southwood, T R; De Iorio, M; Wedderburn, L R; Thomson, W

2014-08-01

Clinical response to methotrexate (MTX) treatment for children with juvenile idiopathic arthritis (JIA) displays considerable heterogeneity. Currently, there are no reliable predictors to identify non-responders: earlier identification could lead to a targeted treatment. We genotyped 759 JIA cases from the UK, the Netherlands and Czech Republic. Clinical variables were measured at baseline and 6 months after start of the treatment. In Phase I analysis, samples were analysed for the association with MTX response using ordinal regression of ACR-pedi categories and linear regression of change in clinical variables, and identified 31 genetic regions (P<0.001). Phase II analysis increased SNP density in the most strongly associated regions, identifying 14 regions (P<1 × 10(-5)): three contain genes of particular biological interest (ZMIZ1, TGIF1 and CFTR). These data suggest a role for novel pathways in MTX response and further investigations within associated regions will help to reach our goal of predicting response to MTX in JIA.

[Prescribing monitoring in clinical practice: from enlightened empiricism to rational strategies].

PubMed

Buclin, Thierry; Herzig, Lilli

2013-05-15

Monitoring of a medical condition is the periodic measurement of one or several physiological or biological variables to detect a signal regarding its clinical progression or its response to treatment. We distinguish different medical situations between diagnostic, clinical and therapeutic process to apply monitoring. Many clinical, variables can be used for monitoring, once their intrinsic properties (normal range, critical difference, kinetics, reactivity) and external validity (pathophysiological importance, predictive power for clinical outcomes) are established. A formal conceptualization of monitoring is being developed and should support the rational development of monitoring strategies and their validation through appropriate clinical trials.

Fear-avoidance beliefs and temporal summation of evoked thermal pain influence self-report of disability in patients with chronic low back pain.

PubMed

George, Steven Z; Wittmer, Virgil T; Fillingim, Roger B; Robinson, Michael E

2006-03-01

Quantitative sensory testing has demonstrated a promising link between experimentally determined pain sensitivity and clinical pain. However, previous studies of quantitative sensory testing have not routinely considered the important influence of psychological factors on clinical pain. This study investigated whether measures of thermal pain sensitivity (temporal summation, first pulse response, and tolerance) contributed to clinical pain reports for patients with chronic low back pain, after controlling for depression or fear-avoidance beliefs about work. Consecutive patients (n=27) with chronic low back pain were recruited from an interdisciplinary pain rehabilitation program in Jacksonville, FL. Patients completed validated self-report questionnaires for depression, fear-avoidance beliefs, clinical pain intensity, and clinical pain related disability. Patients also underwent quantitative sensory testing from previously described protocols to determine thermal pain sensitivity (temporal summation, first pulse response, and tolerance). Hierarchical regression models investigated the contribution of depression and thermal pain sensitivity to clinical pain intensity, and fear-avoidance beliefs and thermal pain sensitivity to clinical pain related disability. None of the measures of thermal pain sensitivity contributed to clinical pain intensity after controlling for depression. Temporal summation of evoked thermal pain significantly contributed to clinical pain disability after controlling for fear-avoidance beliefs about work. Measures of thermal pain sensitivity did not contribute to pain intensity, after controlling for depression. Fear-avoidance beliefs about work and temporal summation of evoked thermal pain significantly influenced pain related disability. These factors should be considered as potential outcome predictors for patients with work-related low back pain. This study supported the neuromatrix theory of pain for patients with CLBP, as cognitive-evaluative factor contributed to pain perception, and cognitive-evaluative and sensory-discriminative factors uniquely contributed to an action program in response to chronic pain. Future research will determine if a predictive model consisting of fear-avoidance beliefs and temporal summation of evoked thermal pain has predictive validity for determining clinical outcome in rehabilitation or vocational settings.

Predictive Biomarkers for Linking Disease Pathology and Drug Effect.

PubMed

Mayer, Bernd; Heinzel, Andreas; Lukas, Arno; Perco, Paul

2017-01-01

Productivity in drug R&D continues seeing significant attrition in clinical stage testing. Approval of new molecular entities proceeds with slow pace specifically when it comes to chronic, age-related diseases, calling for new conceptual approaches, methodological implementation and organizational adoption in drug development. Detailed phenotyping of disease presentation together with comprehensive representation of drug mechanism of action is considered as a path forward, and a big data spectrum has become available covering behavioral, clinical and molecular characteristics, the latter combining reductionist and explorative strategies. On this basis integrative analytics in the realm of Systems Biology has emerged, essentially aiming at traversing associations into causal relationships for bridging molecular disease specifics and clinical phenotype surrogates and finally explaining drug response and outcome. From a conceptual perspective bottom-up modeling approaches are available, with dynamical hierarchies as formalism capable of describing clinical findings as emergent properties of an underlying molecular process network comprehensively resembling disease pathology. In such representation biomarker candidates serve as proxy of a molecular process set, at the interface of a corresponding representation of drug mechanism of action allowing patient stratification and prediction of drug response. In practical implementation network analytics on a protein coding gene level has provided a number of example cases for matching disease presentation and drug molecular effect, and workflows combining computational hypothesis generation and experimental evaluation have become available for systematically optimizing biomarker candidate selection. With biomarker-based enrichment strategies in adaptive clinical trials, implementation routes for tackling development attrition are provided. Predictive biomarkers add precision in drug development and as companion diagnostics in clinical practice. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Does parental consent for birth control affect underage pregnancy rates? The case of Texas.

PubMed

Girma, Sourafel; Paton, David

2013-12-01

Previous work based on conjectural responses of minors predicted that the 2003 Texas requirement for parental consent for state-funded birth control to minors would lead to a large increase in underage pregnancies. We use state- and county-level data to test this prediction. The latter allow us to compare the impact of parental consent in counties with and without state-funded family planning clinics. We control for characteristics systematically correlated with the presence of state-funded clinics by combining difference-in-difference estimation with propensity score-weighted regressions. The evidence suggests that the parental consent mandate led to a large decrease in attendance at family planning clinics among teens but did not lead to an increase in underage pregnancies.

Radiomics in Oncological PET/CT: Clinical Applications.

PubMed

Lee, Jeong Won; Lee, Sang Mi

2018-06-01

18 F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is widely used for staging, evaluating treatment response, and predicting prognosis in malignant diseases. FDG uptake and volumetric PET parameters such as metabolic tumor volume have been used and are still used as conventional PET parameters to assess biological characteristics of tumors. However, in recent years, additional features derived from PET images by computational processing have been found to reflect intratumoral heterogeneity, which is related to biological tumor features, and to provide additional predictive and prognostic information, which leads to the concept of radiomics. In this review, we focus on recent clinical studies of malignant diseases that investigated intratumoral heterogeneity on PET/CT, and we discuss its clinical role in various cancers.

Clinical response to PD-1 blockade correlates with a sub-fraction of peripheral central memory CD4+ T cells in patients with malignant melanoma.

PubMed

Takeuchi, Yoshiko; Tanemura, Atsushi; Tada, Yasuko; Katayama, Ichiro; Kumanogoh, Atsushi; Nishikawa, Hiroyoshi

2018-02-03

Cancer immunotherapy that blocks immune checkpoint molecules, such as PD-1/PD-L1, unleashes dysfunctional antitumor T-cell responses and has durable clinical benefits in various types of cancers. Yet its clinical efficacy is limited to a small proportion of patients, highlighting the need for identifying biomarkers that can predict the clinical response by exploring antitumor responses crucial for tumor regression. Here, we explored comprehensive immune-cell responses associated with clinical benefits using PBMCs from patients with malignant melanoma treated with anti-PD-1 monoclonal antibody. Pre- and post-treatment samples were collected from two different cohorts (discovery set and validation set) and subjected to mass cytometry assays that measured the expression levels of 35 proteins. Screening by high dimensional clustering in the discovery set identified increases in three micro-clusters of CD4+ T cells, a subset of central memory CD4+ T cells harboring the CD27+FAS-CD45RA-CCR7+ phenotype, after treatment in long-term survivors, but not in non-responders. The same increase was also observed in clinical responders in the validation set. We propose that increases in this subset of central memory CD4+ T cells in peripheral blood can be potentially used as a predictor of clinical response to PD-1 blockade therapy in patients with malignant melanoma. © The Japanese Society for Immunology. 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Quantitative imaging features of pretreatment CT predict volumetric response to chemotherapy in patients with colorectal liver metastases.

PubMed

Creasy, John M; Midya, Abhishek; Chakraborty, Jayasree; Adams, Lauryn B; Gomes, Camilla; Gonen, Mithat; Seastedt, Kenneth P; Sutton, Elizabeth J; Cercek, Andrea; Kemeny, Nancy E; Shia, Jinru; Balachandran, Vinod P; Kingham, T Peter; Allen, Peter J; DeMatteo, Ronald P; Jarnagin, William R; D'Angelica, Michael I; Do, Richard K G; Simpson, Amber L

2018-06-19

This study investigates whether quantitative image analysis of pretreatment CT scans can predict volumetric response to chemotherapy for patients with colorectal liver metastases (CRLM). Patients treated with chemotherapy for CRLM (hepatic artery infusion (HAI) combined with systemic or systemic alone) were included in the study. Patients were imaged at baseline and approximately 8 weeks after treatment. Response was measured as the percentage change in tumour volume from baseline. Quantitative imaging features were derived from the index hepatic tumour on pretreatment CT, and features statistically significant on univariate analysis were included in a linear regression model to predict volumetric response. The regression model was constructed from 70% of data, while 30% were reserved for testing. Test data were input into the trained model. Model performance was evaluated with mean absolute prediction error (MAPE) and R 2 . Clinicopatholologic factors were assessed for correlation with response. 157 patients were included, split into training (n = 110) and validation (n = 47) sets. MAPE from the multivariate linear regression model was 16.5% (R 2 = 0.774) and 21.5% in the training and validation sets, respectively. Stratified by HAI utilisation, MAPE in the validation set was 19.6% for HAI and 25.1% for systemic chemotherapy alone. Clinical factors associated with differences in median tumour response were treatment strategy, systemic chemotherapy regimen, age and KRAS mutation status (p < 0.05). Quantitative imaging features extracted from pretreatment CT are promising predictors of volumetric response to chemotherapy in patients with CRLM. Pretreatment predictors of response have the potential to better select patients for specific therapies. • Colorectal liver metastases (CRLM) are downsized with chemotherapy but predicting the patients that will respond to chemotherapy is currently not possible. • Heterogeneity and enhancement patterns of CRLM can be measured with quantitative imaging. • Prediction model constructed that predicts volumetric response with 20% error suggesting that quantitative imaging holds promise to better select patients for specific treatments.

Nurse-Administered, Gut-Directed Hypnotherapy in IBS: Efficacy and Factors Predicting a Positive Response.

PubMed

Lövdahl, Jenny; Ringström, Gisela; Agerforz, Pia; Törnblom, Hans; Simrén, Magnus

2015-07-01

Hypnotherapy is an effective treatment in irritable bowel syndrome (IBS). It is often delivered by a psychotherapist and is costly and time consuming. Nurse-administered hypnotherapy could increase availability and reduce costs. In this study the authors evaluate the effectiveness of nurse-administered, gut-directed hypnotherapy and identify factors predicting treatment outcome. Eighty-five patients were included in the study. Participants received hypnotherapy by a nurse once/week for 12 weeks. Patients reported marked improvement in gastrointestinal (GI) and extra-colonic symptoms after treatment, as well as a reduction in GI-specific anxiety, general anxiety, and depression. Fifty-eight percent were responders after the 12 weeks treatment period, and of these 82% had a favorable clinical response already at week 6. Women were more likely than men to respond favorably to the treatment. Nurse-administered hypnotherapy is an effective treatment for IBS. Being female and reporting a favorable response to treatment by week 6 predicted a positive treatment response at the end of the 12 weeks treatment period.

How to personalize ovarian stimulation in clinical practice.

PubMed

Sighinolfi, Giovanna; Grisendi, Valentina; La Marca, Antonio

2017-09-01

Controlled ovarian stimulation (COS) in in vitro fertilization (IVF) cycles is the starting point from which couple's prognosis depends. Individualization in follicle-stimulating hormone (FSH) starting dose and protocol used is based on ovarian response prediction, which depends on ovarian reserve. Anti-Müllerian hormone levels and the antral follicle count are considered the most accurate and reliable markers of ovarian reserve. A literature search was performed for studies that addressed the ability of ovarian reserve markers to predict poor and high ovarian response in assisted reproductive technology cycles. According to the predicted response to ovarian stimulation (poor- normal- or high- response), it is possible to counsel couples before treatment about the prognosis, and also to individualize ovarian stimulation protocols, choosing among GnRH-agonists or antagonists for endogenous FSH suppression, and the FSH starting dose in order to decrease the risk of cycle cancellation and ovarian hyperstimulation syndrome. In this review we discuss how to choose the best COS therapy, based on ovarian reserve markers, in order to enhance chances in IVF.

Plasma genetic and genomic abnormalities predict treatment response and clinical outcome in advanced prostate cancer.

PubMed

Xia, Shu; Kohli, Manish; Du, Meijun; Dittmar, Rachel L; Lee, Adam; Nandy, Debashis; Yuan, Tiezheng; Guo, Yongchen; Wang, Yuan; Tschannen, Michael R; Worthey, Elizabeth; Jacob, Howard; See, William; Kilari, Deepak; Wang, Xuexia; Hovey, Raymond L; Huang, Chiang-Ching; Wang, Liang

2015-06-30

Liquid biopsies, examinations of tumor components in body fluids, have shown promise for predicting clinical outcomes. To evaluate tumor-associated genomic and genetic variations in plasma cell-free DNA (cfDNA) and their associations with treatment response and overall survival, we applied whole genome and targeted sequencing to examine the plasma cfDNAs derived from 20 patients with advanced prostate cancer. Sequencing-based genomic abnormality analysis revealed locus-specific gains or losses that were common in prostate cancer, such as 8q gains, AR amplifications, PTEN losses and TMPRSS2-ERG fusions. To estimate tumor burden in cfDNA, we developed a Plasma Genomic Abnormality (PGA) score by summing the most significant copy number variations. Cox regression analysis showed that PGA scores were significantly associated with overall survival (p < 0.04). After androgen deprivation therapy or chemotherapy, targeted sequencing showed significant mutational profile changes in genes involved in androgen biosynthesis, AR activation, DNA repair, and chemotherapy resistance. These changes may reflect the dynamic evolution of heterozygous tumor populations in response to these treatments. These results strongly support the feasibility of using non-invasive liquid biopsies as potential tools to study biological mechanisms underlying therapy-specific resistance and to predict disease progression in advanced prostate cancer.

Oxytocin receptor gene variations predict neural and behavioral response to oxytocin in autism

PubMed Central

Watanabe, Takamitsu; Otowa, Takeshi; Abe, Osamu; Kuwabara, Hitoshi; Aoki, Yuta; Natsubori, Tatsunobu; Takao, Hidemasa; Kakiuchi, Chihiro; Kondo, Kenji; Ikeda, Masashi; Iwata, Nakao; Kasai, Kiyoto; Sasaki, Tsukasa

2017-01-01

Abstract Oxytocin appears beneficial for autism spectrum disorder (ASD), and more than 20 single-nucleotide polymorphisms (SNPs) in oxytocin receptor (OXTR) are relevant to ASD. However, neither biological functions of OXTR SNPs in ASD nor critical OXTR SNPs that determine oxytocin’s effects on ASD remains known. Here, using a machine-learning algorithm that was designed to evaluate collective effects of multiple SNPs and automatically identify most informative SNPs, we examined relationships between 27 representative OXTR SNPs and six types of behavioral/neural response to oxytocin in ASD individuals. The oxytocin effects were extracted from our previous placebo-controlled within-participant clinical trial administering single-dose intranasal oxytocin to 38 high-functioning adult Japanese ASD males. Consequently, we identified six different SNP sets that could accurately predict the six different oxytocin efficacies, and confirmed the robustness of these SNP selections against variations of the datasets and analysis parameters. Moreover, major alleles of several prominent OXTR SNPs—including rs53576 and rs2254298—were found to have dissociable effects on the oxytocin efficacies. These findings suggest biological functions of the OXTR SNP variants on autistic oxytocin responses, and implied that clinical oxytocin efficacy may be genetically predicted before its actual administration, which would contribute to establishment of future precision medicines for ASD. PMID:27798253

Stress enhanced calcium kinetics in a neuron.

PubMed

Kant, Aayush; Bhandakkar, Tanmay K; Medhekar, Nikhil V

2018-02-01

Accurate modeling of the mechanobiological response of a Traumatic Brain Injury is beneficial toward its effective clinical examination, treatment and prevention. Here, we present a stress history-dependent non-spatial kinetic model to predict the microscale phenomena of secondary insults due to accumulation of excess calcium ions (Ca[Formula: see text]) induced by the macroscale primary injuries. The model is able to capture the experimentally observed increase and subsequent partial recovery of intracellular Ca[Formula: see text] concentration in response to various types of mechanical impulses. We further establish the accuracy of the model by comparing our predictions with key experimental observations.

The effect of pneumatic dilation in management of postfundoplication dysphagia.

PubMed

Sunjaya, D; Podboy, A; Blackmon, S H; Katzka, D; Halland, M

2017-06-01

Fundoplication surgery is a commonly performed procedure for gastro-esophageal reflux disease or hiatal hernia repair. Up to 10% of patients develop persistent postoperative dysphagia after surgery. Data on the effectiveness of pneumatic dilation for treatment are limited. The aim of this study was to evaluate clinical outcomes and identify clinical factors associated with successful response to pneumatic dilation among patients with persistent postfundoplication dysphagia (PPFD). We retrospectively evaluated patients who had undergone pneumatic dilation for PPFD between 1999 and 2016. Patients with dysphagia or achalasia prior to fundoplication were excluded. Demographic information, surgical history, severity of dysphagia, and clinical outcomes were collected. Data pertaining to esophagram, manometry, endoscopy, and pneumatic dilation were also collected. We identified 38 patients (82% female, 95% Caucasian, and median age 59 years) with PPFD who completed pneumatic dilation. The median postfundoplication dysphagia score was 2. Eleven patients had abnormal peristalsis on manometry. Seventeen patients reported response (seven complete) with an average decrease of 1 in their dysphagia score. Fifteen patients underwent reoperation due to PPFD. Hiatal hernia repair was the only factor that predicts a higher response rate to pneumatic dilation. Only one patient in our study developed complication (pneumoperitoneum) from pneumatic dilation. We found that pneumatic dilation to be a safe treatment option for PPFD with moderate efficacy. Patients who developed PPFD after a hiatal hernia repair may gain the greatest benefit after pneumatic dilation. We were not able to identify additional clinical, radiological, endoscopic, or manometric parameters that were predictive of response. © 2017 John Wiley & Sons Ltd.

Evaluation of stroke volume variation obtained by arterial pulse contour analysis to predict fluid responsiveness intraoperatively.

PubMed

Lahner, D; Kabon, B; Marschalek, C; Chiari, A; Pestel, G; Kaider, A; Fleischmann, E; Hetz, H

2009-09-01

Fluid management guided by oesophageal Doppler monitor has been reported to improve perioperative outcome. Stroke volume variation (SVV) is considered a reliable clinical predictor of fluid responsiveness. Consequently, the aim of the present trial was to evaluate the accuracy of SVV determined by arterial pulse contour (APCO) analysis, using the FloTrac/Vigileo system, to predict fluid responsiveness as measured by the oesophageal Doppler. Patients undergoing major abdominal surgery received intraoperative fluid management guided by oesophageal Doppler monitoring. Fluid boluses of 250 ml each were administered in case of a decrease in corrected flow time (FTc) to <350 ms. Patients were connected to a monitoring device, obtaining SVV by APCO. Haemodynamic variables were recorded before and after fluid bolus application. Fluid responsiveness was defined as an increase in stroke volume index >10%. The ability of SVV to predict fluid responsiveness was assessed by calculation of the area under the receiver operating characteristic (ROC) curve. Twenty patients received 67 fluid boluses. Fifty-two of the 67 fluid boluses administered resulted in fluid responsiveness. SVV achieved an area under the ROC curve of 0.512 [confidence interval (CI) 0.32-0.70]. A cut-off point for fluid responsiveness was found for SVV > or =8.5% (sensitivity: 77%; specificity: 43%; positive predictive value: 84%; and negative predictive value: 33%). This prospective, interventional observer-blinded study demonstrates that SVV obtained by APCO, using the FloTrac/Vigileo system, is not a reliable predictor of fluid responsiveness in the setting of major abdominal surgery.

MADRS symptom subtypes in ECT-treated depressed patients: relationship to response and subsequent ECT.

PubMed

Spashett, Renee; Fernie, Gordon; Reid, Ian C; Cameron, Isobel M

2014-09-01

This study aimed to explore the relationship of Montgomery-Åsberg Depression Rating Scale (MADRS) symptom subtypes with response to electroconvulsive therapy (ECT) and subsequent ECT treatment within 12 months. A consecutive sample of 414 patients with depression receiving ECT in the North East of Scotland was assessed by retrospective chart review. Response rate was defined as greater than or equal to 50% decrease in pretreatment total MADRS score or a posttreatment total MADRS less than or equal to 10. Principal component analyses were conducted on a sample with psychotic features (n = 124) and a sample without psychotic features (n = 290). Scores on extracted factor subscales, clinical and demographic characteristics were assessed for association with response and subsequent ECT treatment within 12 months. Where more than 1 variable was associated with response or subsequent ECT, logistic regression analysis was applied. MADRS symptom subtypes formed 3 separate factors in both samples. Logistic regression revealed older age and high "Despondency" subscale score predicted response in the nonpsychotic group. Older age alone predicted response in the group with psychotic features. Nonpsychotic patients subsequently re-treated with ECT were older than those not prescribed subsequent ECT. No association of variables emerged with subsequent ECT treatment in the group with psychotic features. Being of older age and the presence of psychotic features predicted response. Presence of psychotic features alone predicted subsequent retreatment. Subscale scores of the MADRS are of limited use in predicting which patients with depression will respond to ECT, with the exception of "Despondency" subscale scores in patients without psychotic features.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gilchrist, Kristin H., E-mail: kgilchrist@rti.org; Lewis, Gregory F.; Gay, Elaine A.

Microelectrode arrays (MEAs) recording extracellular field potentials of human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CM) provide a rich data set for functional assessment of drug response. The aim of this work is the development of a method for a systematic analysis of arrhythmia using MEAs, with emphasis on the development of six parameters accounting for different types of cardiomyocyte signal irregularities. We describe a software approach to carry out such analysis automatically including generation of a heat map that enables quick visualization of arrhythmic liability of compounds. We also implemented signal processing techniques for reliable extraction of the repolarization peak formore » field potential duration (FPD) measurement even from recordings with low signal to noise ratios. We measured hiPS-CM's on a 48 well MEA system with 5 minute recordings at multiple time points (0.5, 1, 2 and 4 h) after drug exposure. We evaluated concentration responses for seven compounds with a combination of hERG, QT and clinical proarrhythmia properties: Verapamil, Ranolazine, Flecainide, Amiodarone, Ouabain, Cisapride, and Terfenadine. The predictive utility of MEA parameters as surrogates of these clinical effects were examined. The beat rate and FPD results exhibited good correlations with previous MEA studies in stem cell derived cardiomyocytes and clinical data. The six-parameter arrhythmia assessment exhibited excellent predictive agreement with the known arrhythmogenic potential of the tested compounds, and holds promise as a new method to predict arrhythmic liability. - Highlights: • Six parameters describing arrhythmia were defined and measured for known compounds. • Software for efficient parameter extraction from large MEA data sets was developed. • The proposed cellular parameter set is predictive of clinical drug proarrhythmia.« less

Predictors of response to anti-tumor necrosis factor therapy in ulcerative colitis.

PubMed

Zampeli, Evanthia; Gizis, Michalis; Siakavellas, Spyros I; Bamias, Giorgos

2014-08-15

Ulcerative colitis (UC) is an immune-mediated, chronic inflammatory disease of the large intestine. Its course is characterized by flares of acute inflammation and periods of low-grade chronic inflammatory activity or remission. Monoclonal antibodies against tumor necrosis factor (anti-TNF) are part of the therapeutic armamentarium and are used in cases of moderate to severe UC that is refractory to conventional treatment with corticosteroids and/or immunosuppressants. Therapeutic response to these agents is not uniform and a large percentage of patients either fail to improve (primary non-response) or lose response after a period of improvement (secondary non-response/loss of response). In addition, the use of anti-TNF agents has been related to uncommon but potentially serious adverse effects that preclude their administration or lead to their discontinuation. Finally, use of these medications is associated with a considerable cost for the health system. The identification of parameters that may predict response to anti-TNF drugs in UC would help to better select for patients with a high probability to respond and minimize risk and costs for those who will not respond. Analysis of the major clinical trials and the accumulated experience with the use of anti-TNF drugs in UC has resulted to the report of such prognostic factors. Included are clinical and epidemiological characteristics, laboratory markers, endoscopic indicators and molecular (immunological/genetic) signatures. Such predictive parameters of long-term outcomes may either be present at the commencement of treatment or determined during the early period of therapy. Validation of these prognostic markers in large cohorts of patients with variable characteristics will facilitate their introduction into clinical practice and the best selection of UC patients who will benefit from anti-TNF therapy.

The Glasgow Prognostic Score Predicts Response to Chemotherapy in Patients with Metastatic Breast Cancer.

PubMed

Wang, Dexing; Duan, Li; Tu, Zhiquan; Yan, Fei; Zhang, Cuicui; Li, Xu; Cao, Yuzhu; Wen, Hongsheng

2016-01-01

Breast cancer is one of the most common causes of cancer death in women worldwide. The Glasgow Prognostic Score (GPS), a cumulative prognostic score based on C-reactive protein and albumin, indicates the presence of a systemic inflammatory response. The GPS has been adopted as a powerful prognostic tool for patients with various types of malignant tumors, including breast cancer. The aim of this study was to assess the value of the GPS in predicting the response and toxicity in breast cancer patients treated with chemotherapy. Patients with metastatic breast cancers in a progressive stage for consideration of chemotherapy were eligible. The clinical characteristics and demographics were recorded. The GPS was calculated before the onset of chemotherapy. Data on the response to chemotherapy and progression-free survival (PFS) were also collected. Objective tumor responses were evaluated according to Response Evaluation Criteria in Solid Tumors (RECIST). Toxicities were graded according to National Cancer Institute Common Terminology Criteria for Adverse Events (NCI-CTC) version 3.0 throughout therapy. In total, 106 breast cancer patients were recruited. The GPS was associated with the response rate (p = 0.05), the clinical benefit rate (p = 0.03), and PFS (p = 0.005). The GPS was the only independent predictor of PFS (p = 0.005). The GPS was significantly associated with neutropenia, thrombocytopenia, anorexia, nausea and vomiting, fatigue, and mucositis (p = 0.05-0.001). Our data demonstrate that GPS assessment is associated with poor clinical outcomes and severe chemotherapy-related toxicities in patients with metastatic breast cancer who have undergone chemotherapy, without any specific indication regarding the type of chemotherapy applied. © 2016 S. Karger AG, Basel.

Corticosteroid therapy in ulcerative colitis: Clinical response and predictors

PubMed Central

Li, Jin; Wang, Fan; Zhang, Hong-Jie; Sheng, Jian-Qiu; Yan, Wen-Feng; Ma, Min-Xing; Fan, Ru-Ying; Gu, Fang; Li, Chuan-Feng; Chen, Da-Fan; Zheng, Ping; Gu, Yu-Pei; Cao, Qian; Yang, Hong; Qian, Jia-Ming; Hu, Pin-Jin; Xia, Bing

2015-01-01

AIM: To evaluate clinical response to initial corticosteroid (CS) treatment in Chinese ulcerative colitis patients (UC) and identify predictors of clinical response. METHODS: Four hundred and twenty-three UC patients who were initially treated with oral or intravenous CS from 2007 to 2011 were retrospectively reviewed at eight inflammatory bowel disease centers in China, and 101 consecutive cases with one-year follow-up were analyzed further for clinical response and predictors. Short-term outcomes within one month were classified as primary response and primary non-response. Long-term outcomes within one year were classified as prolonged CS response, CS dependence and secondary non-response. CS refractoriness included primary and secondary non-response. Multivariate analyses were performed to identify predictors associated with clinical response. RESULTS: Within one month, 95.0% and 5.0% of the cases were classified into primary response and non-response, respectively. Within one year, 41.6% of cases were assessed as prolonged CS response, while 49.5% as CS dependence and 4.0% as secondary non-response. The rate of CS refractoriness was 8.9%, while the cumulative rate of surgery was 6.9% within one year. After multivariate analysis of all the variables, tenesmus was found to be a negative predictor of CS dependence (OR = 0.336; 95%CI: 0.147-0.768; P = 0.013) and weight loss as a predictor of CS refractoriness (OR = 5.662; 95%CI: 1.111-28.857; P = 0.040). After one-month treatment, sustained high Sutherland score (≥ 6) also predicted CS dependence (OR = 2.347; 95%CI: 0.935-5.890; P = 0.014). CONCLUSION: Tenesmus was a negative predictor of CS dependence, while weight loss and sustained high Sutherland score were strongly associated with poor CS response. PMID:25780299

Effectiveness of adalimumab for the treatment of ulcerative colitis in clinical practice: comparison between anti-tumour necrosis factor-naïve and non-naïve patients.

PubMed

Iborra, Marisa; Pérez-Gisbert, Javier; Bosca-Watts, Marta Maia; López-García, Alicia; García-Sánchez, Valle; López-Sanromán, Antonio; Hinojosa, Esther; Márquez, Lucía; García-López, Santiago; Chaparro, María; Aceituno, Montserrat; Calafat, Margalida; Guardiola, Jordi; Belloc, Blanca; Ber, Yolanda; Bujanda, Luis; Beltrán, Belén; Rodríguez-Gutiérrez, Cristina; Barrio, Jesús; Cabriada, José Luis; Rivero, Montserrat; Camargo, Raquel; van Domselaar, Manuel; Villoria, Albert; Schuterman, Hugo Salata; Hervás, David; Nos, Pilar

2017-07-01

Ulcerative colitis (UC) treatment is focused to achieve mucosal healing, avoiding disease progression. The study aimed to evaluate the real-world effectiveness of adalimumab (ADA) in UC and to identify predictors of remission to ADA. This cohort study used data from the ENEIDA registry. Clinical response, clinical remission, endoscopic remission, adverse events (AE), colectomy, and hospitalisations were evaluated; baseline characteristics and biological parameters were compared to determine predictors of response. We included 263 patients (87 naïve and 176 previously exposed to anti-tumour necrosis factor alpha, TNF). After 12 weeks, clinical response, clinical remission, and endoscopic remission rates were 51, 26, and 14 %, respectively. The naïve group demonstrated better response to treatment than the anti-TNF-exposed group at short-term. Clinical and endoscopic remission within 1 year of treatment was better in the naïve group (65 vs. 49 and 50 vs. 35 %, respectively). The rates of AE, dose-escalation, hospitalisations, and colectomy during the first year were higher in anti-TNF-exposed patients (40, 43, and 27 % vs. 26, 21, and 11 %, respectively). Patients with primary failure and intolerance to the first anti-TNF and severe disease were associated with worse clinical response. Primary non-response to prior anti-TNF treatment and severe disease were predictive of poorer clinical remission. Low levels of C-reactive protein (CRP) and faecal calprotectin (FC) at baseline were predictors of clinical remission. In clinical practice, ADA was effective in UC, especially in anti-TNF naïve patients. FC and CRP could be predictors of treatment effectiveness.

Outcome definitions and clinical predictors influence pharmacogenetic associations between HTR3A gene polymorphisms and response to clozapine in patients with schizophrenia.

PubMed

Rajkumar, A P; Poonkuzhali, B; Kuruvilla, A; Srivastava, A; Jacob, M; Jacob, K S

2012-12-01

Pharmacogenetics of schizophrenia has not yet delivered anticipated clinical dividends. Clinical heterogeneity of schizophrenia contributes to the poor replication of the findings of pharmacogenetic association studies. Functionally important HTR3A gene single-nucleotide polymorphisms (SNPs) were reported to be associated with response to clozapine. The aim of this study was to investigate how the association between HTR3A gene SNP and response to clozapine is influenced by various clinical predictors and by differing outcome definitions in patients with treatment-resistant schizophrenia (TRS). We recruited 101 consecutive patients with TRS, on stable doses of clozapine, and evaluated their HTR3A gene SNP (rs1062613 and rs2276302), psychopathology, and serum clozapine levels. We assessed their socio-demographic and clinical profiles, premorbid adjustment, traumatic events, cognition, and disability using standard assessment schedules. We evaluated their response to clozapine, by employing six differing outcome definitions. We employed appropriate multivariate statistics to calculate allelic and genotypic association, accounting for the effects of various clinical variables. T allele of rs1062613 and G allele of rs2276302 were significantly associated with good clinical response to clozapine (p = 0.02). However, varying outcome definitions make these associations inconsistent. rs1062613 and rs2276302 could explain only 13.8 % variability in the responses to clozapine, while combined clinical predictors and HTR3A pharmacogenetic association model could explain 38 % variability. We demonstrated that the results of pharmacogenetic studies in schizophrenia depend heavily on their outcome definitions and that combined clinical and pharmacogenetic models have better predictive values. Future pharmacogenetic studies should employ multiple outcome definitions and should evaluate associated clinical variables.

Next-Generation Pathology.

PubMed

Caie, Peter D; Harrison, David J

2016-01-01

The field of pathology is rapidly transforming from a semiquantitative and empirical science toward a big data discipline. Large data sets from across multiple omics fields may now be extracted from a patient's tissue sample. Tissue is, however, complex, heterogeneous, and prone to artifact. A reductionist view of tissue and disease progression, which does not take this complexity into account, may lead to single biomarkers failing in clinical trials. The integration of standardized multi-omics big data and the retention of valuable information on spatial heterogeneity are imperative to model complex disease mechanisms. Mathematical modeling through systems pathology approaches is the ideal medium to distill the significant information from these large, multi-parametric, and hierarchical data sets. Systems pathology may also predict the dynamical response of disease progression or response to therapy regimens from a static tissue sample. Next-generation pathology will incorporate big data with systems medicine in order to personalize clinical practice for both prognostic and predictive patient care.

Response, survival, and long-term toxicity after therapy with the radiolabeled somatostatin analogue [90Y-DOTA]-TOC in metastasized neuroendocrine cancers.

PubMed

Imhof, Anna; Brunner, Philippe; Marincek, Nicolas; Briel, Matthias; Schindler, Christian; Rasch, Helmut; Mäcke, Helmut R; Rochlitz, Christoph; Müller-Brand, Jan; Walter, Martin A

2011-06-10

To investigate response, survival, and safety profile of the somatostatin-based radiopeptide (90)yttrium-labeled tetraazacyclododecane-tetraacetic acid modified Tyr-octreotide ([(90)Y-DOTA]-TOC) in neuroendocrine cancers. In a clinical phase II single-center open-label trial, patients with neuroendocrine cancers were treated with repeated cycles of [(90)Y-DOTA]-TOC. Each cycle consisted of a single intravenous injection of 3.7GBq/m(2) body-surface [(90)Y-DOTA]-TOC. Additional cycles were withheld in case of tumor progression and/or permanent toxicity. Overall, 1,109 patients received 2,472 cycles of [(90)Y-DOTA]-TOC (median, two; range, one to 10 cycles per patient). Of the 1,109 patients, 378 (34.1%) experienced morphologic response; 172 (15.5%), biochemical response; and 329 (29.7%), clinical response. During a median follow-up of 23 months, 491 patients (44.3%) died. Longer survival was correlated with each: morphologic (hazard ratio [HR], 0.46; 95% CI, 0.38 to 0.56; median survival, 44.7 v 18.3 months; P < .001), biochemical (HR, 0.75; 95% CI, 0.59 to 0.96; 35.3 v 25.7 months; P = .023), and clinical response (HR, 0.68; 95% CI, 0.56 to 0.82; 36.8 v 23.5 months; P < .001). Overall, 142 patients (12.8%) developed grade 3 to 4 transient hematologic toxicities, and 103 patients (9.2%) experienced grade 4 to 5 permanent renal toxicity. Multivariable regression revealed that tumoral uptake in the initial imaging study was predictive for overall survival (HR, 0.45; 95% CI, 0.29 to 0.69; P < .001), whereas the initial kidney uptake was predictive for severe renal toxicity (HR, 1.59; 95% CI, 1.17 to 2.17; P = .003). This study documents the long-term outcome of [(90)Y-DOTA]-TOC treatment in a large cohort. Response to [(90)Y-DOTA]-TOC is associated with longer survival. Somatostatin receptor imaging is predictive for both survival after [(90)Y-DOTA]-TOC treatment and occurrence of renal toxicity.

Single nucleotide polymorphisms in multiple sclerosis: disease susceptibility and treatment response biomarkers.

PubMed

Pravica, Vera; Popadic, Dusan; Savic, Emina; Markovic, Milos; Drulovic, Jelena; Mostarica-Stojkovic, Marija

2012-04-01

Multiple sclerosis (MS) is a chronic inflammatory demyelinating and neurodegenerative disease of the central nervous system characterized by unpredictable and variable clinical course. Etiology of MS involves both genetic and environmental factors. New technologies identified genetic polymorphisms associated with MS susceptibility among which immunologically relevant genes are significantly overrepresented. Although individual genes contribute only a small part to MS susceptibility, they might be used as biomarkers, thus helping to identify accurate diagnosis, predict clinical disease course and response to therapy. This review focuses on recent progress in research on MS genetics with special emphasis on the possibility to use single nucleotide polymorphism of candidate genes as biomarkers of susceptibility to disease and response to therapy.

Predicting the dynamics of bacterial growth inhibition by ribosome-targeting antibiotics

NASA Astrophysics Data System (ADS)

Greulich, Philip; Doležal, Jakub; Scott, Matthew; Evans, Martin R.; Allen, Rosalind J.

2017-12-01

Understanding how antibiotics inhibit bacteria can help to reduce antibiotic use and hence avoid antimicrobial resistance—yet few theoretical models exist for bacterial growth inhibition by a clinically relevant antibiotic treatment regimen. In particular, in the clinic, antibiotic treatment is time-dependent. Here, we use a theoretical model, previously applied to steady-state bacterial growth, to predict the dynamical response of a bacterial cell to a time-dependent dose of ribosome-targeting antibiotic. Our results depend strongly on whether the antibiotic shows reversible transport and/or low-affinity ribosome binding (‘low-affinity antibiotic’) or, in contrast, irreversible transport and/or high affinity ribosome binding (‘high-affinity antibiotic’). For low-affinity antibiotics, our model predicts that growth inhibition depends on the duration of the antibiotic pulse, and can show a transient period of very fast growth following removal of the antibiotic. For high-affinity antibiotics, growth inhibition depends on peak dosage rather than dose duration, and the model predicts a pronounced post-antibiotic effect, due to hysteresis, in which growth can be suppressed for long times after the antibiotic dose has ended. These predictions are experimentally testable and may be of clinical significance.

Predicting the dynamics of bacterial growth inhibition by ribosome-targeting antibiotics

PubMed Central

Greulich, Philip; Doležal, Jakub; Scott, Matthew; Evans, Martin R; Allen, Rosalind J

2017-01-01

Understanding how antibiotics inhibit bacteria can help to reduce antibiotic use and hence avoid antimicrobial resistance—yet few theoretical models exist for bacterial growth inhibition by a clinically relevant antibiotic treatment regimen. In particular, in the clinic, antibiotic treatment is time-dependent. Here, we use a theoretical model, previously applied to steady-state bacterial growth, to predict the dynamical response of a bacterial cell to a time-dependent dose of ribosome-targeting antibiotic. Our results depend strongly on whether the antibiotic shows reversible transport and/or low-affinity ribosome binding (‘low-affinity antibiotic’) or, in contrast, irreversible transport and/or high affinity ribosome binding (‘high-affinity antibiotic’). For low-affinity antibiotics, our model predicts that growth inhibition depends on the duration of the antibiotic pulse, and can show a transient period of very fast growth following removal of the antibiotic. For high-affinity antibiotics, growth inhibition depends on peak dosage rather than dose duration, and the model predicts a pronounced post-antibiotic effect, due to hysteresis, in which growth can be suppressed for long times after the antibiotic dose has ended. These predictions are experimentally testable and may be of clinical significance. PMID:28714461

Neuroscience of inhibition for addiction medicine: from prediction of initiation to prediction of relapse.

PubMed

Moeller, Scott J; Bederson, Lucia; Alia-Klein, Nelly; Goldstein, Rita Z

2016-01-01

A core deficit in drug addiction is the inability to inhibit maladaptive drug-seeking behavior. Consistent with this deficit, drug-addicted individuals show reliable cross-sectional differences from healthy nonaddicted controls during tasks of response inhibition accompanied by brain activation abnormalities as revealed by functional neuroimaging. However, it is less clear whether inhibition-related deficits predate the transition to problematic use, and, in turn, whether these deficits predict the transition out of problematic substance use. Here, we review longitudinal studies of response inhibition in children/adolescents with little substance experience and longitudinal studies of already addicted individuals attempting to sustain abstinence. Results show that response inhibition and its underlying neural correlates predict both substance use outcomes (onset and abstinence). Neurally, key roles were observed for multiple regions of the frontal cortex (e.g., inferior frontal gyrus, dorsal anterior cingulate cortex, and dorsolateral prefrontal cortex). In general, less activation of these regions during response inhibition predicted not only the onset of substance use, but interestingly also better abstinence-related outcomes among individuals already addicted. The role of subcortical areas, although potentially important, is less clear because of inconsistent results and because these regions are less classically reported in studies of healthy response inhibition. Overall, this review indicates that response inhibition is not simply a manifestation of current drug addiction, but rather a core neurocognitive dimension that predicts key substance use outcomes. Early intervention in inhibitory deficits could have high clinical and public health relevance. © 2016 Elsevier B.V. All rights reserved.

Predictive and Prognostic Molecular Biomarkers for Response to Neoadjuvant Chemoradiation in Rectal Cancer.

PubMed

Dayde, Delphine; Tanaka, Ichidai; Jain, Rekha; Tai, Mei Chee; Taguchi, Ayumu

2017-03-07

The standard of care in locally advanced rectal cancer is neoadjuvant chemoradiation (nCRT) followed by radical surgery. Response to nCRT varies among patients and pathological complete response is associated with better outcome. However, there is a lack of effective methods to select rectal cancer patients who would or would not have a benefit from nCRT. The utility of clinicopathological and radiological features are limited due to lack of adequate sensitivity and specificity. Molecular biomarkers have the potential to predict response to nCRT at an early time point, but none have currently reached the clinic. Integration of diverse types of biomarkers including clinicopathological and imaging features, identification of mechanistic link to tumor biology, and rigorous validation using samples which represent disease heterogeneity, will allow to develop a sensitive and cost-effective molecular biomarker panel for precision medicine in rectal cancer. Here, we aim to review the recent advance in tissue- and blood-based molecular biomarker research and illustrate their potential in predicting nCRT response in rectal cancer.

Predictive value of dorso-lateral prefrontal connectivity for rTMS response in treatment-resistant depression: A brain perfusion SPECT study.

PubMed

Richieri, Raphaëlle; Verger, Antoine; Boyer, Laurent; Boucekine, Mohamed; David, Anthony; Lançon, Christophe; Cermolacce, Michel; Guedj, Eric

2018-05-18

Previous clinical trials have suggested that repetitive transcranial magnetic stimulation (rTMS) has a significant antidepressant effect in patients with treatment resistant depression (TRD). However, results remain heterogeneous with many patients without effective response. The aim of this SPECT study was to determine before treatment the predictive value of the connectivity of the stimulated area on further rTMS response in patients with TRD. Fifty-eight TRD patients performed a brain perfusion SPECT before high frequency rTMS of the left dorsolateral prefrontal cortex (DLPFC). A voxel based-analysis was achieved to compare connectivity of the left DLPFC in responders and non-responders using inter-regional correlations (p < 0.005, corrected for cluster volume). A multiple logistic regression model was thereafter used with the goal of establishing a predictive score. Before rTMS, responders exhibited increased SPECT connectivity between the left DLPFC and the right cerebellum in comparison to non-responders, independently of age, gender, severity of depression, and severity of treatment resistance. The area under the curve for the combination of these two SPECT clusters to predict rTMS response was 0.756 (p < 0.005). SPECT connectivity of the left DLPFC predicts rTMS response before treatment. Crown Copyright © 2018. Published by Elsevier Inc. All rights reserved.

An exploratory study of adolescent response to fluoxetine using psychological and biological predictors.

PubMed

Zohar, Ada H; Eilat, Tamar; Amitai, Maya; Taler, Michal; Bari, Romi; Chen, Alon; Apter, Alan; Weizman, Avraham; Fennig, Silvana

2018-01-01

Not enough is known about predicting therapeutic response to serotonin-specific reuptake inhibitors, and specifically to fluoxetine. This exploratory study used psychological and biological markers for (retrospective) prediction of treatment-response to fluoxetine in depressed and/or anxious adolescents. Forty-one consecutive adolescent outpatients with a primary diagnosis of severe affective and/or anxiety disorders were assessed and treated with an open-label 8-week trial of fluoxetine. Type D personality was assessed with the 14-item questionnaire, the DS14. In addition, TNFα, IL-6, and IL-1b were measured pre- and post-treatment. There was an elevation of Type D personality in patients, compared to the adolescent population rate. Post-treatment, 44% of patients were classified as non-responders; the relative risk of non-response for Type D personality patients was 2.8. Binary logistic regression predicting response vs. non-response showed a contribution of initial TNFα levels as well as Type D personality to non-response. In this exploratory study, the most significant contributor to non-response was Type D personality. However, the measurement of Type D was not prospective, and thus may be confounded with psychiatric morbidity. The measurement of personality in psychiatric settings may contribute to the understanding of treatment response and have clinical utility.

[Prognostic significance of the cyclic AMP concentration in acute leukemias].

PubMed

Paietta, E; Mittermayer, K; Schwarzmeier, J D

1979-01-01

In patients with acute leukemia (myeloblastic, lymphoblastic, undifferentiated) proliferation kinetics and cyclic adenosine-3', 5'-monophosphate (cAMP) concentration of the leukemic cells were studied for their significance in the prediction of responsiveness to cytostatic therapy. Patients with good clinical response had significantly faster turnover and lower cAMP-levels than those who failed to respond to treatment.

Value of the Gastroesophageal Reflux Disease Questionnaire (GerdQ) in predicting the proton pump inhibitor response in coronary artery disease patients with gastroesophageal reflux-related chest pain.

PubMed

He, S; Liu, Y; Chen, Y; Tang, Y; Xu, J; Tang, C

2016-05-01

Chest pain experienced by patients with coronary artery disease can be partly due to gastroesophageal reflux-induced chest pain (GERP). Empirical proton pump inhibitor (PPI) therapy has been recommended as an initial clinical approach for treating GERP. However, PPI use may lead to some health problems. The Gastroesophageal Reflux Disease Questionnaire (GerdQ) may represent a noninvasive and cost-effective approach for avoiding PPI misuse and for identifying the appropriate patients for the PPI trial test. The aim of this pilot study was to prospectively evaluate the association between GerdQ scores and PPI response in patients with coronary artery disease (CAD) and GERP to determine whether the GerdQ predicts the PPI response in patients with CAD and GERP and to further validate the clinical application value of the GerdQ. A total of 154 consecutive patients with potential GERP were recruited to complete a GerdQ with subsequent PPI therapy. Based on the PPI trial result, patients were divided into a PPI-positive response group and a PPI-negative response group. The difference in the GerdQ scores between the two groups was assessed. The receiver operating characteristic (ROC) curve of GerdQ score was drawn according to the PPI response as the gold standard. The ability of GerdQ to predict the PPI response was assessed. A total of 96 patients completed the entire study; 62 patients (64.6%) were assigned to the PPI-positive response group, and 34 patients (35.4%) to the PPI-negative response group. The GerdQ score of the PPI-positive response group (8.11 ± 3.315) was significantly higher than that of the PPI-negative response group (4.41 ± 2.743), and the difference was statistically significant (t = 5.863, P = 0.000). The ROC curve was drawn according to a PPI response assessment result with a score above 2 as the gold standard. The area under curve was 0.806. When the critical value of GerdQ score was 7.5, Youden index was up to 0.514, the diagnostic sensitivity was 0.661, and the diagnostic specificity was 0.853. A GerdQ score greater than 7.5 better predicts the response to the PPI trial therapy. There is a strong association between the GerdQ score and the response to PPI therapy. Higher GerdQ scores were predictive of a positive PPI response in CAD patients with GERP. The GerdQ may be a reasonable screening tool for GERP in patients with CAD who are prepared to accept PPI therapy. © 2015 International Society for Diseases of the Esophagus.

A Serum Protein Profile Predictive of the Resistance to Neoadjuvant Chemotherapy in Advanced Breast Cancers*

PubMed Central

Hyung, Seok-Won; Lee, Min Young; Yu, Jong-Han; Shin, Byunghee; Jung, Hee-Jung; Park, Jong-Moon; Han, Wonshik; Lee, Kyung-Min; Moon, Hyeong-Gon; Zhang, Hui; Aebersold, Ruedi; Hwang, Daehee; Lee, Sang-Won; Yu, Myeong-Hee; Noh, Dong-Young

2011-01-01

Prediction of the responses to neoadjuvant chemotherapy (NACT) can improve the treatment of patients with advanced breast cancer. Genes and proteins predictive of chemoresistance have been extensively studied in breast cancer tissues. However, noninvasive serum biomarkers capable of such prediction have been rarely exploited. Here, we performed profiling of N-glycosylated proteins in serum from fifteen advanced breast cancer patients (ten patients sensitive to and five patients resistant to NACT) to discover serum biomarkers of chemoresistance using a label-free liquid chromatography-tandem MS method. By performing a series of statistical analyses of the proteomic data, we selected thirteen biomarker candidates and tested their differential serum levels by Western blotting in 13 independent samples (eight patients sensitive to and five patients resistant to NACT). Among the candidates, we then selected the final set of six potential serum biomarkers (AHSG, APOB, C3, C9, CP, and ORM1) whose differential expression was confirmed in the independent samples. Finally, we demonstrated that a multivariate classification model using the six proteins could predict responses to NACT and further predict relapse-free survival of patients. In summary, global N-glycoproteome profile in serum revealed a protein pattern predictive of the responses to NACT, which can be further validated in large clinical studies. PMID:21799047

A panel to predict long-term outcome of infliximab therapy for patients with ulcerative colitis.

PubMed

Arias, Maria Theresa; Vande Casteele, Niels; Vermeire, Séverine; de Buck van Overstraeten, Anthony; Billiet, Thomas; Baert, Filip; Wolthuis, Albert; Van Assche, Gert; Noman, Maja; Hoffman, Ilse; D'Hoore, Andre; Gils, Ann; Rutgeerts, Paul; Ferrante, Marc

2015-03-01

Infliximab is effective for patients with refractory ulcerative colitis (UC), but few factors have been identified that predict long-term outcome of therapy. We aimed to identify a panel of markers associated with outcome of infliximab therapy to help physicians make personalized treatment decisions. We collected data from the first 285 patients with refractory UC (41% female; median age, 39 y) treated with infliximab before July 2012 at University Hospitals Leuven, in Belgium. We performed a Cox regression analysis to identify independent factors that predicted relapse-free and colectomy-free survival, and used these factors to create a panel of markers (risk panel). During a median follow-up period of 5 years, 61% of patients relapsed and 20% required colectomy. Independent predictors of relapse-free survival included short-term complete clinical response (odds ratio [OR], 3.75; 95% confidence interval [CI], 2.35-5.97; P < .001), mucosal healing (OR, 1.87; 95% CI, 1.17-2.98; P = .009), and absence of atypical perinuclear antineutrophil cytoplasmic antibodies (pANCA) (OR, 1.96; 95% CI, 1.23-3.12; P = .005). Independent predictors of colectomy-free survival included short-term clinical response (OR, 7.74; 95% CI, 2.76-21.68; P < .001), mucosal healing (OR, 4.02; 95% CI, 1.16-13.97; P = .028), baseline level of C-reactive protein (CRP) of 5 mg/L or less (OR, 2.95; 95% CI, 1.26-6.89; P = .012), and baseline level of albumin of 35 g/L or greater (OR, 3.03; 95% CI, 1.12-8.22; P = .029). Based on serologic analysis of a subgroup of 112 patients, levels of infliximab greater than 2.5 μg/mL at week 14 of treatment predicted relapse-free survival (P < .001) and colectomy-free survival (P = .034). A risk panel that included levels of pANCA, CRP, albumin, clinical response, and mucosal healing identified patients at risk for UC relapse or colectomy (both P < .001). Clinical response and mucosal healing were confirmed as independent predictors of long-term outcome from infliximab therapy in patients with UC. We identified additional factors (levels of pANCA, CRP, and albumin) to create a risk panel that predicts long-term outcomes of therapy. Serum levels of infliximab at week 14 of treatment also were associated with patient outcomes. Our risk panel and short-term serum levels of infliximab therefore might be used to guide therapy. Copyright © 2015 AGA Institute. Published by Elsevier Inc. All rights reserved.

Emergency Physician Attitudes, Preferences, and Risk Tolerance for Stroke as a Potential Cause of Dizziness Symptoms

PubMed Central

Kene, Mamata V.; Ballard, Dustin W.; Vinson, David R.; Rauchwerger, Adina S.; Iskin, Hilary R.; Kim, Anthony S.

2015-01-01

Introduction We evaluated emergency physicians’ (EP) current perceptions, practice, and attitudes towards evaluating stroke as a cause of dizziness among emergency department patients. Methods We administered a survey to all EPs in a large integrated healthcare delivery system. The survey included clinical vignettes, perceived utility of historical and exam elements, attitudes about the value of and requisite post-test probability of a clinical prediction rule for dizziness. We calculated descriptive statistics and post-test probabilities for such a clinical prediction rule. Results The response rate was 68% (366/535). Respondents’ median practice tenure was eight years (37% female, 92% emergency medicine board certified). Symptom quality and typical vascular risk factors increased suspicion for stroke as a cause of dizziness. Most respondents reported obtaining head computed tomography (CT) (74%). Nearly all respondents used and felt confident using cranial nerve and limb strength testing. A substantial minority of EPs used the Epley maneuver (49%) and HINTS (head-thrust test, gaze-evoked nystagmus, and skew deviation) testing (30%); however, few EPs reported confidence in these tests’ bedside application (35% and 16%, respectively). Respondents favorably viewed applying a properly validated clinical prediction rule for assessment of immediate and 30-day stroke risk, but indicated it would have to reduce stroke risk to <0.5% to be clinically useful. Conclusion EPs report relying on symptom quality, vascular risk factors, simple physical exam elements, and head CT to diagnose stroke as the cause of dizziness, but would find a validated clinical prediction rule for dizziness helpful. A clinical prediction rule would have to achieve a 0.5% post-test stroke probability for acceptability. PMID:26587108

Prediction of anti-cancer drug response by kernelized multi-task learning.

PubMed

Tan, Mehmet

2016-10-01

Chemotherapy or targeted therapy are two of the main treatment options for many types of cancer. Due to the heterogeneous nature of cancer, the success of the therapeutic agents differs among patients. In this sense, determination of chemotherapeutic response of the malign cells is essential for establishing a personalized treatment protocol and designing new drugs. With the recent technological advances in producing large amounts of pharmacogenomic data, in silico methods have become important tools to achieve this aim. Data produced by using cancer cell lines provide a test bed for machine learning algorithms that try to predict the response of cancer cells to different agents. The potential use of these algorithms in drug discovery/repositioning and personalized treatments motivated us in this study to work on predicting drug response by exploiting the recent pharmacogenomic databases. We aim to improve the prediction of drug response of cancer cell lines. We propose to use a method that employs multi-task learning to improve learning by transfer, and kernels to extract non-linear relationships to predict drug response. The method outperforms three state-of-the-art algorithms on three anti-cancer drug screen datasets. We achieved a mean squared error of 3.305 and 0.501 on two different large scale screen data sets. On a recent challenge dataset, we obtained an error of 0.556. We report the methodological comparison results as well as the performance of the proposed algorithm on each single drug. The results show that the proposed method is a strong candidate to predict drug response of cancer cell lines in silico for pre-clinical studies. The source code of the algorithm and data used can be obtained from http://mtan.etu.edu.tr/Supplementary/kMTrace/. Copyright © 2016 Elsevier B.V. All rights reserved.

Mortality, morbidity and refractoriness prediction in status epilepticus: Comparison of STESS and EMSE scores.

PubMed

Giovannini, Giada; Monti, Giulia; Tondelli, Manuela; Marudi, Andrea; Valzania, Franco; Leitinger, Markus; Trinka, Eugen; Meletti, Stefano

2017-03-01

Status epilepticus (SE) is a neurological emergency, characterized by high short-term morbidity and mortality. We evaluated and compared two scores that have been developed to evaluate status epilepticus prognosis: STESS (Status Epilepticus Severity Score) and EMSE (Epidemiology based Mortality score in Status Epilepticus). A prospective observational study was performed on consecutive patients with SE admitted between September 2013 and August 2015. Demographics, clinical variables, STESS-3 and -4, and EMSE-64 scores were calculated for each patient at baseline. SE drug response, 30-day mortality and morbidity were the outcomes measure. 162 episodes of SE were observed: 69% had a STESS ≥3; 34% had a STESS ≥4; 51% patients had an EMSE ≥64. The 30-days mortality was 31.5%: EMSE-64 showed greater negative predictive value (NPV) (97.5%), positive predictive value (PPV) (59.8%) and accuracy in the prediction of death than STESS-3 and STESS-4 (p<0.001). At 30 days, the clinical condition had deteriorated in 59% of the cases: EMSE-64 showed greater NPV (71.3%), PPV (87.8%) and accuracy than STESS-3 and STESS-4 (p<0.001) in the prediction of this outcome. In 23% of all cases, status epilepticus proved refractory to non-anaesthetic treatment. All three scales showed a high NPV (EMSE-64: 87.3%; STESS-4: 89.4%; STESS-3: 87.5%) but a low PPV (EMSE-64: 40.9%; STESS-4: 52.9%; STESS-3: 32%) for the prediction of refractoriness to first and second line drugs. This means that accuracy for the prediction of refractoriness was equally poor for all scales. EMSE-64 appears superior to STESS-3 and STESS-4 in the prediction of 30-days mortality and morbidity. All scales showed poor accuracy in the prediction of response to first and second line antiepileptic drugs. At present, there are no reliable scores capable of predicting treatment responsiveness. Copyright © 2017 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

Empirically Derived Personality Subtyping for Predicting Clinical Symptoms and Treatment Response in Bulimia Nervosa

PubMed Central

Haynos, Ann F.; Pearson, Carolyn M.; Utzinger, Linsey M.; Wonderlich, Stephen A.; Crosby, Ross D.; Mitchell, James E.; Crow, Scott J.; Peterson, Carol B.

2016-01-01

Objective Evidence suggests that eating disorder subtypes reflecting under-controlled, over-controlled, and low psychopathology personality traits constitute reliable phenotypes that differentiate treatment response. This study is the first to use statistical analyses to identify these subtypes within treatment-seeking individuals with bulimia nervosa (BN) and to use these statistically derived clusters to predict clinical outcomes. Methods Using variables from the Dimensional Assessment of Personality Pathology–Basic Questionnaire, K-means cluster analyses identified under-controlled, over-controlled, and low psychopathology subtypes within BN patients (n = 80) enrolled in a treatment trial. Generalized linear models examined the impact of personality subtypes on Eating Disorder Examination global score, binge eating frequency, and purging frequency cross-sectionally at baseline and longitudinally at end of treatment (EOT) and follow-up. In the longitudinal models, secondary analyses were conducted to examine personality subtype as a potential moderator of response to Cognitive Behavioral Therapy-Enhanced (CBT-E) or Integrative Cognitive-Affective Therapy for BN (ICAT-BN). Results There were no baseline clinical differences between groups. In the longitudinal models, personality subtype predicted binge eating (p = .03) and purging (p = .01) frequency at EOT and binge eating frequency at follow-up (p = .045). The over-controlled group demonstrated the best outcomes on these variables. In secondary analyses, there was a treatment by subtype interaction for purging at follow-up (p = .04), which indicated a superiority of CBT-E over ICAT-BN for reducing purging among the over-controlled group. Discussion Empirically derived personality subtyping is appears to be a valid classification system with potential to guide eating disorder treatment decisions. PMID:27611235

Poly-Omic Prediction of Complex Traits: OmicKriging

PubMed Central

Wheeler, Heather E.; Aquino-Michaels, Keston; Gamazon, Eric R.; Trubetskoy, Vassily V.; Dolan, M. Eileen; Huang, R. Stephanie; Cox, Nancy J.; Im, Hae Kyung

2014-01-01

High-confidence prediction of complex traits such as disease risk or drug response is an ultimate goal of personalized medicine. Although genome-wide association studies have discovered thousands of well-replicated polymorphisms associated with a broad spectrum of complex traits, the combined predictive power of these associations for any given trait is generally too low to be of clinical relevance. We propose a novel systems approach to complex trait prediction, which leverages and integrates similarity in genetic, transcriptomic, or other omics-level data. We translate the omic similarity into phenotypic similarity using a method called Kriging, commonly used in geostatistics and machine learning. Our method called OmicKriging emphasizes the use of a wide variety of systems-level data, such as those increasingly made available by comprehensive surveys of the genome, transcriptome, and epigenome, for complex trait prediction. Furthermore, our OmicKriging framework allows easy integration of prior information on the function of subsets of omics-level data from heterogeneous sources without the sometimes heavy computational burden of Bayesian approaches. Using seven disease datasets from the Wellcome Trust Case Control Consortium (WTCCC), we show that OmicKriging allows simple integration of sparse and highly polygenic components yielding comparable performance at a fraction of the computing time of a recently published Bayesian sparse linear mixed model method. Using a cellular growth phenotype, we show that integrating mRNA and microRNA expression data substantially increases performance over either dataset alone. Using clinical statin response, we show improved prediction over existing methods. PMID:24799323

Pharmacogenetic markers to predict the clinical response to methotrexate in south Indian Tamil patients with psoriasis.

PubMed

Indhumathi, S; Rajappa, Medha; Chandrashekar, Laxmisha; Ananthanarayanan, P H; Thappa, D M; Negi, V S

2017-08-01

Despite the advent of several new systemic therapies, methotrexate remains the gold standard for the treatment of moderate to severe psoriasis. However, there exists a significant heterogeneity in individual response to methotrexate. There are no consistently reliable markers to predict methotrexate treatment response till date. We aimed to demonstrate the association of certain genetic variants in the HLA (HLA-A2, HLA-B17, and HLA-Cw6) and the non-HLA genes including T-helper (Th)-1, Th-2, Th-17 cytokine genes (IFN-γ, IL-2, IL-4, IL-10, IL-12B, and IL-23R), and T-regulatory gene (FOXP3) with the methotrexate treatment response in South Indian Tamil patients with psoriasis. Of the 360 patients recruited, 189 patients with moderate to severe psoriasis were treated with methotrexate. Of the 189 patients, 132 patients responded to methotrexate and the remaining 57 patients were non-responders. We analyzed the association of aforesaid polymorphisms with the methotrexate treatment outcome using binary logistic regression. We observed that there were significant differences between genotype frequencies of HLA-Cw6 and FOXP3 (rs3761548) among the responders compared to non-responders, with conservative estimation. We observed that pro-inflammatory cytokines such as IFN-γ, IL-2, IL-12, and IL-23 were markedly reduced with the use of methotrexate, in comparison to the baseline levels, while the plasma IL-4 levels were increased posttreatment. Our results serve as preliminary evidence for the clinical use of genetic markers as predictors of response to methotrexate in psoriasis. This might aid in the future in the development of a point-of-care testing (POCT) gene chip, to predict optimal treatment response in patients with psoriasis, based on their individual genotypic profile.

Predictive models in urology.

PubMed

Cestari, Andrea

2013-01-01

Predictive modeling is emerging as an important knowledge-based technology in healthcare. The interest in the use of predictive modeling reflects advances on different fronts such as the availability of health information from increasingly complex databases and electronic health records, a better understanding of causal or statistical predictors of health, disease processes and multifactorial models of ill-health and developments in nonlinear computer models using artificial intelligence or neural networks. These new computer-based forms of modeling are increasingly able to establish technical credibility in clinical contexts. The current state of knowledge is still quite young in understanding the likely future direction of how this so-called 'machine intelligence' will evolve and therefore how current relatively sophisticated predictive models will evolve in response to improvements in technology, which is advancing along a wide front. Predictive models in urology are gaining progressive popularity not only for academic and scientific purposes but also into the clinical practice with the introduction of several nomograms dealing with the main fields of onco-urology.

Predicting clinical biological responses to dental materials.

PubMed

Wataha, John C

2012-01-01

Methods used to measure and predict clinical biological responses to dental materials remain controversial, confusing, and to some extent, unsuccessful. The current paper reviews significant issues surrounding how we assess the biological safety of materials, with a historical summary and critical look at the biocompatibility literature. The review frames these issues from a U.S. perspective to some degree, but emphasizes their global nature and universal importance. The PubMed database and information from the U.S. Food and Drug Administration, International Standards Organization, and American National Standards Institute were searched for prominent literature addressing the definition of biocompatibility, types of biological tests employed, regulatory and standardization issues, and how biological tests are used together to establish the biological safety of materials. The search encompassed articles published in English from approximately 1965-2011. The review does not comprehensively review the literature, but highlights significant issues that confront the field. Years ago, tests for biological safety sought to establish material inertness as the measure of safety, a criterion that is now deemed naive; the definition of biocompatibility has broadened along with the roles for materials in patient oral health care. Controversies persist about how in vitro or animal tests should be used to evaluate the biological safety of materials for clinical use. Controlled clinical trials remain the single best measure of the clinical response to materials, but even these tests have significant limitations and are less useful to identify mechanisms that shape material performance. Practice-based research networks and practitioner databases are emerging as important supplements to controlled clinical trials, but their final utility remains to be determined. Today we ask materials to play increasingly sophisticated structural and therapeutic roles in patient treatment. To accommodate these roles, strategies to assess, predict, and monitor material safety need to evolve. This evolution will be driven not only by researchers and manufacturers, but also by patients and practitioners, who want to use novel materials in new ways to treat oral disease. Copyright © 2011 Academy of Dental Materials. Published by Elsevier Ltd. All rights reserved.

Anti-Epidermal Growth Factor Receptor Therapy in Head and Neck Squamous Cell Carcinoma: Focus on Potential Molecular Mechanisms of Drug Resistance

PubMed Central

Baay, Marc; Wouters, An; Specenier, Pol; Vermorken, Jan B.; Peeters, Marc; Lardon, Filip

2013-01-01

Targeted therapy against the epidermal growth factor receptor (EGFR) is one of the most promising molecular therapeutics for head and neck squamous cell carcinoma (HNSCC). EGFR is overexpressed in a wide range of malignancies, including HNSCC, and initiates important signal transduction pathways in HNSCC carcinogenesis. However, primary and acquired resistance are serious problems and are responsible for low single-agent response rate and tumor recurrence. Therefore, an improved understanding of the molecular mechanisms of resistance to EGFR inhibitors may provide valuable indications to identify biomarkers that can be used clinically to predict response to EGFR blockade and to establish new treatment options to overcome resistance. To date, no predictive biomarker for HNSCC is available in the clinic. Therapeutic resistance to anti-EGFR therapy may arise from mechanisms that can compensate for reduced EGFR signaling and/or mechanisms that can modulate EGFR-dependent signaling. In this review, we will summarize some of these molecular mechanisms and describe strategies to overcome that resistance. PMID:23821327

Prediction of methylphenidate treatment outcome in adults with attention-deficit/hyperactivity disorder (ADHD).

PubMed

Retz, Wolfgang; Retz-Junginger, Petra

2014-11-01

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent mental disorder of childhood, which often persists in adulthood. Methylphenidate (MPH) is one of the most effective medications to treat ADHD, but also few adult patients show no sufficient response to this drug. In this paper, we give an overview regarding genetic, neuroimaging, clinical and other studies which have tried to reveal the reasons for non-response in adults with ADHD, based on a systematic literature search. Although MPH is a well-established treatment for adults with ADHD, research regarding the prediction of treatment outcome is still limited and has resulted in inconsistent findings. No reliable neurobiological markers of treatment response have been identified so far. Some findings from clinical studies suggest that comorbidity with substance use disorders and personality disorders has an impact on treatment course and outcome. As MPH is widely used in the treatment of adults with ADHD, much more work is needed regarding positive and negative predictors of long-term treatment outcome in order to optimize the pharmacological treatment of adult ADHD patients.

Do Resting Plasma β-Endorphin Levels Predict Responses to Opioid Analgesics?

PubMed

Bruehl, Stephen; Burns, John W; Gupta, Rajnish; Buvanendran, Asokumar; Chont, Melissa; Orlowska, Daria; Schuster, Erik; France, Christopher R

2017-01-01

Clinically feasible predictors of opioid analgesic responses for use in precision pain medicine protocols are needed. This study evaluated whether resting plasma β-endorphin (BE) levels predicted responses to an opioid analgesic, and whether chronic pain status or sex moderated these effects. Participants included 73 individuals with chronic low back pain (CLBP) and 88 pain-free controls, all using no daily opioid analgesics. Participants attended 2 identical laboratory sessions during which they received either intravenous morphine (0.08 mg/kg) or saline placebo, with blood samples obtained before drug administration to assay resting plasma BE levels. Once peak drug activity was achieved in each session, participants engaged in an ischemic forearm pain task (ISC) and a heat pain task. Morphine analgesic effects were derived reflecting the difference in pain outcomes between placebo and morphine conditions. In hierarchical regressions, significant Type (CLBP vs. control)×BE interactions (Ps<0.05) were noted for morphine effects on ISC tolerance, ISC intratask pain ratings, and thermal VAS unpleasantness ratings. These interactions derived primarily from associations between higher BE levels and smaller morphine effects restricted to the CLBP subgroup. All other BE-related effects, including sex interactions, for predicting morphine analgesia failed to reach statistical significance. BE was a predictor of morphine analgesia for only 3 out of 9 outcomes examined, with these effects moderated by chronic pain status but not sex. On the whole, results do not suggest that resting plasma BE levels are likely to be a clinically useful predictor of opioid analgesic responses.

Derivation and Validation of a Biomarker-Based Clinical Algorithm to Rule Out Sepsis From Noninfectious Systemic Inflammatory Response Syndrome at Emergency Department Admission: A Multicenter Prospective Study.

PubMed

Mearelli, Filippo; Fiotti, Nicola; Giansante, Carlo; Casarsa, Chiara; Orso, Daniele; De Helmersen, Marco; Altamura, Nicola; Ruscio, Maurizio; Castello, Luigi Mario; Colonetti, Efrem; Marino, Rossella; Barbati, Giulia; Bregnocchi, Andrea; Ronco, Claudio; Lupia, Enrico; Montrucchio, Giuseppe; Muiesan, Maria Lorenza; Di Somma, Salvatore; Avanzi, Gian Carlo; Biolo, Gianni

2018-05-07

To derive and validate a predictive algorithm integrating a nomogram-based prediction of the pretest probability of infection with a panel of serum biomarkers, which could robustly differentiate sepsis/septic shock from noninfectious systemic inflammatory response syndrome. Multicenter prospective study. At emergency department admission in five University hospitals. Nine-hundred forty-seven adults in inception cohort and 185 adults in validation cohort. None. A nomogram, including age, Sequential Organ Failure Assessment score, recent antimicrobial therapy, hyperthermia, leukocytosis, and high C-reactive protein values, was built in order to take data from 716 infected patients and 120 patients with noninfectious systemic inflammatory response syndrome to predict pretest probability of infection. Then, the best combination of procalcitonin, soluble phospholypase A2 group IIA, presepsin, soluble interleukin-2 receptor α, and soluble triggering receptor expressed on myeloid cell-1 was applied in order to categorize patients as "likely" or "unlikely" to be infected. The predictive algorithm required only procalcitonin backed up with soluble phospholypase A2 group IIA determined in 29% of the patients to rule out sepsis/septic shock with a negative predictive value of 93%. In a validation cohort of 158 patients, predictive algorithm reached 100% of negative predictive value requiring biomarker measurements in 18% of the population. We have developed and validated a high-performing, reproducible, and parsimonious algorithm to assist emergency department physicians in distinguishing sepsis/septic shock from noninfectious systemic inflammatory response syndrome.

Personality traits predict treatment outcome with an antidepressant in patients with functional gastrointestinal disorder.

PubMed

Tanum, L; Malt, U F

2000-09-01

We investigated the relationship between personality traits and response to treatment with the tetracyclic antidepressant mianserin or placebo in patients with functional gastrointestinal disorder (FGD) without psychopathology. Forty-eight patients completed the Buss-Durkee Hostility Inventory, Neuroticism Extroversion Openness -Personality Inventory (NEO-PI), and Eysenck Personality Questionnaire (EPQ), neuroticism + lie subscales, before they were consecutively allocated to a 7-week double-blind treatment study with mianserin or placebo. Treatment response to pain and target symptoms were recorded daily with the Visual Analogue Scale and Clinical Global Improvement Scale at every visit. A low level of neuroticism and little concealed aggressiveness predicted treatment outcome with the antidepressant drug mianserin in non-psychiatric patients with FGD. Inversely, moderate to high neuroticism and marked concealed aggressiveness predicted poor response to treatment. These findings were most prominent in women. Personality traits were better predictors of treatment outcome than serotonergic sensitivity assessed with the fenfluramine test. Assessment of the personality traits negativism, irritability, aggression, and neuroticism may predict response to drug treatment of FGD even when serotonergic sensitivity is controlled for. If confirmed in future studies, the findings point towards a more differential psychopharmacologic treatment of FGD.

TU-C-12A-09: Modeling Pathologic Response of Locally Advanced Esophageal Cancer to Chemo-Radiotherapy Using Quantitative PET/CT Features, Clinical Parameters and Demographics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, H; Chen, W; Kligerman, S

2014-06-15

Purpose: To develop predictive models using quantitative PET/CT features for the evaluation of tumor response to neoadjuvant chemo-radiotherapy (CRT) in patients with locally advanced esophageal cancer. Methods: This study included 20 patients who underwent tri-modality therapy (CRT + surgery) and had {sup 18}F-FDG PET/CT scans before initiation of CRT and 4-6 weeks after completion of CRT but prior to surgery. Four groups of tumor features were examined: (1) conventional PET/CT response measures (SUVmax, tumor diameter, etc.); (2) clinical parameters (TNM stage, histology, etc.) and demographics; (3) spatial-temporal PET features, which characterize tumor SUV intensity distribution, spatial patterns, geometry, and associatedmore » changes resulting from CRT; and (4) all features combined. An optimal feature set was identified with recursive feature selection and cross-validations. Support vector machine (SVM) and logistic regression (LR) models were constructed for prediction of pathologic tumor response to CRT, using cross-validations to avoid model over-fitting. Prediction accuracy was assessed via area under the receiver operating characteristic curve (AUC), and precision was evaluated via confidence intervals (CIs) of AUC. Results: When applied to the 4 groups of tumor features, the LR model achieved AUCs (95% CI) of 0.57 (0.10), 0.73 (0.07), 0.90 (0.06), and 0.90 (0.06). The SVM model achieved AUCs (95% CI) of 0.56 (0.07), 0.60 (0.06), 0.94 (0.02), and 1.00 (no misclassifications). Using spatial-temporal PET features combined with conventional PET/CT measures and clinical parameters, the SVM model achieved very high accuracy (AUC 1.00) and precision (no misclassifications), significantly better than using conventional PET/CT measures or clinical parameters and demographics alone. For groups with a large number of tumor features (groups 3 and 4), the SVM model achieved significantly higher accuracy than the LR model. Conclusion: The SVM model using all features including quantitative PET/CT features accurately and precisely predicted pathologic tumor response to CRT in esophageal cancer. This work was supported in part by National Cancer Institute Grant R21 CA131979 and R01 CA172638. Shan Tan was supported in part by the National Natural Science Foundation of China 60971112 and 61375018, and by Fundamental Research Funds for the Central Universities 2012QN086.« less

AR-V7 in Peripheral Whole Blood of Patients with Castration-resistant Prostate Cancer: Association with Treatment-specific Outcome Under Abiraterone and Enzalutamide.

PubMed

Seitz, Anna Katharina; Thoene, Silvia; Bietenbeck, Andreas; Nawroth, Roman; Tauber, Robert; Thalgott, Mark; Schmid, Sebastian; Secci, Ramona; Retz, Margitta; Gschwend, Jürgen E; Ruland, Jürgen; Winter, Christof; Heck, Matthias M

2017-11-01

It has been demonstrated that androgen receptor splice variant 7 (AR-V7) expression in circulating tumor cells (CTCs) predicts poor treatment response in metastatic castration-resistant prostate cancer (mCRPC) patients treated with abiraterone or enzalutamide. To develop a practical and robust liquid profiling approach for direct quantification of AR-V7 in peripheral whole blood without the need for CTC capture and to determine its potential for predicting treatment response in mCRPC patients. Whole blood samples from a prospective biorepository of 85 mCRPC patients before treatment initiation with abiraterone (n=56) or enzalutamide (n=29) were analyzed via droplet digital polymerase chain reaction. The association of AR-V7 status with prostate-specific antigen (PSA) response defined by PSA decline ≥50% and with PSA-progression-free survival (PSA-PFS), clinical PFS, and overall survival (OS) was assessed. High AR-V7 expression levels in whole blood were detectable in 18% (15/85) of patients. No patient with high AR-V7 expression achieved a PSA response, and AR-V7 status was an independent predictor of PSA response in multivariable logistic regression analysis (p=0.03). High AR-V7 expression was associated with shorter PSA-PFS (median 2.4 vs 3.7 mo; p<0.001), shorter clinical PFS (median 2.7 vs 5.5 mo; p<0.001), and shorter OS (median 4.0 vs. 13.9 mo; p<0.001). On multivariable Cox regression analysis, high AR-V7 expression remained an independent predictor of shorter PSA-PFS (hazard ratio [HR] 7.0, 95% confidence interval [CI] 2.3-20.7; p<0.001), shorter clinical PFS (HR 2.3, 95% CI 1.1-4.9; p=0.02), and shorter OS (HR 3.0, 95% CI 1.4-6.3; p=0.005). Testing of AR-V7 mRNA levels in whole blood is a simple and promising approach to predict poor treatment outcome in mCRPC patients receiving abiraterone or enzalutamide. We established a method for determining AR-V7 status in whole blood. This test predicted treatment resistance in patients with metastatic castration-resistant prostate cancer undergoing treatment with abiraterone or enzalutamide. Prospective validation is needed before application to clinical practice. Copyright © 2017 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Subharmonic Imaging and Pressure Estimation for Monitoring Neoadjuvant Chemotherapy

DTIC Science & Technology

2014-09-01

and therapy response [10]. However, the level of IFP has been shown to predict disease free survival for cervix cancer (34% disease free survival...p. 1951-1961. 11. Milosevic M, et al., Interstitial fluid pressure predicts survival in patients with cervix cancer independent of clinical...12b. DISTRIBUTION CODE 13. ABSTRACT (Maximum 200 Words) Neoadjuvant chemotherapy is currently the standard of care for locally advanced breast cancer

Clinical course, costs and predictive factors for response to treatment in carpal tunnel syndrome: the PALMS study protocol

PubMed Central

2014-01-01

Background Carpal tunnel syndrome (CTS) is the most common neuropathy of the upper limb and a significant contributor to hand functional impairment and disability. Effective treatment options include conservative and surgical interventions, however it is not possible at present to predict the outcome of treatment. The primary aim of this study is to identify which baseline clinical factors predict a good outcome from conservative treatment (by injection) or surgery in patients diagnosed with carpal tunnel syndrome. Secondary aims are to describe the clinical course and progression of CTS, and to describe and predict the UK cost of CTS to the individual, National Health Service (NHS) and society over a two year period. Methods/Design In this prospective observational cohort study patients presenting with clinical signs and symptoms typical of CTS and in whom the diagnosis is confirmed by nerve conduction studies are invited to participate. Data on putative predictive factors are collected at baseline and follow-up through patient questionnaires and include standardised measures of symptom severity, hand function, psychological and physical health, comorbidity and quality of life. Resource use and cost over the 2 year period such as prescribed medications, NHS and private healthcare contacts are also collected through patient self-report at 6, 12, 18 and 24 months. The primary outcome used to classify treatment success or failures will be a 5-point global assessment of change. Secondary outcomes include changes in clinical symptoms, functioning, psychological health, quality of life and resource use. A multivariable model of factors which predict outcome and cost will be developed. Discussion This prospective cohort study will provide important data on the clinical course and UK costs of CTS over a two-year period and begin to identify predictive factors for treatment success from conservative and surgical interventions. PMID:24507749

Computational Modeling and Treatment Identification in the Myelodysplastic Syndromes.

PubMed

Drusbosky, Leylah M; Cogle, Christopher R

2017-10-01

This review discusses the need for computational modeling in myelodysplastic syndromes (MDS) and early test results. As our evolving understanding of MDS reveals a molecularly complicated disease, the need for sophisticated computer analytics is required to keep track of the number and complex interplay among the molecular abnormalities. Computational modeling and digital drug simulations using whole exome sequencing data input have produced early results showing high accuracy in predicting treatment response to standard of care drugs. Furthermore, the computational MDS models serve as clinically relevant MDS cell lines for pre-clinical assays of investigational agents. MDS is an ideal disease for computational modeling and digital drug simulations. Current research is focused on establishing the prediction value of computational modeling. Future research will test the clinical advantage of computer-informed therapy in MDS.

Predictive Value of IL-8 for Sepsis and Severe Infections after Burn Injury - A Clinical Study

PubMed Central

Kraft, Robert; Herndon, David N; Finnerty, Celeste C; Cox, Robert A; Song, Juquan; Jeschke, Marc G

2014-01-01

The inflammatory response induced by burn injury contributes to increased incidence of infections, sepsis, organ failure, and mortality. Thus, monitoring post-burn inflammation is of paramount importance but so far there are no reliable biomarkers available to monitor and/or predict infectious complications after burn. As IL-8 is a major mediator for inflammatory responses, the aim of our study was to determine whether IL-8 expression can be used to predict post-burn sepsis, infections, and mortality other outcomes post-burn. Plasma cytokines, acute phase proteins, constitutive proteins, and hormones were analyzed during the first 60 days post injury from 468 pediatric burn patients. Demographics and clinical outcome variables (length of stay, infection, sepsis, multiorgan failure (MOF), and mortality were recorded. A cut-off level for IL-8 was determined using receiver operating characteristic (ROC) analysis. Statistical significance is set at (p<0.05). ROC analysis identified a cut-off level of 234 pg/ml for IL-8 for survival. Patients were grouped according to their average IL-8 levels relative to this cut off and stratified into high (H) (n=133) and low (L) (n=335) groups. In the L group, regression analysis revealed a significant predictive value of IL-8 to percent of total body surface area (TBSA) burned and incidence of MOF (p<0.001). In the H group IL-8 levels were able to predict sepsis (p<0.002). In the H group, elevated IL-8 was associated with increased inflammatory and acute phase responses compared to the L group (p<0.05). High levels of IL-8 correlated with increased MOF, sepsis, and mortality. These data suggest that serum levels of IL-8 may be a valid biomarker for monitoring sepsis, infections, and mortality in burn patients. PMID:25514427

[Clinical research on clearance of leukemic cell during induction of remission therapy in children with precursor B cell acute lymphoblastic leukemia].

PubMed

Yi, Zhi-gang; Cui, Lei; Gao, Chao; Jin, Mei; Zhang, Rui-dong; Li, Zhi-gang; Wu, Min-yuan

2011-03-01

To investigate the clinical value of clearance of leukemic cell during induction of remission therapy in children with precursor B cell acute lymphoblastic leukemia (BCP-ALL), and to assess the applicative value of different indexes. From April 2005 to April 2008, 206 children with de novo BCP-ALL were admitted. We firstly analyzed the effect of clearance of leukemic cells during induction of remission therapy on relapse-free survival (RFS). Four indexes were used to assess the clearance of leukemic cells including prednisone response on day 8 (d8-PR), percentage of lymphoblast in bone marrow on day 22 (d22-BM) and day 33 (d33-BM), and bone marrow (BM) minimal residual disease (MRD) detection on day 33 (d33-MRD). Then the sensitivity, specificity, positive predictive value and negative predictive value of the four indexes to assess their ability to predict relapse were analyzed. Finally, the consistency between two of the four indexes to explore the relationships among them were analyzed. There were significant differences between RFS of the sub-groups divided according to d8-PR, d22-BM, d33-BM, d33-MRD (P < 0.01); Cox proportional hazard model analysis showed that d33-MRD ≥ 10(-3) and positive BCR/ABL fusion gene were the independent prognostic factors. Sensitivity of d33-MRD was higher than that of morphology detection (d22-BM, d33-BM and d8-PR) in prediction of relapse, and positive predictive value of morphology detection was higher than that of d33-MRD. Sensitivity could be greatly increased by combination with clinical and biological characteristics. Consistency could not be found between d8-PR and d22-BM, d33-BM, d33-MRD, as well as between d22-BM, d33-BM, and d33-MRD. However, all cases of d22-BM, d33-BM M2/M3 were d33-MRD ≥ 10(-3), while the same phenomenon could not be found for patients with poor d8-PR. Clearance of leukemic cell during induction of remission therapy in children with BCP-ALL had important clinical value. Sensitivity of MRD detection after induction of remission therapy was higher than that of morphological analysis to predict relapse. Morphological analysis could only identify a few patients with very high risk of relapse and the sensitivity could be increased by combination with clinical biological characteristics. The simple prednisone response may contain some prognostic information that could not be covered by analysis of BM cells. It may be the best way to assess the clearance of leukemic cells to combine the prednisone response with MRD detection after induction of remission therapy.

Gene expression markers in circulating tumor cells may predict bone metastasis and response to hormonal treatment in breast cancer

PubMed Central

WANG, HAIYING; MOLINA, JULIAN; JIANG, JOHN; FERBER, MATTHEW; PRUTHI, SANDHYA; JATKOE, TIMOTHY; DERECHO, CARLO; RAJPUROHIT, YASHODA; ZHENG, JIAN; WANG, YIXIN

2013-01-01

Circulating tumor cells (CTCs) have recently attracted attention due to their potential as prognostic and predictive markers for the clinical management of metastatic breast cancer patients. The isolation of CTCs from patients may enable the molecular characterization of these cells, which may help establish a minimally invasive assay for the prediction of metastasis and further optimization of treatment. Molecular markers of proven clinical value may therefore be useful in predicting disease aggressiveness and response to treatment. In our earlier study, we identified a gene signature in breast cancer that appears to be significantly associated with bone metastasis. Among the genes that constitute this signature, trefoil factor 1 (TFF1) was identified as the most differentially expressed gene associated with bone metastasis. In this study, we investigated 25 candidate gene markers in the CTCs of metastatic breast cancer patients with different metastatic sites. The panel of the 25 markers was investigated in 80 baseline samples (first blood draw of CTCs) and 30 follow-up samples. In addition, 40 healthy blood donors (HBDs) were analyzed as controls. The assay was performed using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) with RNA extracted from CTCs captured by the CellSearch system. Our study indicated that 12 of the genes were uniquely expressed in CTCs and 10 were highly expressed in the CTCs obtained from patients compared to those obtained from HBDs. Among these genes, the expression of keratin 19 was highly correlated with the CTC count. The TFF1 expression in CTCs was a strong predictor of bone metastasis and the patients with a high expression of estrogen receptor β in CTCs exhibited a better response to hormonal treatment. Molecular characterization of these genes in CTCs may provide a better understanding of the mechanism underlying tumor metastasis and identify gene markers in CTCs for predicting disease progression and response to treatment. PMID:24649289

Gene expression markers in circulating tumor cells may predict bone metastasis and response to hormonal treatment in breast cancer.

PubMed

Wang, Haiying; Molina, Julian; Jiang, John; Ferber, Matthew; Pruthi, Sandhya; Jatkoe, Timothy; Derecho, Carlo; Rajpurohit, Yashoda; Zheng, Jian; Wang, Yixin

2013-11-01

Circulating tumor cells (CTCs) have recently attracted attention due to their potential as prognostic and predictive markers for the clinical management of metastatic breast cancer patients. The isolation of CTCs from patients may enable the molecular characterization of these cells, which may help establish a minimally invasive assay for the prediction of metastasis and further optimization of treatment. Molecular markers of proven clinical value may therefore be useful in predicting disease aggressiveness and response to treatment. In our earlier study, we identified a gene signature in breast cancer that appears to be significantly associated with bone metastasis. Among the genes that constitute this signature, trefoil factor 1 (TFF1) was identified as the most differentially expressed gene associated with bone metastasis. In this study, we investigated 25 candidate gene markers in the CTCs of metastatic breast cancer patients with different metastatic sites. The panel of the 25 markers was investigated in 80 baseline samples (first blood draw of CTCs) and 30 follow-up samples. In addition, 40 healthy blood donors (HBDs) were analyzed as controls. The assay was performed using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) with RNA extracted from CTCs captured by the CellSearch system. Our study indicated that 12 of the genes were uniquely expressed in CTCs and 10 were highly expressed in the CTCs obtained from patients compared to those obtained from HBDs. Among these genes, the expression of keratin 19 was highly correlated with the CTC count. The TFF1 expression in CTCs was a strong predictor of bone metastasis and the patients with a high expression of estrogen receptor β in CTCs exhibited a better response to hormonal treatment. Molecular characterization of these genes in CTCs may provide a better understanding of the mechanism underlying tumor metastasis and identify gene markers in CTCs for predicting disease progression and response to treatment.

Absolute Measurements of Macrophage Migration Inhibitory Factor and Interleukin-1-β mRNA Levels Accurately Predict Treatment Response in Depressed Patients.

PubMed

Cattaneo, Annamaria; Ferrari, Clarissa; Uher, Rudolf; Bocchio-Chiavetto, Luisella; Riva, Marco Andrea; Pariante, Carmine M

2016-10-01

Increased levels of inflammation have been associated with a poorer response to antidepressants in several clinical samples, but these findings have had been limited by low reproducibility of biomarker assays across laboratories, difficulty in predicting response probability on an individual basis, and unclear molecular mechanisms. Here we measured absolute mRNA values (a reliable quantitation of number of molecules) of Macrophage Migration Inhibitory Factor and interleukin-1β in a previously published sample from a randomized controlled trial comparing escitalopram vs nortriptyline (GENDEP) as well as in an independent, naturalistic replication sample. We then used linear discriminant analysis to calculate mRNA values cutoffs that best discriminated between responders and nonresponders after 12 weeks of antidepressants. As Macrophage Migration Inhibitory Factor and interleukin-1β might be involved in different pathways, we constructed a protein-protein interaction network by the Search Tool for the Retrieval of Interacting Genes/Proteins. We identified cutoff values for the absolute mRNA measures that accurately predicted response probability on an individual basis, with positive predictive values and specificity for nonresponders of 100% in both samples (negative predictive value=82% to 85%, sensitivity=52% to 61%). Using network analysis, we identified different clusters of targets for these 2 cytokines, with Macrophage Migration Inhibitory Factor interacting predominantly with pathways involved in neurogenesis, neuroplasticity, and cell proliferation, and interleukin-1β interacting predominantly with pathways involved in the inflammasome complex, oxidative stress, and neurodegeneration. We believe that these data provide a clinically suitable approach to the personalization of antidepressant therapy: patients who have absolute mRNA values above the suggested cutoffs could be directed toward earlier access to more assertive antidepressant strategies, including the addition of other antidepressants or antiinflammatory drugs. © The Author 2016. Published by Oxford University Press on behalf of CINP.

Responsiveness to montelukast is associated with bronchial hyperresponsiveness and total immunoglobulin E but not polymorphisms in the leukotriene C4 synthase and cysteinyl leukotriene receptor 1 genes in Korean children with exercise-induced asthma (EIA).

PubMed

Lee, S-Y; Kim, H-B; Kim, J-H; Kim, B-S; Kang, M-J; Jang, S-O; Seo, H-J; Hong, S-J

2007-10-01

As previous studies have shown that cysteinyl leukotrienes are important mediators in exercise-induced bronchoconstriction (EIB), and leukotriene receptor antagonists (LTRAs) such as montelukast have been shown to improve post-exercise bronchoconstrictor responses, we herein investigated whether clinical responsiveness to montelukast was associated with polymorphisms in the genes encoding leukotriene C4 synthase (LTC4S) and cysteinyl leukotriene receptor 1 (CysLTR1) and/or clinical parameters in Korean asthmatic children with EIB. The study population consisted of 100 asthmatic children with EIB. The individuals studied were given exercise challenge tests before and after receiving montelukast (5 mg/day) for 8 weeks. Responders were defined as children showing>10% post-treatment improvement in forced expiratory volume in 1 s (FEV1). The LTC4S A(-444)C and CysLTR1 T(+927)C polymorphisms were genotyped by PCR-restriction fragment length polymorphism analysis. Of 100 enrolled children, 68 were classified as responders and 32 were classified as non-responders. No significant association was observed between montelukast responsiveness and LTC4S or CysLTR1 genotype, either alone or in combination. In contrast, montelukast-induced improvement in FEV(1) after exercise was correlated with higher pre-treatment PC20 (methacholine) values (r=0.210, P=0.036) and lower total IgE levels (r=-0.216, P=0.031). The LTC4S A(-444)C and CysLTR1 T(+927)C genotypes do not appear to be useful for predicting clinical responsiveness to montelukast, whereas bronchial hyperresponsiveness and total IgE appear to predict the degree of montelukast responsiveness in Korean asthmatic children with EIB.

CD4 responses in the setting or suboptimal virological responses to antiretroviral therapy: features, outcomes, and associated factors.

PubMed

Collazos, Julio; Asensi, Víctor; Cartón, José Antonio

2009-07-01

The factors associated with discordant viroimmunological responses following antiretroviral therapy are unclear. We studied 1380 patients who initiated a protease inhibitor (PI)-based antiretroviral regimen and who fulfilled the criteria for inclusion. Of them, 255 (18.5%) had CD4 increases > or =100 cells/microl after 1 year of therapy despite detectable viral load (immunological responders); they were compared with 669 patients (48.5%) who had CD4 increases <100 cells/microl regardless of their final viral load (immunological nonresponders). Immunological responders had higher rates of sexual acquisition of HIV (p = 0.03), lower rates of clinical progression (p = 0.02), higher probabilities of being naive to antiretroviral therapy (p = 0.006) or to PI if antiretroviral experienced (p = 0.03), higher rates of receiving only nucleoside reverse transcriptase inhibitors in addition to the PI (p = 0.04), and lower baseline CD4 counts (p = 0.007) and higher viral loads (p = 0.009), as compared with nonresponders. Multivariate analysis revealed that sexual transmission of HIV (homosexual p = 0.004, heterosexual p = 0.03), no prior PI experience (p = 0.005), absence of clinical progression (p = 0.02), and lower baseline CD4 counts (p = 0.03) were independently associated with immunological response. However, these factors differed according to the patients' prior antiretroviral status, as higher baseline viral load was also associated with immunological response in antiretroviral-experienced patients (p = 0.02), whereas baseline CD4 count (p = 0.007) was the only predictive parameter in antiretroviral-naive patients. We conclude that immunological responses despite suboptimal viral suppression are common. Prior PI experience, HIV transmission category, baseline CD4 counts, and clinical progression were independently predictive of this condition, although the associated factors were different depending on the patient's prior antiretroviral history.

Identifying anti-cancer drug response related genes using an integrative analysis of transcriptomic and genomic variations with cell line-based drug perturbations.

PubMed

Sun, Yi; Zhang, Wei; Chen, Yunqin; Ma, Qin; Wei, Jia; Liu, Qi

2016-02-23

Clinical responses to anti-cancer therapies often only benefit a defined subset of patients. Predicting the best treatment strategy hinges on our ability to effectively translate genomic data into actionable information on drug responses. To achieve this goal, we compiled a comprehensive collection of baseline cancer genome data and drug response information derived from a large panel of cancer cell lines. This data set was applied to identify the signature genes relevant to drug sensitivity and their resistance by integrating CNVs and the gene expression of cell lines with in vitro drug responses. We presented an efficient in-silico pipeline for integrating heterogeneous cell line data sources with the simultaneous modeling of drug response values across all the drugs and cell lines. Potential signature genes correlated with drug response (sensitive or resistant) in different cancer types were identified. Using signature genes, our collaborative filtering-based drug response prediction model outperformed the 44 algorithms submitted to the DREAM competition on breast cancer cells. The functions of the identified drug response related signature genes were carefully analyzed at the pathway level and the synthetic lethality level. Furthermore, we validated these signature genes by applying them to the classification of the different subtypes of the TCGA tumor samples, and further uncovered their in vivo implications using clinical patient data. Our work may have promise in translating genomic data into customized marker genes relevant to the response of specific drugs for a specific cancer type of individual patients.

Quantitative disease progression model of α‐1 proteinase inhibitor therapy on computed tomography lung density in patients with α‐1 antitrypsin deficiency

PubMed Central

Rogers, James A.; Vit, Oliver; Bexon, Martin; Sandhaus, Robert A.; Burdon, Jonathan; Chorostowska‐Wynimko, Joanna; Thompson, Philip; Stocks, James; McElvaney, Noel G.; Chapman, Kenneth R.; Edelman, Jonathan M.

2017-01-01

Aims Early‐onset emphysema attributed to α‐1 antitrypsin deficiency (AATD) is frequently overlooked and undertreated. RAPID‐RCT/RAPID‐OLE, the largest clinical trials of purified human α‐1 proteinase inhibitor (A1‐PI; 60 mg kg–1 week–1) therapy completed to date, demonstrated for the first time that A1‐PI is clinically effective in slowing lung tissue loss in AATD. A posthoc pharmacometric analysis was undertaken to further explore dose, exposure and response. Methods A disease progression model was constructed, utilizing observed A1‐PI exposure and lung density decline rates (measured by computed tomography) from RAPID‐RCT/RAPID‐OLE, to predict effects of population variability and higher doses on A1‐PI exposure and clinical response. Dose–exposure and exposure–response relationships were characterized using nonlinear and linear mixed effects models, respectively. The dose–exposure model predicts summary exposures and not individual concentration kinetics; covariates included baseline serum A1‐PI, forced expiratory volume in 1 s and body weight. The exposure–response model relates A1‐PI exposure to lung density decline rate at varying exposure levels. Results A dose of 60 mg kg–1 week–1 achieved trough serum levels >11 μmol l–1 (putative ‘protective threshold’) in ≥98% patients. Dose–exposure–response simulations revealed increasing separation between A1‐PI and placebo in the proportions of patients achieving higher reductions in lung density decline rate; improvements in decline rates ≥0.5 g l–1 year–1 occurred more often in patients receiving A1‐PI: 63 vs. 12%. Conclusion Weight‐based A1‐PI dosing reliably raises serum levels above the 11 μmol l–1 threshold. However, our exposure–response simulations question whether this is the maximal, clinically effective threshold for A1‐PI therapy in AATD. The model suggested higher doses of A1‐PI would yield greater clinical effects. PMID:28662542

Multiscale systems pharmacological analysis of everolimus action in hepatocellular carcinoma.

PubMed

Ande, Anusha; Chaar, Maher; Ait-Oudhia, Sihem

2018-05-03

Dysregulation of mTOR pathway is common in hepatocellular carcinoma (HCC). A translational quantitative systems pharmacology (QSP), pharmacokinetic (PK), and pharmacodynamic (PD) model dissecting the circuitry of this pathway was developed to predict HCC patients' response to everolimus, an mTOR inhibitor. The time course of key signaling proteins in the mTOR pathway, HCC cells viability, tumor volume (TV) and everolimus plasma and tumor concentrations in xenograft mice, clinical PK of everolimus and progression free survival (PFS) in placebo and everolimus-treated patients were extracted from literature. A comprehensive and multiscale QSP/PK/PD model was developed, qualified, and translated to clinical settings. Model fittings and simulations were performed using Monolix software. The S6-kinase protein was identified as critical in the mTOR signaling pathway for describing everolimus lack of efficacy in HCC patients. The net growth rate constant (kg) of HCC cells was estimated at 0.02 h -1 (2.88%RSE). The partition coefficient of everolimus into the tumor (kp) was determined at 0.06 (12.98%RSE). The kg in patients was calculated from the doubling time of TV in naturally progressing HCC patients, and was determined at 0.004 day -1 . Model-predicted and observed PFS were in good agreement for placebo and everolimus-treated patients. In conclusion, a multiscale QSP/PK/PD model elucidating everolimus lack of efficacy in HCC patients was successfully developed and predicted PFS reasonably well compared to observed clinical findings. This model may provide insights into clinical response to everolimus-based therapy and serve as a valuable tool for the clinical translation of efficacy for novel mTOR inhibitors.

Molecular Targets in Advanced Therapeutics of Cancers: The Role of Pharmacogenetics.

PubMed

Abubakar, Murtala B; Gan, Siew Hua

2016-01-01

The advent of advanced molecular targeted therapy has resulted in improved prognoses for patients with advanced malignancies. However, despite the significant success and specificity of this advocated targeted therapy, significant on- and off-target adverse effects and inter-individual variability in treatment responses have been reported. The interpatient variability in drug response has been suggested to be partly due to variations in patient genomes. Therefore, the identification of genetic biomarkers by conducting pharmacogenetics studies can help predict patient responses to targeted therapy and may serve as a basis for individualized treatment. In this review, both clinically established and potential molecular targets are highlighted. Overall, current literature suggests that individualization of targeted therapy is promising; however, integrating the clinical benefits of identified biomarkers into clinical practice for personalized medicine remains a major challenge, and further studies to validate these markers and identify novel therapeutic approaches are needed. © 2016 S. Karger AG, Basel.

Current status of predictive biomarkers for neoadjuvant therapy in esophageal cancer

PubMed Central

Uemura, Norihisa; Kondo, Tadashi

2014-01-01

Neoadjuvant therapy has been proven to be extremely valuable and is widely used for advanced esophageal cancer. However, a significant proportion of treated patients (60%-70%) does not respond well to neoadjuvant treatments and develop severe adverse effects. Therefore, predictive markers for individualization of multimodality treatments are urgently needed in esophageal cancer. Recently, molecular biomarkers that predict the response to neoadjuvant therapy have been explored in multimodal approaches in esophageal cancer and successful examples of biomarker identification have been reported. In this review, promising candidates for predictive molecular biomarkers developed by using multiple molecular approaches are reviewed. Moreover, treatment strategies based on the status of predicted biomarkers are discussed, while considering the international differences in the clinical background. However, in the absence of adequate treatment options related to the results of the biomarker test, the usefulness of these diagnostic tools is limited and new effective therapies for biomarker-identified nonresponders to cancer treatment should be concurrent with the progress of predictive technologies. Further improvement in the prognosis of esophageal cancer patients can be achieved through the introduction of novel therapeutic approaches in clinical practice. PMID:25133032

Extreme-Scale Computing Project Aims to Advance Precision Oncology | Poster

Cancer.gov

Two government agencies and five national laboratories are collaborating to develop extremely high-performance computing capabilities that will analyze mountains of research and clinical data to improve scientific understanding of cancer, predict drug response, and improve treatments for patients.

Immunotherapy: a new treatment paradigm in bladder cancer

PubMed Central

Davarpanah, Nicole N.; Yuno, Akira; Trepel, Jane B.; Apolo, Andrea B.

2017-01-01

Purpose of review T-cell checkpoint blockade has become a dynamic immunotherapy for bladder cancer. In 2016, atezolizumab, an immune checkpoint inhibitor, became the first new drug approved in metastatic urothelial carcinoma (mUC) in over 30 years. In 2017, nivolumab was also approved for the same indication. This overview of checkpoint inhibitors in clinical trials focuses on novel immunotherapy combinations, predictive biomarkers including mutational load and neoantigen identification, and an evaluation of the future of bladder cancer immunotherapy. Recent findings Programed cell death protein 1/programed death-ligand 1 (PD-1/PD-L1) checkpoint inhibitors have achieved durable clinical responses in a subset of previously treated and treatment-naïve patients with mUC. The combination of PD-1 and cytotoxic T-lymphocyte antigen 4 (CTLA-4) has successfully improved response rates in multiple malignancies, and combination studies are underway in many tumor types, including bladder cancer, combining T-cell checkpoint blockade with other checkpoint agents and immunomodulatory therapies. Strong tumor responses to checkpoint blockade have been reported to be positively associated with expression of PD-L1 on tumor and tumor-infiltrating immune cells and with increased mutation-associated neoantigen load, which may lead to the development of predictive biomarkers. Summary Recent clinical evidence suggests that mUC is susceptible to T-cell checkpoint blockade. A global effort is underway to achieve higher response rates and more durable remissions, accelerate the development of immunotherapies, employ combination therapies, and test novel immune targets. PMID:28306559

Separate and interactive contributions of weak inhibitory control and threat sensitivity to prediction of suicide risk.

PubMed

Venables, Noah C; Sellbom, Martin; Sourander, Andre; Kendler, Kenneth S; Joiner, Thomas E; Drislane, Laura E; Sillanmäki, Lauri; Elonheimo, Henrik; Parkkola, Kai; Multimaki, Petteri; Patrick, Christopher J

2015-04-30

Biobehavioral dispositions can serve as valuable referents for biologically oriented research on core processes with relevance to many psychiatric conditions. The present study examined two such dispositional variables-weak response inhibition (or disinhibition; INH-) and threat sensitivity (or fearfulness; THT+)-as predictors of the serious transdiagnostic problem of suicide risk in two samples: male and female outpatients from a U.S. clinic (N=1078), and a population-based male military cohort from Finland (N=3855). INH- and THT+ were operationalized through scores on scale measures of disinhibition and fear/fearlessness, known to be related to DSM-defined clinical conditions and brain biomarkers. Suicide risk was assessed by clinician ratings (clinic sample) and questionnaires (both samples). Across samples and alternative suicide indices, INH- and THT+ each contributed uniquely to prediction of suicide risk-beyond internalizing and externalizing problems in the case of the clinic sample where diagnostic data were available. Further, in both samples, INH- and THT+ interactively predicted suicide risk, with individuals scoring concurrently high on both dispositions exhibiting markedly augmented risk. Findings demonstrate that dispositional constructs of INH- and THT+ are predictive of suicide risk, and hold potential as referents for biological research on suicidal behavior. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Specific lymphocyte subsets predict response to adoptive cell therapy using expanded autologous tumor-infiltrating lymphocytes in metastatic melanoma patients

PubMed Central

Radvanyi, Laszlo G.; Bernatchez, Chantale; Zhang, Minying; Fox, Patricia S.; Miller, Priscilla; Chacon, Jessica; Wu, Richard; Lizee, Gregory; Mahoney, Sandy; Alvarado, Gladys; Glass, Michelle; Johnson, Valen E.; McMannis, John D.; Shpall, Elizabeth; Prieto, Victor; Papadopoulos, Nicholas; Kim, Kevin; Homsi, Jade; Bedikian, Agop; Hwu, Wen-Jen; Patel, Sapna; Ross, Merrick I.; Lee, Jeffrey E.; Gershenwald, Jeffrey E.; Lucci, Anthony; Royal, Richard; Cormier, Janice N.; Davies, Michael A.; Mansaray, Rahmatu; Fulbright, Orenthial J.; Toth, Christopher; Ramachandran, Renjith; Wardell, Seth; Gonzalez, Audrey; Hwu, Patrick

2012-01-01

Purpose Adoptive cell therapy (ACT) using autologous tumor-infiltrating lymphocytes (TIL) is a promising treatment for metastatic melanoma unresponsive to conventional therapies. We report here on the results of an ongoing Phase II clinical trial testing the efficacy of ACT using TIL in metastatic melanoma patients and the association of specific patient clinical characteristics and the phenotypic attributes of the infused TIL with clinical response. Experimental Design Altogether, 31 transiently lymphodepleted patients were treated with their expanded TIL followed by two cycles of high-dose (HD) IL-2 therapy. The effects of patient clinical features and the phenotypes of the T-cells infused on clinical response were determined. Results Overall, 15/31 (48.4%) patients had an objective clinical response using immune-related response criteria (irRC), with two patients (6.5%) having a complete response. Progression-free survival of >12 months was observed for 9/15 (60%) of the responding patients. Factors significantly associated with objective tumor regression included a higher number of TIL infused, a higher proportion of CD8+ T-cells in the infusion product, a more differentiated effector phenotype of the CD8+ population and a higher frequency of CD8+ T-cells co-expressing the negative costimulation molecule “B- and T-lymphocyte attenuator” (BTLA). No significant difference in telomere lengths of TIL between responders and non-responders was identified. Conclusion These results indicate that immunotherapy with expanded autologous TIL is capable of achieving durable clinical responses in metastatic melanoma patients and that CD8+ T-cells in the infused TIL, particularly differentiated effectors cells and cells expressing BTLA, are associated with tumor regression. PMID:23032743

Clinical and laboratory parameters predicting a requirement for the reevaluation of growth hormone status during growth hormone treatment: Retesting early in the course of GH treatment.

PubMed

Vuralli, Dogus; Gonc, E Nazli; Ozon, Z Alev; Alikasifoglu, Ayfer; Kandemir, Nurgun

2017-06-01

We aimed to define the predictive criteria, in the form of specific clinical, hormonal and radiological parameters, for children with growth hormone deficiency (GHD) who may benefit from the reevaluation of GH status early in the course of growth hormone (GH) treatment. Two hundred sixty-five children with growth hormone deficiency were retested by GH stimulation at the end of the first year of GH treatment. The initial clinical and laboratory characteristics of those with a normal (GH≥10ng/ml) response and those with a subnormal (GH<10ng/ml) response were compared to predict a normal GH status during reassessment. Sixty-nine patients (40.6%) out of the 170 patients with isolated growth hormone deficiency (IGHD) had a peak GH of ≥10ng/ml during the retest. None of the patients with multiple pituitary hormone deficiency (MPHD) had a peak GH of ≥10ng/ml. Puberty and sex steroid priming in peripubertal cases increased the probability of a normal GH response. Only one patient with IGHD who had an ectopic posterior pituitary without stalk interruption on MRI analysis showed a normal GH response during the retest. Patients with a peak GH between 5 and 10ng/ml, an age at diagnosis of ≥9years or a height gain below 0.61 SDS during the first year of treatment had an increased probability of having a normal GH response at the retest. Early reassessment of GH status during GH treatment is unnecessary in patients who have MPHD with at least 3 hormone deficiencies. Retesting at the end of the first year of therapy is recommended for patients with IGHD who have a height gain of <0.61 SDS in the first year of treatment, especially those with a normal or 'hypoplastic' pituitary on imaging. Priming can increase the likelihood of a normal response in patients in the pubertal age group who do not show overt signs of pubertal development. Copyright © 2017. Published by Elsevier Ltd.

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis.

PubMed

Sieberts, Solveig K; Zhu, Fan; García-García, Javier; Stahl, Eli; Pratap, Abhishek; Pandey, Gaurav; Pappas, Dimitrios; Aguilar, Daniel; Anton, Bernat; Bonet, Jaume; Eksi, Ridvan; Fornés, Oriol; Guney, Emre; Li, Hongdong; Marín, Manuel Alejandro; Panwar, Bharat; Planas-Iglesias, Joan; Poglayen, Daniel; Cui, Jing; Falcao, Andre O; Suver, Christine; Hoff, Bruce; Balagurusamy, Venkat S K; Dillenberger, Donna; Neto, Elias Chaibub; Norman, Thea; Aittokallio, Tero; Ammad-Ud-Din, Muhammad; Azencott, Chloe-Agathe; Bellón, Víctor; Boeva, Valentina; Bunte, Kerstin; Chheda, Himanshu; Cheng, Lu; Corander, Jukka; Dumontier, Michel; Goldenberg, Anna; Gopalacharyulu, Peddinti; Hajiloo, Mohsen; Hidru, Daniel; Jaiswal, Alok; Kaski, Samuel; Khalfaoui, Beyrem; Khan, Suleiman Ali; Kramer, Eric R; Marttinen, Pekka; Mezlini, Aziz M; Molparia, Bhuvan; Pirinen, Matti; Saarela, Janna; Samwald, Matthias; Stoven, Véronique; Tang, Hao; Tang, Jing; Torkamani, Ali; Vert, Jean-Phillipe; Wang, Bo; Wang, Tao; Wennerberg, Krister; Wineinger, Nathan E; Xiao, Guanghua; Xie, Yang; Yeung, Rae; Zhan, Xiaowei; Zhao, Cheng; Greenberg, Jeff; Kremer, Joel; Michaud, Kaleb; Barton, Anne; Coenen, Marieke; Mariette, Xavier; Miceli, Corinne; Shadick, Nancy; Weinblatt, Michael; de Vries, Niek; Tak, Paul P; Gerlag, Danielle; Huizinga, Tom W J; Kurreeman, Fina; Allaart, Cornelia F; Louis Bridges, S; Criswell, Lindsey; Moreland, Larry; Klareskog, Lars; Saevarsdottir, Saedis; Padyukov, Leonid; Gregersen, Peter K; Friend, Stephen; Plenge, Robert; Stolovitzky, Gustavo; Oliva, Baldo; Guan, Yuanfang; Mangravite, Lara M; Bridges, S Louis; Criswell, Lindsey; Moreland, Larry; Klareskog, Lars; Saevarsdottir, Saedis; Padyukov, Leonid; Gregersen, Peter K; Friend, Stephen; Plenge, Robert; Stolovitzky, Gustavo; Oliva, Baldo; Guan, Yuanfang; Mangravite, Lara M

2016-08-23

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2)=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.

Optimal designs for prediction studies of whiplash.

PubMed

Kamper, Steven J; Hancock, Mark J; Maher, Christopher G

2011-12-01

Commentary. To provide guidance for the design and interpretation of predictive studies of whiplash associated disorders (WAD). Numerous studies have sought to define and explain the clinical course and response to treatment of people with WAD. Design of these studies is often suboptimal, which can lead to biased findings and issues with interpreting the results. Literature review and commentary. Predictive studies can be grouped into four broad categories; studies of symptomatic course, studies that aim to identify factors that predict outcome, studies that aim to isolate variables that are causally responsible for outcome, and studies that aim to identify patients who respond best to particular treatments. Although the specific research question will determine the optimal methods, there are a number of generic features that should be incorporated into design of such studies. The aim of these features is to minimize bias, generate adequately precise prognostic estimates, and ensure generalizability of the findings. This paper provides a summary of important considerations in the design, conduct, and reporting of prediction studies in the field of whiplash.

Predictive model of outcome of targeted nodal assessment in colorectal cancer.

PubMed

Nissan, Aviram; Protic, Mladjan; Bilchik, Anton; Eberhardt, John; Peoples, George E; Stojadinovic, Alexander

2010-02-01

Improvement in staging accuracy is the principal aim of targeted nodal assessment in colorectal carcinoma. Technical factors independently predictive of false negative (FN) sentinel lymph node (SLN) mapping should be identified to facilitate operative decision making. To define independent predictors of FN SLN mapping and to develop a predictive model that could support surgical decisions. Data was analyzed from 2 completed prospective clinical trials involving 278 patients with colorectal carcinoma undergoing SLN mapping. Clinical outcome of interest was FN SLN(s), defined as one(s) with no apparent tumor cells in the presence of non-SLN metastases. To assess the independent predictive effect of a covariate for a nominal response (FN SLN), a logistic regression model was constructed and parameters estimated using maximum likelihood. A probabilistic Bayesian model was also trained and cross validated using 10-fold train-and-test sets to predict FN SLN mapping. Area under the curve (AUC) from receiver operating characteristics curves of these predictions was calculated to determine the predictive value of the model. Number of SLNs (<3; P = 0.03) and tumor-replaced nodes (P < 0.01) independently predicted FN SLN. Cross validation of the model created with Bayesian Network Analysis effectively predicted FN SLN (area under the curve = 0.84-0.86). The positive and negative predictive values of the model are 83% and 97%, respectively. This study supports a minimum threshold of 3 nodes for targeted nodal assessment in colorectal cancer, and establishes sufficient basis to conclude that SLN mapping and biopsy cannot be justified in the presence of clinically apparent tumor-replaced nodes.

Deficits in Top-Down Sensory Prediction in Infants At Risk due to Premature Birth.

PubMed

Emberson, Lauren L; Boldin, Alex M; Riccio, Julie E; Guillet, Ronnie; Aslin, Richard N

2017-02-06

A prominent theoretical view is that the brain is inherently predictive [1, 2] and that prediction helps drive the engine of development [3, 4]. Although infants exhibit neural signatures of top-down sensory prediction [5, 6], in order to establish that prediction supports development, it must be established that deficits in early prediction abilities alter trajectories. We investigated prediction in infants born prematurely, a leading cause of neuro-cognitive impairment worldwide [7]. Prematurity, independent of medical complications, leads to developmental disturbances [8-12] and a broad range of developmental delays [13-17]. Is an alteration in early prediction abilities the common cause? Using functional near-infrared spectroscopy (fNIRS), we measured top-down sensory prediction in preterm infants (born <33 weeks gestation) before infants exhibited clinically identifiable developmental delays (6 months corrected age). Whereas preterm infants had typical neural responses to presented visual stimuli, they exhibited altered neural responses to predicted visual stimuli. Importantly, a separate behavioral control confirmed that preterm infants detect pattern violations at the same rate as full-terms, establishing selectivity of this response to top-down predictions (e.g., not in learning an audiovisual association). These findings suggest that top-down sensory prediction plays a crucial role in development and that deficits in this ability may be the reason why preterm infants experience altered developmental trajectories and are at risk for poor developmental outcomes. Moreover, this work presents an opportunity for establishing a neuro-biomarker for early identification of infants at risk and could guide early intervention regimens. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predictive biomarkers of sorafenib efficacy in advanced hepatocellular carcinoma: Are we getting there?

PubMed Central

Shao, Yu-Yun; Hsu, Chih-Hung; Cheng, Ann-Lii

2015-01-01

Sorafenib is the current standard treatment for advanced hepatocellular carcinoma (HCC), but its efficacy is modest with low response rates and short response duration. Predictive biomarkers for sorafenib efficacy are necessary. However, efforts to determine biomarkers for sorafenib have led only to potential candidates rather than clinically useful predictors. Studies based on patient cohorts identified the potential of blood levels of angiopoietin-2, hepatocyte growth factor, insulin-like growth factor-1, and transforming growth factor-β1 for predicting sorafenib efficacy. Alpha-fetoprotein response, dynamic contrast-enhanced magnetic resonance imaging, and treatment-related side effects may serve as early surrogate markers. Novel approaches based on super-responders or experimental mouse models may provide new directions in biomarker research. These studies identified tumor amplification of FGF3/FGF4 or VEGFA and tumor expression of phospho-Mapk14 and phospho-Atf2 as possible predictive markers that await validation. A group effort that considers various prognostic factors and proper collection of tumor tissues before treatment is imperative for the success of future biomarker research in advanced HCC. PMID:26420960

BIM expression in treatment naïve cancers predicts responsiveness to kinase inhibitors

PubMed Central

Faber, Anthony; Corcoran, Ryan B.; Ebi, Hiromichi; Sequist, Lecia V.; Waltman, Belinda A.; Chung, Euiheon; Incio, Joao; Digumarthy, Subba R.; Pollack, Sarah F.; Song, Youngchul; Muzikansky, Alona; Lifshits, Eugene; Roberge, Sylvie; Coffman, Erik J.; Benes, Cyril; Gómez, Henry; Baselga, Jose; Arteaga, Carlos L.; Rivera, Miguel N.; Dias-Santagata, Dora; Jain, Rakesh K.; Engelman, Jeffrey A.

2011-01-01

Cancers with specific genetic mutations are susceptible to selective kinase inhibitors. However, there is wide spectrum of benefit among cancers harboring the same sensitizing genetic mutations. Herein, we measured apoptotic rates among cell lines sharing the same driver oncogene following treatment with the corresponding kinase inhibitor. There was a wide range of kinase inhibitor-induced apoptosis despite comparable inhibition of the target and associated downstream signaling pathways. Surprisingly, pre-treatment RNA levels of the BH3-only pro-apoptotic BIM strongly predicted the capacity of EGFR, HER2, and PI3K inhibitors to induce apoptosis in EGFR mutant, HER2 amplified, and PIK3CA mutant cancers, respectively, but BIM levels did not predict responsiveness to standard chemotherapies. Furthermore, BIM RNA levels in EGFR mutant lung cancer specimens predicted response and duration of clinical benefit from EGFR inhibitors. These findings suggest assessment of BIM levels in treatment naïve tumor biopsies may indicate the degree of benefit from single-agent kinase inhibitors in multiple oncogene-addiction paradigms. PMID:22145099

Predictive biomarkers of sorafenib efficacy in advanced hepatocellular carcinoma: Are we getting there?

PubMed

Shao, Yu-Yun; Hsu, Chih-Hung; Cheng, Ann-Lii

2015-09-28

Sorafenib is the current standard treatment for advanced hepatocellular carcinoma (HCC), but its efficacy is modest with low response rates and short response duration. Predictive biomarkers for sorafenib efficacy are necessary. However, efforts to determine biomarkers for sorafenib have led only to potential candidates rather than clinically useful predictors. Studies based on patient cohorts identified the potential of blood levels of angiopoietin-2, hepatocyte growth factor, insulin-like growth factor-1, and transforming growth factor-β1 for predicting sorafenib efficacy. Alpha-fetoprotein response, dynamic contrast-enhanced magnetic resonance imaging, and treatment-related side effects may serve as early surrogate markers. Novel approaches based on super-responders or experimental mouse models may provide new directions in biomarker research. These studies identified tumor amplification of FGF3/FGF4 or VEGFA and tumor expression of phospho-Mapk14 and phospho-Atf2 as possible predictive markers that await validation. A group effort that considers various prognostic factors and proper collection of tumor tissues before treatment is imperative for the success of future biomarker research in advanced HCC.

Low clinical diagnostic accuracy of early vs advanced Parkinson disease: clinicopathologic study.

PubMed

Adler, Charles H; Beach, Thomas G; Hentz, Joseph G; Shill, Holly A; Caviness, John N; Driver-Dunckley, Erika; Sabbagh, Marwan N; Sue, Lucia I; Jacobson, Sandra A; Belden, Christine M; Dugger, Brittany N

2014-07-29

Determine diagnostic accuracy of a clinical diagnosis of Parkinson disease (PD) using neuropathologic diagnosis as the gold standard. Data from the Arizona Study of Aging and Neurodegenerative Disorders were used to determine the predictive value of a clinical PD diagnosis, using 2 clinical diagnostic confidence levels, PossPD (never treated or not clearly responsive) and ProbPD (responsive to medications). Neuropathologic diagnosis was the gold standard. Based on first visit, 9 of 34 (26%) PossPD cases had neuropathologically confirmed PD while 80 of 97 (82%) ProbPD cases had confirmed PD. PD was confirmed in 8 of 15 (53%) ProbPD cases with <5 years of disease duration and 72 of 82 (88%) with ≥5 years of disease duration. Using final diagnosis at time of death, 91 of 107 (85%) ProbPD cases had confirmed PD. Clinical variables that improved diagnostic accuracy were medication response, motor fluctuations, dyskinesias, and hyposmia. Using neuropathologic findings of PD as the gold standard, this study establishes the novel findings of only 26% accuracy for a clinical diagnosis of PD in untreated or not clearly responsive subjects, 53% accuracy in early PD responsive to medication (<5 years' duration), and >85% diagnostic accuracy of longer duration, medication-responsive PD. Caution is needed when interpreting clinical studies of PD, especially studies of early disease that do not have autopsy confirmation. The need for a tissue or other diagnostic biomarker is reinforced. This study provides Class II evidence that a clinical diagnosis of PD identifies patients who will have pathologically confirmed PD with a sensitivity of 88% and specificity of 68%. © 2014 American Academy of Neurology.

Correlation between Histological Status of the Pulp and Its Response to Sensibility Tests

PubMed Central

Naseri, Mandana; Khayat, Akbar; Zamaheni, Sara; Shojaeian, Shiva

2017-01-01

Introduction: The purpose of this study was to assess the accuracy of sensibility tests by correlating it with histologic pulp condition. Methods and Materials: Assessment of clinical signs and symptoms were performed on 65 permanent teeth that were scheduled to be extracted for periodontal, prosthodontic or orthodontic reasons. The normal pulp and reversible pulpitis were considered as treatable tooth conditions while irreversible pulpitis and necrosis were considered as untreatable conditions. The teeth were then extracted and sectioned for histological analysis of dental pulp. Histologic status and classification corresponded to the treatable or untreatable pulp condition. Comparisons between histological treatable and untreatable pulp condition were performed with chi-square analysis for sensibility test responses. The positive predictive value (PPV), negative predictive value (NPV) and accuracy to detect untreatable from treatable pulp condition were calculated for each test. Results: A significant difference was detected in the normal and a sharp lingered response to heat and cold tests. There was significant difference in the negative response to EPT between histological groups. The kappa agreement coefficient between clinical and histological diagnosis of pulp condition was about 0.843 (P<0.001). The accuracy of cold and heat tests and EPT to detect treatable pulp or untreatable pulp states were 78, 74 and 62%, respectively. The sensibility tests diagnosed untreatable pulpitis with a higher probability (NPV=63%-67% -54%, PPV=83%-91% -95% for heat, cold and EPT, respectively). Conclusion: Sensibility test results were more likely to diagnose pulpal disease or untreatable pulp conditions. However, to increase the diagnostic accuracy patient history, clinical signs and symptoms and also radiographic findings in conjunction with sensibility tests must be used. The result of this small study demonstrated a good agreement between clinical and histological pulp diagnosis. PMID:28179918

Baseline gut microbiota predicts clinical response and colitis in metastatic melanoma patients treated with ipilimumab.

PubMed

Chaput, N; Lepage, P; Coutzac, C; Soularue, E; Le Roux, K; Monot, C; Boselli, L; Routier, E; Cassard, L; Collins, M; Vaysse, T; Marthey, L; Eggermont, A; Asvatourian, V; Lanoy, E; Mateus, C; Robert, C; Carbonnel, F

2017-06-01

Ipilimumab, an immune checkpoint inhibitor targeting CTLA-4, prolongs survival in a subset of patients with metastatic melanoma (MM) but can induce immune-related adverse events, including enterocolitis. We hypothesized that baseline gut microbiota could predict ipilimumab anti-tumor response and/or intestinal toxicity. Twenty-six patients with MM treated with ipilimumab were prospectively enrolled. Fecal microbiota composition was assessed using 16S rRNA gene sequencing at baseline and before each ipilimumab infusion. Patients were further clustered based on microbiota patterns. Peripheral blood lymphocytes immunophenotypes were studied in parallel. A distinct baseline gut microbiota composition was associated with both clinical response and colitis. Compared with patients whose baseline microbiota was driven by Bacteroides (cluster B, n = 10), patients whose baseline microbiota was enriched with Faecalibacterium genus and other Firmicutes (cluster A, n = 12) had longer progression-free survival (P = 0.0039) and overall survival (P = 0.051). Most of the baseline colitis-associated phylotypes were related to Firmicutes (e.g. relatives of Faecalibacterium prausnitzii and Gemmiger formicilis), whereas no colitis-related phylotypes were assigned to Bacteroidetes. A low proportion of peripheral blood regulatory T cells was associated with cluster A, long-term clinical benefit and colitis. Ipilimumab led to a higher inducible T-cell COStimulator induction on CD4+ T cells and to a higher increase in serum CD25 in patients who belonged to Faecalibacterium-driven cluster A. Baseline gut microbiota enriched with Faecalibacterium and other Firmicutes is associated with beneficial clinical response to ipilimumab and more frequent occurrence of ipilimumab-induced colitis. © The Author 2017. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Evaluation of dental pulp sensibility tests in a clinical setting.

PubMed

Jespersen, James J; Hellstein, John; Williamson, Anne; Johnson, William T; Qian, Fang

2014-03-01

The goal of this project was to evaluate the performance of dental pulp sensibility testing with Endo Ice (1,1,1,2-tetrafluoroethane) and an electric pulp tester (EPT) and to determine the effect of several variables on the reliability of these tests. Data were collected from 656 patients seen in the University of Iowa College of Dentistry Endodontic graduate clinic. The results of pulpal sensibility tests, along with the tooth number, age, sex, number of restored surfaces, presence or absence of clinical or radiographic caries, and reported recent use of analgesic medications, were recorded. The presence of vital tissue within the pulp chamber was used to verify the diagnosis. The Endo Ice results showed accuracy, 0.904; sensitivity, 0.916; specificity, 0.896; positive predictive value, 0.862; and negative predictive value, 0.937. The EPT results showed accuracy, 0.75; sensitivity, 0.84; specificity, 0.74; positive predictive value, 0.58; and negative predictive value, 0.90. Patients aged 21-50 years exhibited a more accurate response to cold testing (P = .0043). Vital teeth with caries responded more accurately to cold testing (P = .0077). There was no statistically significant difference noted with any other variable examined. Pulpal sensibility testing with Endo Ice and EPT are accurate and reliable methods of determining pulpal vitality. Patients aged 21-50 exhibited a more accurate response to cold. Sex, tooth type, number of restored surfaces, presence of caries, and recent analgesic use did not significantly alter the results of pulpal sensibility testing in this study. Copyright © 2014 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

Predicting through-focus visual acuity with the eye's natural aberrations.

PubMed

Kingston, Amanda C; Cox, Ian G

2013-10-01

To develop a predictive optical modeling process that utilizes individual computer eye models along with a novel through-focus image quality metric. Individual eye models were implemented in optical design software (Zemax, Bellevue, WA) based on evaluation of ocular aberrations, pupil diameter, visual acuity, and accommodative response of 90 subjects (180 eyes; 24-63 years of age). Monocular high-contrast minimum angle of resolution (logMAR) acuity was assessed at 6 m, 2 m, 1 m, 67 cm, 50 cm, 40 cm, 33 cm, 28 cm, and 25 cm. While the subject fixated on the lowest readable line of acuity, total ocular aberrations and pupil diameter were measured three times each using the Complete Ophthalmic Analysis System (COAS HD VR) at each distance. A subset of 64 mature presbyopic eyes was used to predict the clinical logMAR acuity performance of five novel multifocal contact lens designs. To validate predictability of the design process, designs were manufactured and tested clinically on a population of 24 mature presbyopes (having at least +1.50 D spectacle add at 40 cm). Seven object distances were used in the validation study (6 m, 2 m, 1 m, 67 cm, 50 cm, 40 cm, and 25 cm) to measure monocular high-contrast logMAR acuity. Baseline clinical through-focus logMAR was shown to correlate highly (R² = 0.85) with predicted logMAR from individual eye models. At all object distances, each of the five multifocal lenses showed less than one line difference, on average, between predicted and clinical normalized logMAR acuity. Correlation showed R² between 0.90 and 0.97 for all multifocal designs. Computer-based models that account for patient's aberrations, pupil diameter changes, and accommodative amplitude can be used to predict the performance of contact lens designs. With this high correlation (R² ≥ 0.90) and high level of predictability, more design options can be explored in the computer to optimize performance before a lens is manufactured and tested clinically.

Pharmacological validation of a novel nonhuman primate measure of thermal responsivity with utility for predicting analgesic effects.

PubMed

Vardigan, Joshua D; Houghton, Andrea K; Lange, Henry S; Adarayan, Emily D; Pall, Parul S; Ballard, Jeanine E; Henze, Darrell A; Uslaner, Jason M

2018-01-01

The development of novel analgesics to treat acute or chronic pain has been a challenge due to a lack of translatable measurements. Preclinical end points with improved translatability are necessary to more accurately inform clinical testing paradigms, which may help guide selection of viable drug candidates. In this study, a nonhuman primate biomarker which is sensitive to standard analgesics at clinically relevant plasma concentrations, can differentiate analgesia from sedation and utilizes a protocol very similar to that which can be employed in human clinical studies is described. Specifically, acute heat stimuli were delivered to the volar forearm using a contact heat thermode in the same manner as the clinical setting. Clinically efficacious exposures of morphine, fentanyl, and tramadol produced robust analgesic effects, whereas doses of diazepam that produce sedation had no effect. We propose that this assay has predictive utility that can help improve the probability of success for developing novel analgesics.

Pharmacological validation of a novel nonhuman primate measure of thermal responsivity with utility for predicting analgesic effects

PubMed Central

Vardigan, Joshua D; Houghton, Andrea K; Lange, Henry S; Adarayan, Emily D; Pall, Parul S; Ballard, Jeanine E; Henze, Darrell A; Uslaner, Jason M

2018-01-01

Introduction The development of novel analgesics to treat acute or chronic pain has been a challenge due to a lack of translatable measurements. Preclinical end points with improved translatability are necessary to more accurately inform clinical testing paradigms, which may help guide selection of viable drug candidates. Methods In this study, a nonhuman primate biomarker which is sensitive to standard analgesics at clinically relevant plasma concentrations, can differentiate analgesia from sedation and utilizes a protocol very similar to that which can be employed in human clinical studies is described. Specifically, acute heat stimuli were delivered to the volar forearm using a contact heat thermode in the same manner as the clinical setting. Results Clinically efficacious exposures of morphine, fentanyl, and tramadol produced robust analgesic effects, whereas doses of diazepam that produce sedation had no effect. Conclusion We propose that this assay has predictive utility that can help improve the probability of success for developing novel analgesics. PMID:29692626

Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: A machine learning approach.

PubMed

Lenhard, Fabian; Sauer, Sebastian; Andersson, Erik; Månsson, Kristoffer Nt; Mataix-Cols, David; Rück, Christian; Serlachius, Eva

2018-03-01

There are no consistent predictors of treatment outcome in paediatric obsessive-compulsive disorder (OCD). One reason for this might be the use of suboptimal statistical methodology. Machine learning is an approach to efficiently analyse complex data. Machine learning has been widely used within other fields, but has rarely been tested in the prediction of paediatric mental health treatment outcomes. To test four different machine learning methods in the prediction of treatment response in a sample of paediatric OCD patients who had received Internet-delivered cognitive behaviour therapy (ICBT). Participants were 61 adolescents (12-17 years) who enrolled in a randomized controlled trial and received ICBT. All clinical baseline variables were used to predict strictly defined treatment response status three months after ICBT. Four machine learning algorithms were implemented. For comparison, we also employed a traditional logistic regression approach. Multivariate logistic regression could not detect any significant predictors. In contrast, all four machine learning algorithms performed well in the prediction of treatment response, with 75 to 83% accuracy. The results suggest that machine learning algorithms can successfully be applied to predict paediatric OCD treatment outcome. Validation studies and studies in other disorders are warranted. Copyright © 2017 John Wiley & Sons, Ltd.

Perceived parental rearing behaviours, responsibility attitudes and life events as predictors of obsessive compulsive symptomatology: test of a cognitive model.

PubMed

Haciomeroglu, Bikem; Karanci, A Nuray

2014-11-01

It is important to investigate the role of cognitive, developmental and environmental factors in the development and maintenance of Obsessive Compulsive Symptomatology (OCS). The main objective of this study was to examine the vulnerability factors of OCS in a non-clinical sample. On the basis of Salkovskis' cognitive model of OCD, the study aimed to investigate the role of perceived parental rearing behaviours, responsibility attitudes, and life events in predicting OCS. Furthermore, the mediator role of responsibility attitudes in the relationship between perceived parental rearing behaviours and OCS was examined. Finally, the specificity of these variables to OCS was evaluated by examining the relationship of the same variables with depression and trait anxiety. A total of 300 university students (M = 19.55±1.79) were administered the Padua Inventory-Washington State University Revision, Responsibility Attitudes Scale, s-EMBU (My memories of upbringing), Life Events Inventory for University Students, Beck Depression Inventory, and State-Trait Anxiety Inventory-Trait Form. Regression analysis revealed that perceived mother overprotection, responsibility attitudes and life events significantly predicted OCS. Furthermore, responsibility attitudes mediated the relationship between perceived mother overprotection and OCS. The predictive role of perceived mother overprotection and the mediator role responsibility attitudes were OCS specific. The findings of the present study supported that perceived mother over-protection as a developmental vulnerability factor significantly contributed to the explanation of a cognitive vulnerability factor (namely responsibility attitudes), and perceived maternal overprotection had its predictive role for OCS through responsibility attitudes.

Sustained anterior cingulate cortex activation during reward processing predicts response to psychotherapy in major depressive disorder.

PubMed

Carl, Hannah; Walsh, Erin; Eisenlohr-Moul, Tory; Minkel, Jared; Crowther, Andrew; Moore, Tyler; Gibbs, Devin; Petty, Chris; Bizzell, Josh; Dichter, Gabriel S; Smoski, Moria J

2016-10-01

The purpose of the present investigation was to evaluate whether pre-treatment neural activation in response to rewards is a predictor of clinical response to Behavioral Activation Therapy for Depression (BATD), an empirically validated psychotherapy that decreases depressive symptoms by increasing engagement with rewarding stimuli and reducing avoidance behaviors. Participants were 33 outpatients with major depressive disorder (MDD) and 20 matched controls. We examined group differences in activation, and the capacity to sustain activation, across task runs using functional magnetic resonance imaging (fMRI) and the monetary incentive delay (MID) task. Hierarchical linear modeling was used to investigate whether pre-treatment neural responses predicted change in depressive symptoms over the course of BATD treatment. MDD and Control groups differed in sustained activation during reward outcomes in the right nucleus accumbens, such that the MDD group experienced a significant decrease in activation in this region from the first to second task run relative to controls. Pretreatment anhedonia severity and pretreatment task-related reaction times were predictive of response to treatment. Furthermore, sustained activation in the anterior cingulate cortex during reward outcomes predicted response to psychotherapy; patients with greater sustained activation in this region were more responsive to BATD treatment. The current study only included a single treatment condition, thus it unknown whether these predictors of treatment response are specific to BATD or psychotherapy in general. Findings add to the growing body of literature suggesting that the capacity to sustain neural responses to rewards may be a critical endophenotype of MDD. Copyright © 2016 Elsevier B.V. All rights reserved.

Prefrontal Reactivity to Social Signals of Threat as a Predictor of Treatment Response in Anxious Youth

PubMed Central

Kujawa, Autumn; Swain, James E; Hanna, Gregory L; Koschmann, Elizabeth; Simpson, David; Connolly, Sucheta; Fitzgerald, Kate D; Monk, Christopher S; Phan, K Luan

2016-01-01

Neuroimaging has shown promise as a tool to predict likelihood of treatment response in adult anxiety disorders, with potential implications for clinical decision-making. Despite the relatively high prevalence and emergence of anxiety disorders in youth, very little work has evaluated neural predictors of response to treatment. The goal of the current study was to examine brain function during emotional face processing as a predictor of response to treatment in children and adolescents (age 7–19 years; N=41) with generalized, social, and/or separation anxiety disorder. Prior to beginning treatment with the selective serotonin reuptake inhibitor (SSRI) sertraline or cognitive behavior therapy (CBT), participants completed an emotional faces matching task during functional magnetic resonance imaging (fMRI). Whole brain responses to threatening (ie, angry and fearful) and happy faces were examined as predictors of change in anxiety severity following treatment. Greater activation in inferior and superior frontal gyri, including dorsolateral prefrontal cortex and ventrolateral prefrontal cortex, as well as precentral/postcentral gyri during processing of threatening faces predicted greater response to CBT and SSRI treatment. For processing of happy faces, activation in postcentral gyrus was a significant predictor of treatment response. Post-hoc analyses indicated that effects were not significantly moderated by type of treatment. Findings suggest that greater activation in prefrontal regions involved in appraising and regulating responses to social signals of threat predict better response to SSRI and CBT treatment in anxious youth and that neuroimaging may be a useful tool for predicting how youth will respond to treatment. PMID:26708107

Next Generation Vaccine Biomarkers workshop October 30–31, 2014 – Ottawa, Canada

PubMed Central

Twine, Susan M; Fulton, Kelly M; Spika, John; Ouellette, Marc; Raven, Jennifer F; Conlan, J Wayne; Krishnan, Lakshmi; Barreto, Luis; Richards, James C

2015-01-01

Vaccine biomarkers are critical to many aspects of vaccine development and licensure, including bridging findings in pre-clinical studies to clinical studies, predicting potential adverse events, and predicting vaccine efficacy. Despite advances in our understanding of various biological pathways, and advances in systems analyses of the immune response, there remains much to learn about qualitative and quantitative aspects of the human host response to vaccination. To stimulate discussion and identify opportunities for collaborative ways to advance the field of vaccine biomarkers, A Next Generation Vaccine Biomarker workshop was held in Ottawa. The two day workshop, sponsored by the National Research Council Canada, Canadian Institutes of Health Research, Public Health Agency of Canada, Pfizer, and Medicago, brought together stakeholders from Canadian and international industry, government and academia. The workshop was grouped in themes, covering vaccine biomarker challenges in the pre-clinical and clinical spaces, veterinary vaccines, regulatory challenges, and development of biomarkers for adjuvants and cancer vaccines. The use of case studies allowed participants to identify the needs and gaps requiring innovation. The workshop concluded with a discussion on opportunities for vaccine biomarker discovery, the Canadian context, and approaches for moving forward. This article provides a synopsis of these discussions and identifies steps forward for advancing vaccine biomarker research in Canada. PMID:26383909

Clinical importance of pharmacogenetics in the treatment of hepatitis C virus infection.

PubMed

Kamal, Adina Maria; MitruŢ, Paul; Kamal, Kamal Constantin; Tica, Oana Sorina; Niculescu, Mihaela; Alexandru, Dragoş Ovidiu; Tica, Andrei Adrian

2016-01-01

Globally, over 4% of the world population is affected by hepatitis C virus (HCV) infection. The current standard of care for hepatitis C infection is combination therapy with pegylated interferon and ribavirin for 48 weeks, which yield a sustained virological response in only a little over half of the patients with genotype 1 HCV. We investigated the clinical importance of pharmacogenetics in treatment efficacy and prediction of hematotoxicity. A total of 148 patients infected with HCV were enrolled. All patients were treated for a period of 48 weeks or less with pegylated interferon and ribavirin. Four genotypes were investigated: inosine triphosphatase (ITPA) rs1127354, C20orf194 rs6051702, interferon lambda (IFNL)3 rs8099917, IFNL3÷4 rs12979860 in the population from southwestern Romania. Genetic variants for rs129798660 and rs6051702 proved once more to represent an indisputable clinical tool for predicting sustained virological response (SVR) (69.23%, chi-square p=0.007846, p<0.05 and 63.29%, chi-square p=0.007846, p<0.05, respectively). ITPA genetic variants protect against ribavirin-induced hemolytic anemia and C20orf194 also proved to be protective against thrombocytopenia. These clinical findings strengthen the belief that pharmacogenetics should play a constant role in treatment decisions for patients infected with hepatitis C virus.

A clinical study of electrophysiological correlates of behavioural comfort levels in cochlear implantees.

PubMed

Raghunandhan, S; Ravikumar, A; Kameswaran, Mohan; Mandke, Kalyani; Ranjith, R

2014-05-01

Indications for cochlear implantation have expanded today to include very young children and those with syndromes/multiple handicaps. Programming the implant based on behavioural responses may be tedious for audiologists in such cases, wherein matching an effective Measurable Auditory Percept (MAP) and appropriate MAP becomes the key issue in the habilitation program. In 'Difficult to MAP' scenarios, objective measures become paramount to predict optimal current levels to be set in the MAP. We aimed to (a) study the trends in multi-modal electrophysiological tests and behavioural responses sequentially over the first year of implant use; (b) generate normative data from the above; (c) correlate the multi-modal electrophysiological thresholds levels with behavioural comfort levels; and (d) create predictive formulae for deriving optimal comfort levels (if unknown), using linear and multiple regression analysis. This prospective study included 10 profoundly hearing impaired children aged between 2 and 7 years with normal inner ear anatomy and no additional handicaps. They received the Advanced Bionics HiRes 90 K Implant with Harmony Speech processor and used HiRes-P with Fidelity 120 strategy. They underwent, impedance telemetry, neural response imaging, electrically evoked stapedial response telemetry (ESRT), and electrically evoked auditory brainstem response (EABR) tests at 1, 4, 8, and 12 months of implant use, in conjunction with behavioural mapping. Trends in electrophysiological and behavioural responses were analyzed using paired t-test. By Karl Pearson's correlation method, electrode-wise correlations were derived for neural response imaging (NRI) thresholds versus most comfortable level (M-levels) and offset based (apical, mid-array, and basal array) correlations for EABR and ESRT thresholds versus M-levels were calculated over time. These were used to derive predictive formulae by linear and multiple regression analysis. Such statistically predicted M-levels were compared with the behaviourally recorded M-levels among the cohort, using Cronbach's alpha reliability test method for confirming the efficacy of this method. NRI, ESRT, and EABR thresholds showed statistically significant positive correlations with behavioural M-levels, which improved with implant use over time. These correlations were used to derive predicted M-levels using regression analysis. On an average, predicted M-levels were found to be statistically reliable and they were a fair match to the actual behavioural M-levels. When applied in clinical practice, the predicted values were found to be useful for programming members of the study group. However, individuals showed considerable deviations in behavioural M-levels, above and below the electrophysiologically predicted values, due to various factors. While the current method appears helpful as a reference to predict initial maps in 'difficult to Map' subjects, it is recommended that behavioural measures are mandatory to further optimize the maps for these individuals. The study explores the trends, correlations and individual variabilities that occur between electrophysiological tests and behavioural responses, recorded over time among a cohort of cochlear implantees. The statistical method shown may be used as a guideline to predict optimal behavioural levels in difficult situations among future implantees, bearing in mind that optimal M-levels for individuals can vary from predicted values. In 'Difficult to MAP' scenarios, following a protocol of sequential behavioural programming, in conjunction with electrophysiological correlates will provide the best outcomes.

Bronchodilator Response in FVC Is Larger and More Relevant Than in FEV1 in Severe Airflow Obstruction.

PubMed

Quanjer, Philip H; Ruppel, Gregg L; Langhammer, Arnulf; Krishna, Abhishek; Mertens, Frans; Johannessen, Ane; Menezes, Ana M B; Wehrmeister, Fernando C; Perez-Padilla, Rogelio; Swanney, Maureen P; Tan, Wan C; Bourbeau, Jean

2017-05-01

Recommendations on interpreting tests of bronchodilator responsiveness (BDR) are conflicting. We investigated the dependence of BDR criteria on sex, age, height, ethnicity, and severity of respiratory impairment. BDR test data were available from clinical patients in the Netherlands, New Zealand, and the United States (n = 15,278; female subjects, 51.7%) and from surveys in Canada, Norway, and five Latin-American countries (n = 16,250; female subjects, 54.7%). BDR calculated according to FEV 1 , FVC, and FEV 1 /FVC was expressed as absolute change, a percentage of the baseline level (% baseline), a percentage of the predicted value (% predicted), and z score. Change (Δ) in FEV 1 and FVC, in milliliters, was unrelated to the baseline value but was biased toward age, height, sex, and level of airways obstruction; ΔFEV 1 was significantly lower in African Americans. In 1,106 subjects with low FEV 1 (200-1,621 mL) the FEV 1 increased by 12% to 44.7% relative to baseline but < 200 mL. Expressing BDR as a percentage of the predicted value or as a z score attenuated the bias and made the 200-mL criterion redundant, but reduced positive responses by half. ΔFEV 1 % baseline increased with the level of airflow obstruction but decreased with severe obstruction when expressed as z scores or % predicted; ΔFVC, however expressed, increased with the level of airflow obstruction. Expressing FEV 1 responsiveness as % baseline spuriously suggests that responsiveness increases with the severity of respiratory impairment. Expressing change in FEV 1 or FVC as % predicted or as z scores eliminates this artifact and renders the required 200-mL minimum increase redundant. In severe airways obstruction ΔFVC should be critically evaluated as an index of clinically important relief of hyperinflation, with implications for bronchodilator drug trials. Copyright © 2016 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

Blood oxygenation level-dependent (BOLD) contrast magnetic resonance imaging (MRI) for prediction of breast cancer chemotherapy response: a pilot study.

PubMed

Jiang, Lan; Weatherall, Paul T; McColl, Roderick W; Tripathy, Debu; Mason, Ralph P

2013-05-01

To determine whether a simple noninvasive method of assessing tumor oxygenation is feasible in the clinical setting and can provide useful, potentially predictive information. Tumor microcirculation and oxygenation play critical roles in tumor growth and responsiveness to cytotoxic treatment and may provide prognostic indicators for cancer therapy. Deoxyhemoglobin is paramagnetic and can serve as an endogenous contrast agent causing signal loss in echo planar magnetic resonance imaging (MRI) (blood oxygenation level-dependent [BOLD]-MRI). We used BOLD-MRI to provide early evaluation of response to neoadjuvant chemotherapy in patients with locally advanced breast cancer. MRI was performed on 11 patients with biopsy-proven malignancy. MRI exams were scheduled before, during, and after chemotherapy. The BOLD study applied a 6-minute oxygen breathing challenge. Seven patients successfully completed the exams. Before chemotherapy, BOLD contrast enhancement was observed in all tumors, but the patients, who ultimately had complete pathological response, exhibited a significantly higher BOLD response to oxygen breathing. We have successfully implemented an oxygen-breathing challenge BOLD contrast technique as part of the standard breast MRI exam in patients with locally advanced breast cancer. The preliminary observation that a large BOLD response correlated with better treatment response suggests a predictive capability for BOLD MRI. Copyright © 2012 Wiley Periodicals, Inc.

Person-Centered Fall Risk Awareness Perspectives: Clinical Correlates and Fall Risk.

PubMed

Verghese, Joe

2016-12-01

To identify clinical correlates of person-centered fall risk awareness and their validity for predicting falls. Prospective cohort study. Community. Ambulatory community-dwelling older adults without dementia (N = 316; mean age 78, 55% female). Fall risk awareness was assessed using a two-item questionnaire that asked participants about overall likelihood of someone in their age group having a fall and their own personal risk of falling over the next 12 months. Incident falls were recorded over study follow-up. Fifty-three participants (16.8%) responded positively to the first fall risk awareness question about being likely to have a fall in the next 12 months, and 100 (31.6%) reported being at personal risk of falling over the next 12 months. There was only fair correlation (κ = 0.370) between responses on the two questions. Prior falls and depressive symptoms were associated with positive responses on both fall risk awareness questions. Age and other established fall risk factors were not associated with responses on either fall risk awareness question. The fall risk awareness questionnaire did not predict incident falls or injurious falls. Fall risk awareness is low in older adults. Although person-centered fall risk awareness is not predictive of falls, subjective risk perceptions should be considered when designing fall preventive strategies because they may influence participation and behaviors. © 2016, Copyright the Author Journal compilation © 2016, The American Geriatrics Society.

Clinical decision rules, spinal pain classification and prediction of treatment outcome: A discussion of recent reports in the rehabilitation literature

PubMed Central

2012-01-01

Clinical decision rules are an increasingly common presence in the biomedical literature and represent one strategy of enhancing clinical-decision making with the goal of improving the efficiency and effectiveness of healthcare delivery. In the context of rehabilitation research, clinical decision rules have been predominantly aimed at classifying patients by predicting their treatment response to specific therapies. Traditionally, recommendations for developing clinical decision rules propose a multistep process (derivation, validation, impact analysis) using defined methodology. Research efforts aimed at developing a “diagnosis-based clinical decision rule” have departed from this convention. Recent publications in this line of research have used the modified terminology “diagnosis-based clinical decision guide.” Modifications to terminology and methodology surrounding clinical decision rules can make it more difficult for clinicians to recognize the level of evidence associated with a decision rule and understand how this evidence should be implemented to inform patient care. We provide a brief overview of clinical decision rule development in the context of the rehabilitation literature and two specific papers recently published in Chiropractic and Manual Therapies. PMID:22726639

Tips for Teachers of Evidence-based Medicine: Clinical Prediction Rules (CPRs) and Estimating Pretest Probability

PubMed Central

McGinn, Thomas; Jervis, Ramiro; Wisnivesky, Juan; Keitz, Sheri

2008-01-01

Background Clinical prediction rules (CPR) are tools that clinicians can use to predict the most likely diagnosis, prognosis, or response to treatment in a patient based on individual characteristics. CPRs attempt to standardize, simplify, and increase the accuracy of clinicians’ diagnostic and prognostic assessments. The teaching tips series is designed to give teachers advice and materials they can use to attain specific educational objectives. Educational Objectives In this article, we present 3 teaching tips aimed at helping clinical learners use clinical prediction rules and to more accurately assess pretest probability in every day practice. The first tip is designed to demonstrate variability in physician estimation of pretest probability. The second tip demonstrates how the estimate of pretest probability influences the interpretation of diagnostic tests and patient management. The third tip exposes learners to various examples and different types of Clinical Prediction Rules (CPR) and how to apply them in practice. Pilot Testing We field tested all 3 tips with 16 learners, a mix of interns and senior residents. Teacher preparatory time was approximately 2 hours. The field test utilized a board and a data projector; 3 handouts were prepared. The tips were felt to be clear and the educational objectives reached. Potential teaching pitfalls were identified. Conclusion Teaching with these tips will help physicians appreciate the importance of applying evidence to their every day decisions. In 2 or 3 short teaching sessions, clinicians can also become familiar with the use of CPRs in applying evidence consistently in everyday practice. PMID:18491194

The clinical utility of fibrin-related biomarkers in sepsis.

PubMed

Toh, Julien M H; Ken-Dror, Gie; Downey, Colin; Abrams, Simon T

2013-12-01

Sepsis is associated with systemic inflammatory responses and induction of intravascular fibrin formation. Our aim is to investigate whether three fibrin-related markers (FRM) reflect the extent of coagulation activation in vivo and evaluate their clinical usefulness in identifying as well as monitoring patients with sepsis. Fibrin-degradation products (FDP), D-dimer and soluble fibrin monomer assays were measured on plasma samples from patients in the ICU with sepsis (n = 37), systemic inflammatory response syndrome (SIRS) (n = 35) and healthy individuals (n = 15). The levels were correlated with each other and also with fibrinogen, prothrombin time, platelets and antithrombin III. Clinical correlation was also performed for the diagnosis of sepsis and longitudinal monitoring for survival or death.There was strong correlation between the three FRM (r = 0.38-0.93, P < 0.0001) with only fibrin monomer correlating significantly with prothrombin time, fibrinogen and platelet levels. Clinically, all three FRM could discriminate between patients with sepsis, SIRS and healthy individuals with FDP, and D-dimer showing statistical significance (P < 0.05). No FRM predicted outcome from a single measurement but FDP was significantly able to predict patient survival from serial samples [mean FDP (μg/ml) from 35.36 to 21.37 (first to third ICU-day), P < 0.05]. Fibrin monomer appears the most sensitive indicator of coagulation activation, whereas D-dimer and FDP levels can significantly differentiate ICU patients with sepsis from those without. In addition, FDP would be preferable for monitoring with its statistically significant time-dependent prediction of survival or death from sepsis.

Specific expectancies are associated with symptomatic outcomes and side effect burden in a trial of chamomile extract for generalized anxiety disorder.

PubMed

Keefe, John R; Amsterdam, Jay; Li, Qing S; Soeller, Irene; DeRubeis, Robert; Mao, Jun J

2017-01-01

Patient expectancies are hypothesized to contribute to the efficacy and side effects of psychiatric treatments, but little research has investigated this hypothesis in the context of psychopharmacological therapies for anxiety. We prospectively investigated whether expectancies predicted efficacy and adverse events in oral therapy for Generalized Anxiety Disorder (GAD), controlling for confounding patient characteristics correlating with outcomes. Expectancies regarding treatment efficacy and side effects were assessed at baseline of an eight week open-label phase of a trial of chamomile for Generalized Anxiety Disorder (GAD). The primary outcome was patient-reported GAD-7 scores, with clinical response and treatment-emergent side-effects as secondary outcomes. Expectancies were used to predict symptomatic and side-effect outcomes. Very few baseline patient characteristics predicted either type of expectancy. Controlling for a patient's predicted recovery based on their baseline characteristics, higher efficacy expectancies at baseline predicted greater change on the GAD-7 (adjusted β = -0.19, p = 0.011). Efficacy expectancies also predicted a higher likelihood of attaining clinical response (adjusted odds ratio = 1.69, p = 0.002). Patients with higher side effect expectancies reported more side effects (adjusted log expected count = 0.26, p = 0.038). Efficacy expectancies were unrelated to side effect reports (log expected count = -0.05, p = 0.680), and side effect expectancies were unrelated to treatment efficacy (β = 0.08, p = 0.306). Patients entering chamomile treatment for GAD with more favorable self-generated expectancies for the treatment experience greater improvement and fewer adverse events. Aligning patient expectancies with treatment selections may optimize outcomes. Trial Number NCT01072344 at ClinicalTrials.gov. Copyright © 2016 Elsevier Ltd. All rights reserved.

Biomarker-Based Prediction Models for Response to Treatment in Systemic Sclerosis-Related Interstitial Lung Disease

DTIC Science & Technology

2017-10-01

in the baseline samples of the Scleroderma Lung Study II (SLS II). We are currently analyzing whether these serum proteins have predictive...In this project, we use the valuable samples collected in the Scleroderma Lung Study II (SLSII) clinical trial and the observational cohort, GENISOS...determine key serum protein levels and transcript signatures in whole blood and skin samples collected in the SLSII study . The identified candidate

Multiple sclerosis: individualized disease susceptibility and therapy response.

PubMed

Pravica, Vera; Markovic, Milos; Cupic, Maja; Savic, Emina; Popadic, Dusan; Drulovic, Jelena; Mostarica-Stojkovic, Marija

2013-02-01

Multiple sclerosis (MS) is a heterogeneous disease in which diverse genetic, pathological and clinical backgrounds lead to variable therapy response. Accordingly, MS care should be tailored to address disease traits unique to each person. At the core of personalized management is the emergence of new knowledge, enabling optimized treatment and disease-modifying therapies. This overview analyzes the promise of genetic and nongenetic biomarkers in advancing decision-making algorithms to assist diagnosis or in predicting the disease course and therapy response in any given MS patient.

Qualitative interpretation of PET scans using a Likert scale to assess neck node response to radiotherapy in head and neck cancer.

PubMed

Sjövall, Johanna; Bitzén, Ulrika; Kjellén, Elisabeth; Nilsson, Per; Wahlberg, Peter; Brun, Eva

2016-04-01

The aim of this study was to determine whether PET scans after radiotherapy (RT), visually interpreted as equivocal regarding metabolic neck node response can be used to accurately categorize patients as responders or nonresponders using a Likert scale and/or maximum standardized uptake value (SUVmax). Other aims were to determine the performance of different methods for assessing post-RT PET scans (visual inspection, a Likert scale and SUVmax) and to establish whether any method is superior in predicting regional control (RC) and overall survival (OS). In 105 patients with neck node-positive head and neck cancer, the neck node response was evaluated by FDG PET/CT 6 weeks after RT. The scans were clinically assessed by visual inspection and, for the purposes of this analysis, re-evaluated using the Deauville criteria, a five-point Likert scale previously used in lymphoma studies. In addition, SUVmax was determined. All assessment methods were able to significantly predict RC but not OS. The methods were also able to significantly predict remission of tumour after completion of RT. Of the 105 PET scans, 19 were judged as equivocal on visual inspection. The Likert scale was preferable to SUVmax for grouping patients as responders or nonresponders. All methods (visual inspection, SUVmax and the Likert scale) identified responders and nonresponders and predicted RC. A Likert scale is a promising tool to reduce to a minimum the problem of PET scans judged as equivocal. Consensus regarding qualitative assessment would facilitate PET reporting in clinical practice.

Short and long term improvements in quality of chronic care delivery predict program sustainability.

PubMed

Cramm, Jane Murray; Nieboer, Anna Petra

2014-01-01

Empirical evidence on sustainability of programs that improve the quality of care delivery over time is lacking. Therefore, this study aims to identify the predictive role of short and long term improvements in quality of chronic care delivery on program sustainability. In this longitudinal study, professionals [2010 (T0): n=218, 55% response rate; 2011 (T1): n=300, 68% response rate; 2012 (T2): n=265, 63% response rate] from 22 Dutch disease-management programs completed surveys assessing quality of care and program sustainability. Our study findings indicated that quality of chronic care delivery improved significantly in the first 2 years after implementation of the disease-management programs. At T1, overall quality, self-management support, delivery system design, and integration of chronic care components, as well as health care delivery and clinical information systems and decision support, had improved. At T2, overall quality again improved significantly, as did community linkages, delivery system design, clinical information systems, decision support and integration of chronic care components, and self-management support. Multilevel regression analysis revealed that quality of chronic care delivery at T0 (p<0.001) and quality changes in the first (p<0.001) and second (p<0.01) years predicted program sustainability. In conclusion this study showed that disease-management programs based on the chronic care model improved the quality of chronic care delivery over time and that short and long term changes in the quality of chronic care delivery predicted the sustainability of the projects. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

Clinical and biological effects of demethylating agents on solid tumours - A systematic review.

PubMed

Linnekamp, J F; Butter, R; Spijker, R; Medema, J P; van Laarhoven, H W M

2017-03-01

It is assumed that DNA methylation plays a key role in both tumour development and therapy resistance. Demethylating agents have been shown to be effective in the treatment of haematological malignancies. Based on encouraging preclinical results, demethylating agents may also be effective in solid tumours. This systematic review summarizes the evidence of the effect of demethylating agents on clinical response, methylation and the immune system in solid tumours. We conducted a systematic literature search from 1949 to December 2016, according to the PRISMA guidelines. Studies which evaluated treatment with azacitidine, decitabine, guadecitabine, hydralazine, procaine, MG98 and/or zebularine in patients with solid tumours were included. Data on clinical response, effects on methylation and immune response were extracted. Fifty-eight studies were included: in 13 studies complete responses (CR) were observed, 35 studies showed partial responses (PR), 47 studies stable disease (SD) and all studies except two showed progressive disease (PD). Effects on global methylation were observed in 11/15 studies and demethylation/re-expression of tumour specific genes was seen in 15/17 studies. No clear correlation between (de)methylation and clinical response was observed. In 14 studies immune-related responses were reported, such as re-expression of cancer-testis antigens and upregulation of interferon genes. Demethylating agents are able to improve clinical outcome and alter methylation status in patients with solid tumours. Although beneficial effect has been shown in individual patients, overall response is limited. Further research on biomarker predicting therapy efficacy is indicated, particularly in earlier stage and highly methylated tumours. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

A study on the predictability of acute lymphoblastic leukaemia response to treatment using a hybrid oncosimulator.

PubMed

Ouzounoglou, Eleftherios; Kolokotroni, Eleni; Stanulla, Martin; Stamatakos, Georgios S

2018-02-06

Efficient use of Virtual Physiological Human (VPH)-type models for personalized treatment response prediction purposes requires a precise model parameterization. In the case where the available personalized data are not sufficient to fully determine the parameter values, an appropriate prediction task may be followed. This study, a hybrid combination of computational optimization and machine learning methods with an already developed mechanistic model called the acute lymphoblastic leukaemia (ALL) Oncosimulator which simulates ALL progression and treatment response is presented. These methods are used in order for the parameters of the model to be estimated for retrospective cases and to be predicted for prospective ones. The parameter value prediction is based on a regression model trained on retrospective cases. The proposed Hybrid ALL Oncosimulator system has been evaluated when predicting the pre-phase treatment outcome in ALL. This has been correctly achieved for a significant percentage of patient cases tested (approx. 70% of patients). Moreover, the system is capable of denying the classification of cases for which the results are not trustworthy enough. In that case, potentially misleading predictions for a number of patients are avoided, while the classification accuracy for the remaining patient cases further increases. The results obtained are particularly encouraging regarding the soundness of the proposed methodologies and their relevance to the process of achieving clinical applicability of the proposed Hybrid ALL Oncosimulator system and VPH models in general.

Usefulness of elevated red cell distribution width for predicting systemic inflammatory response syndrome after extracorporeal circulation.

PubMed

Özeren, M; Aytaçoğlu, B; Vezir, Ö; Karaca, K; Akın, R; Sucu, N

2015-10-01

Cardiac surgical operations performed by using extracorporeal circulation (ECC) lead to a systemic inflammatory response (SIR). Sometimes SIR may turn into a severe state, the systemic inflammatory response syndrome (SIRS) that usually has a poor outcome with no specific clinical tools described for its prediction. Red cell distribution width (RDW) is a routine hematological parameter. It has been proposed as a marker of morbidity and mortality in various clinical conditions. We aimed to investigate the relationship between high RDW and SIRS which is triggered by ECC. Eleven hundred consecutive patients who underwent elective heart surgery with the use of ECC were retrospectively analyzed. A total of 19 patients fulfilled the described SIRS criteria and 20 consecutive patients were selected as the control group. RDW and other laboratory parameters, preoperative clinical status, operative data and postoperative data were compared between the SIRS and the control groups. Baseline characteristics of the patient groups were similar. Significant mortality was found in the SIRS group; 18 (94.73%) patients and 2 (10%) patients in the control group (p < 0.002). RDW was found to be significantly higher in the SIRS group vs the control group (15.02 ± 2.03 vs 13.01 ± 1.93, respectively, p < 0.003). Multiple logistic regression analyses showed an association between high RDW levels and SIRS development (OR for RDW levels exceeding 13.5%; 95% confidence limits of 1.0-1.3; p < 0.04). Total operation time and the need for inotropic support were also found to be significant against the SIRS group (p = 0.049). Increased RDW was significantly associated with increased risk of SIRS after ECC. The results of this study suggest that paying attention to RDW might provide valuable clinical information for predicting SIRS development among patients who are candidates for open heart surgery, without incurring additional costs. © The Author(s) 2015.

Validation of Clinical Scoring Systems ART and ABCR after Transarterial Chemoembolization of Hepatocellular Carcinoma.

PubMed

Kloeckner, Roman; Pitton, Michael B; Dueber, Christoph; Schmidtmann, Irene; Galle, Peter R; Koch, Sandra; Wörns, Marcus A; Weinmann, Arndt

2017-01-01

To perform an external validation of the Assessment for Retreatment with Transarterial Chemoembolization (ART) and α-fetoprotein (AFP), Barcelona Clinic Liver Cancer (BCLC), Child-Pugh, and response (ABCR) scores and to compare them in terms of prognostic power. From 2000 to 2015, 871 patients with hepatocellular carcinoma underwent transarterial chemoembolization at a tertiary referral hospital, and 176 met all inclusion and exclusion criteria for both scores and were analyzed. Nineteen percent (n = 34) had BCLC stage A disease and 81% had stage B disease. Thirty-nine patients (22%) presented with elevated AFP levels. Overall survival was calculated. Scores were validated and compared with a Harrell C-index, integrated Brier score (IBS), and prediction error curves. Before the second chemoembolization procedure, 22 patients (12%) showed an increase of 1 point in Child-Pugh score and 51 patients (22%) had an increase of ≥ 2 points. Thirty-one patients (23%) showed a > 25% increase in aspartate aminotransferase level, and 114 (65%) showed a response to treatment. Consequently, 127 patients (72%) had a low ART score and 49 (28%) had a high ART score. One hundred fifty-eight patients (90%) had a low ABCR score, whereas 18 (10%) had a high ABCR score. Low and high ART score groups had median survival durations of 20.8 and 15.3 mo, respectively. Harrell C-indexes were 0.572 and 0.608, and IBSs were 0.135 and 0.128, for ART and ABCR, respectively. For both scores, an increase in Child-Pugh score ≥ 2 points and a radiologic response were significantly associated with survival. Both scores were of limited predictive value, and neither was sufficient to support clear-cut clinical decisions. Further effort is necessary to determine criteria for making valid clinical predictions. Copyright © 2017 SIR. Published by Elsevier Inc. All rights reserved.

Monitoring of anti-cancer treatment with 18F-FDG and 18F-FLT PET: a comprehensive review of pre-clinical studies

PubMed Central

Jensen, Mette Munk; Kjaer, Andreas

2015-01-01

Functional imaging of solid tumors with positron emission tomography (PET) imaging is an evolving field with continuous development of new PET tracers and discovery of new applications for already implemented PET tracers. During treatment of cancer patients, a general challenge is to measure treatment effect early in a treatment course and by that to stratify patients into responders and non-responders. With 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG) and 3’-deoxy-3’-[18F]fluorothymidine(18F-FLT) two of the cancer hallmarks, altered energy metabolism and increased cell proliferation, can be visualized and quantified non-invasively by PET. With 18F-FDG and 18F-FLT PET changes in energy metabolism and cell proliferation can thereby be determined after initiation of cancer treatment in both clinical and pre-clinical studies in order to predict, at an early time-point, treatment response. It is hypothesized that decreases in glycolysis and cell proliferation may occur in tumors that are sensitive to the applied cancer therapeutics and that tumors that are resistant to treatment will show unchanged glucose metabolism and cell proliferation. Whether 18F-FDG and/or 18F-FLT PET can be used for prediction of treatment response has been analyzed in many studies both following treatment with conventional chemotherapeutic agents but also following treatment with different targeted therapies, e.g. monoclonal antibodies and small molecules inhibitors. The results from these studies have been most variable; in some studies early changes in 18F-FDG and 18F-FLT uptake predicted later tumor regression whereas in other studies no change in tracer uptake was observed despite the treatment being effective. The present review gives an overview of pre-clinical studies that have used 18F-FDG and/or 18F-FLT PET for response monitoring of cancer therapeutics. PMID:26550536

Functional neuroimaging of psychotherapeutic processes in anxiety and depression: from mechanisms to predictions.

PubMed

Lueken, Ulrike; Hahn, Tim

2016-01-01

The review provides an update of functional neuroimaging studies that identify neural processes underlying psychotherapy and predict outcomes following psychotherapeutic treatment in anxiety and depressive disorders. Following current developments in this field, studies were classified as 'mechanistic' or 'predictor' studies (i.e., informing neurobiological models about putative mechanisms versus aiming to provide predictive information). Mechanistic evidence points toward a dual-process model of psychotherapy in anxiety disorders with abnormally increased limbic activation being decreased, while prefrontal activity is increased. Partly overlapping findings are reported for depression, albeit with a stronger focus on prefrontal activation following treatment. No studies directly comparing neural pathways of psychotherapy between anxiety and depression were detected. Consensus is accumulating for an overarching role of the anterior cingulate cortex in modulating treatment response across disorders. When aiming to quantify clinical utility, the need for single-subject predictions is increasingly recognized and predictions based on machine learning approaches show high translational potential. Present findings encourage the search for predictors providing clinically meaningful information for single patients. However, independent validation as a crucial prerequisite for clinical use is still needed. Identifying nonresponders a priori creates the need for alternative treatment options that can be developed based on an improved understanding of those neural mechanisms underlying effective interventions.

Predicting tumor responses to mitomycin C on the basis of DT-diaphorase activity or drug metabolism by tumor homogenates: implications for enzyme-directed bioreductive drug development.

PubMed

Phillips, R M; Burger, A M; Loadman, P M; Jarrett, C M; Swaine, D J; Fiebig, H H

2000-11-15

Mitomycin C (MMC) is a clinically used anticancer drug that is reduced to cytotoxic metabolites by cellular reductases via a process known as bioreductive drug activation. The identification of key enzymes responsible for drug activation has been investigated extensively with the ultimate aim of tailoring drug administration to patients whose tumors possess the biochemical machinery required for drug activation. In the case of MMC, considerable interest has been centered upon the enzyme DT-diaphorase (DTD) although conflicting reports of good and poor correlations between enzyme activity and response in vitro and in vivo have been published. The principle aim of this study was to provide a definitive answer to the question of whether tumor response to MMC could be predicted on the basis of DTD activity in a large panel of human tumor xenografts. DTD levels were measured in 45 human tumor xenografts that had been characterized previously in terms of their sensitivity to MMC in vitro and in vivo (the in vivo response profile to MMC was taken from work published previously). A poor correlation between DTD activity and antitumor activity in vitro as well as in vivo was obtained. This study also assessed the predictive value of an alternative approach based upon the ability of tumor homogenates to metabolize MMC. This approach is based on the premise that the overall rate of MMC metabolism may provide a better indicator of response than single enzyme measurements. MMC metabolism was evaluated in tumor homogenates (clarified by centrifugation at 1000 x g for 1 min) by measuring the disappearance of the parent compound by HPLC. In responsive [T/C <10% (T/C defined as the relative size of treated and control tumors)] and resistant (T/C >50%) tumors, the mean half life of MMC was 75+/-48.3 and 280+/-129.6 min, respectively. The difference between the two groups was statistically significant (P < 0.005). In conclusion, these results unequivocally demonstrate that response to MMC in vivo cannot be predicted on the basis of DTD activity. Measurement of MMC metabolism by tumor homogenates on the other hand may provide a better indicator of tumor response, and further studies are required to determine whether this approach has real clinical potential in terms of individualizing patient chemotherapy.

Emotional Responses to Suicidal Patients: Factor Structure, Construct, and Predictive Validity of the Therapist Response Questionnaire-Suicide Form.

PubMed

Barzilay, Shira; Yaseen, Zimri S; Hawes, Mariah; Gorman, Bernard; Altman, Rachel; Foster, Adriana; Apter, Alan; Rosenfield, Paul; Galynker, Igor

2018-01-01

Mental health professionals have a pivotal role in suicide prevention. However, they also often have intense emotional responses, or countertransference, during encounters with suicidal patients. Previous studies of the Therapist Response Questionnaire-Suicide Form (TRQ-SF), a brief novel measure aimed at probing a distinct set of suicide-related emotional responses to patients found it to be predictive of near-term suicidal behavior among high suicide-risk inpatients. The purpose of this study was to validate the TRQ-SF in a general outpatient clinic setting. Adult psychiatric outpatients ( N  = 346) and their treating mental health professionals ( N  = 48) completed self-report assessments following their first clinic meeting. Clinician measures included the TRQ-SF, general emotional states and traits, therapeutic alliance, and assessment of patient suicide risk. Patient suicidal outcomes and symptom severity were assessed at intake and one-month follow-up. Following confirmatory factor analysis of the TRQ-SF, factor scores were examined for relationships with clinician and patient measures and suicidal outcomes. Factor analysis of the TRQ-SF confirmed three dimensions: (1) affiliation, (2) distress, and (3) hope. The three factors also loaded onto a single general factor of negative emotional response toward the patient that demonstrated good internal reliability. The TRQ-SF scores were associated with measures of clinician state anger and anxiety and therapeutic alliance, independently of clinician personality traits after controlling for the state- and patient-specific measures. The total score and three subscales were associated in both concurrent and predictive ways with patient suicidal outcomes, depression severity, and clinicians' judgment of patient suicide risk, but not with global symptom severity, thus indicating specifically suicide-related responses. The TRQ-SF is a brief and reliable measure with a 3-factor structure. It demonstrates construct validity for assessing distinct suicide-related countertransference to psychiatric outpatients. Mental health professionals' emotional responses to their patients are concurrently indicative and prospectively predictive of suicidal thoughts and behaviors. Thus, the TRQ-SF is a useful tool for the study of countertransference in the treatment of suicidal patients and may help clinicians make diagnostic and therapeutic use of their own responses to improve assessment and intervention for individual suicidal patients.

Serial headache drawings by children with migraine: correlation with clinical headache status.

PubMed

Stafstrom, Carl E; Goldenholz, Shira R; Dulli, Douglas A

2005-10-01

Children's artistic self-depictions of headache provide valuable insights into their experience of pain and aid in the diagnostic differentiation of headache types. In a previous study, we compared the clinical diagnosis (gold standard) and artistic diagnosis of headaches in 226 children. In approximately 90% of cases, the drawing predicted the clinical diagnosis of migraine versus nonmigraine headache correctly. In the present study, we explored whether headache drawings correlate with clinical improvement after treatment in children with migraine headaches followed longitudinally. Children seen in the Pediatric Neurology Clinic with the chief complaint of headache were asked to draw a picture of what their headache feels like. On subsequent clinic visits, children with the clinical diagnosis of migraine were asked to draw another picture depicting their current headache. The two drawings were compared to assess whether there was improvement; this "artistic response" was then correlated with the child's clinical status (ie, whether the headaches were improved clinically). One hundred eleven children (66 girls, 45 boys) participated in the study, with a mean interval of 5.3 +/- 2.3 (standard error of the mean) months between the first and second visits. The mean age at the first visit was 11.6 +/- 3.1 years. The raters agreed that serial drawings were both improved or both not improved in 99 of the 111 cases (89%; interrater reliability kappa score of 0.767). Fifty-three children had improvements in their headaches and drawings, 3 children had an improved drawing but no clinical headache improvement, 32 children had no improvement in either their drawing or clinical headaches, and 11 children had improved headaches but no improvement in their drawing. The sensitivity of the drawings for clinical improvement was 0.83, and the specificity was 0.91. The predictive value of an improved headache drawing for an improved clinical response was 0.946. There was no correlation between specific treatment modality and artistic response. We concluded that children's headache drawings are a useful adjunct for the diagnosis of headache type and provide valuable insights into their experience of pain. The present data show that headache drawings can be used longitudinally to provide additional information about the clinical course. The technique is simple, inexpensive, and enjoyable for children and can be applied in a variety of clinical settings.

Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data

PubMed Central

Meng, Xing; Jiang, Rongtao; Lin, Dongdong; Bustillo, Juan; Jones, Thomas; Chen, Jiayu; Yu, Qingbao; Du, Yuhui; Zhang, Yu; Jiang, Tianzi; Sui, Jing; Calhoun, Vince D.

2016-01-01

Neuroimaging techniques have greatly enhanced the understanding of neurodiversity (human brain variation across individuals) in both health and disease. The ultimate goal of using brain imaging biomarkers is to perform individualized predictions. Here we proposed a generalized framework that can predict explicit values of the targeted measures by taking advantage of joint information from multiple modalities. This framework also enables whole brain voxel-wise searching by combining multivariate techniques such as ReliefF, clustering, correlation-based feature selection and multiple regression models, which is more flexible and can achieve better prediction performance than alternative atlas-based methods. For 50 healthy controls and 47 schizophrenia patients, three kinds of features derived from resting-state fMRI (fALFF), sMRI (gray matter) and DTI (fractional anisotropy) were extracted and fed into a regression model, achieving high prediction for both cognitive scores (MCCB composite r = 0.7033, MCCB social cognition r = 0.7084) and symptomatic scores (positive and negative syndrome scale [PANSS] positive r = 0.7785, PANSS negative r = 0.7804). Moreover, the brain areas likely responsible for cognitive deficits of schizophrenia, including middle temporal gyrus, dorsolateral prefrontal cortex, striatum, cuneus and cerebellum, were located with different weights, as well as regions predicting PANSS symptoms, including thalamus, striatum and inferior parietal lobule, pinpointing the potential neuromarkers. Finally, compared to a single modality, multimodal combination achieves higher prediction accuracy and enables individualized prediction on multiple clinical measures. There is more work to be done, but the current results highlight the potential utility of multimodal brain imaging biomarkers to eventually inform clinical decision-making. PMID:27177764

3-D residual eddy current field characterisation: applied to diffusion weighted magnetic resonance imaging.

PubMed

O'Brien, Kieran; Daducci, Alessandro; Kickler, Nils; Lazeyras, Francois; Gruetter, Rolf; Feiweier, Thorsten; Krueger, Gunnar

2013-08-01

Clinical use of the Stejskal-Tanner diffusion weighted images is hampered by the geometric distortions that result from the large residual 3-D eddy current field induced. In this work, we aimed to predict, using linear response theory, the residual 3-D eddy current field required for geometric distortion correction based on phantom eddy current field measurements. The predicted 3-D eddy current field induced by the diffusion-weighting gradients was able to reduce the root mean square error of the residual eddy current field to ~1 Hz. The model's performance was tested on diffusion weighted images of four normal volunteers, following distortion correction, the quality of the Stejskal-Tanner diffusion-weighted images was found to have comparable quality to image registration based corrections (FSL) at low b-values. Unlike registration techniques the correction was not hindered by low SNR at high b-values, and results in improved image quality relative to FSL. Characterization of the 3-D eddy current field with linear response theory enables the prediction of the 3-D eddy current field required to correct eddy current induced geometric distortions for a wide range of clinical and high b-value protocols.

Clinical Response to Vedolizumab in Ulcerative Colitis Patients Is Associated with Changes in Integrin Expression Profiles.

PubMed

Fuchs, Friederike; Schillinger, Daniela; Atreya, Raja; Hirschmann, Simon; Fischer, Sarah; Neufert, Clemens; Atreya, Imke; Neurath, Markus F; Zundler, Sebastian

2017-01-01

Despite large clinical success, deeper insights into the immunological effects of vedolizumab therapy for inflammatory bowel diseases are scarce. In particular, the reasons for differential clinical response in individual patients, the precise impact on the equilibrium of integrin-expressing T cell subsets, and possible associations between these issues are not clear. Blood samples from patients receiving clinical vedolizumab therapy were sequentially collected and analyzed for expression of integrins and chemokine receptors on T cells. Moreover, clinical and laboratory data from the patients were collected, and changes between homing marker expression and clinical parameters were analyzed for possible correlations. While no significant correlation of changes in integrin expression and changes in outcome parameters were identified in Crohn's disease (CD), increasing α4β7 levels in ulcerative colitis (UC) seemed to be associated with favorable clinical development, whereas increasing α4β1 and αEβ7 correlated with negative changes in outcome parameters. Changes in α4β1 integrin expression after 6 weeks were significantly different in responders and non-responders to vedolizumab therapy as assessed after 16 weeks with a cutoff of +4.2% yielding 100% sensitivity and 100% specificity in receiver-operator-characteristic analysis. Our data show that clinical response to vedolizumab therapy in UC but not in CD is associated with specific changes in integrin expression profiles opening novel avenues for mechanistic research and possibly prediction of response to therapy.

T-cell diversification reflects antigen selection in the blood of patients on immune checkpoint inhibition and may be exploited as liquid biopsy biomarker.

PubMed

Akyüz, Nuray; Brandt, Anna; Stein, Alexander; Schliffke, Simon; Mährle, Thorben; Quidde, Julia; Goekkurt, Eray; Loges, Sonja; Haalck, Thomas; Ford, Christopher Thomas; Asemissen, Anne Marie; Thiele, Benjamin; Radloff, Janina; Thenhausen, Toni; Krohn-Grimberghe, Artus; Bokemeyer, Carsten; Binder, Mascha

2017-06-01

Cancer immunotherapy with antibodies targeting immune checkpoints, such as programmed cell death protein 1 (PD-1), shows encouraging results, but reliable biomarkers predicting response to this costly and potentially toxic treatment approach are still lacking. To explore an immune signature predictive for response, we performed liquid biopsy immunoprofiling in 18 cancer patients undergoing PD-1 inhibition before and shortly after initiation of treatment by multicolor flow cytometry and next-generation T- and B-cell immunosequencing (TCRß/IGH). Findings were correlated with clinical outcomes. We found almost complete saturation of surface PD-1 on all T-cell subsets after the first dose of the antibody. Both T- and B-cell compartments quantitatively expanded during treatment. These expansions were mainly driven by an increase in the activated T-cell compartments, as well as of naïve B- and plasma cells. Deep immunosequencing revealed a clear diversification pattern of the clonal T-cell space indicative of antigenic selection in 47% of patients, while the remaining patients showed stable repertoires. 43% of the patients with a diversification pattern showed disease control in response to the PD-1 inhibitor. No disease stabilizations were observed without clonal T-cell space diversification. Our data show for the first time a clear impact of PD-1 targeting not only on circulating T-cells, but also on B-lineage cells, shedding light on the complexity of the anti-tumor immune response. Liquid biopsy T-cell next-generation immunosequencing should be prospectively evaluated as part of a composite response prediction biomarker panel in the context of clinical studies. © 2016 UICC.

Data Mining Approaches for Genomic Biomarker Development: Applications Using Drug Screening Data from the Cancer Genome Project and the Cancer Cell Line Encyclopedia.

PubMed

Covell, David G

2015-01-01

Developing reliable biomarkers of tumor cell drug sensitivity and resistance can guide hypothesis-driven basic science research and influence pre-therapy clinical decisions. A popular strategy for developing biomarkers uses characterizations of human tumor samples against a range of cancer drug responses that correlate with genomic change; developed largely from the efforts of the Cancer Cell Line Encyclopedia (CCLE) and Sanger Cancer Genome Project (CGP). The purpose of this study is to provide an independent analysis of this data that aims to vet existing and add novel perspectives to biomarker discoveries and applications. Existing and alternative data mining and statistical methods will be used to a) evaluate drug responses of compounds with similar mechanism of action (MOA), b) examine measures of gene expression (GE), copy number (CN) and mutation status (MUT) biomarkers, combined with gene set enrichment analysis (GSEA), for hypothesizing biological processes important for drug response, c) conduct global comparisons of GE, CN and MUT as biomarkers across all drugs screened in the CGP dataset, and d) assess the positive predictive power of CGP-derived GE biomarkers as predictors of drug response in CCLE tumor cells. The perspectives derived from individual and global examinations of GEs, MUTs and CNs confirm existing and reveal unique and shared roles for these biomarkers in tumor cell drug sensitivity and resistance. Applications of CGP-derived genomic biomarkers to predict the drug response of CCLE tumor cells finds a highly significant ROC, with a positive predictive power of 0.78. The results of this study expand the available data mining and analysis methods for genomic biomarker development and provide additional support for using biomarkers to guide hypothesis-driven basic science research and pre-therapy clinical decisions.

Qualitative assessment of awake nasopharyngoscopy for prediction of oral appliance treatment response in obstructive sleep apnoea.

PubMed

Sutherland, Kate; Chan, Andrew S L; Ngiam, Joachim; Darendeliler, M Ali; Cistulli, Peter A

2018-01-23

Clinical methods to identify responders to oral appliance (OA) therapy for obstructive sleep apnoea (OSA) are needed. Awake nasopharyngoscopy during mandibular advancement, with image capture and subsequent processing and analysis, may predict treatment response. A qualitative assessment of awake nasopharyngoscopy would be simpler for clinical practice. We aimed to determine if a qualitative classification system of nasopharyngoscopic observations reflects treatment response. OSA patients were recruited for treatment with a customised two-piece OA. A custom scoring sheet was used to record observations of the pharyngeal airway (velopharynx, oropharynx, hypopharynx) during supine nasopharyngoscopy in response to mandibular advancement and performance of the Müller manoeuvre. Qualitative scores for degree (< 25%, 25-50%, 50-75%, > 75%), collapse pattern (concentric, anteroposterior, lateral) and diameter change (uniform, anteroposterior, lateral) were recorded. Treatment outcome was confirmed by polysomnography after a titration period of 14.6 ± 9.8 weeks. Treatment response was defined as (1) Treatment AHI < 5, (2) Treatment AHI < 10 plus > 50% AHI reduction and (3) > 50% AHI reduction. Eighty OSA patients (53.8% male) underwent nasopharyngoscopy. The most common naspharyngoscopic observation with mandibular advancement was a small (< 50%) increase in velopharyngeal lateral diameter (37.5%). The majority of subjects (72.5%) were recorded as having > 75% velopharyngeal collapse on performance of the Müller manoeuvre. Mandibular advancement reduced the observed level of pharyngeal collapse at all three pharyngeal regions (p < 0.001). None of the nasopharyngoscopic qualitative scores differed between responder and non-responder groups. Qualitative assessment of awake nasopharyngoscopy appears useful for assessing the effect of mandibular advancement on upper airway collapsibility. However, it is not sensitive enough to predict oral appliance treatment outcome.

Dysbiosis, inflammation, and response to treatment: a longitudinal study of pediatric subjects with newly diagnosed inflammatory bowel disease.

PubMed

Shaw, Kelly A; Bertha, Madeline; Hofmekler, Tatyana; Chopra, Pankaj; Vatanen, Tommi; Srivatsa, Abhiram; Prince, Jarod; Kumar, Archana; Sauer, Cary; Zwick, Michael E; Satten, Glen A; Kostic, Aleksandar D; Mulle, Jennifer G; Xavier, Ramnik J; Kugathasan, Subra

2016-07-13

Gut microbiome dysbiosis has been demonstrated in subjects with newly diagnosed and chronic inflammatory bowel disease (IBD). In this study we sought to explore longitudinal changes in dysbiosis and ascertain associations between dysbiosis and markers of disease activity and treatment outcome. We performed a prospective cohort study of 19 treatment-naïve pediatric IBD subjects and 10 healthy controls, measuring fecal calprotectin and assessing the gut microbiome via repeated stool samples. Associations between clinical characteristics and the microbiome were tested using generalized estimating equations. Random forest classification was used to predict ultimate treatment response (presence of mucosal healing at follow-up colonoscopy) or non-response using patients' pretreatment samples. Patients with Crohn's disease had increased markers of inflammation and dysbiosis compared to controls. Patients with ulcerative colitis had even higher inflammation and dysbiosis compared to those with Crohn's disease. For all cases, the gut microbial dysbiosis index associated significantly with clinical and biological measures of disease severity, but did not associate with treatment response. We found differences in specific gut microbiome genera between cases/controls and responders/non-responders including Akkermansia, Coprococcus, Fusobacterium, Veillonella, Faecalibacterium, and Adlercreutzia. Using pretreatment microbiome data in a weighted random forest classifier, we were able to obtain 76.5 % accuracy for prediction of responder status. Patient dysbiosis improved over time but persisted even among those who responded to treatment and achieved mucosal healing. Although dysbiosis index was not significantly different between responders and non-responders, we found specific genus-level differences. We found that pretreatment microbiome signatures are a promising avenue for prediction of remission and response to treatment.

Empirically derived personality subtyping for predicting clinical symptoms and treatment response in bulimia nervosa.

PubMed

Haynos, Ann F; Pearson, Carolyn M; Utzinger, Linsey M; Wonderlich, Stephen A; Crosby, Ross D; Mitchell, James E; Crow, Scott J; Peterson, Carol B

2017-05-01

Evidence suggests that eating disorder subtypes reflecting under-controlled, over-controlled, and low psychopathology personality traits constitute reliable phenotypes that differentiate treatment response. This study is the first to use statistical analyses to identify these subtypes within treatment-seeking individuals with bulimia nervosa (BN) and to use these statistically derived clusters to predict clinical outcomes. Using variables from the Dimensional Assessment of Personality Pathology-Basic Questionnaire, K-means cluster analyses identified under-controlled, over-controlled, and low psychopathology subtypes within BN patients (n = 80) enrolled in a treatment trial. Generalized linear models examined the impact of personality subtypes on Eating Disorder Examination global score, binge eating frequency, and purging frequency cross-sectionally at baseline and longitudinally at end of treatment (EOT) and follow-up. In the longitudinal models, secondary analyses were conducted to examine personality subtype as a potential moderator of response to Cognitive Behavioral Therapy-Enhanced (CBT-E) or Integrative Cognitive-Affective Therapy for BN (ICAT-BN). There were no baseline clinical differences between groups. In the longitudinal models, personality subtype predicted binge eating (p = 0.03) and purging (p = 0.01) frequency at EOT and binge eating frequency at follow-up (p = 0.045). The over-controlled group demonstrated the best outcomes on these variables. In secondary analyses, there was a treatment by subtype interaction for purging at follow-up (p = 0.04), which indicated a superiority of CBT-E over ICAT-BN for reducing purging among the over-controlled group. Empirically derived personality subtyping appears to be a valid classification system with potential to guide eating disorder treatment decisions. © 2016 Wiley Periodicals, Inc.(Int J Eat Disord 2017; 50:506-514). © 2016 Wiley Periodicals, Inc.

Role of Quantitative Clinical Pharmacology in Pediatric Approval and Labeling.

PubMed

Mehrotra, Nitin; Bhattaram, Atul; Earp, Justin C; Florian, Jeffry; Krudys, Kevin; Lee, Jee Eun; Lee, Joo Yeon; Liu, Jiang; Mulugeta, Yeruk; Yu, Jingyu; Zhao, Ping; Sinha, Vikram

2016-07-01

Dose selection is one of the key decisions made during drug development in pediatrics. There are regulatory initiatives that promote the use of model-based drug development in pediatrics. Pharmacometrics or quantitative clinical pharmacology enables development of models that can describe factors affecting pharmacokinetics and/or pharmacodynamics in pediatric patients. This manuscript describes some examples in which pharmacometric analysis was used to support approval and labeling in pediatrics. In particular, the role of pharmacokinetic (PK) comparison of pediatric PK to adults and utilization of dose/exposure-response analysis for dose selection are highlighted. Dose selection for esomeprazole in pediatrics was based on PK matching to adults, whereas for adalimumab, exposure-response, PK, efficacy, and safety data together were useful to recommend doses for pediatric Crohn's disease. For vigabatrin, demonstration of similar dose-response between pediatrics and adults allowed for selection of a pediatric dose. Based on model-based pharmacokinetic simulations and safety data from darunavir pediatric clinical studies with a twice-daily regimen, different once-daily dosing regimens for treatment-naïve human immunodeficiency virus 1-infected pediatric subjects 3 to <12 years of age were evaluated. The role of physiologically based pharmacokinetic modeling (PBPK) in predicting pediatric PK is rapidly evolving. However, regulatory review experiences and an understanding of the state of science indicate that there is a lack of established predictive performance of PBPK in pediatric PK prediction. Moving forward, pharmacometrics will continue to play a key role in pediatric drug development contributing toward decisions pertaining to dose selection, trial designs, and assessing disease similarity to adults to support extrapolation of efficacy. Copyright © 2016 U.S. Government work not protected by U.S. copyright.

Systemic Inflammatory Response Syndrome After Major Abdominal Surgery Predicted by Early Upregulation of TLR4 and TLR5.

PubMed

Lahiri, Rajiv; Derwa, Yannick; Bashir, Zora; Giles, Edward; Torrance, Hew D T; Owen, Helen C; O'Dwyer, Michael J; O'Brien, Alastair; Stagg, Andrew J; Bhattacharya, Satyajit; Foster, Graham R; Alazawi, William

2016-05-01

To study innate immune pathways in patients undergoing hepatopancreaticobiliary surgery to understand mechanisms leading to enhanced inflammatory responses and identifying biomarkers of adverse clinical consequences. Patients undergoing major abdominal surgery are at risk of life-threatening systemic inflammatory response syndrome (SIRS) and sepsis. Early identification of at-risk patients would allow tailored postoperative care and improve survival. Two separate cohorts of patients undergoing major hepatopancreaticobiliary surgery were studied (combined n = 69). Bloods were taken preoperatively, on day 1 and day 2 postoperatively. Peripheral blood mononuclear cells and serum were separated and immune phenotype and function assessed ex vivo. Early innate immune dysfunction was evident in 12 patients who subsequently developed SIRS (postoperative day 6) compared with 27 who did not, when no clinical evidence of SIRS was apparent (preoperatively or days 1 and 2). Serum interleukin (IL)-6 concentration and monocyte Toll-like receptor (TLR)/NF-κB/IL-6 functional pathways were significantly upregulated and overactive in patients who developed SIRS (P < 0.0001). Interferon α-mediated STAT1 phosphorylation was higher preoperatively in patients who developed SIRS. Increased TLR4 and TLR5 gene expression in whole blood was demonstrated in a separate validation cohort of 30 patients undergoing similar surgery. Expression of TLR4/5 on monocytes, particularly intermediate CD14CD16 monocytes, on day 1 or 2 predicted SIRS with accuracy 0.89 to 1.0 (areas under receiver operator curves). These data demonstrate the mechanism for IL-6 overproduction in patients who develop postoperative SIRS and identify markers that predict patients at risk of SIRS 5 days before the onset of clinical signs.

Population-based V3 genotypic tropism assay: a retrospective analysis using screening samples from the A4001029 and MOTIVATE studies.

PubMed

McGovern, Rachel A; Thielen, Alexander; Mo, Theresa; Dong, Winnie; Woods, Conan K; Chapman, Douglass; Lewis, Marilyn; James, Ian; Heera, Jayvant; Valdez, Hernan; Harrigan, P Richard

2010-10-23

The MOTIVATE-1 and 2 studies compared maraviroc (MVC) along with optimized background therapy (OBT) vs. placebo along with OBT in treatment-experienced patients screened as having R5-HIV (original Monogram Trofile). A subset screened with non-R5 HIV were treated with MVC or placebo along with OBT in a sister safety trial, A4001029. This analysis retrospectively examined the performance of population-based sequence analysis of HIV-1 env V3-loop to predict coreceptor tropism. Triplicate V3-loop sequences were generated using stored screening plasma samples and data was processed using custom software ('ReCall'), blinded to clinical response. Tropism was inferred using geno2pheno ('g2p'; 5% false positive rate). Primary outcomes were viral load changes after starting maraviroc; and concordance with prior screening Trofile results. Genotype and Trofile results were available for 1164 individuals with virological outcome data (N = 169 non-R5 by Trofile). Compared with Trofile, V3 genotyping had a specificity of 92.6% and a sensitivity of 67.4% for detecting non-R5 virus. However, when compared with clinical outcome, virological responses were consistently similar between Trofile and V3 genotype at weeks 8 and 24 following the initiation of therapy for patients categorized as R5. Despite differences in sensitivity for predicting non-R5 HIV, week 8 and 24 week virological responses were similar in this treatment-experienced population. These findings suggest the potential utility of V3 genotyping as an accessible assay to select patients who may benefit from maraviroc treatment. Optimization of the predictive tropism algorithm may lead to further improvement in the clinical utility of HIV genotypic tropism assays.

Neural responses to social threat and predictors of cognitive behavioral therapy and acceptance and commitment therapy in social anxiety disorder.

PubMed

Burklund, Lisa J; Torre, Jared B; Lieberman, Matthew D; Taylor, Shelley E; Craske, Michelle G

2017-03-30

Previous research has often highlighted hyperactivity in emotion regions to simple, static social threat cues in social anxiety disorder (SAD). Investigation of the neurobiology of SAD using more naturalistic paradigms can further reveal underlying mechanisms and how these relate to clinical outcomes. We used fMRI to investigate responses to novel dynamic rejection stimuli in individuals with SAD (N=70) and healthy controls (HC; N=17), and whether these responses predicted treatment outcomes following cognitive behavioral therapy (CBT) or acceptance and commitment therapy (ACT). Both HC and SAD groups reported greater distress to rejection compared to neutral social stimuli. At the neural level, HCs exhibited greater activations in social pain/rejection regions, including dorsal anterior cingulate cortex and anterior insula, to rejection stimuli. The SAD group evidenced a different pattern, with no differences in these rejection regions and relatively greater activations in the amygdala and other regions to neutral stimuli. Greater responses in anterior cingulate cortex and the amygdala to rejection vs. neutral stimuli predicted better CBT outcomes. In contrast, enhanced activity in sensory-focused posterior insula predicted ACT responses. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Impact of disseminated tumor cells in gastrointestinal cancer.

PubMed

Thorban, Stefan; Rosenberg, Robert; Maak, Matthias; Friederichs, Jan; Gertler, Ralf; Siewert, Jörg-Rüdiger

2006-05-01

The detection of epithelial cells by sensitive immunological and molecular methods in blood, lymph nodes or bone marrow of gastrointestinal cancer patients may open a new approach to clinical metastasis research. The phenotypic and genomic characterization of these cells is of great value in the prediction of the further course of the disease and the monitoring of response to treatment. In addition, the role of ultrastaging in blood, lymph nodes and bone marrow of cancer patients for the indication of multimodal therapy is discussed in this review. The impact of prognostic or predictive factors for new treatment protocols in patients with gastrointestinal cancer was evaluated as well as the correlation with clinical factors.

Testing for clinical inertia in medication treatment of bipolar disorder.

PubMed

Hodgkin, Dominic; Merrick, Elizabeth L; O'Brien, Peggy L; McGuire, Thomas G; Lee, Sue; Deckersbach, Thilo; Nierenberg, Andrew A

2016-11-15

Clinical inertia has been defined as lack of change in medication treatment at visits where a medication adjustment appears to be indicated. This paper seeks to identify the extent of clinical inertia in medication treatment of bipolar disorder. A second goal is to identify patient characteristics that predict this treatment pattern. Data describe 23,406 visits made by 1815 patients treated for bipolar disorder during the STEP-BD practical clinical trial. Visits were classified in terms of whether a medication adjustment appears to be indicated, and also whether or not one occurred. Multivariable regression analyses were conducted to find which patient characteristics were predictive of whether adjustment occurred. 36% of visits showed at least 1 indication for adjustment. The most common indications were non-response to medication, side effects, and start of a new illness episode. Among visits with an indication for adjustment, no adjustment occurred 19% of the time, which may be suggestive of clinical inertia. In multivariable models, presence of any indication for medication adjustment was a predictor of receiving one (OR=1.125, 95% CI =1.015, 1.246), although not as strong as clinical status measures. The associations observed are not necessarily causal, given the study design. The data also lack information about physician-patient communication. Many patients remained on the same medication regimen despite indications of side effects or non-response to treatment. Although lack of adjustment does not necessarily reflect clinical inertia in all cases, the reasons for this treatment pattern merit further examination. Copyright © 2016 Elsevier B.V. All rights reserved.

Testing for Clinical Inertia in Medication Treatment of Bipolar Disorder

PubMed Central

Hodgkin, Dominic; Merrick, Elizabeth L.; O'Brien, Peggy L.; McGuire, Thomas G.; Lee, Sue; Deckersbach, Thilo; Nierenberg, Andrew A.

2016-01-01

Background Clinical inertia has been defined as lack of change in medication treatment at visits where a medication adjustment appears to be indicated. This paper seeks to identify the extent of clinical inertia in medication treatment of bipolar disorder. A second goal is to identify patient characteristics that predict this treatment pattern. Method Data describe 23,406 visits made by 1,815 patients treated for bipolar disorder during the STEP-BD practical clinical trial. Visits were classified in terms of whether a medication adjustment appears to be indicated, and also whether or not one occurred. Multivariable regression analyses were conducted to find which patient characteristics were predictive of whether adjustment occurred. Results 36% of visits showed at least 1 indication for adjustment. The most common indications were non-response to medication, side effects, and start of a new illness episode. Among visits with an indication for adjustment, no adjustment occurred 19% of the time, which may be suggestive of clinical inertia. In multivariable models, presence of any indication for medication adjustment was a predictor of receiving one (OR=1.125, 95% CI = 1.015, 1.246), although not as strong as clinical status measures. Limitations The associations observed are not necessarily causal, given the study design. The data also lack information about physician-patient communication. Conclusions Many patients remained on the same medication regimen despite indications of side effects or non-response to treatment. Although lack of adjustment does not necessarily reflect clinical inertia in all cases, the reasons for this treatment pattern merit further examination. PMID:27391267

Gene expression profile predicting the response to anti-TNF treatment in patients with rheumatoid arthritis; analysis of GEO datasets.

PubMed

Kim, Tae-Hwan; Choi, Sung Jae; Lee, Young Ho; Song, Gwan Gyu; Ji, Jong Dae

2014-07-01

Anti-tumor necrosis factor (TNF) therapy is the treatment of choice for rheumatoid arthritis (RA) patients in whom standard disease-modifying anti-rheumatic drugs are ineffective. However, a substantial proportion of RA patients treated with anti-TNF agents do not show a significant clinical response. Therefore, biomarkers predicting response to anti-TNF agents are needed. Recently, gene expression profiling has been applied in research for developing such biomarkers. We compared gene expression profiles reported by previous studies dealing with the responsiveness of anti-TNF therapy in RA patients and attempted to identify differentially expressed genes (DEGs) that discriminated between responders and non-responders to anti-TNF therapy. We used microarray datasets available at the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO). This analysis included 6 studies and 5 sets of microarray data that used peripheral blood samples for identification of DEGs predicting response to anti-TNF therapy. We found little overlap in the DEGs that were highly ranked in each study. Three DEGs including IL2RB, SH2D2A and G0S2 appeared in more than 1 study. In addition, a meta-analysis designed to increase statistical power found one DEG, G0S2 by the Fisher's method. Our finding suggests the possibility that G0S2 plays as a biomarker to predict response to anti-TNF therapy in patients with rheumatoid arthritis. Further investigations based on larger studies are therefore needed to confirm the significance of G0S2 in predicting response to anti-TNF therapy. Copyright © 2014 Société française de rhumatologie. Published by Elsevier SAS. All rights reserved.

Regulatory T cell frequency, but not plasma IL-33 levels, represents potential immunological biomarker to predict clinical response to intravenous immunoglobulin therapy.

PubMed

Maddur, Mohan S; Stephen-Victor, Emmanuel; Das, Mrinmoy; Prakhar, Praveen; Sharma, Varun K; Singh, Vikas; Rabin, Magalie; Trinath, Jamma; Balaji, Kithiganahalli N; Bolgert, Francis; Vallat, Jean-Michel; Magy, Laurent; Kaveri, Srini V; Bayry, Jagadeesh

2017-03-20

Intravenous immunoglobulin (IVIG) is a polyspecific pooled immunoglobulin G preparation and one of the commonly used therapeutics for autoimmune diseases including those of neurological origin. A recent report in murine model proposed that IVIG expands regulatory T (T reg ) cells via induction of interleukin 33 (IL-33). However, translational insight on these observations is lacking. Ten newly diagnosed Guillain-Barré syndrome (GBS) patients were treated with IVIG at the rate of 0.4 g/kg for three to five consecutive days. Clinical evaluation for muscular weakness was performed by Medical Research Council (MRC) and modified Rankin scoring (MRS) system. Heparinized blood samples were collected before and 1, 2, and 4-5 weeks post-IVIG therapy. Peripheral blood mononuclear cells were stained for surface CD4 and intracellular Foxp3, IFN-γ, and tumor necrosis factor alpha (TNF-α) and were analyzed by flow cytometry. IL-33 and prostaglandin E2 in the plasma were measured by ELISA. The fold changes in plasma IL-33 at week 1 showed no correlation with the MRC and MRS scores at weeks 1, 2, and ≥4 post-IVIG therapy. Clinical recovery following IVIG therapy appears to be associated with T reg cell response. Contrary to murine study, there was no association between the fold changes in IL-33 at week 1 and T reg cell frequency at weeks 1, 2, and ≥4 post-IVIG therapy. T reg cell-mediated clinical response to IVIG therapy in GBS patients was associated with reciprocal regulation of effector T cells-expressing TNF-α. T reg cell expansion by IVIG in patients with autoimmune diseases lack correlation with IL-33. T reg cell frequency, but not plasma IL-33 levels, represents potential immunological biomarker to predict clinical response to IVIG therapy.

Dysfunctional Attitudes and Affective Responses to Daily Stressors: Separating Cognitive, Genetic, and Clinical Influences on Stress Reactivity

PubMed Central

Conway, Christopher C.; Slavich, George M.; Hammen, Constance

2016-01-01

Despite decades of research examining diathesis-stress models of emotional disorders, it remains unclear whether dysfunctional attitudes interact with stressful experiences to shape affect on a daily basis and, if so, how clinical and genetic factors influence these associations. To address these issues, we conducted a multi-level daily diary study that examined how dysfunctional attitudes and stressful events relate to daily fluctuations in negative and positive affect in 104 young adults. Given evidence that clinical and genetic factors underlie stress sensitivity, we also examined how daily affect is influenced by internalizing and externalizing symptoms and brain-derived neurotrophic factor (BDNF) genotype, which have been shown to influence neural, endocrine, and affective responses to stress. In multivariate models, internalizing symptoms and BDNF Val66Met genotype independently predicted heightened negative affect on stressful days, but dysfunctional attitudes did not. Specifically, the BDNF Met allele and elevated baseline internalizing symptomatology predicted greater increases in negative affect in stressful circumstances. These data are the first to demonstrate that BDNF genotype and stress are jointly associated with daily fluctuations in negative affect, and they challenge the assumption that maladaptive beliefs play a strong independent role in determining affective responses to everyday stressors. The results may thus inform the development of new multi-level theories of psychopathology and guide future research on predictors of affective lability. PMID:27041782

Micro-RNAs as Potential Predictors of Response to Breast Cancer Systemic Therapy: Future Clinical Implications

PubMed Central

Campos-Parra, Alma D.; Cuamani Mitznahuatl, Gerardo; Pedroza-Torres, Abraham; Vázquez Romo, Rafael; Porras Reyes, Fany Iris; López-Urrutia, Eduardo; Pérez-Plasencia, Carlos

2017-01-01

Despite advances in diagnosis and new treatments such as targeted therapies, breast cancer (BC) is still the most prevalent tumor in women worldwide and the leading cause of death. The principal obstacle for successful BC treatment is the acquired or de novo resistance of the tumors to the systemic therapy (chemotherapy, endocrine, and targeted therapies) that patients receive. In the era of personalized treatment, several studies have focused on the search for biomarkers capable of predicting the response to this therapy; microRNAs (miRNAs) stand out among these markers due to their broad spectrum or potential clinical applications. miRNAs are conserved small non-coding RNAs that act as negative regulators of gene expression playing an important role in several cellular processes, such as cell proliferation, autophagy, genomic stability, and apoptosis. We reviewed recent data that describe the role of miRNAs as potential predictors of response to systemic treatments in BC. Furthermore, upon analyzing the collected published information, we noticed that the overexpression of miR-155, miR-222, miR-125b, and miR-21 predicts the resistance to the most common systemic treatments; nonetheless, the function of these particular miRNAs must be carefully studied and further analyses are still necessary to increase knowledge about their role and future potential clinical uses in BC. PMID:28574440

Micro-RNAs as Potential Predictors of Response to Breast Cancer Systemic Therapy: Future Clinical Implications.

PubMed

Campos-Parra, Alma D; Mitznahuatl, Gerardo Cuamani; Pedroza-Torres, Abraham; Romo, Rafael Vázquez; Reyes, Fany Iris Porras; López-Urrutia, Eduardo; Pérez-Plasencia, Carlos

2017-06-02

Despite advances in diagnosis and new treatments such as targeted therapies, breast cancer (BC) is still the most prevalent tumor in women worldwide and the leading cause of death. The principal obstacle for successful BC treatment is the acquired or de novo resistance of the tumors to the systemic therapy (chemotherapy, endocrine, and targeted therapies) that patients receive. In the era of personalized treatment, several studies have focused on the search for biomarkers capable of predicting the response to this therapy; microRNAs (miRNAs) stand out among these markers due to their broad spectrum or potential clinical applications. miRNAs are conserved small non-coding RNAs that act as negative regulators of gene expression playing an important role in several cellular processes, such as cell proliferation, autophagy, genomic stability, and apoptosis. We reviewed recent data that describe the role of miRNAs as potential predictors of response to systemic treatments in BC. Furthermore, upon analyzing the collected published information, we noticed that the overexpression of miR-155, miR-222, miR-125b, and miR-21 predicts the resistance to the most common systemic treatments; nonetheless, the function of these particular miRNAs must be carefully studied and further analyses are still necessary to increase knowledge about their role and future potential clinical uses in BC.

Immunological monitoring for prediction of clinical response to antitumor vaccine therapy.

PubMed

Mikhaylova, Irina N; Shubina, Irina Zh; Chkadua, George Z; Petenko, Natalia N; Morozova, Lidia F; Burova, Olga S; Beabelashvili, Robert Sh; Parsunkova, Kermen A; Balatskaya, Natalia V; Chebanov, Dmitrii K; Pospelov, Vadim I; Nazarova, Valeria V; Vihrova, Anastasia S; Cheremushkin, Evgeny A; Molodyk, Alvina A; Kiselevsky, Mikhail V; Demidov, Lev V

2018-05-11

Immunotherapy has shown promising results in a variety of cancers, including melanoma. However, the responses to therapy are usually heterogeneous, and understanding the factors affecting clinical outcome is still not achieved. Here, we show that immunological monitoring of the vaccine therapy for melanoma patients may help to predict the clinical course of the disease. We studied cytokine profile of cellular Th1 (IL-2, IL-12, IFN-γ) and humoral Th2 (IL-4, IL-10) immune response, vascular endothelial growth factor (VEGFA), transforming growth factor-β 2 (TGF-β 2), S100 protein (S100A1B and S100BB), adhesion molecule CD44 and serum cytokines β2-microglobulin to analyze different peripheral blood mononuclear cell subpopuations of patients treated with dendritic vaccines and/or cyclophosphamide in melanoma patients in the course of adjuvant treatment. The obtained data indicate predominance of cellular immunity in the first adjuvant group of patients with durable time to progression and shift to humoral with low cellular immunity in patients with short-term period to progression (increased levels of IL-4 and IL- 10). Beta-2 microglobulin was differentially expressed in adjuvant subgroups: its higher levels correlated with shorter progression-free survival and the total follow-up time. Immunoregulatory index was overall higher in patients with disease progression compared to the group of patients with no signs of disease progression.

Prediction of Sustained Virological Response to Peginterferon-based Therapy for Chronic Hepatitis C: Regression Analysis of a Cohort from Rio Grande do Sul, Brazil

PubMed Central

Fendt, Lúcia; Amaral, Karine; D Picon, Paulo

2017-01-01

Aim: Peginterferon plus ribavirin (peg-IFN/RBV) is still the standard of care for treatment of hepatitis C virus (HCV) in many countries. Given the high toxicity of this regimen, our study aimed to develop a prediction tool that can identify which patients are unlikely to benefit from peg-IFN/RBV and could thus postpone treatment in favor of new-generation direct-acting antivirals. Materials and methods: Binary regression was performed using demographic, clinical, and laboratory covariates and sustained virological response (SVR) outcomes from a prospective cohort of individuals referred for therapy from 2003 to 2008 in a public HCV treatment center in Rio Grande do Sul, Brazil. Results: Of the 743 participants analyzed, 489 completed 48 weeks of treatment (65.8%). A total of 202 of those who completed peg-IFN/RBV therapy achieved SVR (27.2% responders), 196 did not (26.4%), and 91 had missing viral load (VL) at week 72 (12.2% loss to follow-up). The remainder discontinued therapy (n = 254 [34.2%]), 78 (30.7%) doing so due to adverse effects. Baseline covariates included in the regression model were sex, age, human immunodeficiency virus, infection status, aspartate transaminase, alanine transaminase, hemoglobin, platelets, serum creatinine, prothrombin time, pretreatment VL, cirrhosis on liver biopsy, and treatment naivety. A predicted SVR of 17.9% had 90.0% sensitivity for detecting true nonresponders. The negative likelihood ratio at a predicted SVR of 17.9% was 0.16, and the negative predictive value was 92.6%. Conclusion: Easily obtainable variables can identify patients that will likely not benefit from peg-IFN-based therapy. This prediction model might be useful to clinicians. Clinical significance: To our knowledge, this is the only prediction tool that can reliably help clinicians to postpone peg-IFN/RBV therapy for HCV genotype 1 patients. How to cite this article: Picon RV, Fendt L, Amaral K, Picon PD. Prediction of Sustained Virological Response to Peginterferon-based Therapy for Chronic Hepatitis C: Regression Analysis of a Cohort from Rio Grande do Sul, Brazil. Euroasian J Hepato-Gastroenterol 2017;7(1):27-33. PMID:29201768

Classification of childhood asthma phenotypes and long-term clinical responses to inhaled anti-inflammatory medications.

PubMed

Howrylak, Judie A; Fuhlbrigge, Anne L; Strunk, Robert C; Zeiger, Robert S; Weiss, Scott T; Raby, Benjamin A

2014-05-01

Although recent studies have identified the presence of phenotypic clusters in asthmatic patients, the clinical significance and temporal stability of these clusters have not been explored. Our aim was to examine the clinical relevance and temporal stability of phenotypic clusters in children with asthma. We applied spectral clustering to clinical data from 1041 children with asthma participating in the Childhood Asthma Management Program. Posttreatment randomization follow-up data collected over 48 months were used to determine the effect of these clusters on pulmonary function and treatment response to inhaled anti-inflammatory medication. We found 5 reproducible patient clusters that could be differentiated on the basis of 3 groups of features: atopic burden, degree of airway obstruction, and history of exacerbation. Cluster grouping predicted long-term asthma control, as measured by the need for oral prednisone (P < .0001) or additional controller medications (P = .001), as well as longitudinal differences in pulmonary function (P < .0001). We also found that the 2 clusters with the highest rates of exacerbation had different responses to inhaled corticosteroids when compared with the other clusters. One cluster demonstrated a positive response to both budesonide (P = .02) and nedocromil (P = .01) compared with placebo, whereas the other cluster demonstrated minimal responses to both budesonide (P = .12) and nedocromil (P = .56) compared with placebo. Phenotypic clustering can be used to identify longitudinally consistent and clinically relevant patient subgroups, with implications for targeted therapeutic strategies and clinical trials design.

Short-term SSRI treatment normalises amygdala hyperactivity in depressed patients.

PubMed

Godlewska, B R; Norbury, R; Selvaraj, S; Cowen, P J; Harmer, C J

2012-12-01

Antidepressant drugs such as selective serotonin re-uptake inhibitors (SSRIs) remediate negative biases in emotional processing in depressed patients in both behavioural and neural outcome measures. However, it is not clear if these effects occur before, or as a consequence of, changes in clinical state. In the present study, we investigated the effects of short-term SSRI treatment in depressed patients on the neural response to fearful faces prior to clinical improvement in mood. Altogether, 42 unmedicated depressed patients received SSRI treatment (10 mg escitalopram daily) or placebo in a randomised, parallel-group design. The neural response to fearful and happy faces was measured on day 7 of treatment using functional magnetic resonance imaging. A group of healthy controls was imaged in the same way. Amygdala responses to fearful facial expressions were significantly greater in depressed patients compared to healthy controls. However, this response was normalised in patients receiving 7 days treatment with escitalopram. There was no significant difference in clinical depression ratings at 7 days between the escitalopram and placebo-treated patients. Our results suggest that short-term SSRI treatment in depressed patients remediates amygdala hyperactivity in response to negative emotional stimuli prior to clinical improvement in depressed mood. This supports the hypothesis that the clinical effects of antidepressant treatment may be mediated in part through early changes in emotional processing. Further studies will be needed to show if these early effects of antidepressant medication predict eventual clinical outcome.

Functional network architecture predicts psychologically mediated analgesia related to treatment in chronic knee pain patients.

PubMed

Hashmi, Javeria Ali; Kong, Jian; Spaeth, Rosa; Khan, Sheraz; Kaptchuk, Ted J; Gollub, Randy L

2014-03-12

Placebo analgesia is an indicator of how efficiently the brain translates psychological signals conveyed by a treatment procedure into pain relief. It has been demonstrated that functional connectivity between distributed brain regions predicts placebo analgesia in chronic back pain patients. Greater network efficiency in baseline brain networks may allow better information transfer and facilitate adaptive physiological responses to psychological aspects of treatment. Here, we theorized that topological network alignments in resting state scans predict psychologically conditioned analgesic responses to acupuncture treatment in chronic knee osteoarthritis pain patients (n = 45). Analgesia was induced by building positive expectations toward acupuncture treatment with verbal suggestion and heat pain conditioning on a test site of the arm. This procedure induced significantly more analgesia after sham or real acupuncture on the test site than in a control site. The psychologically conditioned analgesia was invariant to sham versus real treatment. Efficiency of information transfer within local networks calculated with graph-theoretic measures (local efficiency and clustering coefficients) significantly predicted conditioned analgesia. Clustering coefficients in regions associated with memory, motivation, and pain modulation were closely involved in predicting analgesia. Moreover, women showed higher clustering coefficients and marginally greater pain reduction than men. Overall, analgesic response to placebo cues can be predicted from a priori resting state data by observing local network topology. Such low-cost synchronizations may represent preparatory resources that facilitate subsequent performance of brain circuits in responding to adaptive environmental cues. This suggests a potential utility of network measures in predicting placebo response for clinical use.

TESTING BALANCE AND FALL RISK IN PERSONS WITH PARKINSON DISEASE, AN ARGUMENT FOR ECOLOGICALLY VALID TESTING

PubMed Central

Foreman, K. Bo; Addison, Odessa; Kim, Han S.; Dibble, Leland E.

2010-01-01

Introduction Despite clear deficits in postural control, most clinical examination tools lack accuracy in identifying persons with Parkinson disease (PD) who have fallen or are at risk for falls. We assert that this is in part due to the lack of ecological validity of the testing. Methods To test this assertion, we examined the responsiveness and predictive validity of the Functional Gait Assessment (FGA), the Pull test, and the Timed up and Go (TUG) during clinically defined ON and OFF medication states. To address responsiveness, ON/OFF medication performance was compared. To address predictive validity, areas under the curve (AUC) of receiver operating characteristic (ROC) curves were compared. Comparisons were made using separate non-parametric tests. Results Thirty-six persons (24 male, 12 female) with PD (22 fallers, 14 non-fallers) participated. Only the FGA was able to detect differences between fallers and non-fallers for both ON/OFF medication testing. The predictive validity of the FGA and the TUG for fall identification was higher during OFF medication compared to ON medication testing. The predictive validity of the FGA was higher than the TUG and the Pull test during ON and OFF medication testing. Discussion In order to most accurately identify fallers, clinicians should test persons with PD in ecologically relevant conditions and tasks. In this study, interpretation of the OFF medication performance and use of the FGA provided more accurate prediction of those who would fall. PMID:21215674

Proposals for enhanced health risk assessment and stratification in an integrated care scenario

PubMed Central

Dueñas-Espín, Ivan; Vela, Emili; Pauws, Steffen; Bescos, Cristina; Cano, Isaac; Cleries, Montserrat; Contel, Joan Carles; de Manuel Keenoy, Esteban; Garcia-Aymerich, Judith; Gomez-Cabrero, David; Kaye, Rachelle; Lahr, Maarten M H; Lluch-Ariet, Magí; Moharra, Montserrat; Monterde, David; Mora, Joana; Nalin, Marco; Pavlickova, Andrea; Piera, Jordi; Ponce, Sara; Santaeugenia, Sebastià; Schonenberg, Helen; Störk, Stefan; Tegner, Jesper; Velickovski, Filip; Westerteicher, Christoph; Roca, Josep

2016-01-01

Objectives Population-based health risk assessment and stratification are considered highly relevant for large-scale implementation of integrated care by facilitating services design and case identification. The principal objective of the study was to analyse five health-risk assessment strategies and health indicators used in the five regions participating in the Advancing Care Coordination and Telehealth Deployment (ACT) programme (http://www.act-programme.eu). The second purpose was to elaborate on strategies toward enhanced health risk predictive modelling in the clinical scenario. Settings The five ACT regions: Scotland (UK), Basque Country (ES), Catalonia (ES), Lombardy (I) and Groningen (NL). Participants Responsible teams for regional data management in the five ACT regions. Primary and secondary outcome measures We characterised and compared risk assessment strategies among ACT regions by analysing operational health risk predictive modelling tools for population-based stratification, as well as available health indicators at regional level. The analysis of the risk assessment tool deployed in Catalonia in 2015 (GMAs, Adjusted Morbidity Groups) was used as a basis to propose how population-based analytics could contribute to clinical risk prediction. Results There was consensus on the need for a population health approach to generate health risk predictive modelling. However, this strategy was fully in place only in two ACT regions: Basque Country and Catalonia. We found marked differences among regions in health risk predictive modelling tools and health indicators, and identified key factors constraining their comparability. The research proposes means to overcome current limitations and the use of population-based health risk prediction for enhanced clinical risk assessment. Conclusions The results indicate the need for further efforts to improve both comparability and flexibility of current population-based health risk predictive modelling approaches. Applicability and impact of the proposals for enhanced clinical risk assessment require prospective evaluation. PMID:27084274

The bitter pill: clinical drugs that activate the human bitter taste receptor TAS2R14.

PubMed

Levit, Anat; Nowak, Stefanie; Peters, Maximilian; Wiener, Ayana; Meyerhof, Wolfgang; Behrens, Maik; Niv, Masha Y

2014-03-01

Bitter taste receptors (TAS2Rs) mediate aversive response to toxic food, which is often bitter. These G-protein-coupled receptors are also expressed in extraoral tissues, and emerge as novel targets for therapeutic indications such as asthma and infection. Our goal was to identify ligands of the broadly tuned TAS2R14 among clinical drugs. Molecular properties of known human bitter taste receptor TAS2R14 agonists were incorporated into pharmacophore- and shape-based models and used to computationally predict additional ligands. Predictions were tested by calcium imaging of TAS2R14-transfected HEK293 cells. In vitro testing of the virtual screening predictions resulted in 30-80% success rates, and 15 clinical drugs were found to activate the TAS2R14. hERG potassium channel, which is predominantly expressed in the heart, emerged as a common off-target of bitter drugs. Despite immense chemical diversity of known TAS2R14 ligands, novel ligands and previously unknown polypharmacology of drugs were unraveled by in vitro screening of computational predictions. This enables rational repurposing of traditional and standard drugs for bitter taste signaling modulation for therapeutic indications.

Rostral Anterior Cingulate Cortex Theta Current Density and Response to Antidepressants and Placebo in Major Depression

PubMed Central

Korb, Alexander S.; Hunter, Aimee M.; Cook, Ian A.; Leuchter, Andrew F.

2009-01-01

Objective To assess whether pretreatment theta current density in the rostral anterior cingulate (rACC) and medial orbitofrontal cortex (mOFC) differentiates responders from non-responders to antidepressant medication or placebo in a double-blinded study. Methods Pretreatment EEGs were collected from 72 subjects with Major Depressive Disorder (MDD) who participated in one of three placebo-controlled trials. Subjects were randomized to receive treatment with fluoxetine, venlafaxine, or placebo. Low-resolution brain electromagnetic tomography (LORETA) was used to assess theta current density in the rACC and mOFC. Results Medication responders showed elevated rACC and mOFC theta current density compared to medication non-responders (rACC: p=0.042; mOFC: p=0.039). There was no significant difference in either brain region between placebo responders and placebo non-responders. Conclusions Theta current density in the rACC and mOFC may be useful as a biomarker for prediction of response to antidepressant medication. Significance This is the first double-blinded treatment study to examine pretreatment rACC and mOFC theta current density in relation to antidepressant response and placebo response. Results support the potential clinical utility of this approach for predicting clinical outcome to antidepressant treatments in MDD. PMID:19539524

Nomograms Predicting Response to Therapy and Outcomes After Bladder-Preserving Trimodality Therapy for Muscle-Invasive Bladder Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coen, John J., E-mail: jcoen@harthosp.org; Paly, Jonathan J.; Niemierko, Andrzej

2013-06-01

Purpose: Selective bladder preservation by use of trimodality therapy is an established management strategy for muscle-invasive bladder cancer. Individual disease features have been associated with response to therapy, likelihood of bladder preservation, and disease-free survival. We developed prognostic nomograms to predict the complete response rate, disease-specific survival, and likelihood of remaining free of recurrent bladder cancer or cystectomy. Methods and Materials: From 1986 to 2009, 325 patients were managed with selective bladder preservation at Massachusetts General Hospital (MGH) and had complete data adequate for nomogram development. Treatment consisted of a transurethral resection of bladder tumor followed by split-course chemoradiation. Patientsmore » with a complete response at midtreatment cystoscopic assessment completed radiation, whereas those with a lesser response underwent a prompt cystectomy. Prognostic nomograms were constructed predicting complete response (CR), disease-specific survival (DSS), and bladder-intact disease-free survival (BI-DFS). BI-DFS was defined as the absence of local invasive or regional recurrence, distant metastasis, bladder cancer-related death, or radical cystectomy. Results: The final nomograms included information on clinical T stage, presence of hydronephrosis, whether a visibly complete transurethral resection of bladder tumor was performed, age, sex, and tumor grade. The predictive accuracy of these nomograms was assessed. For complete response, the area under the receiving operating characteristic curve was 0.69. The Harrell concordance index was 0.61 for both DSS and BI-DFS. Conclusions: Our nomograms allow individualized estimates of complete response, DSS, and BI-DFS. They may assist patients and clinicians making important treatment decisions.« less

Predicting and managing primary and secondary non-response to rituximab using B-cell biomarkers in systemic lupus erythematosus

PubMed Central

Md Yusof, Md Yuzaiful; Shaw, Daniel; El-Sherbiny, Yasser M; Dunn, Emma; Rawstron, Andy C; Emery, Paul; Vital, Edward M

2017-01-01

Objective To assess factors associated with primary and secondary non-response to rituximab in systemic lupus erythematosus (SLE) and evaluate management of secondary non-depletion non-response (2NDNR). Methods 125 patients with SLE treated with rituximab over 12 years were studied prospectively. A major clinical response was defined as improvement of all active British Isles Lupus Assessment Group (BILAG)-2004 domains to grade C/better and no A/B flare. Partial responders were defined by one persistent BILAG B. B-cell subsets were measured using highly sensitive flow cytometry. Patients with 2NDNR, defined by infusion reaction and defective depletion, were treated with ocrelizumab or ofatumumab. Results 117 patients had evaluable data. In cycle 1 (C1), 96/117 (82%) achieved BILAG response (major=50%, partial=32%). In multivariable analysis, younger age (OR 0.97, 95% CI 0.94 to 1.00) and B-cell depletion at 6 weeks (OR 3.22, 95% CI 1.24 to 8.33) increased the odds of major response. Complete depletion was predicted by normal complement and lower pre-rituximab plasmablasts and was not associated with increased serious infection post-rituximab. Seventy-seven (with data on 72) C1 responders were retreated on clinical relapse. Of these, 61/72 (85%) responded in cycle 2 (C2). Of the 11 C2 non-responders, nine met 2NDNR criteria (incidence=12%) and tested positive for anti-rituximab antibodies. Lack of concomitant immunosuppressant and higher pre-rituximab plasmablasts predicted 2NDNR. Five were switched to ocrelizumab/ofatumumab, and all depleted and responded. Conclusion Treatment with anti-CD20 agents can be guided by B-cell monitoring and should aim to achieve complete depletion. 2NDNR is associated with anti-rituximab antibodies, and switching to humanised agents restores depletion and response. In SLE, alternative anti-CD20 antibodies may be more consistently effective. PMID:28684557

Non-invasive prediction of forthcoming cirrhosis-related complications

PubMed Central

Kang, Wonseok; Kim, Seung Up; Ahn, Sang Hoon

2014-01-01

In patients with chronic liver diseases, identification of significant liver fibrosis and cirrhosis is essential for determining treatment strategies, assessing therapeutic response, and stratifying long-term prognosis. Although liver biopsy remains the reference standard for evaluating the extent of liver fibrosis in patients with chronic liver diseases, several non-invasive methods have been developed as alternatives to liver biopsies. Some of these non-invasive methods have demonstrated clinical accuracy for diagnosing significant fibrosis or cirrhosis in many cross-sectional studies with the histological fibrosis stage as a reference standard. However, non-invasive methods cannot be fully validated through cross-sectional studies since liver biopsy is not a perfect surrogate endpoint marker. Accordingly, recent studies have focused on assessing the performance of non-invasive methods through long-term, longitudinal, follow-up studies with solid clinical endpoints related to advanced stages of liver fibrosis and cirrhosis. As a result, current view is that these alternative methods can independently predict future cirrhosis-related complications, such as hepatic decompensation, liver failure, hepatocellular carcinoma, or liver-related death. The clinical role of non-invasive models seems to be shifting from a simple tool for predicting the extent of fibrosis to a surveillance tool for predicting future liver-related events. In this article, we will summarize recent longitudinal studies of non-invasive methods for predicting forthcoming complications related to liver cirrhosis and discuss the clinical value of currently available non-invasive methods based on evidence from the literature. PMID:24627597

Subtype and pathway specific responses to anticancer compounds in breast cancer.

PubMed

Heiser, Laura M; Sadanandam, Anguraj; Kuo, Wen-Lin; Benz, Stephen C; Goldstein, Theodore C; Ng, Sam; Gibb, William J; Wang, Nicholas J; Ziyad, Safiyyah; Tong, Frances; Bayani, Nora; Hu, Zhi; Billig, Jessica I; Dueregger, Andrea; Lewis, Sophia; Jakkula, Lakshmi; Korkola, James E; Durinck, Steffen; Pepin, François; Guan, Yinghui; Purdom, Elizabeth; Neuvial, Pierre; Bengtsson, Henrik; Wood, Kenneth W; Smith, Peter G; Vassilev, Lyubomir T; Hennessy, Bryan T; Greshock, Joel; Bachman, Kurtis E; Hardwicke, Mary Ann; Park, John W; Marton, Laurence J; Wolf, Denise M; Collisson, Eric A; Neve, Richard M; Mills, Gordon B; Speed, Terence P; Feiler, Heidi S; Wooster, Richard F; Haussler, David; Stuart, Joshua M; Gray, Joe W; Spellman, Paul T

2012-02-21

Breast cancers are comprised of molecularly distinct subtypes that may respond differently to pathway-targeted therapies now under development. Collections of breast cancer cell lines mirror many of the molecular subtypes and pathways found in tumors, suggesting that treatment of cell lines with candidate therapeutic compounds can guide identification of associations between molecular subtypes, pathways, and drug response. In a test of 77 therapeutic compounds, nearly all drugs showed differential responses across these cell lines, and approximately one third showed subtype-, pathway-, and/or genomic aberration-specific responses. These observations suggest mechanisms of response and resistance and may inform efforts to develop molecular assays that predict clinical response.

Fronto-limbic effective connectivity as possible predictor of antidepressant response to SSRI administration.

PubMed

Vai, Benedetta; Bulgarelli, Chiara; Godlewska, Beata R; Cowen, Philip J; Benedetti, Francesco; Harmer, Catherine J

2016-12-01

The timely selection of the optimal treatment for depressed patients is critical to improve remission rates. The detection of pre-treatment variables able to predict differential treatment response may provide novel approaches for treatment selection. Selective serotonin reuptake inhibitors (SSRIs) modulate the fronto-limbic functional response and connectivity, an effect preceding the overt clinical antidepressant effects. Here we investigated whether the cortico-limbic connectivity associated with emotional bias measured before SSRI administration predicts the efficacy of antidepressant treatment in MDD patients. fMRI and Dynamic Causal Modeling (DCM) were combined to study if effective connectivity might differentiate healthy controls (HC) and patients affected by major depression who later responded (RMDD, n=21), or failed to respond (nRMDD, n=12), to 6 weeks of escitalopram administration. Sixteen DCMs exploring connectivity between anterior cingulate cortex (ACC), ventrolateral prefrontal cortex (VLPFC), Amygdala (Amy), and fusiform gyrus (FG) were constructed. Analyses revealed that nRMDD had reduced endogenous connectivity from Amy to VLPFC and to ACC, with an increased connectivity and modulation of the ACC to Amy connectivity when processing of fearful emotional stimuli compared to HC. RMDD and HC did not significantly differ among themselves. Pre-treatment effective connectivity in fronto-limbic circuitry could be an important factor affecting antidepressant response, and highlight the mechanisms which may be involved in recovery from depression. These results suggest that fronto-limbic connectivity might provide a neural biomarker to predict the clinical outcome to SSRIs administration in major depression. Copyright © 2016 Elsevier B.V. and ECNP. All rights reserved.

Patient phenotyping in clinical trials of chronic pain treatments: IMMPACT recommendations

PubMed Central

Edwards, Robert R.; Dworkin, Robert H.; Turk, Dennis C.; Angst, Martin S.; Dionne, Raymond; Freeman, Roy; Hansson, Per; Haroutounian, Simon; Arendt-Nielsen, Lars; Attal, Nadine; Baron, Ralf; Brell, Joanna; Bujanover, Shay; Burke, Laurie B.; Carr, Daniel; Chappell, Amy S.; Cowan, Penney; Etropolski, Mila; Fillingim, Roger B.; Gewandter, Jennifer S.; Katz, Nathaniel P.; Kopecky, Ernest A.; Markman, John D.; Nomikos, George; Porter, Linda; Rappaport, Bob A.; Rice, Andrew S.C.; Scavone, Joseph M.; Scholz, Joachim; Simon, Lee S.; Smith, Shannon M.; Tobias, Jeffrey; Tockarshewsky, Tina; Veasley, Christine; Versavel, Mark; Wasan, Ajay D.; Wen, Warren; Yarnitsky, David

2018-01-01

There is tremendous inter-patient variability in the response to analgesic therapy (even for efficacious treatments), which can be the source of great frustration in clinical practice. This has led to calls for “precision medicine”, or personalized pain therapeutics (i.e., empirically-based algorithms that determine the optimal treatments, or treatment combinations, for individual patients) that would presumably improve both the clinical care of patients with pain, and the success rates for putative analgesic drugs in Phase 2 and 3 clinical trials. However, before implementing this approach, the characteristics of individual patients or subgroups of patients that increase or decrease the response to a specific treatment need to be identified. The challenge is to identify the measurable phenotypic characteristics of patients that are most predictive of individual variation in analgesic treatment outcomes, and the measurement tools that are best suited to evaluate these characteristics. In this article, we present evidence on the most promising of these phenotypic characteristics for use in future research, including psychosocial factors, symptom characteristics, sleep patterns, responses to noxious stimulation, endogenous pain-modulatory processes, and response to pharmacologic challenge. We provide evidence-based recommendations for core phenotyping domains and recommend measures of each domain. PMID:27152687

CHL1, ITGB3 and SLC6A4 gene expression and antidepressant drug response: results from the Munich Antidepressant Response Signature (MARS) study.

PubMed

Probst-Schendzielorz, Kristina; Scholl, Catharina; Efimkina, Olga; Ersfeld, Eva; Viviani, Roberto; Serretti, Alessandro; Fabbri, Chiara; Gurwitz, David; Lucae, Susanne; Ising, Marcus; Paul, Anna Maria; Lehmann, Marie-Louise; Steffens, Michael; Crisafulli, Concetta; Calabrò, Marco; Holsboer, Florian; Stingl, Julia

2015-01-01

The identification of antidepressant drugs (ADs) response biomarkers in depression is of high clinical importance. We explored CHL1 and ITGB3 expression as tentative response biomarkers. In vitro sensitivity to ADs, as well as gene expression and genetic variants of the candidate genes CHL1, ITGB3 and SLC6A4 were measured in lymphoblastoid cell lines (LCLs) of 58 depressed patients. An association between the clinical remission of depression and the basal expression of CHL1 and ITGB3 was discovered. Individuals whose LCLs expressed higher levels of CHL1 or ITGB3 showed a significantly better remission upon AD treatment. In addition individuals with the CHL1 rs1516338 TT genotype showed a significantly better remission after 5 weeks AD treatment than those carrying a CC genotype. No association between the in vitro sensitivity of LCLs toward AD and the clinical remission could be detected. CHL1 expression in patient-derived LCLs correlated with the clinical outcome. Thus, it could be a valid biomarker to predict the success of an antidepressant therapy. Original submitted 8 December 2014; Revision submitted 2 March 2015.

Treatment of acute myeloid leukemia in the next decade - Towards real-time functional testing and personalized medicine.

PubMed

Lam, Stephen Sze-Yuen; He, Alex Bai-Liang; Leung, Anskar Yu-Hung

2017-11-01

Information arising from next generation sequencing of leukemia genome has shed important light on the heterogeneous and combinatorial driver events in acute myeloid leukemia (AML). It has also provided insight into its intricate signaling pathways operative in the disease pathogenesis. These have also become biomarkers and targets for therapeutic intervention. Emerging evidence from in vitro drug screening has demonstrated its potential value in predicting clinical drug responses in specific AML subtypes. However, the best culture conditions and readouts have yet to be standardized and the drugs included in these screening exercises frequently revised in view of the rapid emergence of new therapeutic agents in the oncology field. Testing of leukemia cell functions, including BCL2 profiling, has also been used to predict treatment response to conventional chemotherapy and hypomethylating agents as well as BCL2 antagonist in small patient cohorts. These platforms should be integrated into future clinical trials to develop personalized treatment of AML. Copyright © 2017 Elsevier Ltd. All rights reserved.

Mechanisms of change in ERP treatment of compulsive hand washing: does primary threat make a difference?

PubMed

Cougle, Jesse R; Wolitzky-Taylor, Kate B; Lee, Han-Joo; Telch, Michael J

2007-07-01

The present study sought to examine patterns of habituation in exposure and response prevention (ERP) treatment of compulsive hand washing. Sub-clinical compulsive washers (n=27) with illness or non-illness primary threats were compared in order to detect potential differences in response to a single session of ERP. Changes in anxiety, disgust, and urge to wash were analyzed, and significant reductions in both anxiety and disgust were noted. Urge to wash significantly declined among washers primarily concerned with illness; among those concerned with non-illness threats, urge to wash did not significantly decline. Moreover, anxiety was found to decline when controlling for disgust and vice versa. Lastly, when both anxiety and disgust were entered into a model predicting changes in urge to wash, anxiety but not disgust predicted urge to wash for those with illness-related threats; for washers with non-illness threats, the findings were the reverse. Several clinical and theoretical implications are discussed.

Conference scene: DGVS spring conference 2009.

PubMed

Kolligs, Frank Thomas

2009-10-01

The 3rd annual DGVS Spring Conference of the German Society for Gastroenterology (Deutsche Gesellschaft für Verdauungs- und Stoffwechselkrankheiten) was held at the Seminaris Campus Hotel in Berlin, Germany, on 8-9 May, 2009. The conference was organized by Roland Schmid and Matthias Ebert from the Technical University of Munich, Germany. The central theme of the meeting was 'translational gastrointestinal oncology: towards personalized medicine and individualized therapy'. The conference covered talks on markers for diagnosis, screening and surveillance of colorectal cancer, targets for molecular therapy, response prediction in clinical oncology, development and integration of molecular imaging in gastrointestinal oncology and translational research in clinical trial design. Owing to the broad array of topics and limitations of space, this article will focus on biomarkers, response prediction and the integration of biomarkers into clinical trials. Presentations mentioned in this summary were given by Matthias Ebert (Technical University of Munich, Germany), Esmeralda Heiden (Epigenomics, Berlin, Germany), Frank Kolligs (University of Munich, Germany), Florian Lordick (University of Heidelberg, Germany), Hans Jorgen Nielsen (University of Copenhagen, Denmark), Anke Reinacher-Schick (University of Bochum, Germany), Christoph Röcken (University of Berlin, Germany), Wolff Schmiegel (University of Bochum, Germany) and Thomas Seufferlein (University of Halle, Germany).

MACC1 - a novel target for solid cancers.

PubMed

Stein, Ulrike

2013-09-01

The metastatic dissemination of primary tumors is directly linked to patient survival in many tumor entities. The previously undescribed gene metastasis-associated in colon cancer 1 (MACC1) was discovered by genome-wide analyses in colorectal cancer (CRC) tissues. MACC1 is a tumor stage-independent predictor for CRC metastasis linked to metastasis-free survival. In this review, the discovery of MACC1 is briefly presented. In the following, the overwhelming confirmation of these data is provided supporting MACC1 as a new remarkable biomarker for disease prognosis and prediction of therapy response for CRC and also for a variety of additional forms of solid cancers. Lastly, the potential clinical utility of MACC1 as a target for prevention or restriction of tumor progression and metastasis is envisioned. MACC1 has been identified as a prognostic biomarker in a variety of solid cancers. MACC1 correlated with tumor formation and progression, development of metastases and patient survival representing a decisive driver for tumorigenesis and metastasis. MACC1 was also demonstrated to be of predictive value for therapy response. MACC1 is a promising therapeutic target for anti-tumor and anti-metastatic intervention strategies of solid cancers. Its clinical utility, however, must be demonstrated in clinical trials.

Molecular alterations and biomarkers in colorectal cancer

PubMed Central

Grady, William M.; Pritchard, Colin C.

2013-01-01

The promise of precision medicine is now a clinical reality. Advances in our understanding of the molecular genetics of colorectal cancer genetics is leading to the development of a variety of biomarkers that are being used as early detection markers, prognostic markers, and markers for predicting treatment responses. This is no more evident than in the recent advances in testing colorectal cancers for specific molecular alterations in order to guide treatment with the monoclonal antibody therapies cetuximab and panitumumab, which target the epidermal growth factor receptor (EGFR). In this review, we update a prior review published in 2010 and describe our current understanding of the molecular pathogenesis of colorectal cancer and how these alterations relate to emerging biomarkers for early detection and risk stratification (diagnostic markers), prognosis (prognostic markers), and the prediction of treatment responses (predictive markers). PMID:24178577