Science.gov

Sample records for expression signatures predict

  1. Building gene expression signatures indicative of transcription factor activation to predict AOP modulation

    EPA Science Inventory

    Building gene expression signatures indicative of transcription factor activation to predict AOP modulation Adverse outcome pathways (AOPs) are a framework for predicting quantitative relationships between molecular initiatin...

  2. Building gene expression signatures indicative of transcription factor activation to predict AOP modulation

    EPA Science Inventory

    Building gene expression signatures indicative of transcription factor activation to predict AOP modulation Adverse outcome pathways (AOPs) are a framework for predicting quantitative relationships between molecular initiatin...

  3. A microRNA expression signature predicts meningioma recurrence.

    PubMed

    Zhi, Feng; Zhou, Guangxin; Wang, Suinuan; Shi, Yimin; Peng, Ya; Shao, Naiyuan; Guan, Wei; Qu, Hongtao; Zhang, Yi; Wang, Qiang; Yang, Changchun; Wang, Rong; Wu, Sujia; Xia, Xiwei; Yang, Yilin

    2013-01-01

    The aberrant expression of microRNAs (miRNAs) is associated with a variety of diseases, including cancer. In our study, we examined the miRNA expression profile of meningiomas, which is a common type of benign intracranial tumor derived from the protective meninges membranes that surround the brain and spinal cord. To define a typical human meningioma miRNA profile, the expression of 200 miRNAs in a training sample set were screened using quantitative reverse transcription polymerase chain reaction analysis, and then significantly altered miRNAs were validated in a secondary independent sample set. Kaplan-Meier and univariate/multivariate Cox proportional hazard regression analyses were performed to assess whether miRNA expression could predict the recurrence of meningioma after tumor resection. After a two-phase selection and validation process, 14 miRNAs were found to exhibit significantly different expression profiles in meningioma samples compared to normal adjacent tissue (NAT) samples. Unsupervised clustering analysis indicated that the 14-miRNA profile differed between tumor and NAT samples. Downregulation of miR-29c-3p and miR-219-5p were found to be associated with advanced clinical stages of meningioma. Kaplan-Meier analysis showed that high expression of miR-190a and low expression of miR-29c-3p and miR-219-5p correlated significantly with higher recurrence rates in meningioma patients. Cox proportional hazard regression analysis revealed that miR-190a expression level is an important prognostic predictor that is independent of other clinicopathological factors. Our results suggest that the use of miRNA profiling has significant potential as an effective diagnostic and prognostic marker in defining the expression signature of meningiomas and in predicting postsurgical outcomes. Copyright © 2012 UICC.

  4. An endometrial gene expression signature accurately predicts recurrent implantation failure after IVF

    PubMed Central

    Koot, Yvonne E. M.; van Hooff, Sander R.; Boomsma, Carolien M.; van Leenen, Dik; Groot Koerkamp, Marian J. A.; Goddijn, Mariëtte; Eijkemans, Marinus J. C.; Fauser, Bart C. J. M.; Holstege, Frank C. P.; Macklon, Nick S.

    2016-01-01

    The primary limiting factor for effective IVF treatment is successful embryo implantation. Recurrent implantation failure (RIF) is a condition whereby couples fail to achieve pregnancy despite consecutive embryo transfers. Here we describe the collection of gene expression profiles from mid-luteal phase endometrial biopsies (n = 115) from women experiencing RIF and healthy controls. Using a signature discovery set (n = 81) we identify a signature containing 303 genes predictive of RIF. Independent validation in 34 samples shows that the gene signature predicts RIF with 100% positive predictive value (PPV). The strength of the RIF associated expression signature also stratifies RIF patients into distinct groups with different subsequent implantation success rates. Exploration of the expression changes suggests that RIF is primarily associated with reduced cellular proliferation. The gene signature will be of value in counselling and guiding further treatment of women who fail to conceive upon IVF and suggests new avenues for developing intervention. PMID:26797113

  5. Building predictive gene signatures through simultaneous assessment of transcription factor activation and gene expression.

    EPA Science Inventory

    Building predictive gene signatures through simultaneous assessment of transcription factor activation and gene expression Exposure to many drugs and environmentally-relevant chemicals can cause adverse outcomes. These adverse outcomes, such as cancer, have been linked to mol...

  6. Building predictive gene signatures through simultaneous assessment of transcription factor activation and gene expression.

    EPA Science Inventory

    Building predictive gene signatures through simultaneous assessment of transcription factor activation and gene expression Exposure to many drugs and environmentally-relevant chemicals can cause adverse outcomes. These adverse outcomes, such as cancer, have been linked to mol...

  7. A gene expression signature that can predict the recurrence of tamoxifen-treated primary breast cancer

    PubMed Central

    Chanrion, Maïa; Negre, Vincent; Fontaine, Hélène; Salvetat, Nicolas; Bibeau, Frédéric; Grogan, Gaëtan Mac; Mauriac, Louis; Katsaros, Dionyssios; Molina, Franck; Theillet, Charles; Darbon, Jean-Marie

    2008-01-01

    Purpose Identification of a molecular signature predicting the relapse of tamoxifen-treated primary breast cancers should help the therapeutical management of ER-positive cancers. Experimental Design A series of 132 primary tumors from patients who received adjuvant tamoxifen were analysed for expression profiles at the whole genome level by 70-mer oligonucleotide microarrays. A supervised analysis was performed to identify an expression signature. Results We defined a 36-gene signature that classified correctly 78% of patients with relapse and 80% of relapse-free patients (79% accuracy). Using 23 independent tumors, we confirmed the accuracy of the signature (78%), whose relevance was further demonstrated by using published microarray data from 60 tamoxifen-treated patients (63% accuracy). Univariate analysis using the validation set of 83 tumors demonstrated that the 36-gene classifier was more efficient to predict disease-free survival than the traditional histo-pathological prognostic factors and as effective as the Nottingham Prognostic Index or the “Adjuvant!“ software. Multivariate analysis demonstrated that the molecular signature was the only independent prognostic factor. Comparison with several already published signatures demonstated that the 36-gene signature was among the best to classify tumors from both training and validation sets. Kaplan-Meier analyses emphasized its prognostic power both on the whole cohort of patients and on a subgroup with an intermediate risk of recurrence as defined by the St Gallen criteria. Conclusion This study identifies a molecular signature specifying a subgroup of patients who do not gain benefits from tamoxifen treatment. These patients may therefore be eligible for alternative endocrine therapies and/or chemotherapy. PMID:18347175

  8. A gene expression signature that can predict the recurrence of tamoxifen-treated primary breast cancer.

    PubMed

    Chanrion, Maïa; Negre, Vincent; Fontaine, Hélène; Salvetat, Nicolas; Bibeau, Frédéric; Mac Grogan, Gaëtan; Mauriac, Louis; Katsaros, Dionyssios; Molina, Franck; Theillet, Charles; Darbon, Jean-Marie

    2008-03-15

    The identification of a molecular signature predicting the relapse of tamoxifen-treated primary breast cancers should help the therapeutic management of estrogen receptor-positive cancers. A series of 132 primary tumors from patients who received adjuvant tamoxifen were analyzed for expression profiles at the whole-genome level by 70-mer oligonucleotide microarrays. A supervised analysis was done to identify an expression signature. We defined a 36-gene signature that correctly classified 78% of patients with relapse and 80% of relapse-free patients (79% accuracy). Using 23 independent tumors, we confirmed the accuracy of the signature (78%) whose relevance was further shown by using published microarray data from 60 tamoxifen-treated patients (63% accuracy). Univariate analysis using the validation set of 83 tumors showed that the 36-gene classifier is more efficient in predicting disease-free survival than the traditional histopathologic prognostic factors and is as effective as the Nottingham Prognostic Index or the "Adjuvant!" software. Multivariate analysis showed that the molecular signature is the only independent prognostic factor. A comparison with several already published signatures demonstrated that the 36-gene signature is among the best to classify tumors from both training and validation sets. Kaplan-Meier analyses emphasized its prognostic power both on the whole cohort of patients and on a subgroup with an intermediate risk of recurrence as defined by the St. Gallen criteria. This study identifies a molecular signature specifying a subgroup of patients who do not gain benefits from tamoxifen treatment. These patients may therefore be eligible for alternative endocrine therapies and/or chemotherapy.

  9. A composite gene expression signature optimizes prediction of colorectal cancer metastasis and outcome

    PubMed Central

    Schell, Michael J.; Yang, Mingli; Missiaglia, Edoardo; Delorenzi, Mauro; Soneson, Charlotte; Yue, Binglin; Nebozhyn, Michael V.; Loboda, Andrey; Bloom, Gregory; Yeatman, Timothy J

    2015-01-01

    Purpose We previously found that an epithelial-to-mesenchymal transition (EMT)-based gene expression signature was highly correlated to the first principal component (PC1) of 326 colorectal cancer (CRC) tumors and was prognostic. This study was designed to improve these signatures for better prediction of metastasis and outcome. Experimental Design 468 CRC tumors including all stages (I–IV) and metastatic lesions were used to develop a new prognostic score (ΔPC1.EMT) by subtracting the EMT signature score from its correlated PC1 signature score. The score was validated on six other independent datasets with total 3697 tumors. Results ΔPC1.EMT was found to be far more predictive of metastasis and outcome than its parent scores. It performed well in Stages I–III, amongst MSI subtypes, and across multiple mutation-based subclasses, demonstrating a refined capacity to predict distant metastatic potential in tumors even with a “good” prognosis. For example, in the PETACC-3 clinical trial dataset it predicted worse overall survival in an adjusted multivariable model for Stage III patients (HR by IQR=1.50, 95%CI=1.25–1.81, P=0.000016, N=644). The improved performance of ΔPC1.EMT was related to its propensity of identifying epithelial-like subpopulations as well as mesenchymal-like subpopulations. Biologically, the signature was correlated positively with RAS signaling but negatively with mitochondrial metabolism. ΔPC1.EMT was a “best of assessed” prognostic score when compared to ten other known prognostic signatures. Conclusion The study developed a prognostic signature score with a propensity of detecting non-EMT features, including epithelial cancer stem cell-related properties, thereby improving its potential to predict metastasis and poorer outcome in Stages I-III patients. PMID:26446941

  10. mRNA Expression Signature of Gleason Grade Predicts Lethal Prostate Cancer

    PubMed Central

    Penney, Kathryn L.; Sinnott, Jennifer A.; Fall, Katja; Pawitan, Yudi; Hoshida, Yujin; Kraft, Peter; Stark, Jennifer R.; Fiorentino, Michelangelo; Perner, Sven; Finn, Stephen; Calza, Stefano; Flavin, Richard; Freedman, Matthew L.; Setlur, Sunita; Sesso, Howard D.; Andersson, Swen-Olof; Martin, Neil; Kantoff, Philip W.; Johansson, Jan-Erik; Adami, Hans-Olov; Rubin, Mark A.; Loda, Massimo; Golub, Todd R.; Andrén, Ove; Stampfer, Meir J.; Mucci, Lorelei A.

    2011-01-01

    Purpose Prostate-specific antigen screening has led to enormous overtreatment of prostate cancer because of the inability to distinguish potentially lethal disease at diagnosis. We reasoned that by identifying an mRNA signature of Gleason grade, the best predictor of prognosis, we could improve prediction of lethal disease among men with moderate Gleason 7 tumors, the most common grade, and the most indeterminate in terms of prognosis. Patients and Methods Using the complementary DNA–mediated annealing, selection, extension, and ligation assay, we measured the mRNA expression of 6,100 genes in prostate tumor tissue in the Swedish Watchful Waiting cohort (n = 358) and Physicians' Health Study (PHS; n = 109). We developed an mRNA signature of Gleason grade comparing individuals with Gleason ≤ 6 to those with Gleason ≥ 8 tumors and applied the model among patients with Gleason 7 to discriminate lethal cases. Results We built a 157-gene signature using the Swedish data that predicted Gleason with low misclassification (area under the curve [AUC] = 0.91); when this signature was tested in the PHS, the discriminatory ability remained high (AUC = 0.94). In men with Gleason 7 tumors, who were excluded from the model building, the signature significantly improved the prediction of lethal disease beyond knowing whether the Gleason score was 4 + 3 or 3 + 4 (P = .006). Conclusion Our expression signature and the genes identified may improve our understanding of the de-differentiation process of prostate tumors. Additionally, the signature may have clinical applications among men with Gleason 7, by further estimating their risk of lethal prostate cancer and thereby guiding therapy decisions to improve outcomes and reduce overtreatment. PMID:21537050

  11. Long noncoding RNA expression signature to predict platinum-based chemotherapeutic sensitivity of ovarian cancer patients.

    PubMed

    Liu, Rong; Zeng, Ying; Zhou, Cheng-Fang; Wang, Ying; Li, Xi; Liu, Zhao-Qian; Chen, Xiao-Ping; Zhang, Wei; Zhou, Hong-Hao

    2017-12-01

    Dysregulated long noncoding RNAs (lncRNAs) are potential markers of several tumor prognoses. This study aimed to develop a lncRNA expression signature that can predict chemotherapeutic sensitivity for patients with advanced stage and high-grade serous ovarian cancer (HGS-OvCa) treated with platinum-based chemotherapy. The lncRNA expression profiles of 258 HGS-OvCa patients from The Cancer Genome Atlas were analyzed. Results revealed that an eight-lncRNA signature was significantly associated with chemosensitivity in the multivariate logistic regression model, which can accurately predict the chemosensitivity of patients [Area under curve (AUC) = 0.83]. The association of a chemosensitivity predictor with molecular subtypes indicated the excellent prognosis performance of this marker in differentiated, mesenchymal, and immunoreactive subtypes (AUC > 0.8). The significant correlation between ZFAS1 expression and chemosensitivity was confirmed in 233 HGS-OvCa patients from the Gene Expression Omnibus datasets (GSE9891, GSE63885, and GSE51373). In vitro experiments demonstrated that the ZFAS1 expression was upregulated by cisplatin in A2008, HeyA8, and HeyC2 cell lines. This finding suggested that ZFAS1 may participate in platinum resistance. Therefore, the evaluation of the eight-lncRNA signature may be clinically implicated in the selection of platinum-resistant HGS-OvCa patients. The role of ZFAS1 in platinum resistance should be further investigated.

  12. Predicting Autism Spectrum Disorder Using Blood-based Gene Expression Signatures and Machine Learning

    PubMed Central

    Oh, Dong Hoon; Kim, Il Bin; Kim, Seok Hyeon; Ahn, Dong Hyun

    2017-01-01

    Objective The aim of this study was to identify a transcriptomic signature that could be used to classify subjects with autism spectrum disorder (ASD) compared to controls on the basis of blood gene expression profiles. The gene expression profiles could ultimately be used as diagnostic biomarkers for ASD. Methods We used the published microarray data (GSE26415) from the Gene Expression Omnibus database, which included 21 young adults with ASD and 21 age- and sex-matched unaffected controls. Nineteen differentially expressed probes were identified from a training dataset (n=26, 13 ASD cases and 13 controls) using the limma package in R language (adjusted p value <0.05) and were further analyzed in a test dataset (n=16, 8 ASD cases and 8 controls) using machine learning algorithms. Results Hierarchical cluster analysis showed that subjects with ASD were relatively well-discriminated from controls. Based on the support vector machine and K-nearest neighbors analysis, validation of 19-DE probes with a test dataset resulted in an overall class prediction accuracy of 93.8% as well as a sensitivity and specificity of 100% and 87.5%, respectively. Conclusion The results of our exploratory study suggest that the gene expression profiles identified from the peripheral blood samples of young adults with ASD can be used to identify a biological signature for ASD. Further study using a larger cohort and more homogeneous datasets is required to improve the diagnostic accuracy. PMID:28138110

  13. A hemocyte gene expression signature correlated with predictive capacity of oysters to survive Vibrio infections

    PubMed Central

    2012-01-01

    Background The complex balance between environmental and host factors is an important determinant of susceptibility to infection. Disturbances of this equilibrium may result in multifactorial diseases as illustrated by the summer mortality syndrome, a worldwide and complex phenomenon that affects the oysters, Crassostrea gigas. The summer mortality syndrome reveals a physiological intolerance making this oyster species susceptible to diseases. Exploration of genetic basis governing the oyster resistance or susceptibility to infections is thus a major goal for understanding field mortality events. In this context, we used high-throughput genomic approaches to identify genetic traits that may characterize inherent survival capacities in C. gigas. Results Using digital gene expression (DGE), we analyzed the transcriptomes of hemocytes (immunocompetent cells) of oysters able or not able to survive infections by Vibrio species shown to be involved in summer mortalities. Hemocytes were nonlethally collected from oysters before Vibrio experimental infection, and two DGE libraries were generated from individuals that survived or did not survive. Exploration of DGE data and microfluidic qPCR analyses at individual level showed an extraordinary polymorphism in gene expressions, but also a set of hemocyte-expressed genes whose basal mRNA levels discriminate oyster capacity to survive infections by the pathogenic V. splendidus LGP32. Finally, we identified a signature of 14 genes that predicted oyster survival capacity. Their expressions are likely driven by distinct transcriptional regulation processes associated or not associated to gene copy number variation (CNV). Conclusions We provide here for the first time in oyster a gene expression survival signature that represents a useful tool for understanding mortality events and for assessing genetic traits of interest for disease resistance selection programs. PMID:22708697

  14. A gene expression inflammatory signature specifically predicts multiple myeloma evolution and patients survival

    PubMed Central

    Botta, C; Di Martino, M T; Ciliberto, D; Cucè, M; Correale, P; Rossi, M; Tagliaferri, P; Tassone, P

    2016-01-01

    Multiple myeloma (MM) is closely dependent on cross-talk between malignant plasma cells and cellular components of the inflammatory/immunosuppressive bone marrow milieu, which promotes disease progression, drug resistance, neo-angiogenesis, bone destruction and immune-impairment. We investigated the relevance of inflammatory genes in predicting disease evolution and patient survival. A bioinformatics study by Ingenuity Pathway Analysis on gene expression profiling dataset of monoclonal gammopathy of undetermined significance, smoldering and symptomatic-MM, identified inflammatory and cytokine/chemokine pathways as the most progressively affected during disease evolution. We then selected 20 candidate genes involved in B-cell inflammation and we investigated their role in predicting clinical outcome, through univariate and multivariate analyses (log-rank test, logistic regression and Cox-regression model). We defined an 8-genes signature (IL8, IL10, IL17A, CCL3, CCL5, VEGFA, EBI3 and NOS2) identifying each condition (MGUS/smoldering/symptomatic-MM) with 84% accuracy. Moreover, six genes (IFNG, IL2, LTA, CCL2, VEGFA, CCL3) were found independently correlated with patients' survival. Patients whose MM cells expressed high levels of Th1 cytokines (IFNG/LTA/IL2/CCL2) and low levels of CCL3 and VEGFA, experienced the longest survival. On these six genes, we built a prognostic risk score that was validated in three additional independent datasets. In this study, we provide proof-of-concept that inflammation has a critical role in MM patient progression and survival. The inflammatory-gene prognostic signature validated in different datasets clearly indicates novel opportunities for personalized anti-MM treatment. PMID:27983725

  15. A chemokine gene expression signature derived from meta-analysis predicts the pathogenicity of viral respiratory infections

    PubMed Central

    2011-01-01

    Background During respiratory viral infections host injury occurs due in part to inappropriate host responses. In this study we sought to uncover the host transcriptional responses underlying differences between high- and low-pathogenic infections. Results From a compendium of 12 studies that included responses to influenza A subtype H5N1, reconstructed 1918 influenza A virus, and SARS coronavirus, we used meta-analysis to derive multiple gene expression signatures. We compared these signatures by their capacity to segregate biological conditions by pathogenicity and predict pathogenicity in a test data set. The highest-performing signature was expressed as a continuum in low-, medium-, and high-pathogenicity samples, suggesting a direct, analog relationship between expression and pathogenicity. This signature comprised 57 genes including a subnetwork of chemokines, implicating dysregulated cell recruitment in injury. Conclusions Highly pathogenic viruses elicit expression of many of the same key genes as lower pathogenic viruses but to a higher degree. This increased degree of expression may result in the uncontrolled co-localization of inflammatory cell types and lead to irreversible host damage. PMID:22189154

  16. A gene expression signature that can predict green tea exposure and chemopreventive efficacy of lung cancer in mice.

    PubMed

    Lu, Yan; Yao, Ruisheng; Yan, Ying; Wang, Yian; Hara, Yukihiko; Lubet, Ronald A; You, Ming

    2006-02-15

    Green tea has been shown to be a potent chemopreventive agent against lung tumorigenesis in animal models. Previously, we found that treatment of A/J mice with either green tea (0.6% in water) or a defined green tea catechin extract (polyphenon E; 2.0 g/kg in diet) inhibited lung tumor tumorigenesis. Here, we described expression profiling of lung tissues derived from these studies to determine the gene expression signature that can predict the exposure and efficacy of green tea in mice. We first profiled global gene expressions in normal lungs versus lung tumors to determine genes which might be associated with the tumorigenic process (TUM genes). Gene expression in control tumors and green tea-treated tumors (either green tea or polyphenon E) were compared to determine those TUM genes whose expression levels in green tea-treated tumors returned to levels seen in normal lungs. We established a 17-gene expression profile specific for exposure to effective doses of either green tea or polyphenon E. This gene expression signature was altered both in normal lungs and lung adenomas when mice were exposed to green tea or polyphenon E. These experiments identified patterns of gene expressions that both offer clues for green tea's potential mechanisms of action and provide a molecular signature specific for green tea exposure.

  17. A Cell Type-Specific Expression Signature Predicts Haploinsufficient Autism-Susceptibility Genes.

    PubMed

    Zhang, Chaolin; Shen, Yufeng

    2017-02-01

    Recent studies have identified many genes with rare de novo mutations in autism, but a limited number of these have been conclusively established as disease-susceptibility genes due to the lack of recurrence and confounding background mutations. Such extreme genetic heterogeneity severely limits recurrence-based statistical power even in studies with a large sample size. Here, we use cell-type specific expression profiles to differentiate mutations in autism patients from those in unaffected siblings. We report a gene expression signature in different neuronal cell types shared by genes with likely gene-disrupting (LGD) mutations in autism cases. This signature reflects haploinsufficiency of risk genes enriched in transcriptional and post-transcriptional regulators, with the strongest positive associations with specific types of neurons in different brain regions, including cortical neurons, cerebellar granule cells, and striatal medium spiny neurons. When used to prioritize genes with a single LGD mutation in cases, a D-score derived from the signature achieved a precision of 40% as compared with the 15% baseline with a minimal loss in sensitivity. An ensemble model combining D-score with mutation intolerance metrics from Exome Aggregation Consortium further improved the precision to 60%, resulting in 117 high-priority candidates. These prioritized lists can facilitate identification of additional autism-susceptibility genes.

  18. Gene-Expression Signature Predicts Postoperative Recurrence in Stage I Non-Small Cell Lung Cancer Patients

    PubMed Central

    Lu, Yan; Wang, Liang; Liu, Pengyuan; Yang, Ping; You, Ming

    2012-01-01

    About 30% stage I non-small cell lung cancer (NSCLC) patients undergoing resection will recur. Robust prognostic markers are required to better manage therapy options. The purpose of this study is to develop and validate a novel gene-expression signature that can predict tumor recurrence of stage I NSCLC patients. Cox proportional hazards regression analysis was performed to identify recurrence-related genes and a partial Cox regression model was used to generate a gene signature of recurrence in the training dataset −142 stage I lung adenocarcinomas without adjunctive therapy from the Director's Challenge Consortium. Four independent validation datasets, including GSE5843, GSE8894, and two other datasets provided by Mayo Clinic and Washington University, were used to assess the prediction accuracy by calculating the correlation between risk score estimated from gene expression and real recurrence-free survival time and AUC of time-dependent ROC analysis. Pathway-based survival analyses were also performed. 104 probesets correlated with recurrence in the training dataset. They are enriched in cell adhesion, apoptosis and regulation of cell proliferation. A 51-gene expression signature was identified to distinguish patients likely to develop tumor recurrence (Dxy = −0.83, P<1e-16) and this signature was validated in four independent datasets with AUC >85%. Multiple pathways including leukocyte transendothelial migration and cell adhesion were highly correlated with recurrence-free survival. The gene signature is highly predictive of recurrence in stage I NSCLC patients, which has important prognostic and therapeutic implications for the future management of these patients. PMID:22292069

  19. Gene-expression signature predicts postoperative recurrence in stage I non-small cell lung cancer patients.

    PubMed

    Lu, Yan; Wang, Liang; Liu, Pengyuan; Yang, Ping; You, Ming

    2012-01-01

    About 30% stage I non-small cell lung cancer (NSCLC) patients undergoing resection will recur. Robust prognostic markers are required to better manage therapy options. The purpose of this study is to develop and validate a novel gene-expression signature that can predict tumor recurrence of stage I NSCLC patients. Cox proportional hazards regression analysis was performed to identify recurrence-related genes and a partial Cox regression model was used to generate a gene signature of recurrence in the training dataset -142 stage I lung adenocarcinomas without adjunctive therapy from the Director's Challenge Consortium. Four independent validation datasets, including GSE5843, GSE8894, and two other datasets provided by Mayo Clinic and Washington University, were used to assess the prediction accuracy by calculating the correlation between risk score estimated from gene expression and real recurrence-free survival time and AUC of time-dependent ROC analysis. Pathway-based survival analyses were also performed. 104 probesets correlated with recurrence in the training dataset. They are enriched in cell adhesion, apoptosis and regulation of cell proliferation. A 51-gene expression signature was identified to distinguish patients likely to develop tumor recurrence (Dxy = -0.83, P<1e-16) and this signature was validated in four independent datasets with AUC >85%. Multiple pathways including leukocyte transendothelial migration and cell adhesion were highly correlated with recurrence-free survival. The gene signature is highly predictive of recurrence in stage I NSCLC patients, which has important prognostic and therapeutic implications for the future management of these patients.

  20. Vitamin D receptor expression and associated gene signature in tumour stromal fibroblasts predict clinical outcome in colorectal cancer

    PubMed Central

    Ferrer-Mayorga, Gemma; Gómez-López, Gonzalo; Barbáchano, Antonio; Fernández-Barral, Asunción; Peña, Cristina; Pisano, David G; Cantero, Ramón; Rojo, Federico; Muñoz, Alberto; Larriba, María Jesús

    2017-01-01

    Objective Colorectal cancer (CRC) is a major health concern. Vitamin D deficiency is associated with high CRC incidence and mortality, suggesting a protective effect of vitamin D against this disease. Given the strong influence of tumour stroma on cancer progression, we investigated the potential effects of the active vitamin D metabolite 1α,25-dihydroxyvitamin D3 (1,25(OH)2D3) on CRC stroma. Design Expression of vitamin D receptor (VDR) and two 1,25(OH)2D3 target genes was analysed in 658 patients with CRC with prolonged clinical follow-up. 1,25(OH)2D3 effects on primary cultures of patient-derived colon normal fibroblasts (NFs) and cancer-associated fibroblasts (CAFs) were studied using collagen gel contraction and migration assays and global gene expression analyses. Publicly available data sets (n=877) were used to correlate the 1,25(OH)2D3-associated gene signature in CAFs with CRC outcome. Results High VDR expression in tumour stromal fibroblasts was associated with better overall survival (OS) and progression-free survival in CRC, independently of its expression in carcinoma cells. 1,25(OH)2D3 inhibited the protumoural activation of NFs and CAFs and imposed in CAFs a 1,25(OH)2D3-associated gene signature that correlated with longer OS and disease-free survival in CRC. Furthermore, expression of two genes from the signature, CD82 and S100A4, correlated with stromal VDR expression and clinical outcome in our cohort of patients with CRC. Conclusions 1,25(OH)2D3 has protective effects against CRC through the regulation of stromal fibroblasts. Accordingly, expression of VDR and 1,25(OH)2D3-associated gene signature in stromal fibroblasts predicts a favourable clinical outcome in CRC. Therefore, treatment of patients with CRC with VDR agonists could be explored even in the absence of VDR expression in carcinoma cells. PMID:27053631

  1. A gene expression signature that predicts the therapeutic response of the basal-like breast cancer to neoadjuvant chemotherapy

    PubMed Central

    Lin, Yiing; Lin, Shin; Watson, Mark; Trinkaus, Kathryn M.; Kuo, Sacha; Naughton, Michael J.; Weilbaecher, Katherine; Fleming, Timothy P.

    2014-01-01

    Several gene expression profiles have been reported to predict breast cancer response to neoadjuvant chemotherapy. These studies often consider breast cancer as a homogeneous entity, although higher rates of pathologic complete response (pCR) are known to occur within the basal-like subclass. We postulated that profiles with higher predictive accuracy could be derived from a subset analysis of basal-like tumors in isolation. Using a previously described “intrinsic” signature to differentiate breast tumor subclasses, we identified 50 basal-like tumors from two independent clinical trials associated with gene expression profile data. 24 tumor data sets were derived from a 119-patient neoadjuvant trial at our institution and an additional 26 tumor data sets were identified from a published data set (Hess et al. J Clin Oncol 24:4236–4244, 2006). The combined 50 basal-like tumors were partitioned to form a 37 sample training set with 13 sequestered for validation. Clinical surveillance occurred for a mean of 26 months. We identified a 23-gene profile which predicted pCR in basal-like breast cancers with 92% predictive accuracy in the sequestered validation data set. Furthermore, distinct cluster of patients with high rates of cancer recurrence was observed based on cluster analysis with the 23-gene signature. Disease-free survival analysis of these three clusters revealed significantly reduced survival in the patients of this high recurrence cluster. We identified a 23-gene signature which predicts response of basal-like breast cancer to neoadjuvant chemotherapy as well as disease-free survival. This signature is independent of tissue collection method and chemotherapeutic regimen. PMID:19967557

  2. No Specific Gene Expression Signature in Human Granulosa and Cumulus Cells for Prediction of Oocyte Fertilisation and Embryo Implantation

    PubMed Central

    Burnik Papler, Tanja; Vrtacnik Bokal, Eda; Lovrecic, Luca; Kopitar, Andreja Natasa; Maver, Ales

    2015-01-01

    In human IVF procedures objective and reliable biomarkers of oocyte and embryo quality are needed in order to increase the use of single embryo transfer (SET) and thus prevent multiple pregnancies. During folliculogenesis there is an intense bi-directional communication between oocyte and follicular cells. For this reason gene expression profile of follicular cells could be an important indicator and biomarker of oocyte and embryo quality. The objective of this study was to identify gene expression signature(s) in human granulosa (GC) and cumulus (CC) cells predictive of successful embryo implantation and oocyte fertilization. Forty-one patients were included in the study and individual GC and CC samples were collected; oocytes were cultivated separately, allowing a correlation with IVF outcome and elective SET was performed. Gene expression analysis was performed using microarrays, followed by a quantitative real-time PCR validation. After statistical analysis of microarray data, there were no significantly differentially expressed genes (FDR<0,05) between non-fertilized and fertilized oocytes and non-implanted and implanted embryos in either of the cell type. Furthermore, the results of quantitative real-time PCR were in consent with microarray data as there were no significant differences in gene expression of genes selected for validation. In conclusion, we did not find biomarkers for prediction of oocyte fertilization and embryo implantation in IVF procedures in the present study. PMID:25769026

  3. A Five-Gene Expression Signature Predicts Clinical Outcome of Ovarian Serous Cystadenocarcinoma

    PubMed Central

    Guo, Wenna

    2016-01-01

    Ovarian serous cystadenocarcinoma is a common malignant tumor of female genital organs. Treatment is generally less effective as patients are usually diagnosed in the late stage. Therefore, a well-designed prognostic marker provides valuable data for optimizing therapy. In this study, we analyzed 303 samples of ovarian serous cystadenocarcinoma and the corresponding RNA-seq data. We observed the correlation between gene expression and patients' survival and eventually established a risk assessment model of five factors using Cox proportional hazards regression analysis. We found that the survival time in high-risk patients was significantly shorter than in low-risk patients in both training and testing sets after Kaplan-Meier analysis. The AUROC value was 0.67 when predicting the survival time in testing set, which indicates a relatively high specificity and sensitivity. The results suggest diagnostic and therapeutic applications of our five-gene model for ovarian serous cystadenocarcinoma. PMID:27478834

  4. Gene expression signature for early prediction of late occurring pancytopenia in irradiated baboons.

    PubMed

    Port, Matthias; Hérodin, Francis; Valente, Marco; Drouet, Michel; Lamkowski, Andreas; Majewski, Matthäus; Abend, Michael

    2017-02-24

    Based on gene expression changes measured in the peripheral blood within the first 2 days after irradiation, we predicted a pancytopenia in a baboon model. Eighteen baboons were irradiated with 2.5 or 5 Gy. According to changes in blood cell counts, the surviving baboons (n = 17) exhibited a hematological acute radiation syndrome (HARS) either with or without a pancytopenia. We used a two stage study design where stage I was a whole genome screen (microarrays) for mRNA combined with a qRT-PCR platform for simultaneous detection of 667 miRNAs using a part of the samples. Candidate mRNAs and miRNAs differentially upregulated or downregulated (>2-fold, p < 0.05) during the first 2 days after irradiation were chosen for validation in stage II using the remaining samples and using throughout more sensitive qRT-PCR. We detected about twice as many upregulated (mean 2128) than downregulated genes (mean 789) in baboons developing an HARS either with or without a pancytopenia. From 51 candidate mRNAs altogether, 11 mRNAs were validated using qRT-PCR. These mRNAs showed only significant differences between HARS groups and H0, but not between HARS groups with and without pancytopenia. Six miRNA species (e.g., miR-574-3p, p = 0.009, ROC = 0.94) revealed significant gene expression differences between HARS groups with and without pancytopenia and are known to sensitize irradiated cells. Hence, in particular, the newly identified miRNA species for prediction of pancytopenia will support the medical management decision making.

  5. Multisensors signature prediction workbench

    NASA Astrophysics Data System (ADS)

    Latger, Jean; Cathala, Thierry

    2015-10-01

    Guidance of weapon systems relies on sensors to analyze targets signature. Defense weapon systems also need to detect then identify threats also using sensors. The sensors performance is very dependent on conditions e.g. time of day, atmospheric propagation, background ... Visible camera are very efficient for diurnal fine weather conditions, long wave infrared sensors for night vision, radar systems very efficient for seeing through atmosphere and/or foliage ... Besides, multi sensors systems, combining several collocated sensors with associated algorithms of fusion, provide better efficiency (typically for Enhanced Vision Systems). But these sophisticated systems are all the more difficult to conceive, assess and qualify. In that frame, multi sensors simulation is highly required. This paper focuses on multi sensors simulation tools. A first part makes a state of the Art of such simulation workbenches with a special focus on SE-Workbench. SEWorkbench is described with regards to infrared/EO sensors, millimeter waves sensors, active EO sensors and GNSS sensors. Then a general overview of simulation of targets and backgrounds signature objectives is presented, depending on the type of simulation required (parametric studies, open loop simulation, closed loop simulation, hybridization of SW simulation and HW ...). After the objective review, the paper presents some basic requirements for simulation implementation such as the deterministic behavior of simulation, mandatory to repeat it many times for parametric studies... Several technical topics are then discussed, such as the rendering technique (ray tracing vs. rasterization), the implementation (CPU vs. GP GPU) and the tradeoff between physical accuracy and performance of computation. Examples of results using SE-Workbench are showed and commented.

  6. Transcription factor regulation can be accurately predicted from the presence of target gene signatures in microarray gene expression data

    PubMed Central

    Essaghir, Ahmed; Toffalini, Federica; Knoops, Laurent; Kallin, Anders; van Helden, Jacques; Demoulin, Jean-Baptiste

    2010-01-01

    Deciphering transcription factor networks from microarray data remains difficult. This study presents a simple method to infer the regulation of transcription factors from microarray data based on well-characterized target genes. We generated a catalog containing transcription factors associated with 2720 target genes and 6401 experimentally validated regulations. When it was available, a distinction between transcriptional activation and inhibition was included for each regulation. Next, we built a tool (www.tfacts.org) that compares submitted gene lists with target genes in the catalog to detect regulated transcription factors. TFactS was validated with published lists of regulated genes in various models and compared to tools based on in silico promoter analysis. We next analyzed the NCI60 cancer microarray data set and showed the regulation of SOX10, MITF and JUN in melanomas. We then performed microarray experiments comparing gene expression response of human fibroblasts stimulated by different growth factors. TFactS predicted the specific activation of Signal transducer and activator of transcription factors by PDGF-BB, which was confirmed experimentally. Our results show that the expression levels of transcription factor target genes constitute a robust signature for transcription factor regulation, and can be efficiently used for microarray data mining. PMID:20215436

  7. Gene expression signature in organized and growth arrested mammaryacini predicts good outcome in breast cancer

    SciTech Connect

    Fournier, Marcia V.; Martin, Katherine J.; Kenny, Paraic A.; Xhaja, Kris; Bosch, Irene; Yaswen, Paul; Bissell, Mina J.

    2006-02-08

    To understand how non-malignant human mammary epithelial cells (HMEC) transit from a disorganized proliferating to an organized growth arrested state, and to relate this process to the changes that occur in breast cancer, we studied gene expression changes in non-malignant HMEC grown in three-dimensional cultures, and in a previously published panel of microarray data for 295 breast cancer samples. We hypothesized that the gene expression pattern of organized and growth arrested mammary acini would share similarities with breast tumors with good prognoses. Using Affymetrix HG-U133A microarrays, we analyzed the expression of 22,283 gene transcripts in two HMEC cell lines, 184 (finite life span) and HMT3522 S1 (immortal non-malignant), on successive days post-seeding in a laminin-rich extracellular matrix assay. Both HMECs underwent growth arrest in G0/G1 and differentiated into polarized acini between days 5 and 7. We identified gene expression changes with the same temporal pattern in both lines. We show that genes that are significantly lower in the organized, growth arrested HMEC than in their proliferating counterparts can be used to classify breast cancer patients into poor and good prognosis groups with high accuracy. This study represents a novel unsupervised approach to identifying breast cancer markers that may be of use clinically.

  8. Identification of expression signatures predictive of sensitivity to the Bcl-2 family member inhibitor ABT-263 in small cell lung carcinoma and leukemia/lymphoma cell lines.

    PubMed

    Tahir, Stephen K; Wass, John; Joseph, Mary K; Devanarayan, Viswanath; Hessler, Paul; Zhang, Haichao; Elmore, Steve W; Kroeger, Paul E; Tse, Christin; Rosenberg, Saul H; Anderson, Mark G

    2010-03-01

    ABT-263 inhibits the antiapoptotic proteins Bcl-2, Bcl-x(L), and Bcl-w and has single-agent efficacy in numerous small cell lung carcinoma (SCLC) and leukemia/lymphoma cell lines in vitro and in vivo. It is currently in clinical trials for treating patients with SCLC and various leukemia/lymphomas. Identification of predictive markers for response will benefit the clinical development of ABT-263. We identified the expression of Bcl-2 family genes that correlated best with sensitivity to ABT-263 in a panel of 36 SCLC and 31 leukemia/lymphoma cell lines. In cells sensitive to ABT-263, expression of Bcl-2 and Noxa is elevated, whereas expression of Mcl-1 is higher in resistant cells. We also examined global expression differences to identify gene signature sets that correlated with sensitivity to ABT-263 to generate optimal signature sets predictive of sensitivity to ABT-263. Independent cell lines were used to verify the predictive power of the gene sets and to refine the optimal gene signatures. When comparing normal lung tissue and SCLC primary tumors, the expression pattern of these genes in the tumor tissue is most similar to sensitive SCLC lines, whereas normal tissue is most similar to resistant SCLC lines. Most of the genes identified using global expression patterns are related to the apoptotic pathway; however, all but Bcl-rambo are distinct from the Bcl-2 family. This study leverages global expression data to identify key gene expression patterns for sensitivity to ABT-263 in SCLC and leukemia/lymphoma and may provide guidance in the selection of patients in future clinical trials.

  9. High Expression of Three-Gene Signature Improves Prediction of Relapse-Free Survival in Estrogen Receptor-Positive and Node-Positive Breast Tumors

    PubMed Central

    Thakkar, Arvind; Raj, Hemanth; Ravishankar; Muthuvelan, Bhaskaran; Balakrishnan, Arun; Padigaru, Muralidhara

    2015-01-01

    The objective of the present study was to validate prognostic gene signature for estrogen receptor alpha-positive (ER03B1+) and lymph node (+) breast cancer for improved selection of patients for adjuvant therapy. In our previous study, we identified a group of seven genes (GATA3, NTN4, SLC7A8, ENPP1, MLPH, LAMB2, and PLAT) that show elevated messenger RNA (mRNA) expression levels in ERα (+) breast cancer patient samples. The prognostic values of these genes were evaluated using gene expression data from three public data sets of breast cancer patients (n = 395). Analysis of ERα (+) breast cancer cohort (n = 195) showed high expression of GATA3, NTN4, and MLPH genes significantly associated with longer relapse-free survival (RFS). Next cohort of ERα (+) and node (+) samples (n = 109) revealed high mRNA expression of GATA3, SLC7A8, and MLPH significantly associated with longer RFS. Multivariate analysis of combined three-gene signature for ERα (+) cohort, and ERα (+) and node (+) cohorts showed better hazard ratio than individual genes. The validated three-gene signature sets for ERα (+) cohort, and ERα (+) and node (+) cohort may have potential clinical utility since they demonstrated predictive and prognostic ability in three independent public data sets. PMID:26648682

  10. miRNA expression profiling of inflammatory breast cancer identifies a 5-miRNA signature predictive of breast tumor aggressiveness.

    PubMed

    Lerebours, Florence; Cizeron-Clairac, Geraldine; Susini, Aurelie; Vacher, Sophie; Mouret-Fourme, Emmanuelle; Belichard, Catherine; Brain, Etienne; Alberini, Jean-Louis; Spyratos, Frédérique; Lidereau, Rosette; Bieche, Ivan

    2013-10-01

    IBC (inflammatory breast cancer) is a rare but very aggressive form of breast cancer with a particular phenotype. The molecular mechanisms responsible for IBC remain largely unknown. In particular, genetic and epigenetic alterations specific to IBC remain to be identified. MicroRNAs, a class of small noncoding RNAs able to regulate gene expression, are deregulated in breast cancer and may therefore serve as tools for diagnosis and prediction. This study was designed to determine miRNA expression profiling (microRNAome) in IBC. Quantitative RT-PCR was used to determine expression levels of 804 miRNAs in a screening series of 12 IBC compared to 31 non-stage-matched non-IBC and 8 normal breast samples. The differentially expressed miRNAs were then validated in a series of 65 IBC and 95 non-IBC. From a set of 18 miRNAs of interest selected from the screening series, 13 were differentially expressed with statistical significance in the validation series of IBC compared to non-IBC. Among these, a 5-miRNA signature comprising miR-421, miR-486, miR-503, miR-720 and miR-1303 was shown to be predictive for IBC phenotype with an overall accuracy of 89%. Moreover, multivariate analysis showed that this signature was an independent predictor of poor Metastasis-Free Survival in non-IBC patients.

  11. Effects of Sample Size on Differential Gene Expression, Rank Order and Prediction Accuracy of a Gene Signature

    PubMed Central

    Stretch, Cynthia; Khan, Sheehan; Asgarian, Nasimeh; Eisner, Roman; Vaisipour, Saman; Damaraju, Sambasivarao; Graham, Kathryn; Bathe, Oliver F.; Steed, Helen; Greiner, Russell; Baracos, Vickie E.

    2013-01-01

    Top differentially expressed gene lists are often inconsistent between studies and it has been suggested that small sample sizes contribute to lack of reproducibility and poor prediction accuracy in discriminative models. We considered sex differences (69♂, 65♀) in 134 human skeletal muscle biopsies using DNA microarray. The full dataset and subsamples (n = 10 (5♂, 5♀) to n = 120 (60♂, 60♀)) thereof were used to assess the effect of sample size on the differential expression of single genes, gene rank order and prediction accuracy. Using our full dataset (n = 134), we identified 717 differentially expressed transcripts (p<0.0001) and we were able predict sex with ∼90% accuracy, both within our dataset and on external datasets. Both p-values and rank order of top differentially expressed genes became more variable using smaller subsamples. For example, at n = 10 (5♂, 5♀), no gene was considered differentially expressed at p<0.0001 and prediction accuracy was ∼50% (no better than chance). We found that sample size clearly affects microarray analysis results; small sample sizes result in unstable gene lists and poor prediction accuracy. We anticipate this will apply to other phenotypes, in addition to sex. PMID:23755224

  12. Expression signature based on TP53 target genes doesn't predict response to TP53-MDM2 inhibitor in wild type TP53 tumors.

    PubMed

    Sonkin, Dmitriy

    2015-10-22

    A number of TP53-MDM2 inhibitors are currently under investigation as therapeutic agents in a variety of clinical trials in patients with TP53 wild type tumors. Not all wild type TP53 tumors are sensitive to such inhibitors. In an attempt to improve selection of patients with TP53 wild type tumors, an mRNA expression signature based on 13 TP53 transcriptional target genes was recently developed (Jeay et al. 2015). Careful reanalysis of TP53 status in the study validation data set of cancer cell lines considered to be TP53 wild type detected TP53 inactivating alterations in 23% of cell lines. The subsequent reanalysis of the remaining TP53 wild type cell lines clearly demonstrated that unfortunately the 13-gene signature cannot predict response to TP53-MDM2 inhibitor in TP53 wild type tumors.

  13. A gene expression signature of CD34+ cells to predict major cytogenetic response in chronic-phase chronic myeloid leukemia patients treated with imatinib

    PubMed Central

    McWeeney, Shannon K.; Pemberton, Lucy C.; Loriaux, Marc M.; Vartanian, Kristina; Willis, Stephanie G.; Yochum, Gregory; Wilmot, Beth; Turpaz, Yaron; Pillai, Raji; Druker, Brian J.; Snead, Jennifer L.; MacPartlin, Mary; O'Brien, Stephen G.; Melo, Junia V.; Lange, Thoralf; Harrington, Christina A.

    2010-01-01

    In chronic-phase chronic myeloid leukemia (CML) patients, the lack of a major cytogenetic response (< 36% Ph+ metaphases) to imatinib within 12 months indicates failure and mandates a change of therapy. To identify biomarkers predictive of imatinib failure, we performed gene expression array profiling of CD34+ cells from 2 independent cohorts of imatinib-naive chronic-phase CML patients. The learning set consisted of retrospectively selected patients with a complete cytogenetic response or more than 65% Ph+ metaphases within 12 months of imatinib therapy. Based on analysis of variance P less than .1 and fold difference 1.5 or more, we identified 885 probe sets with differential expression between responders and nonresponders, from which we extracted a 75-probe set minimal signature (classifier) that separated the 2 groups. On application to a prospectively accrued validation set, the classifier correctly predicted 88% of responders and 83% of nonresponders. Bioinformatics analysis and comparison with published studies revealed overlap of classifier genes with CML progression signatures and implicated β-catenin in their regulation, suggesting that chronic-phase CML patients destined to fail imatinib have more advanced disease than evident by morphologic criteria. Our classifier may allow directing more aggressive therapy upfront to the patients most likely to benefit while sparing good-risk patients from unnecessary toxicity. PMID:19837975

  14. A reported 20-gene expression signature to predict lymph node-positive disease at radical cystectomy for muscle-invasive bladder cancer is clinically not applicable

    PubMed Central

    van Kessel, Kim E. M.; van de Werken, Harmen J. G.; Lurkin, Irene; Ziel – van der Made, Angelique C. J.; Zwarthoff, Ellen C.; Boormans, Joost L.

    2017-01-01

    Background Neoadjuvant chemotherapy (NAC) for muscle-invasive bladder cancer (MIBC) provides a small but significant survival benefit. Nevertheless, controversies on applying NAC remain because the limited benefit must be weight against chemotherapy-related toxicity and the delay of definitive local treatment. Therefore, there is a clear clinical need for tools to guide treatment decisions on NAC in MIBC. Here, we aimed to validate a previously reported 20-gene expression signature that predicted lymph node-positive disease at radical cystectomy in clinically node-negative MIBC patients, which would be a justification for upfront chemotherapy. Methods We studied diagnostic transurethral resection of bladder tumors (dTURBT) of 150 MIBC patients (urothelial carcinoma) who were subsequently treated by radical cystectomy and pelvic lymph node dissection. RNA was isolated and the expression level of the 20 genes was determined on a qRT-PCR platform. Normalized Ct values were used to calculate a risk score to predict the presence of node-positive disease. The Cancer Genome Atlas (TCGA) RNA expression data was analyzed to subsequently validate the results. Results In a univariate regression analysis, none of the 20 genes significantly correlated with node-positive disease. The area under the curve of the risk score calculated by the 20-gene expression signature was 0.54 (95% Confidence Interval: 0.44-0.65) versus 0.67 for the model published by Smith et al. Node-negative patients had a significantly lower tumor grade at TURBT (p = 0.03), a lower pT stage (p<0.01) and less frequent lymphovascular invasion (13% versus 38%, p<0.01) at radical cystectomy than node-positive patients. In addition, in the TCGA data, none of the 20 genes was differentially expressed in node-negative versus node-positive patients. Conclusions We conclude that a 20-gene expression signature developed for nodal staging of MIBC at radical cystectomy could not be validated on a qRT-PCR platform in a

  15. First Generation Gene Expression Signature for Early Prediction of Late Occurring Hematological Acute Radiation Syndrome in Baboons.

    PubMed

    Port, M; Herodin, F; Valente, M; Drouet, M; Lamkowski, A; Majewski, M; Abend, M

    2016-07-01

    We implemented a two-stage study to predict late occurring hematologic acute radiation syndrome (HARS) in a baboon model based on gene expression changes measured in peripheral blood within the first two days after irradiation. Eighteen baboons were irradiated to simulate different patterns of partial-body and total-body exposure, which corresponded to an equivalent dose of 2.5 or 5 Gy. According to changes in blood cell counts the surviving baboons (n = 17) exhibited mild (H1-2, n = 4) or more severe (H2-3, n = 13) HARS. Blood samples taken before irradiation served as unexposed control (H0, n = 17). For stage I of this study, a whole genome screen (mRNA microarrays) was performed using a portion of the samples (H0, n = 5; H1-2, n = 4; H2-3, n = 5). For stage II, using the remaining samples and the more sensitive methodology, qRT-PCR, validation was performed on candidate genes that were differentially up- or down-regulated during the first two days after irradiation. Differential gene expression was defined as significant (P < 0.05) and greater than or equal to a twofold difference above a H0 classification. From approximately 20,000 genes, on average 46% appeared to be expressed. On day 1 postirradiation for H2-3, approximately 2-3 times more genes appeared up-regulated (1,418 vs. 550) or down-regulated (1,603 vs. 735) compared to H1-2. This pattern became more pronounced at day 2 while the number of differentially expressed genes decreased. The specific genes showed an enrichment of biological processes coding for immune system processes, natural killer cell activation and immune response (P = 1 × E-06 up to 9 × E-14). Based on the P values, magnitude and sustained differential gene expression over time, we selected 89 candidate genes for validation using qRT-PCR. Ultimately, 22 genes were confirmed for identification of H1-3 classifications and seven genes for identification of H2-3 classifications using qRT-PCR. For H1-3 classifications, most genes were

  16. Irma multisensor predictive signature model

    NASA Astrophysics Data System (ADS)

    Watson, John S.; Flynn, David S.; Wellfare, Michael R.; Richards, Mike; Prestwood, Lee

    1995-06-01

    The Irma synthetic signature model was one of the first high resolution synthetic infrared (IR) target and background signature models to be developed for tactical air-to-surface weapon scenarios. Originally developed in 1980 by the Armament Directorate of the Air Force Wright Laboratory (WL/MN), the Irma model was used exclusively to generate IR scenes for smart weapons research and development. In 1988, a number of significant upgrades to Irma were initiated including the addition of a laser channel. This two channel version, Irma 3.0, was released to the user community in 1990. In 1992, an improved scene generator was incorporated into the Irma model which supported correlated frame-to-frame imagery. This and other improvements were released in Irma 2.2. Recently, Irma 3.2, a passive IR/millimeter wave (MMW) code, was completed. Currently, upgrades are underway to include an active MMW channel. Designated Irma 4.0, this code will serve as a cornerstone of sensor fusion research in the laboratory from 6.1 concept development to 6.3 technology demonstration programs for precision guided munitions. Several significant milestones have been reached in this development process and are demonstrated. The Irma 4.0 software design has been developed and interim results are available. Irma is being developed to facilitate multi-sensor smart weapons research and development. It is currently in distribution to over 80 agencies within the U.S. Air Force, U.S. Army, U.S. Navy, ARPA, NASA, Department of Transportation, academia, and industry.

  17. Irma multisensor predictive signature model

    NASA Astrophysics Data System (ADS)

    Watson, John S.; Wellfare, Michael R.; Foster, Joseph; Owens, Monte A.; Vechinski, Douglas A.; Richards, Mike; Resnick, Andrew; Underwood, Vincent

    1998-07-01

    The Irma synthetic signature model was one of the first high resolution infrared (IR) target and background signature models to be developed for tactical weapons application. Originally developed in 1980 by the Munitions Directorate of the Air Force Research Laboratory, the Irma model was used exclusively to generate IR scenes for smart weapons research and development. In 1988, a number of significant upgrades to Irma were initiated including the addition of a laser channel. This two channel version, Irma 3.0, was released to the user community in 1990. In 1992, an improved scene generator was incorporated into the Irma model which supported correlated frame-to-frame imagery. This and other improvements were released in Irma 2.2. Irma 3.2, a passive IR/millimeter wave (MMW) code, was completed in 1994. This served as the cornerstone for the development of the co- registered active/passive IR/MMW model, Irma 4.0. Currently, upgrades are underway to include a near IR (NIR)/visible channel; a facet editor; utilities to support image viewing and scaling; and additional target/data files. The Irma 4.1 software development effort is nearly completion. The purpose of this paper is to illustrate the results of the development. Planned upgrades for Irma 5.0 will be provided as well. Irma is being developed to facilitate multi-sensor research and development. It is currently being used to support a number of civilian and military applications. The current Irma user base includes over 100 agencies within the Air Force, Army, Navy, DARPA, NASA, Department of Transportation, academia, and industry.

  18. Identifying regulatory mechanisms underlying tumorigenesis using locus expression signature analysis.

    PubMed

    Lee, Eunjee; de Ridder, Jeroen; Kool, Jaap; Wessels, Lodewyk F A; Bussemaker, Harmen J

    2014-04-15

    Retroviral insertional mutagenesis is a powerful tool for identifying putative cancer genes in mice. To uncover the regulatory mechanisms by which common insertion loci affect downstream processes, we supplemented genotyping data with genome-wide mRNA expression profiling data for 97 tumors induced by retroviral insertional mutagenesis. We developed locus expression signature analysis, an algorithm to construct and interpret the differential gene expression signature associated with each common insertion locus. Comparing locus expression signatures to promoter affinity profiles allowed us to build a detailed map of transcription factors whose protein-level regulatory activity is modulated by a particular locus. We also predicted a large set of drugs that might mitigate the effect of the insertion on tumorigenesis. Taken together, our results demonstrate the potential of a locus-specific signature approach for identifying mammalian regulatory mechanisms in a cancer context.

  19. MicroRNA expression signatures for the prediction of BRCA1/2 mutation-associated hereditary breast cancer in paraffin-embedded formalin-fixed breast tumors.

    PubMed

    Tanic, Miljana; Yanowski, Kira; Gómez-López, Gonzalo; Rodriguez-Pinilla, María Socorro; Marquez-Rodas, Iván; Osorio, Ana; Pisano, David G; Martinez-Delgado, Beatriz; Benítez, Javier

    2015-02-01

    Screening for germline mutations in breast cancer-associated genes BRCA1 and BRCA2 is indicated for patients with breast cancer from high-risk breast cancer families and influences both treatment options and clinical management. However, only 25% of selected patients test positive for BRCA1/2 mutation, indicating that additional diagnostic biomarkers are necessary. We analyzed 124 formalin-fixed paraffin-embedded (FFPE) tumor samples from patients with hereditary (104) and sporadic (20) invasive breast cancer, divided into two series (A and B). Microarray expression profiling of 829 human miRNAs was performed on 76 samples (Series A), and bioinformatics tool Prophet was used to develop and test a microarray classifier. Samples were stratified into a training set (n = 38) for microarray classifier generation and a test set (n = 38) for signature validation. A 35-miRNA microarray classifier was generated for the prediction of BRCA1/2 mutation status with a reported 95% (95% CI = 0.88-1.0) and 92% (95% CI: 0.84-1.0) accuracy in the training and the test set, respectively. Differential expression of 12 miRNAs between BRCA1/2 mutation carriers versus noncarriers was validated by qPCR in an independent tumor series B (n = 48). Logistic regression model based on the expression of six miRNAs (miR-142-3p, miR-505*, miR-1248, miR-181a-2*, miR-25* and miR-340*) discriminated between tumors from BRCA1/2 mutation carriers and noncarriers with 92% (95% CI: 0.84-0.99) accuracy. In conclusion, we identified miRNA expression signatures predictive of BRCA1/2 mutation status in routinely available FFPE breast tumor samples, which may be useful to complement current patient selection criteria for gene testing by identifying individuals with high likelihood of being BRCA1/2 mutation carriers.

  20. Irma multisensor predictive signature model

    NASA Astrophysics Data System (ADS)

    Watson, John S.; Wellfare, Michael R.; Chenault, David B.; Talele, Sunjay E.; Blume, Bradley T.; Richards, Mike; Prestwood, Lee

    1997-06-01

    Development of target acquisition and target recognition algorithms in highly cluttered backgrounds in a variety of battlefield conditions demands a flexible, high fidelity capability for synthetic image generation. Cost effective smart weapons research and testing also requires extensive scene generation capability. The Irma software package addresses this need through a first principles, phenomenology based scene generator that enhances research into new algorithms, novel sensors, and sensor fusion approaches. Irma was one of the first high resolution synthetic infrared target and background signature models developed for tactical air-to-surface weapon scenarios. Originally developed in 1980 by the Armament Directorate of the Air Force Wright Laboratory, the Irma model was used exclusively to generate IR scenes for smart weapons research and development. in 1987, Nichols Research Corporation took over the maintenance of Irma and has since added substantial capabilities. The development of Irma has culminated in a program that includes not only passive visible, IR, and millimeter wave (MMW) channels but also active MMW and ladar channels. Each of these channels is co-registered providing the capability to develop algorithms for multi-band sensor fusion concepts and associated algorithms. In this paper, the capabilities of the latest release of Irma, Irma 4.0, will be described. A brief description of the elements of the software that are common to all channels will be provided. Each channel will be described briefly including a summary of the phenomenological effects and the sensor effects modeled in the software. Examples of Irma multi- channel imagery will be presented.

  1. Numerical prediction of meteoric infrasound signatures

    NASA Astrophysics Data System (ADS)

    Nemec, Marian; Aftosmis, Michael J.; Brown, Peter G.

    2017-06-01

    We present a thorough validation of a computational approach to predict infrasonic signatures of centimeter-sized meteoroids. This is the first direct comparison of computational results with well-calibrated observations that include trajectories, optical masses and ground pressure signatures. We assume that the energy deposition along the meteor trail is dominated by atmospheric drag and simulate a steady, inviscid flow of air in thermochemical equilibrium to compute a near-body pressure signature of the meteoroid. This signature is then propagated through a stratified and windy atmosphere to the ground using a methodology from aircraft sonic-boom analysis. The results show that when the source of the signature is the cylindrical Mach-cone, the simulations closely match the observations. The prediction of the shock rise-time, the zero-peak amplitude of the waveform and the duration of the positive pressure phase are consistently within 10% of the measurements. Uncertainty in primarily the shape of the meteoroid results in a poorer prediction of the trailing part of the waveform. Overall, our results independently verify energy deposition estimates deduced from optical observations.

  2. Gene expression signatures for colorectal cancer microsatellite status and HNPCC

    PubMed Central

    Kruhøffer, M; Jensen, J L; Laiho, P; Dyrskjøt, L; Salovaara, R; Arango, D; Birkenkamp-Demtroder, K; Sørensen, F B; Christensen, L L; Buhl, L; Mecklin, J-P; Järvinen, H; Thykjaer, T; Wikman, F P; Bech-Knudsen, F; Juhola, M; Nupponen, N N; Laurberg, S; Andersen, C L; Aaltonen, L A; Ørntoft, T F

    2005-01-01

    The majority of microsatellite instable (MSI) colorectal cancers are sporadic, but a subset belongs to the syndrome hereditary nonpolyposis colorectal cancer (HNPCC). Microsatellite instability is caused by dysfunction of the mismatch repair (MMR) system that leads to a mutator phenotype, and MSI is correlated to prognosis and response to chemotherapy. Gene expression signatures as predictive markers are being developed for many cancers, and the identification of a signature for MMR deficiency would be of interest both clinically and biologically. To address this issue, we profiled the gene expression of 101 stage II and III colorectal cancers (34 MSI, 67 microsatellite stable (MSS)) using high-density oligonucleotide microarrays. From these data, we constructed a nine-gene signature capable of separating the mismatch repair proficient and deficient tumours. Subsequently, we demonstrated the robustness of the signature by transferring it to a real-time RT-PCR platform. Using this platform, the signature was validated on an independent test set consisting of 47 tumours (10 MSI, 37 MSS), of which 45 were correctly classified. In a second step, we constructed a signature capable of separating MMR-deficient tumours into sporadic MSI and HNPCC cases, and validated this by a mathematical cross-validation approach. The demonstration that this two-step classification approach can identify MSI as well as HNPCC cases merits further gene expression studies to identify prognostic signatures. PMID:15956967

  3. Determination of minimal transcriptional signatures of compounds for target prediction

    PubMed Central

    2012-01-01

    The identification of molecular target and mechanism of action of compounds is a key hurdle in drug discovery. Multiplexed techniques for bead-based expression profiling allow the measurement of transcriptional signatures of compound-treated cells in high-throughput mode. Such profiles can be used to gain insight into compounds' mode of action and the protein targets they are modulating. Through the proxy of target prediction from such gene signatures we explored important aspects of the use of transcriptional profiles to capture biological variability of perturbed cellular assays. We found that signatures derived from expression data and signatures derived from biological interaction networks performed equally well, and we showed that gene signatures can be optimised using a genetic algorithm. Gene signatures of approximately 128 genes seemed to be most generic, capturing a maximum of the perturbation inflicted on cells through compound treatment. Moreover, we found evidence for oxidative phosphorylation to be one of the most general ways to capture compound perturbation. PMID:22574917

  4. Gene-expression signature of vascular invasion in hepatocellular carcinoma

    PubMed Central

    Mínguez, Beatriz; Hoshida, Yujin; Villanueva, Augusto; Toffanin, Sara; Cabellos, Laia; Thung, Swan; Mandeli, John; Sia, Daniela; April, Craig; Fan, Jian-Bing; Lachenmayer, Anja; Savic, Radoslav; Roayaie, Sasan; Mazzaferro, Vincenzo; Bruix, Jordi; Schwartz, Myron; Friedman, Scott L.; Llovet, Josep M.

    2013-01-01

    Background & Aims Vascular invasion is a major predictor of tumor recurrence after surgical treatments for hepatocellular carcinoma (HCC). While macroscopic vascular invasion can be detected by radiological techniques, pre-operative detection of microscopic vascular invasion, which complicates 30–40% of patients with early tumors, remains elusive. Methods A total of 214 patients with hepatocellular carcinoma who underwent resection were included in the study. By using genome-wide gene-expression profiling of 79 hepatitis C-related hepatocellular carcinoma samples (training set), a gene-expression signature associated with vascular invasion was defined. The signature was validated in formalin-fixed paraffin-embedded tissues obtained from an independent set of 135 patients with various etiologies. Results A 35-gene signature of vascular invasion was defined in the training set, predicting vascular invasion with an accuracy of 69%. The signature was independently associated with the presence of vascular invasion (OR 3.38, 95% CI 1.48–7.71, p = 0.003) along with tumor size (diameter greater than 3 cm, OR 2.66, 95% CI 1.17–6.05, p = 0.02). In the validation set, the signature discarded the presence of vascular invasion with a negative predictive value of 0.77, and significantly improved the diagnostic power of tumor size alone (p = 0.045). Conclusions The assessment of a gene-expression signature obtained from resected biopsied tumor specimens improved the diagnosis of vascular invasion beyond clinical variable-based prediction. The signature may aid in candidate selection for liver transplantation, and guide the design of clinical trials with experimental adjuvant therapies. PMID:21703203

  5. Evaluation of public cancer datasets and signatures identifies TP53 mutant signatures with robust prognostic and predictive value.

    PubMed

    Lehmann, Brian David; Ding, Yan; Viox, Daniel Joseph; Jiang, Ming; Zheng, Yi; Liao, Wang; Chen, Xi; Xiang, Wei; Yi, Yajun

    2015-03-26

    Systematic analysis of cancer gene-expression patterns using high-throughput transcriptional profiling technologies has led to the discovery and publication of hundreds of gene-expression signatures. However, few public signature values have been cross-validated over multiple studies for the prediction of cancer prognosis and chemosensitivity in the neoadjuvant setting. To analyze the prognostic and predictive values of publicly available signatures, we have implemented a systematic method for high-throughput and efficient validation of a large number of datasets and gene-expression signatures. Using this method, we performed a meta-analysis including 351 publicly available signatures, 37,000 random signatures, and 31 breast cancer datasets. Survival analyses and pathologic responses were used to assess prediction of prognosis, chemoresponsiveness, and chemo-drug sensitivity. Among 31 breast cancer datasets and 351 public signatures, we identified 22 validation datasets, two robust prognostic signatures (BRmet50 and PMID18271932Sig33) in breast cancer and one signature (PMID20813035Sig137) specific for prognosis prediction in patients with ER-negative tumors. The 22 validation datasets demonstrated enhanced ability to distinguish cancer gene profiles from random gene profiles. Both prognostic signatures are composed of genes associated with TP53 mutations and were able to stratify the good and poor prognostic groups successfully in 82%and 68% of the 22 validation datasets, respectively. We then assessed the abilities of the two signatures to predict treatment responses of breast cancer patients treated with commonly used chemotherapeutic regimens. Both BRmet50 and PMID18271932Sig33 retrospectively identified those patients with an insensitive response to neoadjuvant chemotherapy (mean positive predictive values 85%-88%). Among those patients predicted to be treatment sensitive, distant relapse-free survival (DRFS) was improved (negative predictive values 87

  6. Metastasis predictive signature profiles pre-exist in normal tissues

    PubMed Central

    Yang, Haiyan; Crawford, Nigel; Lukes, Luanne; Finney, Richard; Lancaster, Mindy; Hunter., Kent W.

    2006-01-01

    Previous studies from our laboratory have demonstrated that metastatic propensity is significantly influenced by the genetic background upon which tumors arise. We have also established that human gene expression profiles predictive of metastasis are not only present in mouse tumors with both high and low metastatic capacity, but also correlate with genetic background. These results suggest that human metastasis-predictive gene expression signatures may be significantly driven by genetic background, rather than acquired somatic mutations. To test this hypothesis, gene expression profiling was performed on inbred mouse strains with significantly different metastatic efficiencies. Analysis of previously described human metastasis signature gene expression patterns in normal tissues permitted accurate categorization of high or low metastatic mouse genotypes. Furthermore, prospective identification of animals at high risk of metastasis was achieved by using mass spectrometry to characterize salivary peptide polymorphisms in a genetically heterogeneous population. These results strongly support the role of constitutional genetic variation in modulation of metastatic efficiency and suggest that predictive signature profiles could be developed from normal tissues in humans. The ability to identify those individuals at high risk of disseminated disease at the time of clinical manifestation of a primary cancer could have a significant impact on cancer management. PMID:16475030

  7. Digital gene expression signatures for maize development

    USDA-ARS?s Scientific Manuscript database

    Genome-wide expression signatures detect specific perturbations in developmental programs and contribute to functional resolution of key regulatory networks. In maize (Zea mays) inflorescences, mutations in the RAMOSA (RA) genes affect determinacy of axillary meristems and thus alter branching patt...

  8. Digital gene expression signatures for maize development.

    PubMed

    Eveland, Andrea L; Satoh-Nagasawa, Namiko; Goldshmidt, Alexander; Meyer, Sandra; Beatty, Mary; Sakai, Hajime; Ware, Doreen; Jackson, David

    2010-11-01

    Genome-wide expression signatures detect specific perturbations in developmental programs and contribute to functional resolution of key regulatory networks. In maize (Zea mays) inflorescences, mutations in the RAMOSA (RA) genes affect the determinacy of axillary meristems and thus alter branching patterns, an important agronomic trait. In this work, we developed and tested a framework for analysis of tag-based, digital gene expression profiles using Illumina's high-throughput sequencing technology and the newly assembled B73 maize reference genome. We also used a mutation in the RA3 gene to identify putative expression signatures specific to stem cell fate in axillary meristem determinacy. The RA3 gene encodes a trehalose-6-phosphate phosphatase and may act at the interface between developmental and metabolic processes. Deep sequencing of digital gene expression libraries, representing three biological replicate ear samples from wild-type and ra3 plants, generated 27 million 20- to 21-nucleotide reads with frequencies spanning 4 orders of magnitude. Unique sequence tags were anchored to 3'-ends of individual transcripts by DpnII and NlaIII digests, which were multiplexed during sequencing. We mapped 86% of nonredundant signature tags to the maize genome, which associated with 37,117 gene models and unannotated regions of expression. In total, 66% of genes were detected by at least nine reads in immature maize ears. We used comparative genomics to leverage existing information from Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa) in functional analyses of differentially expressed maize genes. Results from this study provide a basis for the analysis of short-read expression data in maize and resolved specific expression signatures that will help define mechanisms of action for the RA3 gene.

  9. Cancer gene expression signatures - the rise and fall?

    PubMed

    Chibon, Frederic

    2013-05-01

    A 'gene expression signature' can be defined as a single or a combined gene expression alteration with validated specificity in terms of diagnosis, prognosis or prediction of therapeutic response. Since the publication of the first signature in the late 90s, high-throughput gene expression analysis has revolutionised genetics over the last 15 years. The scientific community has used this new technology to find responses to these fundamental questions; from understanding tumour biology, to prediction of progression, and treatments to which it will respond. Nevertheless, legitimate excitement about the attractiveness of molecular technologies and the promise of discovery-based research should not overlook adherence to the rules of evidence, otherwise it may result in claims that are not meaningful and lead to disappointment. This review will thus focus on the approaches developed to answer these three fundamental questions and the results evidenced both at biological and clinical level. On looking at this huge amount of data that have become increasingly minute, and at times contradictory, we discuss how gene expression signature improve our understanding of cancer biology, our ability to predict progression and response, and finally, our capacity to treat cancers more efficiently. Copyright © 2013 Elsevier Ltd. All rights reserved.

  10. Predictive chromatin signatures in the mammalian genome

    PubMed Central

    Hon, Gary C.; Hawkins, R. David; Ren, Bing

    2009-01-01

    The DNA sequence of an organism is a blueprint of life: it harbors not only the information about proteins and other molecules produced in each cell, but also instructions on when and where such molecules are made. Chromatin, the structure of histone and DNA that has co-evolved with eukaryotic genome, also contains information that indicates the function and activity of the underlying DNA sequences. Such information exists in the form of covalent modifications to the histone proteins that comprise the nucleosome. Thanks to the development of high throughput technologies such as DNA microarrays and next generation DNA sequencing, we have begun to associate the various combinations of chromatin modification patterns with functional sequences in the human genome. Here, we review the rapid progress from descriptive observations of histone modification profiles to highly predictive models enabling use of chromatin signatures to enumerate novel functional sequences in mammalian genomes that have escaped previous detection. PMID:19808796

  11. Hereditary family signature of facial expression

    PubMed Central

    Peleg, Gili; Katzir, Gadi; Peleg, Ofer; Kamara, Michal; Brodsky, Leonid; Hel-Or, Hagit; Keren, Daniel; Nevo, Eviatar

    2006-01-01

    Although facial expressions of emotion are universal, individual differences create a facial expression “signature” for each person; but, is there a unique family facial expression signature? Only a few family studies on the heredity of facial expressions have been performed, none of which compared the gestalt of movements in various emotional states; they compared only a few movements in one or two emotional states. No studies, to our knowledge, have compared movements of congenitally blind subjects with their relatives to our knowledge. Using two types of analyses, we show a correlation between movements of congenitally blind subjects with those of their relatives in think-concentrate, sadness, anger, disgust, joy, and surprise and provide evidence for a unique family facial expression signature. In the analysis “in-out family test,” a particular movement was compared each time across subjects. Results show that the frequency of occurrence of a movement of a congenitally blind subject in his family is significantly higher than that outside of his family in think-concentrate, sadness, and anger. In the analysis “the classification test,” in which congenitally blind subjects were classified to their families according to the gestalt of movements, results show 80% correct classification over the entire interview and 75% in anger. Analysis of the movements' frequencies in anger revealed a correlation between the movements' frequencies of congenitally blind individuals and those of their relatives. This study anticipates discovering genes that influence facial expressions, understanding their evolutionary significance, and elucidating repair mechanisms for syndromes lacking facial expression, such as autism. PMID:17043232

  12. Combining Gene Signatures Improves Prediction of Breast Cancer Survival

    PubMed Central

    Zhao, Xi; Naume, Bjørn; Langerød, Anita; Frigessi, Arnoldo; Kristensen, Vessela N.; Børresen-Dale, Anne-Lise; Lingjærde, Ole Christian

    2011-01-01

    Background Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study. Principal Findings To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. Conclusion Combining the predictive strength of multiple gene signatures improves prediction of breast

  13. A prognostic gene expression signature in infratentorial ependymoma.

    PubMed

    Wani, Khalida; Armstrong, Terri S; Vera-Bolanos, Elizabeth; Raghunathan, Aditya; Ellison, David; Gilbertson, Richard; Vaillant, Brian; Goldman, Stewart; Packer, Roger J; Fouladi, Maryam; Pollack, Ian; Mikkelsen, Tom; Prados, Michael; Omuro, Antonio; Soffietti, Riccardo; Ledoux, Alicia; Wilson, Charmaine; Long, Lihong; Gilbert, Mark R; Aldape, Ken

    2012-05-01

    Patients with ependymoma exhibit a wide range of clinical outcomes that are currently unexplained by clinical or histological factors. Little is known regarding molecular biomarkers that could predict clinical behavior. Since recent data suggest that these tumors display biological characteristics according to their location (cerebral vs. infratentorial vs. spinal cord), rather than explore a broad spectrum of ependymoma, we focused on molecular alterations in ependymomas arising in the infratentorial compartment. Unsupervised clustering of available gene expression microarray data revealed two major subgroups of infratentorial ependymoma. Group 1 tumors over expressed genes that were associated with mesenchyme, Group 2 tumors showed no distinct gene ontologies. To assess the prognostic significance of these gene expression subgroups, real-time reverse transcriptase polymerase chain reaction assays were performed on genes defining the subgroups in a training set. This resulted in a 10-gene prognostic signature. Multivariate analysis showed that the 10-gene signature was an independent predictor of recurrence-free survival after adjusting for clinical factors. Evaluation of an external dataset describing subgroups of infratentorial ependymomas showed concordance of subgroup definition, including validation of the mesenchymal subclass. Importantly, the 10-gene signature was validated as a predictor of recurrence-free survival in this dataset. Taken together, the results indicate a link between clinical outcome and biologically identified subsets of infratentorial ependymoma and offer the potential for prognostic testing to estimate clinical aggressiveness in these tumors.

  14. Expression profile of skin papillomas with high cancer risk displays a unique genetic signature that clusters with squamous cell carcinomas and predicts risk for malignant conversion.

    PubMed

    Darwiche, N; Ryscavage, A; Perez-Lorenzo, R; Wright, L; Bae, D-S; Hennings, H; Yuspa, S H; Glick, A B

    2007-10-18

    Chemical induction of squamous tumors in the mouse skin induces multiple benign papillomas: high-frequency terminally benign low-risk papillomas and low-frequency high-risk papillomas, the putative precursor lesions to squamous cell carcinoma (SCC). We have compared the gene expression profile of twenty different early low- and high-risk papillomas with normal skin and SCC. Unsupervised clustering of 514 differentially expressed genes (P<0.001) showed that 9/10 high-risk papillomas clustered with SCC, while 1/10 clustered with low-risk papillomas, and this correlated with keratin markers of tumor progression. Prediction analysis for microarrays (PAM) identified 87 genes that distinguished the two papilloma classes, and a majority of these had a similar expression pattern in both high-risk papillomas and SCC. Additional classifier algorithms generated a gene list that correctly classified unknown benign tumors as low- or high-risk concordant with promotion protocol and keratin profiling. Reduced expression of immune function genes characterized the high-risk papillomas and SCC. Immunohistochemistry confirmed reduced T-cell number in high-risk papillomas, suggesting that reduced adaptive immunity defines papillomas that progress to SCC. These results demonstrate that murine premalignant lesions can be segregated into subgroups by gene expression patterns that correlate with risk for malignant conversion, and suggest a paradigm for generating diagnostic biomarkers for human premalignant lesions with unknown individual risk for malignant conversion.

  15. Reactive oxygen species-associated molecular signature predicts survival in patients with sepsis.

    PubMed

    Bime, Christian; Zhou, Tong; Wang, Ting; Slepian, Marvin J; Garcia, Joe G N; Hecker, Louise

    2016-06-01

    Sepsis-related multiple organ dysfunction syndrome is a leading cause of death in intensive care units. There is overwhelming evidence that oxidative stress plays a significant role in the pathogenesis of sepsis-associated multiple organ failure; however, reactive oxygen species (ROS)-associated biomarkers and/or diagnostics that define mortality or predict survival in sepsis are lacking. Lung or peripheral blood gene expression analysis has gained increasing recognition as a potential prognostic and/or diagnostic tool. The objective of this study was to identify ROS-associated biomarkers predictive of survival in patients with sepsis. In-silico analyses of expression profiles allowed the identification of a 21-gene ROS-associated molecular signature that predicts survival in sepsis patients. Importantly, this signature performed well in a validation cohort consisting of sepsis patients aggregated from distinct patient populations recruited from different sites. Our signature outperforms randomly generated signatures of the same signature gene size. Our findings further validate the critical role of ROSs in the pathogenesis of sepsis and provide a novel gene signature that predicts survival in sepsis patients. These results also highlight the utility of peripheral blood molecular signatures as biomarkers for predicting mortality risk in patients with sepsis, which could facilitate the development of personalized therapies.

  16. Reactive oxygen species–associated molecular signature predicts survival in patients with sepsis

    PubMed Central

    Zhou, Tong; Wang, Ting; Slepian, Marvin J.; Garcia, Joe G. N.; Hecker, Louise

    2016-01-01

    Abstract Sepsis-related multiple organ dysfunction syndrome is a leading cause of death in intensive care units. There is overwhelming evidence that oxidative stress plays a significant role in the pathogenesis of sepsis-associated multiple organ failure; however, reactive oxygen species (ROS)–associated biomarkers and/or diagnostics that define mortality or predict survival in sepsis are lacking. Lung or peripheral blood gene expression analysis has gained increasing recognition as a potential prognostic and/or diagnostic tool. The objective of this study was to identify ROS-associated biomarkers predictive of survival in patients with sepsis. In-silico analyses of expression profiles allowed the identification of a 21-gene ROS-associated molecular signature that predicts survival in sepsis patients. Importantly, this signature performed well in a validation cohort consisting of sepsis patients aggregated from distinct patient populations recruited from different sites. Our signature outperforms randomly generated signatures of the same signature gene size. Our findings further validate the critical role of ROSs in the pathogenesis of sepsis and provide a novel gene signature that predicts survival in sepsis patients. These results also highlight the utility of peripheral blood molecular signatures as biomarkers for predicting mortality risk in patients with sepsis, which could facilitate the development of personalized therapies. PMID:27252846

  17. Developing a Predictive Capability for Bioluminescence Signatures

    DTIC Science & Technology

    2011-09-30

    naval nighttime operations because the flow field associated with their motion stimulates naturally occurring plankton . In the littoral, the primary...sources of bioluminescence are dinoflagellates, common unicellular plankton that are also known to form red tides. Dinoflagellate bioluminescence is...bioluminescent signatures of some swimming fish are distinct enough to differentiate species; nocturnally foraging predators may use bioluminescent

  18. MSH2/BRCA1 expression as a DNA-repair signature predicting survival in early–stage lung cancer patients from the IFCT-0002 Phase 3 Trial

    PubMed Central

    Levallet, Guénaëlle; Dubois, Fatéméh; Fouret, Pierre; Antoine, Martine; Brosseau, Solenn; Bergot, Emmanuel; Beau-Faller, Michèle; Gounant, Valérie; Brambilla, Elisabeth; Debieuvre, Didier; Molinier, Olivier; Galateau-Sallé, Françoise; Mazieres, Julien; Quoix, Elisabeth; Pujol, Jean-Louis; Moro-Sibilot, Denis; Langlais, Alexandra; Morin, Franck; Westeel, Virginie; Zalcman, Gérard

    2017-01-01

    Introduction DNA repair is a double-edged sword in lung carcinogenesis. When defective, it promotes genetic instability and accumulated genetic alterations. Conversely these defects could sensitize cancer cells to therapeutic agents inducing DNA breaks. Methods We used immunohistochemistry (IHC) to assess MSH2, XRCC5, and BRCA1 expression in 443 post-chemotherapy specimens from patients randomized in a Phase 3 trial, comparing two neoadjuvant regimens in 528 Stage I-II non-small cell lung cancer (NSCLC) patients (IFCT-0002). O6MGMT promoter gene methylation was analyzed in a subset of 208 patients of the same trial with available snap-frozen specimens. Results Median follow-up was from 90 months onwards. Only high BRCA1 (n = 221, hazard ratio [HR] = 1.58, 95% confidence interval [CI] [1.07-2.34], p = 0.02) and low MSH2 expression (n = 356, HR = 1.52, 95% CI [1.11-2.08], p = 0.008) significantly predicted better overall survival (OS) in univariate and multivariate analysis. A bootstrap re-sampling strategy distinguished three patient groups at high (n = 55, low BRCA1 and high MSH2, median OS >96 months, HR = 2.5, 95% CI [1.45-4.33], p = 0.001), intermediate (n = 82, median OS = 73.4 p = 0.0596), and low (high BRCA1 and low MSH2, n = 67, median OS = ND, HR = 0.51, 95% CI [0.31-0.83], p = 0.006) risk of death. Interpretation DNA repair protein expression assessment identified three different groups of risk of death in early-stage lung cancer patients, according to their tumor MSH2 and BRCA1 expression levels. These results deserve prospective evaluation of MSH2/BRCA1 theranostic value in lung cancer patients treated with combinations of DNA-damaging chemotherapy and drugs targeting DNA repair, such as Poly(ADP-ribose) polymerase (PARP) inhibitors. PMID:28008145

  19. MSH2/BRCA1 expression as a DNA-repair signature predicting survival in early-stage lung cancer patients from the IFCT-0002 Phase 3 Trial.

    PubMed

    Levallet, Guénaëlle; Dubois, Fatéméh; Fouret, Pierre; Antoine, Martine; Brosseau, Solenn; Bergot, Emmanuel; Beau-Faller, Michèle; Gounant, Valérie; Brambilla, Elisabeth; Debieuvre, Didier; Molinier, Olivier; Galateau-Sallé, Françoise; Mazieres, Julien; Quoix, Elisabeth; Pujol, Jean-Louis; Moro-Sibilot, Denis; Langlais, Alexandra; Morin, Franck; Westeel, Virginie; Zalcman, Gérard

    2017-01-17

    DNA repair is a double-edged sword in lung carcinogenesis. When defective, it promotes genetic instability and accumulated genetic alterations. Conversely these defects could sensitize cancer cells to therapeutic agents inducing DNA breaks. We used immunohistochemistry (IHC) to assess MSH2, XRCC5, and BRCA1 expression in 443 post-chemotherapy specimens from patients randomized in a Phase 3 trial, comparing two neoadjuvant regimens in 528 Stage I-II non-small cell lung cancer (NSCLC) patients (IFCT-0002). O6MGMT promoter gene methylation was analyzed in a subset of 208 patients of the same trial with available snap-frozen specimens. Median follow-up was from 90 months onwards. Only high BRCA1 (n = 221, hazard ratio [HR] = 1.58, 95% confidence interval [CI] [1.07-2.34], p = 0.02) and low MSH2 expression (n = 356, HR = 1.52, 95% CI [1.11-2.08], p = 0.008) significantly predicted better overall survival (OS) in univariate and multivariate analysis. A bootstrap re-sampling strategy distinguished three patient groups at high (n = 55, low BRCA1 and high MSH2, median OS >96 months, HR = 2.5, 95% CI [1.45-4.33], p = 0.001), intermediate (n = 82, median OS = 73.4 p = 0.0596), and low (high BRCA1 and low MSH2, n = 67, median OS = ND, HR = 0.51, 95% CI [0.31-0.83], p = 0.006) risk of death. DNA repair protein expression assessment identified three different groups of risk of death in early-stage lung cancer patients, according to their tumor MSH2 and BRCA1 expression levels. These results deserve prospective evaluation of MSH2/BRCA1 theranostic value in lung cancer patients treated with combinations of DNA-damaging chemotherapy and drugs targeting DNA repair, such as Poly(ADP-ribose) polymerase (PARP) inhibitors.

  20. Molecular classification of prostate cancer using curated expression signatures.

    PubMed

    Markert, Elke K; Mizuno, Hideaki; Vazquez, Alexei; Levine, Arnold J

    2011-12-27

    High Gleason score is currently the best prognostic indicator for poor prognosis in prostate cancer. However, a significant number of patients with low Gleason scores develop aggressive disease as well. In an effort to understand molecular signatures associated with poor outcome in prostate cancer, we analyzed a microarray dataset characterizing 281 prostate cancers from a Swedish watchful-waiting cohort. Patients were classified on the basis of their mRNA microarray signature profiles indicating embryonic stem cell expression patterns (stemness), inactivation of the tumor suppressors p53 and PTEN, activation of several oncogenic pathways, and the TMPRSS2-ERG fusion. Unsupervised clustering identified a subset of tumors manifesting stem-like signatures together with p53 and PTEN inactivation, which had very poor survival outcome, a second group with intermediate survival outcome, characterized by the TMPRSS2-ERG fusion, and three groups with benign outcome. The stratification was validated on a second independent dataset of 150 tumor and metastatic samples from a clinical cohort at Memorial Sloan-Kettering Cancer Center. This classification is independent of Gleason score and therefore provides useful unique molecular profiles for prostate cancer prognosis, helping to predict poor outcome in patients with low or average Gleason scores.

  1. Clinical Value of Prognosis Gene Expression Signatures in Colorectal Cancer: A Systematic Review

    PubMed Central

    Cordero, David; Riccadonna, Samantha; Solé, Xavier; Crous-Bou, Marta; Guinó, Elisabet; Sanjuan, Xavier; Biondo, Sebastiano; Soriano, Antonio; Jurman, Giuseppe; Capella, Gabriel; Furlanello, Cesare; Moreno, Victor

    2012-01-01

    Introduction The traditional staging system is inadequate to identify those patients with stage II colorectal cancer (CRC) at high risk of recurrence or with stage III CRC at low risk. A number of gene expression signatures to predict CRC prognosis have been proposed, but none is routinely used in the clinic. The aim of this work was to assess the prediction ability and potential clinical usefulness of these signatures in a series of independent datasets. Methods A literature review identified 31 gene expression signatures that used gene expression data to predict prognosis in CRC tissue. The search was based on the PubMed database and was restricted to papers published from January 2004 to December 2011. Eleven CRC gene expression datasets with outcome information were identified and downloaded from public repositories. Random Forest classifier was used to build predictors from the gene lists. Matthews correlation coefficient was chosen as a measure of classification accuracy and its associated p-value was used to assess association with prognosis. For clinical usefulness evaluation, positive and negative post-tests probabilities were computed in stage II and III samples. Results Five gene signatures showed significant association with prognosis and provided reasonable prediction accuracy in their own training datasets. Nevertheless, all signatures showed low reproducibility in independent data. Stratified analyses by stage or microsatellite instability status showed significant association but limited discrimination ability, especially in stage II tumors. From a clinical perspective, the most predictive signatures showed a minor but significant improvement over the classical staging system. Conclusions The published signatures show low prediction accuracy but moderate clinical usefulness. Although gene expression data may inform prognosis, better strategies for signature validation are needed to encourage their widespread use in the clinic. PMID:23145004

  2. The 82-plex plasma protein signature that predicts increasing inflammation

    PubMed Central

    Tepel, Martin; Beck, Hans C.; Tan, Qihua; Borst, Christoffer; Rasmussen, Lars M.

    2015-01-01

    The objective of the study was to define the specific plasma protein signature that predicts the increase of the inflammation marker C-reactive protein from index day to next-day using proteome analysis and novel bioinformatics tools. We performed a prospective study of 91 incident kidney transplant recipients and quantified 359 plasma proteins simultaneously using nano-Liquid-Chromatography-Tandem Mass-Spectrometry in individual samples and plasma C-reactive protein on the index day and the next day. Next-day C-reactive protein increased in 59 patients whereas it decreased in 32 patients. The prediction model selected and validated 82 plasma proteins which determined increased next-day C-reactive protein (area under receiver-operator-characteristics curve, 0.772; 95% confidence interval, 0.669 to 0.876; P < 0.0001). Multivariable logistic regression showed that 82-plex protein signature (P < 0.001) was associated with observed increased next-day C-reactive protein. The 82-plex protein signature outperformed routine clinical procedures. The category-free net reclassification index improved with 82-plex plasma protein signature (total net reclassification index, 88.3%). Using the 82-plex plasma protein signature increased net reclassification index with a clinical meaningful 10% increase of risk mainly by the improvement of reclassification of subjects in the event group. An 82-plex plasma protein signature predicts an increase of the inflammatory marker C-reactive protein. PMID:26445912

  3. Tamoxifen therapy benefit predictive signature coupled with prognostic signature of post-operative recurrent risk for early stage ER+ breast cancer.

    PubMed

    Cai, Hao; Li, Xiangyu; Li, Jing; Ao, Lu; Yan, Haidan; Tong, Mengsha; Guan, Qingzhou; Li, Mengyao; Guo, Zheng

    2015-12-29

    Two types of prognostic signatures for predicting recurrent risk of ER+ breast cancer patients have been developed: one type for patients accepting surgery only and another type for patients receiving post-operative tamoxifen therapy. However, the first type of signature cannot distinguish high-risk patients who cannot benefit from tamoxifen therapy, while the second type of signature cannot identify patients who will be at low risk of recurrence even if they accept surgery only. In this study, we proposed to develop two coupled signatures to solve these problems based on within-sample relative expression orderings (REOs) of gene pairs. Firstly, we identified a prognostic signature of post-operative recurrent risk using 544 samples of ER+ breast cancer patients accepting surgery only. Then, applying this drug-free signature to 840 samples of patients receiving post-operative tamoxifen therapy, we recognized 553 samples of patients who would have been at high risk of recurrence if they had accepted surgery only and used these samples to develop a tamoxifen therapy benefit predictive signature. The two coupled signatures were validated in independent data. The signatures developed in this study are robust against experimental batch effects and applicable at the individual levels, which can facilitate the clinical decision of tamoxifen therapy.

  4. Tamoxifen therapy benefit predictive signature coupled with prognostic signature of post-operative recurrent risk for early stage ER+ breast cancer

    PubMed Central

    Cai, Hao; Li, Xiangyu; Li, Jing; Ao, Lu; Yan, Haidan; Tong, Mengsha; Guan, Qingzhou; Li, Mengyao; Guo, Zheng

    2015-01-01

    Two types of prognostic signatures for predicting recurrent risk of ER+ breast cancer patients have been developed: one type for patients accepting surgery only and another type for patients receiving post-operative tamoxifen therapy. However, the first type of signature cannot distinguish high-risk patients who cannot benefit from tamoxifen therapy, while the second type of signature cannot identify patients who will be at low risk of recurrence even if they accept surgery only. In this study, we proposed to develop two coupled signatures to solve these problems based on within-sample relative expression orderings (REOs) of gene pairs. Firstly, we identified a prognostic signature of post-operative recurrent risk using 544 samples of ER+ breast cancer patients accepting surgery only. Then, applying this drug-free signature to 840 samples of patients receiving post-operative tamoxifen therapy, we recognized 553 samples of patients who would have been at high risk of recurrence if they had accepted surgery only and used these samples to develop a tamoxifen therapy benefit predictive signature. The two coupled signatures were validated in independent data. The signatures developed in this study are robust against experimental batch effects and applicable at the individual levels, which can facilitate the clinical decision of tamoxifen therapy. PMID:26527319

  5. Predicting Electromagnetic Signatures of Gravitational Wave Sources

    NASA Astrophysics Data System (ADS)

    D'Orazio, Daniel John

    This dissertation investigates the signatures of electromagnetic radiation that may accompany two specific sources of gravitational radiation: the inspiral and merger of massive black hole binaries (MBHBs) in galactic nuclei, and the coalescence of black hole neutron star (BHNS) pairs. Part I considers the interaction of MBHBs, at sub-pc separations, with a circumbinary gas disk. Accretion rates onto the MBHB are calculated from two-dimensional hydrodynamical simulations as a function of the relative masses of the black holes. The results are applied to interpretation of the recent, sub-pc separation MBHB candidate in the nucleus of the periodically variable Quasar PG 1302-102. We advance an interpretation of the variability observed in PG 1302-102 as being caused by Doppler-boosted emission sourced by the orbital velocity of the smaller black hole in a MBHB with disparate relative masses. Part II considers BHNS binaries in which the black hole is large enough to swallow the neutron star whole before it is disrupted. As the pair nears merger, orbital motion of the black hole through the magnetosphere of the neutron star generates an electromotive force, a black-hole-battery, which, for the strongest neutron star magnetic field strengths, could power luminosities large enough to make the merging pair observable out to cosmic distances. Relativistic solutions for vacuum fields of a magnetic dipole near a horizon are given, and a mechanism for harnessing the power of the black-hole-battery is put forth in the form of a fireball emitting in hard X-rays to gamma-rays.

  6. Spatiotemporal Signatures of Lexical–Semantic Prediction

    PubMed Central

    Lau, Ellen F.; Weber, Kirsten; Gramfort, Alexandre; Hämäläinen, Matti S.; Kuperberg, Gina R.

    2016-01-01

    Although there is broad agreement that top-down expectations can facilitate lexical–semantic processing, the mechanisms driving these effects are still unclear. In particular, while previous electroencephalography (EEG) research has demonstrated a reduction in the N400 response to words in a supportive context, it is often challenging to dissociate facilitation due to bottom-up spreading activation from facilitation due to top-down expectations. The goal of the current study was to specifically determine the cortical areas associated with facilitation due to top-down prediction, using magnetoencephalography (MEG) recordings supplemented by EEG and functional magnetic resonance imaging (fMRI) in a semantic priming paradigm. In order to modulate expectation processes while holding context constant, we manipulated the proportion of related pairs across 2 blocks (10 and 50% related). Event-related potential results demonstrated a larger N400 reduction when a related word was predicted, and MEG source localization of activity in this time-window (350–450 ms) localized the differential responses to left anterior temporal cortex. fMRI data from the same participants support the MEG localization, showing contextual facilitation in left anterior superior temporal gyrus for the high expectation block only. Together, these results provide strong evidence that facilitatory effects of lexical–semantic prediction on the electrophysiological response 350–450 ms postonset reflect modulation of activity in left anterior temporal cortex. PMID:25316341

  7. Design of a multi-signature ensemble classifier predicting neuroblastoma patients' outcome

    PubMed Central

    2012-01-01

    Background Neuroblastoma is the most common pediatric solid tumor of the sympathetic nervous system. Development of improved predictive tools for patients stratification is a crucial requirement for neuroblastoma therapy. Several studies utilized gene expression-based signatures to stratify neuroblastoma patients and demonstrated a clear advantage of adding genomic analysis to risk assessment. There is little overlapping among signatures and merging their prognostic potential would be advantageous. Here, we describe a new strategy to merge published neuroblastoma related gene signatures into a single, highly accurate, Multi-Signature Ensemble (MuSE)-classifier of neuroblastoma (NB) patients outcome. Methods Gene expression profiles of 182 neuroblastoma tumors, subdivided into three independent datasets, were used in the various phases of development and validation of neuroblastoma NB-MuSE-classifier. Thirty three signatures were evaluated for patients' outcome prediction using 22 classification algorithms each and generating 726 classifiers and prediction results. The best-performing algorithm for each signature was selected, validated on an independent dataset and the 20 signatures performing with an accuracy > = 80% were retained. Results We combined the 20 predictions associated to the corresponding signatures through the selection of the best performing algorithm into a single outcome predictor. The best performance was obtained by the Decision Table algorithm that produced the NB-MuSE-classifier characterized by an external validation accuracy of 94%. Kaplan-Meier curves and log-rank test demonstrated that patients with good and poor outcome prediction by the NB-MuSE-classifier have a significantly different survival (p < 0.0001). Survival curves constructed on subgroups of patients divided on the bases of known prognostic marker suggested an excellent stratification of localized and stage 4s tumors but more data are needed to prove this point. Conclusions The

  8. MicroRNA Expression Signature in Degenerative Aortic Stenosis

    PubMed Central

    2016-01-01

    Degenerative aortic stenosis, characterized by narrowing of the exit of the left ventricle of the heart, has become the most common valvular heart disease in the elderly. The aim of this study was to investigate the microRNA (miRNA) signature in degenerative AS. Through microarray analysis, we identified the miRNA expression signature in the tissue samples from healthy individuals (n = 4) and patients with degenerative AS (n = 4). Six miRNAs (hsa-miR-193a-3p, hsa-miR-29b-1-5p, hsa-miR-505-5p, hsa-miR-194-5p, hsa-miR-99b-3p, and hsa-miR-200b-3p) were overexpressed and 14 (hsa-miR-3663-3p, hsa-miR-513a-5p, hsa-miR-146b-5p, hsa-miR-1972, hsa-miR-718, hsa-miR-3138, hsa-miR-21-5p, hsa-miR-630, hsa-miR-575, hsa-miR-301a-3p, hsa-miR-636, hsa-miR-34a-3p, hsa-miR-21-3p, and hsa-miR-516a-5p) were downregulated in aortic tissue from AS patients. GeneSpring 13.1 was used to identify potential human miRNA target genes by comparing a 3-way comparison of predictions from TargetScan, PITA, and microRNAorg databases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to identify potential pathways and functional annotations associated with AS. Twenty miRNAs were significantly differentially expressed between patients with AS samples and normal controls and identified potential miRNA targets and molecular pathways associated with this morbidity. This study describes the miRNA expression signature in degenerative AS and provides an improved understanding of the molecular pathobiology of this disease. PMID:27579316

  9. MicroRNA Expression Signature in Degenerative Aortic Stenosis.

    PubMed

    Shi, Jing; Liu, Hui; Wang, Hui; Kong, Xiangqing

    2016-01-01

    Degenerative aortic stenosis, characterized by narrowing of the exit of the left ventricle of the heart, has become the most common valvular heart disease in the elderly. The aim of this study was to investigate the microRNA (miRNA) signature in degenerative AS. Through microarray analysis, we identified the miRNA expression signature in the tissue samples from healthy individuals (n = 4) and patients with degenerative AS (n = 4). Six miRNAs (hsa-miR-193a-3p, hsa-miR-29b-1-5p, hsa-miR-505-5p, hsa-miR-194-5p, hsa-miR-99b-3p, and hsa-miR-200b-3p) were overexpressed and 14 (hsa-miR-3663-3p, hsa-miR-513a-5p, hsa-miR-146b-5p, hsa-miR-1972, hsa-miR-718, hsa-miR-3138, hsa-miR-21-5p, hsa-miR-630, hsa-miR-575, hsa-miR-301a-3p, hsa-miR-636, hsa-miR-34a-3p, hsa-miR-21-3p, and hsa-miR-516a-5p) were downregulated in aortic tissue from AS patients. GeneSpring 13.1 was used to identify potential human miRNA target genes by comparing a 3-way comparison of predictions from TargetScan, PITA, and microRNAorg databases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to identify potential pathways and functional annotations associated with AS. Twenty miRNAs were significantly differentially expressed between patients with AS samples and normal controls and identified potential miRNA targets and molecular pathways associated with this morbidity. This study describes the miRNA expression signature in degenerative AS and provides an improved understanding of the molecular pathobiology of this disease.

  10. A Myc Activity Signature Predicts Poor Clinical Outcomes in Myc-Associated Cancers.

    PubMed

    Jung, MoonSun; Russell, Amanda J; Liu, Bing; George, Joshy; Liu, Pei Yan; Liu, Tao; DeFazio, Anna; Bowtell, David D L; Oberthuer, André; London, Wendy B; Fletcher, Jamie I; Haber, Michelle; Norris, Murray D; Henderson, Michelle J

    2017-02-15

    Myc transcriptional activity is frequently deregulated in human cancers, but a Myc-driven gene signature with prognostic ability across multiple tumor types remains lacking. Here, we selected 18 Myc-regulated genes from published studies of Myc family targets in epithelial ovarian cancer (EOC) and neuroblastoma. A Myc family activity score derived from the 18 genes was correlated to MYC/MYCN/MYCL1 expression in a panel of 35 cancer cell lines. The prognostic ability of this signature was evaluated in neuroblastoma, medulloblastoma, diffuse large B-cell lymphoma (DLBCL), and EOC microarray gene expression datasets using Kaplan-Meier and multivariate Cox regression analyses and was further validated in 42 primary neuroblastomas using qPCR. Cell lines with high MYC, MYCN, and/or MYCL1 gene expression exhibited elevated expression of the signature genes. Survival analysis showed that the signature was associated with poor outcome independently of well-defined prognostic factors in neuroblastoma, breast cancer, DLBCL, and medulloblastoma. In EOC, the 18-gene Myc activity signature was capable of identifying a group of patients with poor prognosis in a "high-MYCN" molecular subtype but not in the overall cohort. The predictive ability of this signature was reproduced using qPCR analysis of an independent cohort of neuroblastomas, including a subset of tumors without MYCN amplification. These data reveal an 18-gene Myc activity signature that is highly predictive of poor prognosis in diverse Myc-associated malignancies and suggest its potential clinical application in the identification of Myc-driven tumors that might respond to Myc-targeted therapies. Cancer Res; 77(4); 971-81. ©2016 AACR. ©2016 American Association for Cancer Research.

  11. Derivation of cancer diagnostic and prognostic signatures from gene expression data

    PubMed Central

    Goodison, Steve; Sun, Yijun; Urquidi, Virginia

    2010-01-01

    The ability to compare genome-wide expression profiles in human tissue samples has the potential to add an invaluable molecular pathology aspect to the detection and evaluation of multiple diseases. Applications include initial diagnosis, evaluation of disease subtype, monitoring of response to therapy and the prediction of disease recurrence. The derivation of molecular signatures that can predict tumor recurrence in breast cancer has been a particularly intense area of investigation and a number of studies have shown that molecular signatures can outperform currently used clinicopathologic factors in predicting relapse in this disease. However, many of these predictive models have been derived using relatively simple computational algorithms and whether these models are at a stage of development worthy of large-cohort clinical trial validation is currently a subject of debate. In this review, we focus on the derivation of optimal molecular signatures from high-dimensional data and discuss some of the expected future developments in the field. PMID:21083217

  12. Uncertainty of hydrological signatures predicted for ungauged basins

    NASA Astrophysics Data System (ADS)

    Westerberg, Ida; Coxon, Gemma; Wagener, Thorsten; McMillan, Hilary; Montanari, Alberto; Castellarin, Attilio; Freer, Jim

    2015-04-01

    Reliable information about the hydrological behaviour of an ungauged catchment is needed for a wide range of water resource management decisions, and it has been a central topic of research in hydrology for the last decade through the Predictions in Ungauged Basins initiative. Such information derived as an index value from observed data in a gauged basin is known as a hydrological signature, and has been used in a variety of studies for, e.g., change detection, model calibration and diagnostic model-structural evaluation. When signature values are predicted for ungauged catchments, they are not only affected by uncertainties in the regionalisation procedure, but also by uncertainties in the observed data for the gauged catchments used for the prediction. In this study we investigated a method for regionalisation of hydrological signatures to ungauged catchments that accounted for both of these uncertainty sources. This also enabled us to assess the role of the different uncertainty sources in defining the overall regionalisation uncertainty - e.g. for what signatures and conditions are the data uncertainties more important than the regionalisation uncertainties and vice versa? The study was made using an extensive dataset of catchments in England and Wales, incorporating gauging (stage-discharge) data from all the discharge stations. The uncertainties were assessed within a Monte Carlo framework that incorporated different types of uncertainties in the data as well as uncertainties in the regionalisation procedure. The regionalisation results had a high reliability when the gauged discharge uncertainties were accounted for. The magnitude of the gauged uncertainty was often larger than the differences between deterministic gauged and regionalised values, which shows that deterministic comparisons are insufficient for evaluation of regionalisation results. The results were better for medium and high-flow signatures than for low-flow signatures. The data

  13. Applying Signature Extraction and Classification Algorithms on Express on Profiles of CD Markers and Toll Like Receptors to Classify and Predict Exposures to Various Pathogens

    DTIC Science & Technology

    2016-02-10

    to confidently  identify transcriptional responses induced by bacteria ( anthrax , plague, Brucella), toxins (CT, SEB,  BoNTA), or viruses (Dengue, VEE...Cytokines &  Chemokines,”  anthrax , Brucella and SEB showed major up regulation of most genes coding for  inflammatory mediators; the other 5 agents had mixed...when compared with Brucella and  anthrax .  Not surprisingly, the superantigen SEB displayed kinetic patterns for over expression of interferon‐γ, IL‐ 2

  14. A Gene Expression Signature for Chemoradiosensitivity of Colorectal Cancer Cells

    SciTech Connect

    Spitzner, Melanie; Emons, Georg; Kramer, Frank; Gaedcke, Jochen; Rave-Fraenk, Margret; Scharf, Jens-Gerd; Burfeind, Peter; Becker, Heinz; Beissbarth, Tim; Ghadimi, B. Michael; Ried, Thomas; Grade, Marian

    2010-11-15

    Purpose: The standard treatment of patients with locally advanced rectal cancers comprises preoperative 5-fluorouracil-based chemoradiotherapy followed by standardized surgery. However, tumor response to multimodal treatment has varied greatly, ranging from complete resistance to complete pathologic regression. The prediction of the response is, therefore, an important clinical need. Methods and Materials: To establish in vitro models for studying the molecular basis of this heterogeneous tumor response, we exposed 12 colorectal cancer cell lines to 3 {mu}M of 5-fluorouracil and 2 Gy of radiation. The differences in treatment sensitivity were then correlated with the pretherapeutic gene expression profiles of these cell lines. Results: We observed a heterogeneous response, with surviving fractions ranging from 0.28 to 0.81, closely recapitulating clinical reality. Using a linear model analysis, we identified 4,796 features whose expression levels correlated significantly with the sensitivity to chemoradiotherapy (Q <.05), including many genes involved in the mitogen-activated protein kinase signaling pathway or cell cycle genes. These data have suggested a potential relevance of the insulin and Wnt signaling pathways for treatment response, and we identified STAT3, RASSF1, DOK3, and ERBB2 as potential therapeutic targets. The microarray measurements were independently validated for a subset of these genes using real-time polymerase chain reactions. Conclusion: We are the first to report a gene expression signature for the in vitro chemoradiosensitivity of colorectal cancer cells. We anticipate that this analysis will unveil molecular biomarkers predictive of the response of rectal cancers to chemoradiotherapy and enable the identification of genes that could serve as targets to sensitize a priori resistant primary tumors.

  15. Cancer-cell intrinsic gene expression signatures overcome intratumoural heterogeneity bias in colorectal cancer patient classification

    PubMed Central

    Dunne, Philip D.; Alderdice, Matthew; O'Reilly, Paul G.; Roddy, Aideen C.; McCorry, Amy M. B.; Richman, Susan; Maughan, Tim; McDade, Simon S.; Johnston, Patrick G.; Longley, Daniel B.; Kay, Elaine; McArt, Darragh G.; Lawler, Mark

    2017-01-01

    Stromal-derived intratumoural heterogeneity (ITH) has been shown to undermine molecular stratification of patients into appropriate prognostic/predictive subgroups. Here, using several clinically relevant colorectal cancer (CRC) gene expression signatures, we assessed the susceptibility of these signatures to the confounding effects of ITH using gene expression microarray data obtained from multiple tumour regions of a cohort of 24 patients, including central tumour, the tumour invasive front and lymph node metastasis. Sample clustering alongside correlative assessment revealed variation in the ability of each signature to cluster samples according to patient-of-origin rather than region-of-origin within the multi-region dataset. Signatures focused on cancer-cell intrinsic gene expression were found to produce more clinically useful, patient-centred classifiers, as exemplified by the CRC intrinsic signature (CRIS), which robustly clustered samples by patient-of-origin rather than region-of-origin. These findings highlight the potential of cancer-cell intrinsic signatures to reliably stratify CRC patients by minimising the confounding effects of stromal-derived ITH. PMID:28561046

  16. Genomic signatures of germline gene expression.

    PubMed

    McVicker, Graham; Green, Phil

    2010-11-01

    Transcribed regions in the human genome differ from adjacent intergenic regions in transposable element density, crossover rates, and asymmetric substitution and sequence composition patterns. We tested whether these differences reflect selection or are instead a byproduct of germline transcription, using publicly available gene expression data from a variety of germline and somatic tissues. Crossover rate shows a strong negative correlation with gene expression in meiotic tissues, suggesting that crossover is inhibited by transcription. Strand-biased composition (G+T content) and A → G versus T → C substitution asymmetry are both positively correlated with germline gene expression. We find no evidence for a strand bias in allele frequency data, implying that the substitution asymmetry reflects a mutation rather than a fixation bias. The density of transposable elements is positively correlated with germline expression, suggesting that such elements preferentially insert into regions that are actively transcribed. For each of the features examined, our analyses favor a nonselective explanation for the observed trends and point to the role of germline gene expression in shaping the mammalian genome.

  17. A framework for polarized radiance signature prediction for natural scenes

    NASA Astrophysics Data System (ADS)

    Devaraj, Chabitha; Brown, Scott; Messinger, David; Goodenough, Adam; Pogorzala, David

    2007-04-01

    As the interest in polarization sensitive imaging systems increases, the modeling tools used to perform instrument trade studies and to generate data for algorithm testing must be adapted to correctly predict polarization signatures. The incorporation of polarization into the image chain simulated by these tools must address the modeling of the natural illuminants (e.g. Sun, Moon, Sky), background sources (e.g. adjacent objects), the polarized Bidirectional Reflectance Distribution Function (pBRDF) of surfaces, atmospheric propagation (extinction, scattering and self-emission) and sensor effects (e.g. optics, filters). Although, each of these links in the image chain may utilize unique modeling approaches, they must be integrated under a framework that addresses important aspects such as a unified coordinate space and a common polarization state convention. This paper presents a modeling framework for the prediction of polarized signatures within a natural scene. The proposed image chain utilizes community developed modeling tools including an experimental version of MODTRAN and BRDF models that have been either derived or extended for polarization (e.g. Beard-Maxwell, Priest-Germer, etc.). This description also includes the theory utilized in the modeling tools incorporated into the image chain model to integrate these links into a full signature prediction capability. Analytical and experimental lab studies are presented to demonstrate the correct implementation and integration of the described image chain framework within the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model.

  18. NF-κB gene signature predicts prostate cancer progression

    PubMed Central

    Jin, Renjie; Yi, Yajun; Yull, Fiona E.; Blackwell, Timothy S.; Clark, Peter E.; Koyama, Tatsuki; Smith, Joseph A.; Matusik, Robert J.

    2014-01-01

    In many prostate cancer (PCa) patients, the cancer will be recurrent and eventually progress to lethal metastatic disease after primary treatment, such as surgery or radiation therapy. Therefore, it would be beneficial to better predict which patients with early-stage PCa would progress or recur after primary definitive treatment. In addition, many studies indicate that activation of NF-κB signaling correlates with PCa progression; however, the precise underlying mechanism is not fully understood. Our studies show that activation of NF-κB signaling via deletion of one allele of its inhibitor, IκBα, did not induce prostatic tumorigenesis in our mouse model. However, activation of NF-κB signaling did increase the rate of tumor progression in the Hi-Myc mouse PCa model when compared to Hi-Myc alone. Using the non-malignant NF-κB activated androgen depleted mouse prostate, a NF-κB Activated Recurrence Predictor 21 (NARP21) gene signature was generated. The NARP21 signature successfully predicted disease-specific survival and distant metastases-free survival in patients with PCa. This transgenic mouse model derived gene signature provides a useful and unique molecular profile for human PCa prognosis, which could be used on a prostatic biopsy to predict indolent versus aggressive behavior of the cancer after surgery. PMID:24686169

  19. A gene expression signature for RSV: clinical implications and limitations.

    PubMed

    Openshaw, Peter J M

    2013-11-01

    Peter Openshaw discusses the challenges in advancing respiratory syncytial virus (RSV) treatments and the implications of a study by Mejias and colleagues using a newly identified gene signature for diagnosis and prediction of RSV severity. Please see later in the article for the Editors' Summary.

  20. Bayesian profiling of molecular signatures to predict event times

    PubMed Central

    Zhang, Dabao; Zhang, Min

    2007-01-01

    Background It is of particular interest to identify cancer-specific molecular signatures for early diagnosis, monitoring effects of treatment and predicting patient survival time. Molecular information about patients is usually generated from high throughput technologies such as microarray and mass spectrometry. Statistically, we are challenged by the large number of candidates but only a small number of patients in the study, and the right-censored clinical data further complicate the analysis. Results We present a two-stage procedure to profile molecular signatures for survival outcomes. Firstly, we group closely-related molecular features into linkage clusters, each portraying either similar or opposite functions and playing similar roles in prognosis; secondly, a Bayesian approach is developed to rank the centroids of these linkage clusters and provide a list of the main molecular features closely related to the outcome of interest. A simulation study showed the superior performance of our approach. When it was applied to data on diffuse large B-cell lymphoma (DLBCL), we were able to identify some new candidate signatures for disease prognosis. Conclusion This multivariate approach provides researchers with a more reliable list of molecular features profiled in terms of their prognostic relationship to the event times, and generates dependable information for subsequent identification of prognostic molecular signatures through either biological procedures or further data analysis. PMID:17239251

  1. Gene Expression Signature in Endemic Osteoarthritis by Microarray Analysis

    PubMed Central

    Wang, Xi; Ning, Yujie; Zhang, Feng; Yu, Fangfang; Tan, Wuhong; Lei, Yanxia; Wu, Cuiyan; Zheng, Jingjing; Wang, Sen; Yu, Hanjie; Li, Zheng; Lammi, Mikko J.; Guo, Xiong

    2015-01-01

    Kashin-Beck Disease (KBD) is an endemic osteochondropathy with an unknown pathogenesis. Diagnosis of KBD is effective only in advanced cases, which eliminates the possibility of early treatment and leads to an inevitable exacerbation of symptoms. Therefore, we aim to identify an accurate blood-based gene signature for the detection of KBD. Previously published gene expression profile data on cartilage and peripheral blood mononuclear cells (PBMCs) from adults with KBD were compared to select potential target genes. Microarray analysis was conducted to evaluate the expression of the target genes in a cohort of 100 KBD patients and 100 healthy controls. A gene expression signature was identified using a training set, which was subsequently validated using an independent test set with a minimum redundancy maximum relevance (mRMR) algorithm and support vector machine (SVM) algorithm. Fifty unique genes were differentially expressed between KBD patients and healthy controls. A 20-gene signature was identified that distinguished between KBD patients and controls with 90% accuracy, 85% sensitivity, and 95% specificity. This study identified a 20-gene signature that accurately distinguishes between patients with KBD and controls using peripheral blood samples. These results promote the further development of blood-based genetic biomarkers for detection of KBD. PMID:25997002

  2. An Integrated mRNA and microRNA Expression Signature for Glioblastoma Multiforme Prognosis

    PubMed Central

    Xiong, Jie; Bing, Zhitong; Su, Yanlin; Deng, Defeng; Peng, Xiaoning

    2014-01-01

    Although patients with Glioblastoma multiforme (GBM) have grave prognosis, significant variability in patient outcome is observed. The objective of this study is to identify a molecular signature for GBM prognosis. We subjected 355 mRNA and microRNA expression profiles to elastic net-regulated Cox regression for identification of an integrated RNA signature for GBM prognosis. A prognostic index (PI) was generated for patient stratification. Survival comparison was conducted by Kaplan-Meier method and a general multivariate Cox regression procedure was applied to evaluate the independence of the PI. The abilities and efficiencies of signatures to predict GBM patient outcome was assessed and compared by the area under the curve (AUC) of the receiver-operator characteristic (ROC). An integrated RNA prognostic signature consisted by 4 protective mRNAs, 12 risky mRNAs, and 1 risky microRNA was identified. Decreased survival was associated with being in the high-risk group (hazard ratio = 2.864, P<0.0001). The prognostic value of the integrated signature was validated in five independent GBM expression datasets (n = 201, hazard ratio = 2.453, P<0.0001). The PI outperformed the known clinical factors, mRNA-only, and miRNA-only prognostic signatures for GBM prognosis (area under the ROC curve for the integrated RNA, mRNA-only, and miRNA-only signatures were 0.828, 0.742, and 0.757 at 3 years of overall survival, respectively, P<0.0001 by permutation test). We describe the first, to our knowledge, robust transcriptome-based integrated RNA signature that improves the current GBM prognosis based on clinical variables, mRNA-only, and miRNA-only signatures. PMID:24871302

  3. Derivation of molecular signatures for breast cancer recurrence prediction using a two-way validation approach

    PubMed Central

    Sun, Yijun; Urquidi, Virginia

    2010-01-01

    Previous studies have demonstrated the potential value of gene expression signatures in assessing the risk of post-surgical breast cancer recurrence, however, many of these predictive models have been derived using simple computational algorithms and validated internally or using one-way validation on a single dataset. We have recently developed a new feature selection algorithm that overcomes some limitations inherent to high-dimensional data analysis. In this study, we applied this algorithm to two publicly available gene expression datasets obtained from over 400 patients with breast cancer to investigate whether we could derive more accurate prognostic signatures and reveal common predictive factors across independent datasets. We compared the performance of three advanced computational algorithms using a robust two-way validation method, where one dataset was used for training and to establish a prediction model that was then blindly tested on the other dataset. The experiment was then repeated in the reverse direction. Analyses identified prognostic signatures that while comprised of only 10–13 genes, significantly outperformed previously reported signatures for breast cancer evaluation. The cross-validation approach revealed CEGP1 and PRAME as major candidates for breast cancer biomarker development. PMID:19291396

  4. GeneSigDB: a manually curated database and resource for analysis of gene expression signatures

    PubMed Central

    Culhane, Aedín C.; Schröder, Markus S.; Sultana, Razvan; Picard, Shaita C.; Martinelli, Enzo N.; Kelly, Caroline; Haibe-Kains, Benjamin; Kapushesky, Misha; St Pierre, Anne-Alyssa; Flahive, William; Picard, Kermshlise C.; Gusenleitner, Daniel; Papenhausen, Gerald; O'Connor, Niall; Correll, Mick; Quackenbush, John

    2012-01-01

    GeneSigDB (http://www.genesigdb.org or http://compbio.dfci.harvard.edu/genesigdb/) is a database of gene signatures that have been extracted and manually curated from the published literature. It provides a standardized resource of published prognostic, diagnostic and other gene signatures of cancer and related disease to the community so they can compare the predictive power of gene signatures or use these in gene set enrichment analysis. Since GeneSigDB release 1.0, we have expanded from 575 to 3515 gene signatures, which were collected and transcribed from 1604 published articles largely focused on gene expression in cancer, stem cells, immune cells, development and lung disease. We have made substantial upgrades to the GeneSigDB website to improve accessibility and usability, including adding a tag cloud browse function, facetted navigation and a ‘basket’ feature to store genes or gene signatures of interest. Users can analyze GeneSigDB gene signatures, or upload their own gene list, to identify gene signatures with significant gene overlap and results can be viewed on a dynamic editable heatmap that can be downloaded as a publication quality image. All data in GeneSigDB can be downloaded in numerous formats including .gmt file format for gene set enrichment analysis or as a R/Bioconductor data file. GeneSigDB is available from http://www.genesigdb.org. PMID:22110038

  5. Shape Signatures: New Descriptors for Predicting Cardiotoxicity In Silico

    PubMed Central

    Chekmarev, Dmitriy S.; Kholodovych, Vladyslav; Balakin, Konstantin V.; Ivanenkov, Yan; Ekins, Sean; Welsh, William J.

    2009-01-01

    Shape Signatures is a new computational tool that is being evaluated for applications in computational toxicology and drug discovery. The method employs a customized ray-tracing algorithm to explore the volume enclosed by the surface of a molecule and then uses the output to construct compact histograms (i.e., signatures) that encode for molecular shape and polarity. In the present study, we extend the application of the Shape Signatures methodology to the domain of computational models for cardiotoxicity. The Shape Signatures method is used to generate molecular descriptors that are then utilized with widely used classification techniques such as k nearest neighbors (k-NN), support vector machines (SVM), and Kohonen self-organizing maps (SOM). The performances of these approaches were assessed by applying them to a data set of compounds with varying affinity toward the 5-HT2B receptor as well as a set of human ether-a-go-go-related gene (hERG) potassium channel inhibitors. Our classification models for 5-HT2B represented the first attempt at global computational models for this receptor and exhibited average accuracies in the range of 73−83%. This level of performance is comparable to using commercially available molecular descriptors. The overall accuracy of the hERG Shape Signatures–SVM models was 69−73%, in line with other computational models published to date. Our data indicate that Shape Signatures descriptors can be used with SVM and Kohonen SOM and perform better in classification problems related to the analysis of highly clustered and heterogeneous property spaces. Such models may have utility for predicting the potential for cardiotoxicity in drug discovery mediated by the 5-HT2B receptor and hERG. PMID:18461975

  6. ToxCast: Developing Predictive Signatures of Chemically ...

    EPA Pesticide Factsheets

    ToxCast, the United States Environmental Protection Agency’s chemical prioritization research program, is developing methods for utilizing computational chemistry, bioactivity profiling and toxicogenomic data to predict potential for toxicity and prioritize limited testing resources (www.epa.gov/tocast). In the proof-of-concept phase, we are focused upon evaluating chemicals with an existing, rich toxicological database in order to provide an interpretive context for the high through put screening data. This set of 320 reference chemicals are largely derived from the active ingredients in food use pesticides and represent numerous structural classes and phenotypic outcomes, including tumorigens, developmental and reproductive toxicants, neurotoxicants and irnrnunotoxicants. The goal of the program is to develop signatures based on the combined use of physico-chemical properties (the traditional independent variables in structure activity models) and the bioactivity data (derived from a broad spectrum of more than 400 readouts from biochemical assays, cell-based phenotypic assays, and genomic analyses of cells) that are predictive of responses in animal bioassays. The signatures derived for chemicals with toxicity data gaps could then be compared with those of the well characterized chemicals, and those with significant signatures would become priority candidates for testing in traditional animal bioassays. These data are being generated through a series of

  7. Gene-expression signatures of Atlantic salmon's plastic life cycle.

    PubMed

    Aubin-Horth, Nadia; Letcher, Benjamin H; Hofmann, Hans A

    2009-09-15

    How genomic expression differs as a function of life history variation is largely unknown. Atlantic salmon exhibits extreme alternative life histories. We defined the gene-expression signatures of wild-caught salmon at two different life stages by comparing the brain expression profiles of mature sneaker males and immature males, and early migrants and late migrants. In addition to life-stage-specific signatures, we discovered a surprisingly large gene set that was differentially regulated-at similar magnitudes, yet in opposite direction-in both life history transitions. We suggest that this co-variation is not a consequence of many independent cellular and molecular switches in the same direction but rather represents the molecular equivalent of a physiological shift orchestrated by one or very few master regulators.

  8. Gene-expression signatures of Atlantic salmon's plastic life cycle

    USGS Publications Warehouse

    Aubin-Horth, N.; Letcher, B.H.; Hofmann, H.A.

    2009-01-01

    How genomic expression differs as a function of life history variation is largely unknown. Atlantic salmon exhibits extreme alternative life histories. We defined the gene-expression signatures of wild-caught salmon at two different life stages by comparing the brain expression profiles of mature sneaker males and immature males, and early migrants and late migrants. In addition to life-stage-specific signatures, we discovered a surprisingly large gene set that was differentially regulated-at similar magnitudes, yet in opposite direction-in both life history transitions. We suggest that this co-variation is not a consequence of many independent cellular and molecular switches in the same direction but rather represents the molecular equivalent of a physiological shift orchestrated by one or very few master regulators. ?? 2009 Elsevier Inc. All rights reserved.

  9. Identification of Gene-Expression Signatures and Protein Markers for Breast Cancer Grading and Staging.

    PubMed

    Yao, Fang; Zhang, Chi; Du, Wei; Liu, Chao; Xu, Ying

    2015-01-01

    The grade of a cancer is a measure of the cancer's malignancy level, and the stage of a cancer refers to the size and the extent that the cancer has spread. Here we present a computational method for prediction of gene signatures and blood/urine protein markers for breast cancer grades and stages based on RNA-seq data, which are retrieved from the TCGA breast cancer dataset and cover 111 pairs of disease and matching adjacent noncancerous tissues with pathologists-assigned stages and grades. By applying a differential expression and an SVM-based classification approach, we found that 324 and 227 genes in cancer have their expression levels consistently up-regulated vs. their matching controls in a grade- and stage-dependent manner, respectively. By using these genes, we predicted a 9-gene panel as a gene signature for distinguishing poorly differentiated from moderately and well differentiated breast cancers, and a 19-gene panel as a gene signature for discriminating between the moderately and well differentiated breast cancers. Similarly, a 30-gene panel and a 21-gene panel are predicted as gene signatures for distinguishing advanced stage (stages III-IV) from early stage (stages I-II) cancer samples and for distinguishing stage II from stage I samples, respectively. We expect these gene panels can be used as gene-expression signatures for cancer grade and stage classification. In addition, of the 324 grade-dependent genes, 188 and 66 encode proteins that are predicted to be blood-secretory and urine-excretory, respectively; and of the 227 stage-dependent genes, 123 and 51 encode proteins predicted to be blood-secretory and urine-excretory, respectively. We anticipate that some combinations of these blood and urine proteins could serve as markers for monitoring breast cancer at specific grades and stages through blood and urine tests.

  10. Identification of Gene-Expression Signatures and Protein Markers for Breast Cancer Grading and Staging

    PubMed Central

    Yao, Fang; Zhang, Chi; Du, Wei; Liu, Chao; Xu, Ying

    2015-01-01

    The grade of a cancer is a measure of the cancer's malignancy level, and the stage of a cancer refers to the size and the extent that the cancer has spread. Here we present a computational method for prediction of gene signatures and blood/urine protein markers for breast cancer grades and stages based on RNA-seq data, which are retrieved from the TCGA breast cancer dataset and cover 111 pairs of disease and matching adjacent noncancerous tissues with pathologists-assigned stages and grades. By applying a differential expression and an SVM-based classification approach, we found that 324 and 227 genes in cancer have their expression levels consistently up-regulated vs. their matching controls in a grade- and stage-dependent manner, respectively. By using these genes, we predicted a 9-gene panel as a gene signature for distinguishing poorly differentiated from moderately and well differentiated breast cancers, and a 19-gene panel as a gene signature for discriminating between the moderately and well differentiated breast cancers. Similarly, a 30-gene panel and a 21-gene panel are predicted as gene signatures for distinguishing advanced stage (stages III-IV) from early stage (stages I-II) cancer samples and for distinguishing stage II from stage I samples, respectively. We expect these gene panels can be used as gene-expression signatures for cancer grade and stage classification. In addition, of the 324 grade-dependent genes, 188 and 66 encode proteins that are predicted to be blood-secretory and urine-excretory, respectively; and of the 227 stage-dependent genes, 123 and 51 encode proteins predicted to be blood-secretory and urine-excretory, respectively. We anticipate that some combinations of these blood and urine proteins could serve as markers for monitoring breast cancer at specific grades and stages through blood and urine tests. PMID:26375396

  11. Gene Expression Signature in Peripheral Blood Detects Thoracic Aortic Aneurysm

    PubMed Central

    Shiffman, Dov; Balasubramanian, Sriram; Iakoubova, Olga; Tranquilli, Maryann; Albornoz, Gonzalo; Blake, Julie; Mehmet, Necip N.; Ngadimo, Dewi; Poulter, Karen; Chan, Frances; Samaha, Raymond R.; Elefteriades, John A.

    2007-01-01

    Background Thoracic aortic aneurysm (TAA) is usually asymptomatic and associated with high mortality. Adverse clinical outcome of TAA is preventable by elective surgical repair; however, identifying at-risk individuals is difficult. We hypothesized that gene expression patterns in peripheral blood cells may correlate with TAA disease status. Our goal was to identify a distinct gene expression signature in peripheral blood that may identify individuals at risk for TAA. Methods and Findings Whole genome gene expression profiles from 94 peripheral blood samples (collected from 58 individuals with TAA and 36 controls) were analyzed. Significance Analysis of Microarray (SAM) identified potential signature genes characterizing TAA vs. normal, ascending vs. descending TAA, and sporadic vs. familial TAA. Using a training set containing 36 TAA patients and 25 controls, a 41-gene classification model was constructed for detecting TAA status and an overall accuracy of 78±6% was achieved. Testing this classifier on an independent validation set containing 22 TAA samples and 11 controls yielded an overall classification accuracy of 78%. These 41 classifier genes were further validated by TaqMan® real-time PCR assays. Classification based on the TaqMan® data replicated the microarray results and achieved 80% classification accuracy on the testing set. Conclusions This study identified informative gene expression signatures in peripheral blood cells that can characterize TAA status and subtypes of TAA. Moreover, a 41-gene classifier based on expression signature can identify TAA patients with high accuracy. The transcriptional programs in peripheral blood leading to the identification of these markers also provide insights into the mechanism of development of aortic aneurysms and highlight potential targets for therapeutic intervention. The classifier genes identified in this study, and validated by TaqMan® real-time PCR, define a set of promising potential diagnostic markers

  12. Predictive gene signatures: molecular markers distinguishing colon adenomatous polyp and carcinoma.

    PubMed

    Drew, Janice E; Farquharson, Andrew J; Mayer, Claus Dieter; Vase, Hollie F; Coates, Philip J; Steele, Robert J; Carey, Francis A

    2014-01-01

    Cancers exhibit abnormal molecular signatures associated with disease initiation and progression. Molecular signatures could improve cancer screening, detection, drug development and selection of appropriate drug therapies for individual patients. Typically only very small amounts of tissue are available from patients for analysis and biopsy samples exhibit broad heterogeneity that cannot be captured using a single marker. This report details application of an in-house custom designed GenomeLab System multiplex gene expression assay, the hCellMarkerPlex, to assess predictive gene signatures of normal, adenomatous polyp and carcinoma colon tissue using archived tissue bank material. The hCellMarkerPlex incorporates twenty-one gene markers: epithelial (EZR, KRT18, NOX1, SLC9A2), proliferation (PCNA, CCND1, MS4A12), differentiation (B4GANLT2, CDX1, CDX2), apoptotic (CASP3, NOX1, NTN1), fibroblast (FSP1, COL1A1), structural (ACTG2, CNN1, DES), gene transcription (HDAC1), stem cell (LGR5), endothelial (VWF) and mucin production (MUC2). Gene signatures distinguished normal, adenomatous polyp and carcinoma. Individual gene targets significantly contributing to molecular tissue types, classifier genes, were further characterised using real-time PCR, in-situ hybridisation and immunohistochemistry revealing aberrant epithelial expression of MS4A12, LGR5 CDX2, NOX1 and SLC9A2 prior to development of carcinoma. Identified gene signatures identify aberrant epithelial expression of genes prior to cancer development using in-house custom designed gene expression multiplex assays. This approach may be used to assist in objective classification of disease initiation, staging, progression and therapeutic responses using biopsy material.

  13. Binding Affinity prediction with Property Encoded Shape Distribution signatures

    PubMed Central

    Das, Sourav; Krein, Michael P.

    2010-01-01

    We report the use of the molecular signatures known as “Property-Encoded Shape Distributions” (PESD) together with standard Support Vector Machine (SVM) techniques to produce validated models that can predict the binding affinity of a large number of protein ligand complexes. This “PESD-SVM” method uses PESD signatures that encode molecular shapes and property distributions on protein and ligand surfaces as features to build SVM models that require no subjective feature selection. A simple protocol was employed for tuning the SVM models during their development, and the results were compared to SFCscore – a regression-based method that was previously shown to perform better than 14 other scoring functions. Although the PESD-SVM method is based on only two surface property maps, the overall results were comparable. For most complexes with a dominant enthalpic contribution to binding (ΔH/-TΔS > 3), a good correlation between true and predicted affinities was observed. Entropy and solvent were not considered in the present approach and further improvement in accuracy would require accounting for these components rigorously. PMID:20095526

  14. Conserved Expression Signatures between Medaka and Human Pigment Cell Tumors

    PubMed Central

    Schartl, Manfred; Kneitz, Susanne; Wilde, Brigitta; Wagner, Toni; Henkel, Christiaan V.; Spaink, Herman P.; Meierjohann, Svenja

    2012-01-01

    Aberrations in gene expression are a hallmark of cancer cells. Differential tumor-specific transcript levels of single genes or whole sets of genes may be critical for the neoplastic phenotype and important for therapeutic considerations or useful as biomarkers. As an approach to filter out such relevant expression differences from the plethora of changes noted in global expression profiling studies, we searched for changes of gene expression levels that are conserved. Transcriptomes from massive parallel sequencing of different types of melanoma from medaka were generated and compared to microarray datasets from zebrafish and human melanoma. This revealed molecular conservation at various levels between fish models and human tumors providing a useful strategy for identifying expression signatures strongly associated with disease phenotypes and uncovering new melanoma molecules. PMID:22693581

  15. Integrative analysis of lung development-cancer expression associations reveals the roles of signatures with inverse expression patterns.

    PubMed

    Zhang, Chunlong; Li, Chunquan; Xu, Yanjun; Feng, Li; Shang, Desi; Yang, Xinmiao; Han, Junwei; Sun, Zeguo; Li, Yixue; Li, Xia

    2015-05-01

    Recent studies have focused on exploring the associations between organ development and malignant tumors; however, the clinical relevance of the development signatures was inadequately addressed in lung cancer. In this study, we explored the associations between lung development and lung cancer progression by analyzing a total of two development and seven cancer datasets. We identified representative expression patterns (continuously up- and down-regulated) from development and cancer profiles, and inverse pattern associations were observed at both the gene and functional levels. Furthermore, we dissected the biological processes dominating the associations, and found that proliferation and immunity were respectively involved in the two inverse development-cancer expression patterns. Through sub-pathway analysis of the signatures with inverse expression patterns, we finally identified a 13-gene risk signature from the cell cycle sub-pathway, and evaluated its predictive performance for lung cancer patient clinical outcome using independent cohorts. Our findings indicated that the integrative analysis of development and cancer expression patterns provided a framework for identifying effective molecular signatures for clinical utility.

  16. Differentially Expressed Genes and Signature Pathways of Human Prostate Cancer

    PubMed Central

    Myers, Jennifer S.; von Lersner, Ariana K.; Robbins, Charles J.; Sang, Qing-Xiang Amy

    2015-01-01

    Genomic technologies including microarrays and next-generation sequencing have enabled the generation of molecular signatures of prostate cancer. Lists of differentially expressed genes between malignant and non-malignant states are thought to be fertile sources of putative prostate cancer biomarkers. However such lists of differentially expressed genes can be highly variable for multiple reasons. As such, looking at differential expression in the context of gene sets and pathways has been more robust. Using next-generation genome sequencing data from The Cancer Genome Atlas, differential gene expression between age- and stage- matched human prostate tumors and non-malignant samples was assessed and used to craft a pathway signature of prostate cancer. Up- and down-regulated genes were assigned to pathways composed of curated groups of related genes from multiple databases. The significance of these pathways was then evaluated according to the number of differentially expressed genes found in the pathway and their position within the pathway using Gene Set Enrichment Analysis and Signaling Pathway Impact Analysis. The “transforming growth factor-beta signaling” and “Ran regulation of mitotic spindle formation” pathways were strongly associated with prostate cancer. Several other significant pathways confirm reported findings from microarray data that suggest actin cytoskeleton regulation, cell cycle, mitogen-activated protein kinase signaling, and calcium signaling are also altered in prostate cancer. Thus we have demonstrated feasibility of pathway analysis and identified an underexplored area (Ran) for investigation in prostate cancer pathogenesis. PMID:26683658

  17. GESearch: An Interactive GUI Tool for Identifying Gene Expression Signature.

    PubMed

    Ye, Ning; Yin, Hengfu; Liu, Jingjing; Dai, Xiaogang; Yin, Tongming

    2015-01-01

    The huge amount of gene expression data generated by microarray and next-generation sequencing technologies present challenges to exploit their biological meanings. When searching for the coexpression genes, the data mining process is largely affected by selection of algorithms. Thus, it is highly desirable to provide multiple options of algorithms in the user-friendly analytical toolkit to explore the gene expression signatures. For this purpose, we developed GESearch, an interactive graphical user interface (GUI) toolkit, which is written in MATLAB and supports a variety of gene expression data files. This analytical toolkit provides four models, including the mean, the regression, the delegate, and the ensemble models, to identify the coexpression genes, and enables the users to filter data and to select gene expression patterns by browsing the display window or by importing knowledge-based genes. Subsequently, the utility of this analytical toolkit is demonstrated by analyzing two sets of real-life microarray datasets from cell-cycle experiments. Overall, we have developed an interactive GUI toolkit that allows for choosing multiple algorithms for analyzing the gene expression signatures.

  18. Minimising Immunohistochemical False Negative ER Classification Using a Complementary 23 Gene Expression Signature of ER Status

    PubMed Central

    Li, Qiyuan; Eklund, Aron C.; Juul, Nicolai; Haibe-Kains, Benjamin; Workman, Christopher T.; Richardson, Andrea L.; Szallasi, Zoltan; Swanton, Charles

    2010-01-01

    Background Expression of the oestrogen receptor (ER) in breast cancer predicts benefit from endocrine therapy. Minimising the frequency of false negative ER status classification is essential to identify all patients with ER positive breast cancers who should be offered endocrine therapies in order to improve clinical outcome. In routine oncological practice ER status is determined by semi-quantitative methods such as immunohistochemistry (IHC) or other immunoassays in which the ER expression level is compared to an empirical threshold[1], [2]. The clinical relevance of gene expression-based ER subtypes as compared to IHC-based determination has not been systematically evaluated. Here we attempt to reduce the frequency of false negative ER status classification using two gene expression approaches and compare these methods to IHC based ER status in terms of predictive and prognostic concordance with clinical outcome. Methodology/Principal Findings Firstly, ER status was discriminated by fitting the bimodal expression of ESR1 to a mixed Gaussian model. The discriminative power of ESR1 suggested bimodal expression as an efficient way to stratify breast cancer; therefore we identified a set of genes whose expression was both strongly bimodal, mimicking ESR expression status, and highly expressed in breast epithelial cell lines, to derive a 23-gene ER expression signature-based classifier. We assessed our classifiers in seven published breast cancer cohorts by comparing the gene expression-based ER status to IHC-based ER status as a predictor of clinical outcome in both untreated and tamoxifen treated cohorts. In untreated breast cancer cohorts, the 23 gene signature-based ER status provided significantly improved prognostic power compared to IHC-based ER status (P = 0.006). In tamoxifen-treated cohorts, the 23 gene ER expression signature predicted clinical outcome (HR = 2.20, P = 0.00035). These complementary ER signature-based strategies estimated that

  19. Molecular subsets in the gene expression signatures of scleroderma skin.

    PubMed

    Milano, Ausra; Pendergrass, Sarah A; Sargent, Jennifer L; George, Lacy K; McCalmont, Timothy H; Connolly, M Kari; Whitfield, Michael L

    2008-07-16

    Scleroderma is a clinically heterogeneous disease with a complex phenotype. The disease is characterized by vascular dysfunction, tissue fibrosis, internal organ dysfunction, and immune dysfunction resulting in autoantibody production. We analyzed the genome-wide patterns of gene expression with DNA microarrays in skin biopsies from distinct scleroderma subsets including 17 patients with systemic sclerosis (SSc) with diffuse scleroderma (dSSc), 7 patients with SSc with limited scleroderma (lSSc), 3 patients with morphea, and 6 healthy controls. 61 skin biopsies were analyzed in a total of 75 microarray hybridizations. Analysis by hierarchical clustering demonstrates nearly identical patterns of gene expression in 17 out of 22 of the forearm and back skin pairs of SSc patients. Using this property of the gene expression, we selected a set of 'intrinsic' genes and analyzed the inherent data-driven groupings. Distinct patterns of gene expression separate patients with dSSc from those with lSSc and both are easily distinguished from normal controls. Our data show three distinct patient groups among the patients with dSSc and two groups among patients with lSSc. Each group can be distinguished by unique gene expression signatures indicative of proliferating cells, immune infiltrates and a fibrotic program. The intrinsic groups are statistically significant (p<0.001) and each has been mapped to clinical covariates of modified Rodnan skin score, interstitial lung disease, gastrointestinal involvement, digital ulcers, Raynaud's phenomenon and disease duration. We report a 177-gene signature that is associated with severity of skin disease in dSSc. Genome-wide gene expression profiling of skin biopsies demonstrates that the heterogeneity in scleroderma can be measured quantitatively with DNA microarrays. The diversity in gene expression demonstrates multiple distinct gene expression programs in the skin of patients with scleroderma.

  20. Molecular Subsets in the Gene Expression Signatures of Scleroderma Skin

    PubMed Central

    Milano, Ausra; Pendergrass, Sarah A.; Sargent, Jennifer L.; George, Lacy K.; McCalmont, Timothy H.; Connolly, M. Kari; Whitfield, Michael L.

    2008-01-01

    Background Scleroderma is a clinically heterogeneous disease with a complex phenotype. The disease is characterized by vascular dysfunction, tissue fibrosis, internal organ dysfunction, and immune dysfunction resulting in autoantibody production. Methodology and Findings We analyzed the genome-wide patterns of gene expression with DNA microarrays in skin biopsies from distinct scleroderma subsets including 17 patients with systemic sclerosis (SSc) with diffuse scleroderma (dSSc), 7 patients with SSc with limited scleroderma (lSSc), 3 patients with morphea, and 6 healthy controls. 61 skin biopsies were analyzed in a total of 75 microarray hybridizations. Analysis by hierarchical clustering demonstrates nearly identical patterns of gene expression in 17 out of 22 of the forearm and back skin pairs of SSc patients. Using this property of the gene expression, we selected a set of ‘intrinsic’ genes and analyzed the inherent data-driven groupings. Distinct patterns of gene expression separate patients with dSSc from those with lSSc and both are easily distinguished from normal controls. Our data show three distinct patient groups among the patients with dSSc and two groups among patients with lSSc. Each group can be distinguished by unique gene expression signatures indicative of proliferating cells, immune infiltrates and a fibrotic program. The intrinsic groups are statistically significant (p<0.001) and each has been mapped to clinical covariates of modified Rodnan skin score, interstitial lung disease, gastrointestinal involvement, digital ulcers, Raynaud's phenomenon and disease duration. We report a 177-gene signature that is associated with severity of skin disease in dSSc. Conclusions and Significance Genome-wide gene expression profiling of skin biopsies demonstrates that the heterogeneity in scleroderma can be measured quantitatively with DNA microarrays. The diversity in gene expression demonstrates multiple distinct gene expression programs in the skin of

  1. A TCRβ Repertoire Signature Can Predict Experimental Cerebral Malaria.

    PubMed

    Mariotti-Ferrandiz, Encarnita; Pham, Hang-Phuong; Dulauroy, Sophie; Gorgette, Olivier; Klatzmann, David; Cazenave, Pierre-André; Pied, Sylviane; Six, Adrien

    2016-01-01

    Cerebral Malaria (CM) is associated with a pathogenic T cell response. Mice infected by P. berghei ANKA clone 1.49 (PbA) developing CM (CM+) present an altered PBL TCR repertoire, partly due to recurrently expanded T cell clones, as compared to non-infected and CM- infected mice. To analyse the relationship between repertoire alteration and CM, we performed a kinetic analysis of the TRBV repertoire during the course of the infection until CM-related death in PbA-infected mice. The repertoires of PBL, splenocytes and brain lymphocytes were compared between infected and non-infected mice using a high-throughput CDR3 spectratyping method. We observed a modification of the whole TCR repertoire in the spleen and blood of infected mice, from the fifth and the sixth day post-infection, respectively, while only three TRBV were significantly perturbed in the brain of infected mice. Using multivariate analysis and statistical modelling, we identified a unique TCRβ signature discriminating CM+ from CTR mice, enriched during the course of the infection in the spleen and the blood and predicting CM onset. These results highlight a dynamic modification and compartmentalization of the TCR diversity during the course of PbA infection, and provide a novel method to identify disease-associated TCRβ signature as diagnostic and prognostic biomarkers.

  2. A TCRβ Repertoire Signature Can Predict Experimental Cerebral Malaria

    PubMed Central

    Dulauroy, Sophie; Gorgette, Olivier; Klatzmann, David; Cazenave, Pierre-André; Pied, Sylviane; Six, Adrien

    2016-01-01

    Cerebral Malaria (CM) is associated with a pathogenic T cell response. Mice infected by P. berghei ANKA clone 1.49 (PbA) developing CM (CM+) present an altered PBL TCR repertoire, partly due to recurrently expanded T cell clones, as compared to non-infected and CM- infected mice. To analyse the relationship between repertoire alteration and CM, we performed a kinetic analysis of the TRBV repertoire during the course of the infection until CM-related death in PbA-infected mice. The repertoires of PBL, splenocytes and brain lymphocytes were compared between infected and non-infected mice using a high-throughput CDR3 spectratyping method. We observed a modification of the whole TCR repertoire in the spleen and blood of infected mice, from the fifth and the sixth day post-infection, respectively, while only three TRBV were significantly perturbed in the brain of infected mice. Using multivariate analysis and statistical modelling, we identified a unique TCRβ signature discriminating CM+ from CTR mice, enriched during the course of the infection in the spleen and the blood and predicting CM onset. These results highlight a dynamic modification and compartmentalization of the TCR diversity during the course of PbA infection, and provide a novel method to identify disease-associated TCRβ signature as diagnostic and prognostic biomarkers. PMID:26844551

  3. Irma 5.2 multi-sensor signature prediction model

    NASA Astrophysics Data System (ADS)

    Savage, James; Coker, Charles; Thai, Bea; Aboutalib, Omar; Pau, John

    2008-04-01

    The Irma synthetic signature prediction code is being developed by the Munitions Directorate of the Air Force Research Laboratory (AFRL/RW) to facilitate the research and development of multi-sensor systems. There are over 130 users within the Department of Defense, NASA, Department of Transportation, academia, and industry. Irma began as a high-resolution, physics-based Infrared (IR) target and background signature model for tactical weapon applications and has grown to include: a laser (or active) channel (1990), improved scene generator to support correlated frame-to-frame imagery (1992), and passive IR/millimeter wave (MMW) channel for a co-registered active/passive IR/MMW model (1994). Irma version 5.0 was released in 2000 and encompassed several upgrades to both the physical models and software; host support was expanded to Windows, Linux, Solaris, and SGI Irix platforms. In 2005, version 5.1 was released after extensive verification and validation of an upgraded and reengineered ladar channel. The reengineering effort then shifted focus to the Irma passive channel. Field measurements for the validation effort include both polarized and unpolarized data collection. Irma 5.2 was released in 2007 with a reengineered passive channel. This paper summarizes the capabilities of Irma and the progress toward Irma 5.3, which includes a reengineered radar channel.

  4. Irma 5.2 multi-sensor signature prediction model

    NASA Astrophysics Data System (ADS)

    Savage, James; Coker, Charles; Thai, Bea; Aboutalib, Omar; Chow, Anthony; Yamaoka, Neil; Kim, Charles

    2007-04-01

    The Irma synthetic signature prediction code is being developed by the Munitions Directorate of the Air Force Research Laboratory (AFRL/MN) to facilitate the research and development of multi-sensor systems. There are over 130 users within the Department of Defense, NASA, Department of Transportation, academia, and industry. Irma began as a high-resolution, physics-based Infrared (IR) target and background signature model for tactical weapon applications and has grown to include: a laser (or active) channel (1990), improved scene generator to support correlated frame-to-frame imagery (1992), and passive IR/millimeter wave (MMW) channel for a co-registered active/passive IR/MMW model (1994). Irma version 5.0 was released in 2000 and encompassed several upgrades to both the physical models and software; host support was expanded to Windows, Linux, Solaris, and SGI Irix platforms. In 2005, version 5.1 was released after an extensive verification and validation of an upgraded and reengineered active channel. Since 2005, the reengineering effort has focused on the Irma passive channel. Field measurements for the validation effort include the unpolarized data collection. Irma 5.2 is scheduled for release in the summer of 2007. This paper will report the validation test results of the Irma passive models and discuss the new features in Irma 5.2.

  5. A gene expression signature of retinoblastoma loss-of-function is a predictive biomarker of resistance to palbociclib in breast cancer cell lines and is prognostic in patients with ER positive early breast cancer.

    PubMed

    Malorni, Luca; Piazza, Silvano; Ciani, Yari; Guarducci, Cristina; Bonechi, Martina; Biagioni, Chiara; Hart, Christopher D; Verardo, Roberto; Di Leo, Angelo; Migliaccio, Ilenia

    2016-09-13

    Palbociclib is a CDK4/6 inhibitor that received FDA approval for treatment of hormone receptor positive (HR+) HER2 negative (HER2neg) advanced breast cancer. To better personalize patients treatment it is critical to identify subgroups that would mostly benefit from it. We hypothesize that complex alterations of the Retinoblastoma (Rb) pathway might be implicated in resistance to CDK4/6 inhibitors and aim to investigate whether signatures of Rb loss-of-function would identify breast cancer cell lines resistant to palbociclib. We established a gene expression signature of Rb loss-of-function (RBsig) by identifying genes correlated with E2F1 and E2F2 expression in breast cancers within The Cancer Genome Atlas. We assessed the RBsig prognostic role in the METABRIC and in a comprehensive breast cancer meta-dataset. Finally, we analyzed whether RBsig would discriminate palbociclib-sensitive and -resistant breast cancer cells in a large RNA sequencing-based dataset. The RBsig was associated with RB1 genetic status in all tumors (p <7e-32) and in luminal or basal subtypes (p < 7e-11 and p < 0.002, respectively). The RBsig was prognostic in the METABRIC dataset (discovery: HR = 1.93 [1.5-2.4] p = 1.4e-08; validation: HR = 2.01 [1.6-2.5] p = 1.3e-09). Untreated and endocrine treated patients with estrogen receptor positive breast cancer expressing high RBsig had significantly worse recurrence free survival compared to those with low RBsig (HR = 2.37 [1.8 - 3.2] p = 1.87e-08 and HR = 2.62 [1.9- 3.5] p = 8.6e-11, respectively). The RBsig was able to identify palbociclib resistant and sensitive breast cancer cells (ROC AUC = 0,7778). Signatures of RB loss might be helpful in personalizing treatment of patients with HR+/HER2neg breast cancer. Further validation in patients receiving palbociclib is warranted.

  6. A gene expression signature of retinoblastoma loss-of-function is a predictive biomarker of resistance to palbociclib in breast cancer cell lines and is prognostic in patients with ER positive early breast cancer

    PubMed Central

    Malorni, Luca; Piazza, Silvano; Ciani, Yari; Guarducci, Cristina; Bonechi, Martina; Biagioni, Chiara; Hart, Christopher D.; Verardo, Roberto; Leo, Angelo Di; Migliaccio, Ilenia

    2016-01-01

    Palbociclib is a CDK4/6 inhibitor that received FDA approval for treatment of hormone receptor positive (HR+) HER2 negative (HER2neg) advanced breast cancer. To better personalize patients treatment it is critical to identify subgroups that would mostly benefit from it. We hypothesize that complex alterations of the Retinoblastoma (Rb) pathway might be implicated in resistance to CDK4/6 inhibitors and aim to investigate whether signatures of Rb loss-of-function would identify breast cancer cell lines resistant to palbociclib. We established a gene expression signature of Rb loss-of-function (RBsig) by identifying genes correlated with E2F1 and E2F2 expression in breast cancers within The Cancer Genome Atlas. We assessed the RBsig prognostic role in the METABRIC and in a comprehensive breast cancer meta-dataset. Finally, we analyzed whether RBsig would discriminate palbociclib-sensitive and -resistant breast cancer cells in a large RNA sequencing-based dataset. The RBsig was associated with RB1 genetic status in all tumors (p <7e-32) and in luminal or basal subtypes (p < 7e-11 and p < 0.002, respectively). The RBsig was prognostic in the METABRIC dataset (discovery: HR = 1.93 [1.5-2.4] p = 1.4e-08; validation: HR = 2.01 [1.6-2.5] p = 1.3e-09). Untreated and endocrine treated patients with estrogen receptor positive breast cancer expressing high RBsig had significantly worse recurrence free survival compared to those with low RBsig (HR = 2.37 [1.8 − 3.2] p = 1.87e−08 and HR = 2.62 [1.9− 3.5] p = 8.6e−11, respectively). The RBsig was able to identify palbociclib resistant and sensitive breast cancer cells (ROC AUC = 0,7778). Signatures of RB loss might be helpful in personalizing treatment of patients with HR+/HER2neg breast cancer. Further validation in patients receiving palbociclib is warranted. PMID:27634906

  7. A novel gene expression signature for bone metastasis in breast carcinomas.

    PubMed

    Savci-Heijink, C Dilara; Halfwerk, Hans; Koster, Jan; van de Vijver, Marc J

    2016-04-01

    Metastatic cancer remains the leading cause of death for patients with breast cancer. To understand the mechanisms underlying the development of distant metastases to specific sites is therefore important and of potential clinical value. From 157 primary breast tumours of the patients with known metastatic disease, gene expression profiling data were generated and correlated to metastatic behaviour including site-specific metastasis, metastasis pattern and survival outcomes. We analysed gene expression signatures specifically associated with the development of bone metastases. As a validation cohort, we used a published dataset of 376 breast carcinomas for which gene expression data and site-specific metastasis information were available. 80.5 % of luminal-type tumours developed bone metastasis as opposed to 41.7 % of basal and 55.6 % of HER2-like tumours. A novel 15-gene signature identified 82.4 % of the tumours with bone metastasis, 85.2 % of the tumours which had bone metastasis as first site of metastasis and 100 % of the ones with bone metastasis only (p 9.99e-09), in the training set. In the independent dataset, 81.2 % of the positive tested tumours had known metastatic disease to the bone (p 4.28e-10). This 15-gene signature showed much better correlation with the development of bone metastases than previously identified signatures and was predictive in both ER-positive as well as in ER-negative tumours. Multivariate analyses revealed that together with the molecular subtype, our 15-gene expression signature was significantly correlated to bone metastasis status (p <0.001, 95 % CI 3.86-48.02 in the training set; p 0.001, 95 % CI 1.54-5.00 in the independent set). The 15 genes, APOPEC3B, ATL2, BBS1, C6orf61, C6orf167, MMS22L, KCNS1, MFAP3L, NIP7, NUP155, PALM2, PH-4, PGD5, SFT2D2 and STEAP3, encoded mainly membrane-bound molecules with molecular function of protein binding. The expression levels of the up-regulated genes (NAT1, BBS1 and PH-4) were

  8. Translational signatures and mRNA levels are highly correlated in human stably expressed genes.

    PubMed

    Line, Sergio R P; Liu, Xiaoming; de Souza, Ana Paula; Yu, Fuli

    2013-04-19

    Gene expression is one of the most relevant biological processes of living cells. Due to the relative small population sizes, it is predicted that human gene sequences are not strongly influenced by selection towards expression efficiency. One of the major problems in estimating to what extent gene characteristics can be selected to maximize expression efficiency is the wide variation that exists in RNA and protein levels among physiological states and different tissues. Analyses of datasets of stably expressed genes (i.e. with consistent expression between physiological states and tissues) would provide more accurate and reliable measurements of associations between variations of a specific gene characteristic and expression, and how distinct gene features work to optimize gene expression. Using a dataset of human genes with consistent expression between physiological states we selected gene sequence signatures related to translation that can predict about 42% of mRNA variation. The prediction can be increased to 51% when selecting genes that are stably expressed in more than 1 tissue. These genes are enriched for translation and ribosome biosynthesis processes and have higher translation efficiency scores, smaller coding sequences and 3' UTR sizes and lower folding energies when compared to other datasets. Additionally, the amino acid frequencies weighted by expression showed higher correlations with isoacceptor tRNA gene copy number, and smaller absolute correlation values with biosynthetic costs. Our results indicate that human gene sequence characteristics related to transcription and translation processes can co-evolve in an integrated manner in order to optimize gene expression.

  9. An Endotoxin Tolerance Signature Predicts Sepsis and Organ Dysfunction at Initial Clinical Presentation

    PubMed Central

    Pena, Olga M.; Hancock, David G.; Lyle, Ngan H.; Linder, Adam; Russell, James A.; Xia, Jianguo; Fjell, Christopher D.; Boyd, John H.; Hancock, Robert E.W.

    2014-01-01

    Background Sepsis involves aberrant immune responses to infection, but the exact nature of this immune dysfunction remains poorly defined. Bacterial endotoxins like lipopolysaccharide (LPS) are potent inducers of inflammation, which has been associated with the pathophysiology of sepsis, but repeated exposure can also induce a suppressive effect known as endotoxin tolerance or cellular reprogramming. It has been proposed that endotoxin tolerance might be associated with the immunosuppressive state that was primarily observed during late-stage sepsis. However, this relationship remains poorly characterised. Here we clarify the underlying mechanisms and timing of immune dysfunction in sepsis. Methods We defined a gene expression signature characteristic of endotoxin tolerance. Gene-set test approaches were used to correlate this signature with early sepsis, both newly and retrospectively analysing microarrays from 593 patients in 11 cohorts. Then we recruited a unique cohort of possible sepsis patients at first clinical presentation in an independent blinded controlled observational study to determine whether this signature was associated with the development of confirmed sepsis and organ dysfunction. Findings All sepsis patients presented an expression profile strongly associated with the endotoxin tolerance signature (p < 0.01; AUC 96.1%). Importantly, this signature further differentiated between suspected sepsis patients who did, or did not, go on to develop confirmed sepsis, and predicted the development of organ dysfunction. Interpretation Our data support an updated model of sepsis pathogenesis in which endotoxin tolerance-mediated immune dysfunction (cellular reprogramming) is present throughout the clinical course of disease and related to disease severity. Thus endotoxin tolerance might offer new insights guiding the development of new therapies and diagnostics for early sepsis. PMID:25685830

  10. A rank-based transcriptional signature for predicting relapse risk of stage II colorectal cancer identified with proper data sources

    PubMed Central

    Zhao, Wenyuan; Chen, Beibei; Guo, Xin; Wang, Ruiping; Chang, Zhiqiang; Dong, Yu; Song, Kai; Wang, Wen; Qi, Lishuang; Gu, Yunyan; Wang, Chenguang; Yang, Da; Guo, Zheng

    2016-01-01

    The irreproducibility problem seriously hinders the studies on transcriptional signatures for predicting relapse risk of early stage colorectal cancer (CRC) patients. Through reviewing recently published 34 literatures for the development of CRC prognostic signatures based on gene expression profiles, we revealed a surprising phenomenon that 33 of these studies analyzed CRC samples with and without adjuvant chemotherapy together in the training and/or validation datasets. This data misuse problem could be partially attributed to the unclear and incomplete data annotation in public data sources. Furthermore, all the signatures proposed by these studies were based on risk scores summarized from gene expression levels, which are sensitive to experimental batch effects and risk compositions of the samples analyzed together. To avoid the above-mentioned problems, we carefully selected three qualified large datasets to develop and validate a signature consisting of three pairs of genes. The within-sample relative expression orderings of these gene pairs could robustly predict relapse risk of stage II CRC samples assessed in different laboratories. The transcriptional and functional analyses provided clear evidence that the high risk patients predicted by the proposed signature represent patients with micro-metastases. PMID:26967049

  11. A gene expression signature that defines breast cancer metastases.

    PubMed

    Ellsworth, Rachel E; Seebach, Jeff; Field, Lori A; Heckman, Caroline; Kane, Jennifer; Hooke, Jeffrey A; Love, Brad; Shriver, Craig D

    2009-01-01

    The most important predictor of prognosis in breast cancer is lymph node status, yet little is known about molecular changes associated with lymph node metastasis. Here, gene expression analysis was performed on primary breast (PBT) and corresponding metastatic lymph node (MLN) tumors to identify molecular signatures associated with nodal metastasis. RNA was isolated after laser microdissection from frozen PBT and MLN from 20 patients with positive lymph nodes and hybridized to the microarray chips. Differential expression was determined using Mann-Whitney testing; Bonferroni corrected P values of 0.05 and 0.001 were calculated. Results were validated using TaqMan assays. Fifty-one genes were differentially expressed (P < 1 x 10(-5), less than twofold differences) between the PBT and paired MLN; 13 with significantly higher expression in the MLN and 38 in the PBT. qRT-PCR validated the differential expression of 40/51 genes. Of the 40 validated genes, NTS and PAX5 were found to have >100-fold higher expression in MLT while COL11A1, KRT14, MMP13, TAC1 and WNT2 had >100-fold higher expression in PBT. Gene expression differences between PBT and MLN suggests that expression of a unique set of genes is required for successful lymph node colonization. Genes expressed at higher levels in PBT are involved in degradation of the extracellular matrix, enabling cells with metastatic potential to disseminate, while genes expressed at higher levels in metastases are involved in transcription, signal transduction and immune response, providing cells with proliferation and survival advantages. These data improve our understanding of the biological processes involved in successful metastatis and provide new targets to arrest tumor cell dissemination and metastatic colonization.

  12. Irma 5.1 multisensor signature prediction model

    NASA Astrophysics Data System (ADS)

    Savage, James; Coker, Charles; Edwards, Dave; Thai, Bea; Aboutalib, Omar; Chow, Anthony; Yamaoka, Neil; Kim, Charles

    2006-05-01

    The Irma synthetic signature prediction code is being developed to facilitate the research and development of multi-sensor systems. Irma was one of the first high resolution, physics-based Infrared (IR) target and background signature models to be developed for tactical weapon applications. Originally developed in 1980 by the Munitions Directorate of the Air Force Research Laboratory (AFRL/MN), the Irma model was used exclusively to generate IR scenes. In 1988, a number of significant upgrades to Irma were initiated including the addition of a laser (or active) channel. This two-channel version was released to the user community in 1990. In 1992, an improved scene generator was incorporated into the Irma model, which supported correlated frame-to-frame imagery. A passive IR/millimeter wave (MMW) code was completed in 1994. This served as the cornerstone for the development of the co-registered active/passive IR/MMW model, Irma 4.0. In 2000, Irma version 5.0 was released which encompassed several upgrades to both the physical models and software. Circular polarization was added to the passive channel, and a Doppler capability was added to the active MMW channel. In 2002, the multibounce technique was added to the Irma passive channel. In the ladar channel, a user-friendly Ladar Sensor Assistant (LSA) was incorporated which provides capability and flexibility for sensor modeling. Irma 5.0 runs on several platforms including Windows, Linux, Solaris, and SGI Irix. Irma is currently used to support a number of civilian and military applications. The Irma user base includes over 130 agencies within the Air Force, Army, Navy, DARPA, NASA, Department of Transportation, academia, and industry. In 2005, Irma version 5.1 was released to the community. In addition to upgrading the Ladar channel code to an object oriented language (C++) and providing a new graphical user interface to construct scenes, this new release significantly improves the modeling of the ladar channel and

  13. Irma 5.1 multisensor signature prediction model

    NASA Astrophysics Data System (ADS)

    Savage, James; Coker, Charles; Thai, Bea; Aboutalib, Omar; Yamaoka, Neil; Kim, Charles

    2005-05-01

    The Irma synthetic signature prediction code is being developed to facilitate the research and development of multisensor systems. Irma was one of the first high resolution Infrared (IR) target and background signature models to be developed for tactical weapon application. Originally developed in 1980 by the Munitions Directorate of the Air Force Research Laboratory (AFRL/MN), the Irma model was used exclusively to generate IR scenes. In 1988, a number of significant upgrades to Irma were initiated including the addition of a laser (or active) channel. This two-channel version was released to the user community in 1990. In 1992, an improved scene generator was incorporated into the Irma model, which supported correlated frame-to-frame imagery. A passive IR/millimeter wave (MMW) code was completed in 1994. This served as the cornerstone for the development of the co-registered active/passive IR/MMW model, Irma 4.0. In 2000, Irma version 5.0 was released which encompassed several upgrades to both the physical models and software. Circular polarization was added to the passive channel and the doppler capability was added to the active MMW channel. In 2002, the multibounce technique was added to the Irma passive channel. In the ladar channel, a user-friendly Ladar Sensor Assistant (LSA) was incorporated which provides capability and flexibility for sensor modeling. Irma 5.0 runs on several platforms including Windows, Linux, Solaris, and SGI Irix. Since 2000, additional capabilities and enhancements have been added to the ladar channel including polarization and speckle effect. Work is still ongoing to add time-jittering model to the ladar channel. A new user interface has been introduced to aid users in the mechanism of scene generation and running the Irma code. The user interface provides a canvas where a user can add and remove objects using mouse clicks to construct a scene. The scene can then be visualized to find the desired sensor position. The synthetic ladar

  14. Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd

    NASA Astrophysics Data System (ADS)

    Wang, Zichen; Monteiro, Caroline D.; Jagodnik, Kathleen M.; Fernandez, Nicolas F.; Gundersen, Gregory W.; Rouillard, Andrew D.; Jenkins, Sherry L.; Feldmann, Axel S.; Hu, Kevin S.; McDermott, Michael G.; Duan, Qiaonan; Clark, Neil R.; Jones, Matthew R.; Kou, Yan; Goff, Troy; Woodland, Holly; Amaral, Fabio M. R.; Szeto, Gregory L.; Fuchs, Oliver; Schüssler-Fiorenza Rose, Sophia M.; Sharma, Shvetank; Schwartz, Uwe; Bausela, Xabier Bengoetxea; Szymkiewicz, Maciej; Maroulis, Vasileios; Salykin, Anton; Barra, Carolina M.; Kruth, Candice D.; Bongio, Nicholas J.; Mathur, Vaibhav; Todoric, Radmila D.; Rubin, Udi E.; Malatras, Apostolos; Fulp, Carl T.; Galindo, John A.; Motiejunaite, Ruta; Jüschke, Christoph; Dishuck, Philip C.; Lahl, Katharina; Jafari, Mohieddin; Aibar, Sara; Zaravinos, Apostolos; Steenhuizen, Linda H.; Allison, Lindsey R.; Gamallo, Pablo; de Andres Segura, Fernando; Dae Devlin, Tyler; Pérez-García, Vicente; Ma'Ayan, Avi

    2016-09-01

    Gene expression data are accumulating exponentially in public repositories. Reanalysis and integration of themed collections from these studies may provide new insights, but requires further human curation. Here we report a crowdsourcing project to annotate and reanalyse a large number of gene expression profiles from Gene Expression Omnibus (GEO). Through a massive open online course on Coursera, over 70 participants from over 25 countries identify and annotate 2,460 single-gene perturbation signatures, 839 disease versus normal signatures, and 906 drug perturbation signatures. All these signatures are unique and are manually validated for quality. Global analysis of these signatures confirms known associations and identifies novel associations between genes, diseases and drugs. The manually curated signatures are used as a training set to develop classifiers for extracting similar signatures from the entire GEO repository. We develop a web portal to serve these signatures for query, download and visualization.

  15. Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd

    PubMed Central

    Wang, Zichen; Monteiro, Caroline D.; Jagodnik, Kathleen M.; Fernandez, Nicolas F.; Gundersen, Gregory W.; Rouillard, Andrew D.; Jenkins, Sherry L.; Feldmann, Axel S.; Hu, Kevin S.; McDermott, Michael G.; Duan, Qiaonan; Clark, Neil R.; Jones, Matthew R.; Kou, Yan; Goff, Troy; Woodland, Holly; Amaral, Fabio M R.; Szeto, Gregory L.; Fuchs, Oliver; Schüssler-Fiorenza Rose, Sophia M.; Sharma, Shvetank; Schwartz, Uwe; Bausela, Xabier Bengoetxea; Szymkiewicz, Maciej; Maroulis, Vasileios; Salykin, Anton; Barra, Carolina M.; Kruth, Candice D.; Bongio, Nicholas J.; Mathur, Vaibhav; Todoric, Radmila D; Rubin, Udi E.; Malatras, Apostolos; Fulp, Carl T.; Galindo, John A.; Motiejunaite, Ruta; Jüschke, Christoph; Dishuck, Philip C.; Lahl, Katharina; Jafari, Mohieddin; Aibar, Sara; Zaravinos, Apostolos; Steenhuizen, Linda H.; Allison, Lindsey R.; Gamallo, Pablo; de Andres Segura, Fernando; Dae Devlin, Tyler; Pérez-García, Vicente; Ma'ayan, Avi

    2016-01-01

    Gene expression data are accumulating exponentially in public repositories. Reanalysis and integration of themed collections from these studies may provide new insights, but requires further human curation. Here we report a crowdsourcing project to annotate and reanalyse a large number of gene expression profiles from Gene Expression Omnibus (GEO). Through a massive open online course on Coursera, over 70 participants from over 25 countries identify and annotate 2,460 single-gene perturbation signatures, 839 disease versus normal signatures, and 906 drug perturbation signatures. All these signatures are unique and are manually validated for quality. Global analysis of these signatures confirms known associations and identifies novel associations between genes, diseases and drugs. The manually curated signatures are used as a training set to develop classifiers for extracting similar signatures from the entire GEO repository. We develop a web portal to serve these signatures for query, download and visualization. PMID:27667448

  16. Integrating multiple molecular sources into a clinical risk prediction signature by extracting complementary information.

    PubMed

    Hieke, Stefanie; Benner, Axel; Schlenl, Richard F; Schumacher, Martin; Bullinger, Lars; Binder, Harald

    2016-08-30

    High-throughput technology allows for genome-wide measurements at different molecular levels for the same patient, e.g. single nucleotide polymorphisms (SNPs) and gene expression. Correspondingly, it might be beneficial to also integrate complementary information from different molecular levels when building multivariable risk prediction models for a clinical endpoint, such as treatment response or survival. Unfortunately, such a high-dimensional modeling task will often be complicated by a limited overlap of molecular measurements at different levels between patients, i.e. measurements from all molecular levels are available only for a smaller proportion of patients. We propose a sequential strategy for building clinical risk prediction models that integrate genome-wide measurements from two molecular levels in a complementary way. To deal with partial overlap, we develop an imputation approach that allows us to use all available data. This approach is investigated in two acute myeloid leukemia applications combining gene expression with either SNP or DNA methylation data. After obtaining a sparse risk prediction signature e.g. from SNP data, an automatically selected set of prognostic SNPs, by componentwise likelihood-based boosting, imputation is performed for the corresponding linear predictor by a linking model that incorporates e.g. gene expression measurements. The imputed linear predictor is then used for adjustment when building a prognostic signature from the gene expression data. For evaluation, we consider stability, as quantified by inclusion frequencies across resampling data sets. Despite an extremely small overlap in the application example with gene expression and SNPs, several genes are seen to be more stably identified when taking the (imputed) linear predictor from the SNP data into account. In the application with gene expression and DNA methylation, prediction performance with respect to survival also indicates that the proposed approach might

  17. Sequence signatures extracted from proximal promoters can be used to predict distal enhancers

    PubMed Central

    2013-01-01

    Background Gene expression is controlled by proximal promoters and distal regulatory elements such as enhancers. While the activity of some promoters can be invariant across tissues, enhancers tend to be highly tissue-specific. Results We compiled sets of tissue-specific promoters based on gene expression profiles of 79 human tissues and cell types. Putative transcription factor binding sites within each set of sequences were used to train a support vector machine classifier capable of distinguishing tissue-specific promoters from control sequences. We obtained reliable classifiers for 92% of the tissues, with an area under the receiver operating characteristic curve between 60% (for subthalamic nucleus promoters) and 98% (for heart promoters). We next used these classifiers to identify tissue-specific enhancers, scanning distal non-coding sequences in the loci of the 200 most highly and lowly expressed genes. Thirty percent of reliable classifiers produced consistent enhancer predictions, with significantly higher densities in the loci of the most highly expressed compared to lowly expressed genes. Liver enhancer predictions were assessed in vivo using the hydrodynamic tail vein injection assay. Fifty-eight percent of the predictions yielded significant enhancer activity in the mouse liver, whereas a control set of five sequences was completely negative. Conclusions We conclude that promoters of tissue-specific genes often contain unambiguous tissue-specific signatures that can be learned and used for the de novo prediction of enhancers. PMID:24156763

  18. Transcriptional pathway signatures predict MEK addiction and response to selumetinib (AZD6244).

    PubMed

    Dry, Jonathan R; Pavey, Sandra; Pratilas, Christine A; Harbron, Chris; Runswick, Sarah; Hodgson, Darren; Chresta, Christine; McCormack, Rose; Byrne, Natalie; Cockerill, Mark; Graham, Alexander; Beran, Garry; Cassidy, Andrew; Haggerty, Carolyn; Brown, Helen; Ellison, Gillian; Dering, Judy; Taylor, Barry S; Stark, Mitchell; Bonazzi, Vanessa; Ravishankar, Sugandha; Packer, Leisl; Xing, Feng; Solit, David B; Finn, Richard S; Rosen, Neal; Hayward, Nicholas K; French, Tim; Smith, Paul D

    2010-03-15

    Selumetinib (AZD6244, ARRY-142886) is a selective, non-ATP-competitive inhibitor of mitogen-activated protein/extracellular signal-regulated kinase kinase (MEK)-1/2. The range of antitumor activity seen preclinically and in patients highlights the importance of identifying determinants of response to this drug. In large tumor cell panels of diverse lineage, we show that MEK inhibitor response does not have an absolute correlation with mutational or phospho-protein markers of BRAF/MEK, RAS, or phosphoinositide 3-kinase (PI3K) activity. We aimed to enhance predictivity by measuring pathway output through coregulated gene networks displaying differential mRNA expression exclusive to resistant cell subsets and correlated to mutational or dynamic pathway activity. We discovered an 18-gene signature enabling measurement of MEK functional output independent of tumor genotype. Where the MEK pathway is activated but the cells remain resistant to selumetinib, we identified a 13-gene signature that implicates the existence of compensatory signaling from RAS effectors other than PI3K. The ability of these signatures to stratify samples according to functional activation of MEK and/or selumetinib sensitivity was shown in multiple independent melanoma, colon, breast, and lung tumor cell lines and in xenograft models. Furthermore, we were able to measure these signatures in fixed archival melanoma tumor samples using a single RT-qPCR-based test and found intergene correlations and associations with genetic markers of pathway activity to be preserved. These signatures offer useful tools for the study of MEK biology and clinical application of MEK inhibitors, and the novel approaches taken may benefit other targeted therapies.

  19. Gene Expression Signature in Adipose Tissue of Acromegaly Patients.

    PubMed

    Hochberg, Irit; Tran, Quynh T; Barkan, Ariel L; Saltiel, Alan R; Chandler, William F; Bridges, Dave

    2015-01-01

    To study the effect of chronic excess growth hormone on adipose tissue, we performed RNA sequencing in adipose tissue biopsies from patients with acromegaly (n = 7) or non-functioning pituitary adenomas (n = 11). The patients underwent clinical and metabolic profiling including assessment of HOMA-IR. Explants of adipose tissue were assayed ex vivo for lipolysis and ceramide levels. Patients with acromegaly had higher glucose, higher insulin levels and higher HOMA-IR score. We observed several previously reported transcriptional changes (IGF1, IGFBP3, CISH, SOCS2) that are known to be induced by GH/IGF-1 in liver but are also induced in adipose tissue. We also identified several novel transcriptional changes, some of which may be important for GH/IGF responses (PTPN3 and PTPN4) and the effects of acromegaly on growth and proliferation. Several differentially expressed transcripts may be important in GH/IGF-1-induced metabolic changes. Specifically, induction of LPL, ABHD5, and NRIP1 can contribute to enhanced lipolysis and may explain the elevated adipose tissue lipolysis in acromegalic patients. Higher expression of TCF7L2 and the fatty acid desaturases FADS1, FADS2 and SCD could contribute to insulin resistance. Ceramides were not different between the two groups. In summary, we have identified the acromegaly gene expression signature in human adipose tissue. The significance of altered expression of specific transcripts will enhance our understanding of the metabolic and proliferative changes associated with acromegaly.

  20. Gene Expression Signature in Adipose Tissue of Acromegaly Patients

    PubMed Central

    Hochberg, Irit; Tran, Quynh T.; Barkan, Ariel L.; Saltiel, Alan R.; Chandler, William F.; Bridges, Dave

    2015-01-01

    To study the effect of chronic excess growth hormone on adipose tissue, we performed RNA sequencing in adipose tissue biopsies from patients with acromegaly (n = 7) or non-functioning pituitary adenomas (n = 11). The patients underwent clinical and metabolic profiling including assessment of HOMA-IR. Explants of adipose tissue were assayed ex vivo for lipolysis and ceramide levels. Patients with acromegaly had higher glucose, higher insulin levels and higher HOMA-IR score. We observed several previously reported transcriptional changes (IGF1, IGFBP3, CISH, SOCS2) that are known to be induced by GH/IGF-1 in liver but are also induced in adipose tissue. We also identified several novel transcriptional changes, some of which may be important for GH/IGF responses (PTPN3 and PTPN4) and the effects of acromegaly on growth and proliferation. Several differentially expressed transcripts may be important in GH/IGF-1-induced metabolic changes. Specifically, induction of LPL, ABHD5, and NRIP1 can contribute to enhanced lipolysis and may explain the elevated adipose tissue lipolysis in acromegalic patients. Higher expression of TCF7L2 and the fatty acid desaturases FADS1, FADS2 and SCD could contribute to insulin resistance. Ceramides were not different between the two groups. In summary, we have identified the acromegaly gene expression signature in human adipose tissue. The significance of altered expression of specific transcripts will enhance our understanding of the metabolic and proliferative changes associated with acromegaly. PMID:26087292

  1. A five-long non-coding RNA signature to improve prognosis prediction of clear cell renal cell carcinoma.

    PubMed

    Shi, Da; Qu, Qinghua; Chang, Qimeng; Wang, Yilin; Gui, Yaping; Dong, Dong

    2017-08-29

    Recent works have reported that long non-coding RNAs (lncRNAs) play critical roles in tumorigenesis and prognosis of cancers, suggesting the potential utility of lncRNAs as cancer prognostic markers. However, lncRNA signatures in predicting the survival of patients with clear cell renal cell carcinoma (ccRCC) remain unknown. In this study, we attempted to identify lncRNA signatures and their prognostic values in ccRCC. Using lncRNA expression profiling data in 440 ccRCC tumors from The Cancer Genome Atlas (TCGA) data, a five-lncRNA signature (AC069513.4, AC003092.1, CTC-205M6.2, RP11-507K2.3, U91328.21) has been identified to be significantly associated with ccRCC patients' overall survival in both training set and testing set. Based on the lncRNA signature, ccRCC patients could be divided into high-risk and low-risk group with significantly different survival rate. Further multivariable Cox regression analysis suggested that the prognostic value of this signature was independent of clinical factors. Functional enrichment analyses showed the potential functional roles of the five prognostic lncRNAs in ccRCC oncogenesis. These results indicated that this five-lncRNA signature could be used as an independent prognostic biomarker in the prediction of ccRCC patients' survival.

  2. Optimized outcome prediction in breast cancer by combining the 70-gene signature with clinical risk prediction algorithms.

    PubMed

    Drukker, C A; Nijenhuis, M V; Bueno-de-Mesquita, J M; Retèl, V P; van Harten, W H; van Tinteren, H; Wesseling, J; Schmidt, M K; Van't Veer, L J; Sonke, G S; Rutgers, E J T; van de Vijver, M J; Linn, S C

    2014-06-01

    Clinical guidelines for breast cancer treatment differ in their selection of patients at a high risk of recurrence who are eligible to receive adjuvant systemic treatment (AST). The 70-gene signature is a molecular tool to better guide AST decisions. The aim of this study was to evaluate whether adding the 70-gene signature to clinical risk prediction algorithms can optimize outcome prediction and consequently treatment decisions in early stage, node-negative breast cancer patients. A 70-gene signature was available for 427 patients participating in the RASTER study (cT1-3N0M0). Median follow-up was 61.6 months. Based on 5-year distant-recurrence free interval (DRFI) probabilities survival areas under the curve (AUC) were calculated and compared for risk estimations based on the six clinical risk prediction algorithms: Adjuvant! Online (AOL), Nottingham Prognostic Index (NPI), St. Gallen (2003), the Dutch National guidelines (CBO 2004 and NABON 2012), and PREDICT plus. Also, survival AUC were calculated after adding the 70-gene signature to these clinical risk estimations. Systemically untreated patients with a high clinical risk estimation but a low risk 70-gene signature had an excellent 5-year DRFI varying between 97.1 and 100 %, depending on the clinical risk prediction algorithms used in the comparison. The best risk estimation was obtained in this cohort by adding the 70-gene signature to CBO 2012 (AUC: 0.644) and PREDICT (AUC: 0.662). Clinical risk estimations by all clinical algorithms improved by adding the 70-gene signature. Patients with a low risk 70-gene signature have an excellent survival, independent of their clinical risk estimation. Adding the 70-gene signature to clinical risk prediction algorithms improves risk estimations and therefore might improve the identification of early stage node-negative breast cancer patients for whom AST has limited value. In this cohort, the PREDICT plus tool in combination with the 70-gene signature provided the

  3. Characteristics and Predictive Value of Blood Transcriptome Signature in Males with Autism Spectrum Disorders

    PubMed Central

    Shimizu-Motohashi, Yuko; Holm, Ingrid A.; Campbell, Malcolm G.; Lee, In-Hee; Brewster, Stephanie J.; Hanson, Ellen; Harris, Heather K.; Lowe, Kathryn R.; Saada, Adrianna; Mora, Andrea; Madison, Kimberly; Hundley, Rachel; Egan, Jessica; McCarthy, Jillian; Eran, Ally; Galdzicki, Michal; Rappaport, Leonard; Kunkel, Louis M.; Kohane, Isaac S.

    2012-01-01

    Autism Spectrum Disorders (ASD) is a spectrum of highly heritable neurodevelopmental disorders in which known mutations contribute to disease risk in 20% of cases. Here, we report the results of the largest blood transcriptome study to date that aims to identify differences in 170 ASD cases and 115 age/sex-matched controls and to evaluate the utility of gene expression profiling as a tool to aid in the diagnosis of ASD. The differentially expressed genes were enriched for the neurotrophin signaling, long-term potentiation/depression, and notch signaling pathways. We developed a 55-gene prediction model, using a cross-validation strategy, on a sample cohort of 66 male ASD cases and 33 age-matched male controls (P1). Subsequently, 104 ASD cases and 82 controls were recruited and used as a validation set (P2). This 55-gene expression signature achieved 68% classification accuracy with the validation cohort (area under the receiver operating characteristic curve (AUC): 0.70 [95% confidence interval [CI]: 0.62–0.77]). Not surprisingly, our prediction model that was built and trained with male samples performed well for males (AUC 0.73, 95% CI 0.65–0.82), but not for female samples (AUC 0.51, 95% CI 0.36–0.67). The 55-gene signature also performed robustly when the prediction model was trained with P2 male samples to classify P1 samples (AUC 0.69, 95% CI 0.58–0.80). Our result suggests that the use of blood expression profiling for ASD detection may be feasible. Further study is required to determine the age at which such a test should be deployed, and what genetic characteristics of ASD can be identified. PMID:23227143

  4. Prognostic breast cancer signature identified from 3D culture model accurately predicts clinical outcome across independent datasets

    SciTech Connect

    Martin, Katherine J.; Patrick, Denis R.; Bissell, Mina J.; Fournier, Marcia V.

    2008-10-20

    One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasets having 295, 286, and 118 samples, respectively. Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome. The 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds prognostic

  5. Specific molecular signatures predict decitabine response in chronic myelomonocytic leukemia.

    PubMed

    Meldi, Kristen; Qin, Tingting; Buchi, Francesca; Droin, Nathalie; Sotzen, Jason; Micol, Jean-Baptiste; Selimoglu-Buet, Dorothée; Masala, Erico; Allione, Bernardino; Gioia, Daniela; Poloni, Antonella; Lunghi, Monia; Solary, Eric; Abdel-Wahab, Omar; Santini, Valeria; Figueroa, Maria E

    2015-05-01

    Myelodysplastic syndromes and chronic myelomonocytic leukemia (CMML) are characterized by mutations in genes encoding epigenetic modifiers and aberrant DNA methylation. DNA methyltransferase inhibitors (DMTis) are used to treat these disorders, but response is highly variable, with few means to predict which patients will benefit. Here, we examined baseline differences in mutations, DNA methylation, and gene expression in 40 CMML patients who were responsive or resistant to decitabine (DAC) in order to develop a molecular means of predicting response at diagnosis. While somatic mutations did not differentiate responders from nonresponders, we identified 167 differentially methylated regions (DMRs) of DNA at baseline that distinguished responders from nonresponders using next-generation sequencing. These DMRs were primarily localized to nonpromoter regions and overlapped with distal regulatory enhancers. Using the methylation profiles, we developed an epigenetic classifier that accurately predicted DAC response at the time of diagnosis. Transcriptional analysis revealed differences in gene expression at diagnosis between responders and nonresponders. In responders, the upregulated genes included those that are associated with the cell cycle, potentially contributing to effective DAC incorporation. Treatment with CXCL4 and CXCL7, which were overexpressed in nonresponders, blocked DAC effects in isolated normal CD34+ and primary CMML cells, suggesting that their upregulation contributes to primary DAC resistance.

  6. An 8-gene signature for prediction of prognosis and chemoresponse in non-small cell lung cancer

    PubMed Central

    Nguyen, Minh Nam; Matondo, Abel; Jo, Yong Hwa; Yoo, Ji Youn; Nguyen, Ngoc Ngo Yen; Yun, Hyeong Rok; Kim, Jieun; Akter, Salima; Kang, Insug; Ha, Joohun; Maeng, Chi Hoon; Kim, Si-Young; Lee, Ju-seog; Kim, Jayoung; Kim, Sung Soo

    2016-01-01

    Identification of a potential gene signature for improved diagnosis in non-small cell lung cancer (NSCLC) patient is necessary. Here, we aim to establish and validate the prognostic efficacy of a gene set that can predict prognosis and benefits of adjuvant chemotherapy (ACT) in NSCLC patients from various ethnicities. An 8-gene signature was calculated from the gene expression of 181 patients using univariate Cox proportional hazard regression analysis. The prognostic value of the signature was robustly validated in 1,477 patients from five microarray independent data sets and one RNA-seq data set. The 8-gene signature was identified as an independent predictor of patient survival in the presence of clinical parameters in univariate and multivariate analyses [hazard ratio (HR): 2.84, 95% confidence interval CI (1.74-4.65), p=3.06e-05, [HR] 2.62, 95% CI (1.51-4.53), p=0.001], respectively. Subset analysis demonstrated that the 8-gene signature could identify high-risk patients in stage II-III with improved survival from ACT [(HR) 1.47, 95% CI (1.01-2.14), p=0.044]. The 8-gene signature also stratified risk groups in EGFR-mutated and wild-type patients. In conclusion, the 8-gene signature is a strong and independent predictor that can significantly stratify patients into low- and high-risk groups. Our gene signature also has the potential to predict patients in stage II-III that are likely to benefit from ACT. PMID:27863408

  7. Quantifying uncertainties in streamflow predictions through signature based inference of hydrological model parameters

    NASA Astrophysics Data System (ADS)

    Fenicia, Fabrizio; Reichert, Peter; Kavetski, Dmitri; Albert, Calro

    2016-04-01

    The calibration of hydrological models based on signatures (e.g. Flow Duration Curves - FDCs) is often advocated as an alternative to model calibration based on the full time series of system responses (e.g. hydrographs). Signature based calibration is motivated by various arguments. From a conceptual perspective, calibration on signatures is a way to filter out errors that are difficult to represent when calibrating on the full time series. Such errors may for example occur when observed and simulated hydrographs are shifted, either on the "time" axis (i.e. left or right), or on the "streamflow" axis (i.e. above or below). These shifts may be due to errors in the precipitation input (time or amount), and if not properly accounted in the likelihood function, may cause biased parameter estimates (e.g. estimated model parameters that do not reproduce the recession characteristics of a hydrograph). From a practical perspective, signature based calibration is seen as a possible solution for making predictions in ungauged basins. Where streamflow data are not available, it may in fact be possible to reliably estimate streamflow signatures. Previous research has for example shown how FDCs can be reliably estimated at ungauged locations based on climatic and physiographic influence factors. Typically, the goal of signature based calibration is not the prediction of the signatures themselves, but the prediction of the system responses. Ideally, the prediction of system responses should be accompanied by a reliable quantification of the associated uncertainties. Previous approaches for signature based calibration, however, do not allow reliable estimates of streamflow predictive distributions. Here, we illustrate how the Bayesian approach can be employed to obtain reliable streamflow predictive distributions based on signatures. A case study is presented, where a hydrological model is calibrated on FDCs and additional signatures. We propose an approach where the likelihood

  8. A Predictable Terrestrial Signature to Riverine Dissolved Organic Carbon?

    NASA Astrophysics Data System (ADS)

    Sanderman, J.; Amundson, R.; Baldock, J. A.

    2007-12-01

    . In these ecosystems, riverine DOC is not simply terrestrial or aquatic in origin. Within the "terrestrial" signature, we have found nearly as large a range of ages and chemistries as found within the soil profile itself, and the resulting DOC composition is largely predictable with knowledge of runoff generating mechanisms.

  9. Identifying developmental vascular disruptor compounds using a predictive signature and alternative toxicity models

    EPA Science Inventory

    Identifying Developmental Vascular Disruptor Compounds Using a Predictive Signature and Alternative Toxicity Models Presenting Author: Tamara Tal Affiliation: U.S. EPA/ORD/ISTD, RTP, NC, USA Chemically induced vascular toxicity during embryonic development can result in a wide...

  10. Identifying developmental vascular disruptor compounds using a predictive signature and alternative toxicity models

    EPA Science Inventory

    Identifying Developmental Vascular Disruptor Compounds Using a Predictive Signature and Alternative Toxicity Models Presenting Author: Tamara Tal Affiliation: U.S. EPA/ORD/ISTD, RTP, NC, USA Chemically induced vascular toxicity during embryonic development can result in a wide...

  11. Serum-based microRNA signatures in early diagnosis and prognosis prediction of colon cancer.

    PubMed

    Vychytilova-Faltejskova, Petra; Radova, Lenka; Sachlova, Milana; Kosarova, Zdenka; Slaba, Katerina; Fabian, Pavel; Grolich, Tomas; Prochazka, Vladimir; Kala, Zdenek; Svoboda, Marek; Kiss, Igor; Vyzula, Rostislav; Slaby, Ondrej

    2016-10-01

    Early detection of colorectal cancer is the main prerequisite for successful treatment and reduction of mortality. Circulating microRNAs were previously identified as promising diagnostic, prognostic and predictive biomarkers. The purpose of this study was to identify serum microRNAs enabling early diagnosis and prognosis prediction of colon cancer. In total, serum samples from 427 colon cancer patients and 276 healthy donors were included in three-phase biomarker study. Large-scale microRNA expression profiling was performed using Illumina small RNA sequencing. Diagnostic and prognostic potential of identified microRNAs was validated on independent training and validation sets of samples using RT-qPCR. Fifty-four microRNAs were found to be significantly deregulated in serum of colon cancer patients compared to healthy donors (P < 0.01). A diagnostic four-microRNA signature consisting of miR-23a-3p, miR-27a-3p, miR-142-5p and miR-376c-3p was established (AUC = 0.917), distinguishing colon cancer patients from healthy donors with sensitivity of 89% and specificity of 81% (AUC = 0.922). This panel of microRNAs exhibited high diagnostic performance also when analyzed separately in colon cancer patients in early stages of the disease (T1-4N0M0; AUC = 0.877). Further, a prognostic panel based on the expression of miR-23a-3p and miR-376c-3p independent of TNM stage was established (HR 2.30; 95% CI 1.44-3.66; P < 0.0004). In summary, highly sensitive signatures of circulating microRNAs enabling non-invasive early detection and prognosis prediction of colon cancer were identified. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  12. Melanoma long non-coding RNA signature predicts prognostic survival and directs clinical risk-specific treatments.

    PubMed

    Chen, Xijia; Guo, Wenna; Xu, Xin-Jian; Su, Fangchu; Wang, Yi; Zhang, Yingzheng; Wang, Qiang; Zhu, Liucun

    2017-03-01

    Various studies have demonstrated that the Breslow thickness, tumor ulceration and mitotic index could serve as prognostic markers in patients with cutaneous melanoma. Recently, however, as these clinicopathological biomarkers lack efficient interpretation of endogenous mechanism of melanoma, the emphasis on the prognosis of melanoma has transformed to molecular tumor markers. This study was designed to identify survival-related long non-coding RNAs (lncRNAs), and based on the different expressions of these lncRNAs, clinical risk-specific diagnosis and adjuvant therapy could be employed on melanoma patients, especially patients in the early course of disease or patients with a Breslow thickness no more than 2mm. The clinical information and corresponding RNA expression data were obtained from The Cancer Genome Atlas dataset and Gene Expression Omnibus dataset (GSE65904). All samples were categorized into one training dataset and two validation datasets. Cox proportional hazard regression analysis was then used to identify survival-related lncRNAs and risk assessment signature was constructed in training dataset. Kaplan-Meier method was used to estimate the utility of this signature in predicting the duration of survival of patients both in the training dataset and two validation datasets. Meanwhile receiver operating characteristic analyses were used to evaluate the predictive effectiveness of this signature in two validation datasets. It was found that the signature was effective while used for risk stratification, and Kaplan-Meier analyses indicated that the duration of survival of patients in high-risk groups were significantly shorter than that of low-risk groups. Moreover, areas under the receiver operating characteristic curve were 0.711 (95% confidence interval: 0.618-0.804) and 0.698 (95% confidence interval: 0.614-0.782) when this signature was used to predict the patients' duration of survival in two validation datasets respectively, indicating the

  13. Identification of a Genomic Signature Predicting for Recurrence in Early Stage Ovarian Cancer

    DTIC Science & Technology

    2013-10-01

    0521 TITLE: Identification of a genomic signature predicting for recurrence in early stage ovarian cancer PRINCIPAL INVESTIGATOR: Michael...Identification of a genomic signature predicting for recurrence in early stage ovarian 5b. GRANT NUMBER cancer 5c. PROGRAM ELEMENT NUMBER 6...obtained IRB approval for using these cancer FFPE samples to identify molecular features that distinguish recurrent and non-recurrent tumors through RNA

  14. DNA Copy Number Signature to Predict Recurrence in Early-Stage Ovarian Cancer

    DTIC Science & Technology

    2015-08-01

    AWARD NUMBER: W81XWH-14-1-0194 TITLE: DNA Copy Number Signature to Predict Recurrence in Early-Stage Ovarian Cancer PRINCIPAL INVESTIGATOR...SUBTITLE 5a. CONTRACT NUMBER DNA Copy Number Signature to Predict Recurrence in Early Stage Ovarian Cancer 5b. GRANT NUMBER W81XWH-14-1-0194 5c...tasks Major Task 1: Obtain DNA samples from consortium specimens • Subtask 1 Pathological review of 592 early-stage high-grade ovarian cancer specimens

  15. An Orthologous Epigenetic Gene Expression Signature Derived from Differentiating Embryonic Stem Cells Identifies Regulators of Cardiogenesis.

    PubMed

    Busser, Brian W; Lin, Yongshun; Yang, Yanqin; Zhu, Jun; Chen, Guokai; Michelson, Alan M

    2015-01-01

    Here we used predictive gene expression signatures within a multi-species framework to identify the genes that underlie cardiac cell fate decisions in differentiating embryonic stem cells. We show that the overlapping orthologous mouse and human genes are the most accurate candidate cardiogenic genes as these genes identified the most conserved developmental pathways that characterize the cardiac lineage. An RNAi-based screen of the candidate genes in Drosophila uncovered numerous novel cardiogenic genes. shRNA knockdown combined with transcriptome profiling of the newly-identified transcription factors zinc finger protein 503 and zinc finger E-box binding homeobox 2 and the well-known cardiac regulatory factor NK2 homeobox 5 revealed that zinc finger E-box binding homeobox 2 activates terminal differentiation genes required for cardiomyocyte structure and function whereas zinc finger protein 503 and NK2 homeobox 5 are required for specification of the cardiac lineage. We further demonstrated that an essential role of NK2 homeobox 5 and zinc finger protein 503 in specification of the cardiac lineage is the repression of gene expression programs characteristic of alternative cell fates. Collectively, these results show that orthologous gene expression signatures can be used to identify conserved cardiogenic pathways.

  16. Validation study of existing gene expression signatures for anti-TNF treatment in patients with rheumatoid arthritis.

    PubMed

    Toonen, Erik J M; Gilissen, Christian; Franke, Barbara; Kievit, Wietske; Eijsbouts, Agnes M; den Broeder, Alfons A; van Reijmersdal, Simon V; Veltman, Joris A; Scheffer, Hans; Radstake, Timothy R D J; van Riel, Piet L C M; Barrera, Pilar; Coenen, Marieke J H

    2012-01-01

    So far, there are no means of identifying rheumatoid arthritis (RA) patients who will fail to respond to tumour necrosis factor blocking agents (anti-TNF), prior to treatment. We set out to validate eight previously reported gene expression signatures predicting therapy outcome. Genome-wide expression profiling using Affymetrix GeneChip Exon 1.0 ST arrays was performed on RNA isolated from whole blood of 42 RA patients starting treatment with infliximab or adalimumab. Clinical response according to EULAR criteria was determined at week 14 of therapy. Genes that have been reported to be associated with anti-TNF treatment were extracted from our dataset. K-means partition clustering was performed to assess the predictive value of the gene-sets. We performed a hypothesis-driven analysis of the dataset using eight existing gene sets predictive of anti-TNF treatment outcome. The set that performed best reached a sensitivity of 71% and a specificity of 61%, for classifying the patients in the current study. We successfully validated one of eight previously reported predictive expression profile. This replicated expression signature is a good starting point for developing a prediction model for anti-TNF treatment outcome that can be used in a daily clinical setting. Our results confirm that gene expression profiling prior to treatment is a useful tool to predict anti-TNF (non) response.

  17. Validation Study of Existing Gene Expression Signatures for Anti-TNF Treatment in Patients with Rheumatoid Arthritis

    PubMed Central

    Toonen, Erik J. M.; Gilissen, Christian; Franke, Barbara; Kievit, Wietske; Eijsbouts, Agnes M.; den Broeder, Alfons A.; van Reijmersdal, Simon V.; Veltman, Joris A.; Scheffer, Hans; Radstake, Timothy R. D. J.; van Riel, Piet L. C. M.; Barrera, Pilar; Coenen, Marieke J. H.

    2012-01-01

    So far, there are no means of identifying rheumatoid arthritis (RA) patients who will fail to respond to tumour necrosis factor blocking agents (anti-TNF), prior to treatment. We set out to validate eight previously reported gene expression signatures predicting therapy outcome. Genome-wide expression profiling using Affymetrix GeneChip Exon 1.0 ST arrays was performed on RNA isolated from whole blood of 42 RA patients starting treatment with infliximab or adalimumab. Clinical response according to EULAR criteria was determined at week 14 of therapy. Genes that have been reported to be associated with anti-TNF treatment were extracted from our dataset. K-means partition clustering was performed to assess the predictive value of the gene-sets. We performed a hypothesis-driven analysis of the dataset using eight existing gene sets predictive of anti-TNF treatment outcome. The set that performed best reached a sensitivity of 71% and a specificity of 61%, for classifying the patients in the current study. We successfully validated one of eight previously reported predictive expression profile. This replicated expression signature is a good starting point for developing a prediction model for anti-TNF treatment outcome that can be used in a daily clinical setting. Our results confirm that gene expression profiling prior to treatment is a useful tool to predict anti-TNF (non) response. PMID:22457743

  18. A Signature of Circulating microRNAs Predicts the Susceptibility of Acute Mountain Sickness.

    PubMed

    Liu, Bao; Huang, He; Wu, Gang; Xu, Gang; Sun, Bing-Da; Zhang, Er-Long; Chen, Jian; Gao, Yu-Qi

    2017-01-01

    Background: Acute mountain sickness (AMS) is a common disabling condition in individuals experiencing high altitudes, which may progress to life-threatening high altitude cerebral edema. Today, no established biomarkers are available for prediction the susceptibility of AMS. MicroRNAs emerge as promising sensitive and specific biomarkers for a variety of diseases. Thus, we sought to identify circulating microRNAs suitable for prediction the susceptible of AMS before exposure to high altitude. Methods: We enrolled 109 healthy man adults and collected blood samples before their exposure to high altitude. Then we took them to an elevation of 3648 m for 5 days. Circulating microRNAs expression was measured by microarray and quantitative reverse-transcription polymerase chain reaction (qRT-PCR). AMS was defined as Lake Louise score ≥3 and headache using Lake Louise Acute Mountain Sickness Scoring System. Results: A total of 31 microRNAs were differentially expressed between AMS and Non-AMS groups, 15 up-regulated and 16 down-regulated. Up-regulation of miR-369-3p, miR-449b-3p, miR-136-3p, and miR-4791 in patients with AMS compared with Non-AMS individuals were quantitatively confirmed using qRT-PCR (all, P < 0.001). With multiple logistic regression analysis, a unique signature encompassing miR-369-3p, miR-449b-3p, and miR-136-3p discriminate AMS from Non-AMS (area under the curve 0.986, 95%CI 0.970-1.000, P < 0.001, LR+: 14.21, LR-: 0.08). This signature yielded a 92.68% sensitivity and a 93.48% specificity for AMS vs. Non-AMS. Conclusion: The study here, for the first time, describes a signature of three circulating microRNAs as a robust biomarker to predict the susceptibility of AMS before exposure to high altitude.

  19. A Signature of Circulating microRNAs Predicts the Susceptibility of Acute Mountain Sickness

    PubMed Central

    Liu, Bao; Huang, He; Wu, Gang; Xu, Gang; Sun, Bing-Da; Zhang, Er-Long; Chen, Jian; Gao, Yu-Qi

    2017-01-01

    Background: Acute mountain sickness (AMS) is a common disabling condition in individuals experiencing high altitudes, which may progress to life-threatening high altitude cerebral edema. Today, no established biomarkers are available for prediction the susceptibility of AMS. MicroRNAs emerge as promising sensitive and specific biomarkers for a variety of diseases. Thus, we sought to identify circulating microRNAs suitable for prediction the susceptible of AMS before exposure to high altitude. Methods: We enrolled 109 healthy man adults and collected blood samples before their exposure to high altitude. Then we took them to an elevation of 3648 m for 5 days. Circulating microRNAs expression was measured by microarray and quantitative reverse-transcription polymerase chain reaction (qRT-PCR). AMS was defined as Lake Louise score ≥3 and headache using Lake Louise Acute Mountain Sickness Scoring System. Results: A total of 31 microRNAs were differentially expressed between AMS and Non-AMS groups, 15 up-regulated and 16 down-regulated. Up-regulation of miR-369-3p, miR-449b-3p, miR-136-3p, and miR-4791 in patients with AMS compared with Non-AMS individuals were quantitatively confirmed using qRT-PCR (all, P < 0.001). With multiple logistic regression analysis, a unique signature encompassing miR-369-3p, miR-449b-3p, and miR-136-3p discriminate AMS from Non-AMS (area under the curve 0.986, 95%CI 0.970–1.000, P < 0.001, LR+: 14.21, LR–: 0.08). This signature yielded a 92.68% sensitivity and a 93.48% specificity for AMS vs. Non-AMS. Conclusion: The study here, for the first time, describes a signature of three circulating microRNAs as a robust biomarker to predict the susceptibility of AMS before exposure to high altitude. PMID:28228730

  20. Using Protein Interaction Database and Support Vector Machines to Improve Gene Signatures for Prediction of Breast Cancer Recurrence

    PubMed Central

    Sehhati, Mohammad Reza; Dehnavi, Alireza Mehri; Rabbani, Hossein; Javanmard, Shaghayegh Haghjoo

    2013-01-01

    Numerous studies used microarray gene expression data to extract metastasis-driving gene signatures for the prediction of breast cancer relapse. However, the accuracy and generality of the previously introduced biomarkers are not acceptable for reliable usage in independent datasets. This inadequacy is attributed to ignoring gene interactions by simple feature selection methods, due to their computational burden. In this study, an integrated approach with low computational cost was proposed for identifying a more predictive gene signature, for prediction of breast cancer recurrence. First, a small set of genes was primarily selected as signature by an appropriate filter feature selection (FFS) method. Then, a binary sub-class of protein-protein interaction (PPI) network was used to expand the primary set by adding adjacent proteins of each gene signature from the PPI-network. Subsequently, the support vector machine-based recursive feature elimination (SVMRFE) method was applied to the expression level of all the genes in the expanded set. Finally, the genes with the highest score by SVMRFE were selected as the new biomarkers. Accuracy of the final selected biomarkers was evaluated to classify four datasets on breast cancer patients, including 800 cases, into two cohorts of poor and good prognosis. The results of the five-fold cross validation test, using the support vector machine as a classifier, showed more than 13% improvement in the average accuracy, after modifying the primary selected signatures. Moreover, the method used in this study showed a lower computational cost compared to the other PPI-based methods. The proposed method demonstrated more robust and accurate biomarkers using the PPI network, at a low computational cost. This approach could be used as a supplementary procedure in microarray studies after applying various gene selection methods. PMID:24098862

  1. Using protein interaction database and support vector machines to improve gene signatures for prediction of breast cancer recurrence.

    PubMed

    Sehhati, Mohammad Reza; Dehnavi, Alireza Mehri; Rabbani, Hossein; Javanmard, Shaghayegh Haghjoo

    2013-04-01

    Numerous studies used microarray gene expression data to extract metastasis-driving gene signatures for the prediction of breast cancer relapse. However, the accuracy and generality of the previously introduced biomarkers are not acceptable for reliable usage in independent datasets. This inadequacy is attributed to ignoring gene interactions by simple feature selection methods, due to their computational burden. In this study, an integrated approach with low computational cost was proposed for identifying a more predictive gene signature, for prediction of breast cancer recurrence. First, a small set of genes was primarily selected as signature by an appropriate filter feature selection (FFS) method. Then, a binary sub-class of protein-protein interaction (PPI) network was used to expand the primary set by adding adjacent proteins of each gene signature from the PPI-network. Subsequently, the support vector machine-based recursive feature elimination (SVMRFE) method was applied to the expression level of all the genes in the expanded set. Finally, the genes with the highest score by SVMRFE were selected as the new biomarkers. Accuracy of the final selected biomarkers was evaluated to classify four datasets on breast cancer patients, including 800 cases, into two cohorts of poor and good prognosis. The results of the five-fold cross validation test, using the support vector machine as a classifier, showed more than 13% improvement in the average accuracy, after modifying the primary selected signatures. Moreover, the method used in this study showed a lower computational cost compared to the other PPI-based methods. The proposed method demonstrated more robust and accurate biomarkers using the PPI network, at a low computational cost. This approach could be used as a supplementary procedure in microarray studies after applying various gene selection methods.

  2. Multiclass cancer diagnosis using tumor gene expression signatures

    SciTech Connect

    Ramaswamy, S.; Tamayo, P.; Rifkin, R.; Mukherjee, S.; Yeang, C. -H.; Angelo, M.; Ladd, C.; Reich, M.; Latulippe, E.; Mesirov, J. P.; Poggio, T.; Gerald, W.; Loda, M.; Lander, E. S.; Golub, T. R.

    2001-12-11

    The optimal treatment of patients with cancer depends on establishing accurate diagnoses by using a complex combination of clinical and histopathological data. In some instances, this task is difficult or impossible because of atypical clinical presentation or histopathology. To determine whether the diagnosis of multiple common adult malignancies could be achieved purely by molecular classification, we subjected 218 tumor samples, spanning 14 common tumor types, and 90 normal tissue samples to oligonucleotide microarray gene expression analysis. The expression levels of 16,063 genes and expressed sequence tags were used to evaluate the accuracy of a multiclass classifier based on a support vector machine algorithm. Overall classification accuracy was 78%, far exceeding the accuracy of random classification (9%). Poorly differentiated cancers resulted in low-confidence predictions and could not be accurately classified according to their tissue of origin, indicating that they are molecularly distinct entities with dramatically different gene expression patterns compared with their well differentiated counterparts. Taken together, these results demonstrate the feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics.

  3. Multiclass cancer diagnosis using tumor gene expression signatures

    DOE PAGES

    Ramaswamy, S.; Tamayo, P.; Rifkin, R.; ...

    2001-12-11

    The optimal treatment of patients with cancer depends on establishing accurate diagnoses by using a complex combination of clinical and histopathological data. In some instances, this task is difficult or impossible because of atypical clinical presentation or histopathology. To determine whether the diagnosis of multiple common adult malignancies could be achieved purely by molecular classification, we subjected 218 tumor samples, spanning 14 common tumor types, and 90 normal tissue samples to oligonucleotide microarray gene expression analysis. The expression levels of 16,063 genes and expressed sequence tags were used to evaluate the accuracy of a multiclass classifier based on a supportmore » vector machine algorithm. Overall classification accuracy was 78%, far exceeding the accuracy of random classification (9%). Poorly differentiated cancers resulted in low-confidence predictions and could not be accurately classified according to their tissue of origin, indicating that they are molecularly distinct entities with dramatically different gene expression patterns compared with their well differentiated counterparts. Taken together, these results demonstrate the feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics.« less

  4. Pathway-based identification of a smoking associated 6-gene signature predictive of lung cancer risk and survival

    PubMed Central

    Guo, Nancy Lan; Wan, Ying-Wooi

    2012-01-01

    Objective Smoking is a prominent risk factor for lung cancer. However, it is not an established prognostic factor for lung cancer in clinics. To date, no gene test is available for diagnostic screening of lung cancer risk or prognostication of clinical outcome in smokers. This study sought to identify a smoking associated gene signature in order to provide a more precise diagnosis and prognosis of lung cancer in smokers. Methods and materials An implication network based methodology was used to identify biomarkers by modeling crosstalk with major lung cancer signaling pathways. Specifically, the methodology contains the following steps: 1) identifying genes significantly associated with lung cancer survival; 2) selecting candidate genes which are differentially expressed in smokers versus non-smokers from the survival genes identified in Step 1; 3) from these candidate genes, constructing gene coexpression networks based on prediction logic for the smoker group and the non-smoker group, respectively; 4) identifying smoking-mediated differential components, i.e., the unique gene coexpression patterns specific to each group; and 5) from the differential components, identifying genes directly co-expressed with major lung cancer signaling hallmarks. Results A smoking-associated 6-gene signature was identified for prognosis of lung cancer from a training cohort (n=256). The 6-gene signature could separate lung cancer patients into two risk groups with distinct post-operative survival (log-rank P < 0.04, Kaplan-Meier analyses) in three independent cohorts (n=427). The expression-defined prognostic prediction is strongly related to smoking association and smoking cessation (P < 0.02; Pearson’s Chi-squared tests). The 6-gene signature is an accurate prognostic factor (hazard ratio = 1.89, 95% CI: [1.04, 3.43]) compared to common clinical covariates in multivariate Cox analysis. The 6-gene signature also provides an accurate diagnosis of lung cancer with an overall

  5. Irma 5.0 multisensor signature prediction model

    NASA Astrophysics Data System (ADS)

    Owens, Monte A.; Wellfare, Michael R.; Foster, Joseph; Watson, John S.; Vechinski, Douglas A.; Richards, Mike; Underwood, Vincent

    1999-07-01

    The Irma synthetic signature model was one of the first high resolution Infrared (IR) target and background signature models to be developed for tactical weapons application. Originally developed in 1980 by the Munitions Directorate of the Air Force Research Laboratory (AFRL/MN), the Irma model was used exclusively to generate IR scenes for smart weapons research and development. In 1988, a number of significant upgrades to Irma were initiated including the addition of a laser channel. This two channel version was released to the user community in 1990. In 1992, an improved scene generator was incorporated into the Irma model which supported correlated frame-to-frame imagery. A passive IR/millimeter wave (MMW) code was completed in 1994. This served as the cornerstone for the development of the co-registered active/passive IR/MMW model, Irma 4.0. The latest version of Irma, 4.1, was released in April 1998 during the Aerosense Conference. It incorporated a number of upgrades to both the physical models and software. Current development efforts are focused on the inclusion of circular polarization, hybrid ladar signature blending, an RF air-to-air channel, reconfigurable sensor model, and enhance user interface. These capabilities will be integrated into the next release, Irma 5.0, scheduled for completion in FY00. The purpose of this paper is to demonstrate the progress of the Irma 5.0 development effort. Irma is being developed to facilitate multi-sensor research and development. It is currently being used to support a number of civilian and military applications. The Irma user base includes over 130 agencies within the Air Force, Army, Navy, DARPA, NASA, Department of Transportation, academia, and industry.

  6. Irma 5.0 multisensor signature prediction model

    NASA Astrophysics Data System (ADS)

    Wellfare, Michael R.; Vechinski, Douglas A.; Watson, John S.; Foster, Joe L., Jr.; Edwards, John W.; Richards, Mike; Coker, Jason S.; Coker, Charles F.

    2000-07-01

    The Irma synthetic signature model was one of the first high resolution Infrared (IR) target and background signature models to be developed for tactical weapons application. Originally developed in 1980 by the Munitions Directorate of the Air Force Research Laboratory (AFRL/MN), the Irma model was used exclusively to generate IR scenes for smart weapons research and development. In 1988, a number of significant upgrades to Irma were initiated including the addition of a laser channel. This two-channel version was released to the user community in 1990. In 1992, an improved scene generator was incorporated into the Irma model, which supported correlated frame-to-frame imagery. A passive IR/millimeter wave (MMW) code was completed in 1994. This served as the cornerstone for the development of the co-registered active/passive IR IR/MMW model, Irma 4.0. The latest release of Irma, version 4.1, incorporated a number of upgrades to both the physical models and software. Since that time several upgrades to the model have been accomplished including the inclusion of circular polarization, hybrid LADAR signature blending, and a RF air-to-air channel. Work is still ongoing towards the development of a reconfigurable sensor model, a Scannerless Range Imaging (SRI) sensor modeling capability, a PC version, and an enhanced user interface. These capabilities will be integrated into the next release, Irma 5.0, scheduled for completion in FY00.The purpose of this paper is to demonstrate the progress of the Irma 5.0 development effort. Irma is being developed to facilitate multi-sensor research and development. It is currently being used to support a number of civilian and military applications. The Irma user base includes over 130 agencies within the Air Force, Army, Navy, DARPA, NASA, Department of Transportation, academia, and industry.

  7. Characteristics and Validation Techniques for PCA-Based Gene-Expression Signatures

    PubMed Central

    Welsh, Eric A.

    2017-01-01

    Background. Many gene-expression signatures exist for describing the biological state of profiled tumors. Principal Component Analysis (PCA) can be used to summarize a gene signature into a single score. Our hypothesis is that gene signatures can be validated when applied to new datasets, using inherent properties of PCA. Results. This validation is based on four key concepts. Coherence: elements of a gene signature should be correlated beyond chance. Uniqueness: the general direction of the data being examined can drive most of the observed signal. Robustness: if a gene signature is designed to measure a single biological effect, then this signal should be sufficiently strong and distinct compared to other signals within the signature. Transferability: the derived PCA gene signature score should describe the same biology in the target dataset as it does in the training dataset. Conclusions. The proposed validation procedure ensures that PCA-based gene signatures perform as expected when applied to datasets other than those that the signatures were trained upon. Complex signatures, describing multiple independent biological components, are also easily identified. PMID:28265563

  8. A microRNA expression signature for clinical response in locally advanced cervical cancer.

    PubMed

    Pedroza-Torres, Abraham; Fernández-Retana, Jorge; Peralta-Zaragoza, Oscar; Jacobo-Herrera, Nadia; Cantú de Leon, David; Cerna-Cortés, Jorge F; Lopez-Camarillo, Cesar; Pérez-Plasencia, Carlos

    2016-09-01

    Nearly 50% of patients who are diagnosed with locally advanced cervical cancer have an unfavorable pathological response to conventional treatment. MicroRNAs (miRNAs) are potential biomarkers in cervical cancer; however, their role in identifying patients who do not respond to conventional treatment remains poorly investigated. Here, we identify a set of miRNAs that can be used as molecular markers to predict the pathological response in locally advanced cervical cancer patients receiving radiation and chemotherapy treatment. Forty-one patients diagnosed with locally advanced cervical cancer were invited to participate in this study and enrolled after they signed an informed consent. Two patient cohorts were randomized for miRNA expression profiling, a discovery cohort (n=10) and a validation cohort (n=31); profiling was performed by means of a miScript miRNA PCR Array. After a median clinical follow-up of 45months, statistical analysis was performed to identify miRNAs that could discriminate non-responders from complete pathological responders to conventional treatment. miRNA expression profiling identified 101 miRNAs that showed significant differences between non-responders and complete pathological responders (p<0.05). Seven differentially expressed miRNAs were selected, and their expression patterns were confirmed in the validation phase; thus, miR-31-3p, -3676, -125a-5p, -100-5p, -125b-5p, and -200a-5p and miR-342 were significantly associated with clinical response. Expression of this miRNA signature above the median level was a significant predictor of non-response to standard treatment (p<0.001). These seven validated miRNA signatures could be used as molecular biomarkers of chemo- and radio-resistance in locally advanced cervical cancer patients. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  9. Genefu: an R/Bioconductor package for computation of gene expression-based signatures in breast cancer.

    PubMed

    Gendoo, Deena M A; Ratanasirigulchai, Natchar; Schröder, Markus S; Paré, Laia; Parker, Joel S; Prat, Aleix; Haibe-Kains, Benjamin

    2016-04-01

    Breast cancer is one of the most frequent cancers among women. Extensive studies into the molecular heterogeneity of breast cancer have produced a plethora of molecular subtype classification and prognosis prediction algorithms, as well as numerous gene expression signatures. However, reimplementation of these algorithms is a tedious but important task to enable comparison of existing signatures and classification models between each other and with new models. Here, we present the genefu R/Bioconductor package, a multi-tiered compendium of bioinformatics algorithms and gene signatures for molecular subtyping and prognostication in breast cancer. The genefu package is available from Bioconductor. http://www.bioconductor.org/packages/devel/bioc/html/genefu.html Source code is also available on Github https://github.com/bhklab/genefu bhaibeka@uhnresearch.ca Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  10. Towards Simulating Non-Axisymmetric Influences on Aircraft Plumes for Signature Prediction

    NASA Technical Reports Server (NTRS)

    Kenzakowski, D. C.; Shipman, J. D.; Dash, S. M.

    2000-01-01

    A methodology for efficiently including three-dimensional effects on aircraft plume signature is presented. First, exploratory work on the use of passive mixing enhancement devices, namely chevrons and tabs, in IR signature reduction for external turbofan plumes is demonstrated numerically and experimentally. Such small attachments, when properly designed, cause an otherwise axisymmetric plume to have significant 3D structures, affecting signature prediction. Second, an approach for including non-axisymmetric and installation effects in plume signature prediction is discussed using unstructured methodology. Unstructured flow solvers, using advanced turbulence modeling and plume thermochemistry, facilitate the modeling of aircraft effects on plume structure that previously have been neglected due to gridding complexities. The capabilities of the CRUNCH unstructured Navier-Stokes solver for plume modeling is demonstrated for a passively mixed turbofan nozzle, a generic fighter nozzle, and a complete aircraft.

  11. Towards Simulating Non-Axisymmetric Influences on Aircraft Plumes for Signature Prediction

    NASA Technical Reports Server (NTRS)

    Kenzakowski, D. C.; Shipman, J. D.; Dash, S. M.

    2000-01-01

    A methodology for efficiently including three-dimensional effects on aircraft plume signature is presented. First, exploratory work on the use of passive mixing enhancement devices, namely chevrons and tabs, in IR signature reduction for external turbofan plumes is demonstrated numerically and experimentally. Such small attachments, when properly designed, cause an otherwise axisymmetric plume to have significant 3D structures, affecting signature prediction. Second, an approach for including non-axisymmetric and installation effects in plume signature prediction is discussed using unstructured methodology. Unstructured flow solvers, using advanced turbulence modeling and plume thermochemistry, facilitate the modeling of aircraft effects on plume structure that previously have been neglected due to gridding complexities. The capabilities of the CRUNCH unstructured Navier-Stokes solver for plume modeling is demonstrated for a passively mixed turbofan nozzle, a generic fighter nozzle, and a complete aircraft.

  12. Gene expression-based prognostic signatures in lung cancer: ready for clinical use?

    PubMed

    Subramanian, Jyothi; Simon, Richard

    2010-04-07

    A substantial number of studies have reported the development of gene expression-based prognostic signatures for lung cancer. The ultimate aim of such studies should be the development of well-validated clinically useful prognostic signatures that improve therapeutic decision making beyond current practice standards. We critically reviewed published studies reporting the development of gene expression-based prognostic signatures for non-small cell lung cancer to assess the progress made toward this objective. Studies published between January 1, 2002, and February 28, 2009, were identified through a PubMed search. Following hand-screening of abstracts of the identified articles, 16 were selected as relevant. Those publications were evaluated in detail for appropriateness of the study design, statistical validation of the prognostic signature on independent datasets, presentation of results in an unbiased manner, and demonstration of medical utility for the new signature beyond that obtained using existing treatment guidelines. Based on this review, we found little evidence that any of the reported gene expression signatures are ready for clinical application. We also found serious problems in the design and analysis of many of the studies. We suggest a set of guidelines to aid the design, analysis, and evaluation of prognostic signature studies. These guidelines emphasize the importance of focused study planning to address specific medically important questions and the use of unbiased analysis methods to evaluate whether the resulting signatures provide evidence of medical utility beyond standard of care-based prognostic factors.

  13. A gene expression signature from human breast cancer cells with acquired hormone independence identifies MYC as a mediator of antiestrogen resistance

    PubMed Central

    Miller, Todd W.; Balko, Justin M.; Ghazoui, Zara; Dunbier, Anita; Anderson, Helen; Dowsett, Mitch; González-Angulo, Ana M.; Mills, Gordon B.; Miller, William R.; Wu, Huiyun; Shyr, Yu; Arteaga, Carlos L.

    2011-01-01

    Purpose Although most patients with estrogen receptor α (ER)-positive breast cancer initially respond to endocrine therapy, many ultimately develop resistance to antiestrogens. However, mechanisms of antiestrogen resistance and biomarkers predictive of such resistance are underdeveloped. Experimental Design We adapted four ER+ human breast cancer cell lines to grow in an estrogen-depleted medium. A gene signature of estrogen independence was developed by comparing expression profiles of long-term estrogen-deprived (LTED) cells to their parental counterparts. We evaluated the ability of the LTED signature to predict tumor response to neoadjuvant therapy with an aromatase inhibitor, and disease outcome following adjuvant tamoxifen. We utilized Gene Set Analysis (GSA) of LTED cell gene expression profiles and a loss-of-function approach to identify pathways causally associated with resistance to endocrine therapy. Results The LTED gene expression signature was predictive of high tumor cell proliferation following neoadjuvant therapy with anastrozole and letrozole, each in different patient cohorts. This signature was also predictive of poor recurrence-free survival in two studies of patients treated with adjuvant tamoxifen. Bioinformatic interrogation of expression profiles in LTED cells revealed a signature of MYC activation. The MYC activation signature and high MYC protein levels were both predictive of poor outcome following tamoxifen therapy. Finally, knockdown of MYC inhibited LTED cell growth. Conclusions A gene expression signature derived from ER+ breast cancer cells with acquired hormone independence predicted tumor response to aromatase inhibitors and associated with clinical markers of resistance to tamoxifen. In some cases, activation of the MYC pathway was associated with this resistance. PMID:21346144

  14. Identification of a Genomic Signature Predicting for Recurrence in Early Stage Ovarian Cancer

    DTIC Science & Technology

    2014-10-01

    RNAseq of 400 training specimens (Months 12-18) 2. Import raw data into public databases (Months 12-18) 3. Generate preliminary gene signature through...This included sequencing a sample test of 10 tumors and comparing the sequencing results of these early stage samples with publicly available RNAseq ...addressed and fulfill an important unmet need. KEYWORDS: Early Stage Ovarian Cancer, genomic predictive signature, recurrence, RNAseq Research

  15. A miRNA-based signature detected in primary melanoma tissue predicts development of brain metastasis

    PubMed Central

    Hanniford, Doug; Zhong, Judy; Koetz, Lisa; Gaziel-Sovran, Avital; Lackaye, Daniel J.; Shang, Shulian; Pavlick, Anna; Shapiro, Richard; Berman, Russell; Darvishian, Farbod; Shao, Yongzhao; Osman, Iman; Hernando, Eva

    2015-01-01

    Purpose Brain metastasis is the major cause of mortality among melanoma patients. A molecular prognostic test that can reliably stratify patients at initial melanoma diagnosis by risk of developing brain metastasis may inform the clinical management of these patients. Experimental Design We performed a retrospective, cohort-based study analyzing genome-wide and targeted microRNA expression profiling of primary melanoma tumors of three patient cohorts (n= 92, n= 119, n= 45) with extensive clinical follow up. We used Cox regression analysis to establish a microRNA-based signature that improves the ability of the current clinicopathologic staging system to predict the development of brain metastasis. Results Our analyses identified a 4-microRNA (miR-150–5p, miR-15b-5p, miR-16–5p, and miR-374b-3p) prognostic signature that, in combination with stage, distinguished primary melanomas that metastasized to the brain from non-recurrent and non-brain-metastatic primary tumors (training cohort: C-index=81.4%, validation cohort: C-index=67.4%, independent cohort: C-index=76.9%). Corresponding Kaplan-Meier curves of high- vs. low-risk patients displayed a clear separation in brain-metastasis-free and overall survival (training: p<0.001, p<0.001, validation: p=0.033, p=0.007, independent: p=0.021, p=0.022, respectively). Finally, of the microRNA in the prognostic model, we found that the expression of a key lymphocyte miRNA, miR-150–5p, which is less abundant in primary melanomas metastatic to brain, correlated with presence of CD45+ tumor infiltrating lymphocytes. Conclusions A prognostic assay based on the described miRNA expression signature combined with the currently used staging criteria may improve accuracy of primary melanoma patient prognoses and aid clinical management of patients, including selection for adjuvant treatment or clinical trials of adjuvant therapies. PMID:26089374

  16. Systems Biology Approach Predicts Antibody Signature Associated with Brucella melitensis Infection in Humans

    PubMed Central

    2011-01-01

    A complete understanding of the factors that determine selection of antigens recognized by the humoral immune response following infectious agent challenge is lacking. Here we illustrate a systems biology approach to identify the antibody signature associated with Brucella melitensis (Bm) infection in humans and predict proteomic features of serodiagnostic antigens. By taking advantage of a full proteome microarray expressing previously cloned 1406 and newly cloned 1640 Bm genes, we were able to identify 122 immunodominant antigens and 33 serodiagnostic antigens. The reactive antigens were then classified according to annotated functional features (COGs), computationally predicted features (e.g., subcellular localization, physical properties), and protein expression estimated by mass spectrometry (MS). Enrichment analyses indicated that membrane association and secretion were significant enriching features of the reactive antigens, as were proteins predicted to have a signal peptide, a single transmembrane domain, and outer membrane or periplasmic location. These features accounted for 67% of the serodiagnostic antigens. An overlay of the seroreactive antigen set with proteomic data sets generated by MS identified an additional 24%, suggesting that protein expression in bacteria is an additional determinant in the induction of Brucella-specific antibodies. This analysis indicates that one-third of the proteome contains enriching features that account for 91% of the antigens recognized, and after B. melitensis infection the immune system develops significant antibody titers against 10% of the proteins with these enriching features. This systems biology approach provides an empirical basis for understanding the breadth and specificity of the immune response to B. melitensis and a new framework for comparing the humoral responses against other microorganisms. PMID:21863892

  17. Predicted signatures of rotating Bose-Einstein Condensates

    SciTech Connect

    Butts, D.A.; Rokhsar, D.S.

    1999-01-01

    Superfluids are distinguished from normal fluidsby theirpeculiar response1 to rotation: circulating flow in superfuid helium2,3,astrongly coupled Bose liquid, can appear only as quantized vortices4-6.The newly created Bose-Einstein condensates7,9--clouds of millions ofultracold, weakly interacting alkali-metal atoms that occupy a singlequantum state Doffer the possibility of investigating superuidity in theweak-coupling regime. An outstanding question is whether Bose-Einsteincondensates exhibit a mesoscopic quantum analogue of the macroscopicvortices in superfluids, and what its experimental signature would be.Here we report calculations of the low-energy states of a rotating,weakly interacting Bose gas. We find a succession of transitions betweenstab reement with observations5 of rotating super-fluid helium, astrong-coupling superfuid. Counterintuitively, the angular momentum perparticle is not quantized. Some angular momenta are forbidden,corresponding to asymmetrical unstablestates that provide a physicalmechanism for the entry of vorticity into the condensate.

  18. Independent confirmation of a prognostic gene-expression signature in adult acute myeloid leukemia with a normal karyotype: a Cancer and Leukemia Group B study

    PubMed Central

    Radmacher, Michael D.; Marcucci, Guido; Ruppert, Amy S.; Mrózek, Krzysztof; Whitman, Susan P.; Vardiman, James W.; Paschka, Peter; Vukosavljevic, Tamara; Baldus, Claudia D.; Kolitz, Jonathan E.; Caligiuri, Michael A.; Larson, Richard A.; Bloomfield, Clara D.

    2006-01-01

    Patients with acute myeloid leukemia (AML) and normal karyotype are classified in an intermediate-risk group, albeit this subset is heterogeneous for clinical outcome. A recent complementary DNA microarray study identified a gene-expression signature that—when used to cluster normal karyotype patients—separated them into 2 prognostically relevant subgroups. We sought the first independent validation of the prognostic value of this signature. Using oligonucleotide microarrays to measure gene expression in samples from uniformly treated adults with karyotypically normal AML, we performed cluster analysis based on the previously identified signature. We also developed a well-defined classification rule using the signature to predict outcome for individual patients. Cluster analysis confirmed the prognostic utility of the signature: patient clusters differed in overall (P = .001) and disease-free (P = .001) survival. The signature-based classifier identified groups with differences in overall (P = .02) and disease-free (P = .05) survival. A strong association of the outcome classifier with the prognostically adverse FLT3 internal tandem duplication (FLT3 ITD) potentially explained the prognostic significance of the signature. However, in the subgroup of patients without FLT3 ITD there was a moderate difference in survival for the classifier-derived groups. Our analysis confirms the applicability of the gene-expression profiling strategy for outcome prediction in cytogenetically normal AML. PMID:16670265

  19. Refinement of breast cancer risk prediction with concordant leading edge subsets from prognostic gene signatures.

    PubMed

    Huang, Chi-Cheng; Tu, Shih-Hsin; Lien, Heng-Hui; Huang, Ching-Shui; Huang, Chi-Jung; Lai, Liang-Chuan; Tsai, Mon-Hsun; Chuang, Eric Y

    2014-09-01

    Several prognostic signatures have been identified for breast cancer. However, these signatures vary extensively in their gene compositions, and the poor concordance of the risk groups defined by the prognostic signatures hinders their clinical applicability. Breast cancer risk prediction was refined with a novel approach to finding concordant genes from leading edge analysis of prognostic signatures. Each signature was split into two gene sets, which contained either up-regulated or down-regulated genes, and leading edge analysis was performed within each array study for all up-/down-regulated gene sets of the same signature from all training datasets. Consensus of leading edge subsets among all training microarrays was used to synthesize a predictive model, which was then tested in independent studies by partial least squares regression. Only a small portion of six prognostic signatures (Amsterdam, Rotterdam, Genomic Grade Index, Recurrence Score, and Hu306 and PAM50 of intrinsic subtypes) was significantly enriched in the leading edge analysis in five training datasets (n = 2,380), and that the concordant leading edge subsets (43 genes) could identify the core signature genes that account for the enrichment signals providing prognostic power across all assayed samples. The proposed concordant leading edge algorithm was able to discriminate high-risk from low-risk patients in terms of relapse-free or distant metastasis-free survival in all training samples (hazard ratios: 1.84-2.20) and in three out of four independent studies (hazard ratios: 3.91-8.31). In some studies, the concordant leading edge subset remained a significant prognostic factor independent of clinical ER, HER2, and lymph node status. The present study provides a statistical framework for identifying core consensus across microarray studies with leading edge analysis, and a breast cancer risk predictive model was established.

  20. Unsupervised Extraction of Stable Expression Signatures from Public Compendia with an Ensemble of Neural Networks.

    PubMed

    Tan, Jie; Doing, Georgia; Lewis, Kimberley A; Price, Courtney E; Chen, Kathleen M; Cady, Kyle C; Perchuk, Barret; Laub, Michael T; Hogan, Deborah A; Greene, Casey S

    2017-07-26

    Cross-experiment comparisons in public data compendia are challenged by unmatched conditions and technical noise. The ADAGE method, which performs unsupervised integration with denoising autoencoder neural networks, can identify biological patterns, but because ADAGE models, like many neural networks, are over-parameterized, different ADAGE models perform equally well. To enhance model robustness and better build signatures consistent with biological pathways, we developed an ensemble ADAGE (eADAGE) that integrated stable signatures across models. We applied eADAGE to a compendium of Pseudomonas aeruginosa gene expression profiling experiments performed in 78 media. eADAGE revealed a phosphate starvation response controlled by PhoB in media with moderate phosphate and predicted that a second stimulus provided by the sensor kinase, KinB, is required for this PhoB activation. We validated this relationship using both targeted and unbiased genetic approaches. eADAGE, which captures stable biological patterns, enables cross-experiment comparisons that can highlight measured but undiscovered relationships. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

  1. A predictive signature gene set for discriminating active from latent tuberculosis in Warao Amerindian children

    PubMed Central

    2013-01-01

    Background Tuberculosis (TB) continues to cause a high toll of disease and death among children worldwide. The diagnosis of childhood TB is challenged by the paucibacillary nature of the disease and the difficulties in obtaining specimens. Whereas scientific and clinical research efforts to develop novel diagnostic tools have focused on TB in adults, childhood TB has been relatively neglected. Blood transcriptional profiling has improved our understanding of disease pathogenesis of adult TB and may offer future leads for diagnosis and treatment. No studies applying gene expression profiling of children with TB have been published so far. Results We identified a 116-gene signature set that showed an average prediction error of 11% for TB vs. latent TB infection (LTBI) and for TB vs. LTBI vs. healthy controls (HC) in our dataset. A minimal gene set of only 9 genes showed the same prediction error of 11% for TB vs. LTBI in our dataset. Furthermore, this minimal set showed a significant discriminatory value for TB vs. LTBI for all previously published adult studies using whole blood gene expression, with average prediction errors between 17% and 23%. In order to identify a robust representative gene set that would perform well in populations of different genetic backgrounds, we selected ten genes that were highly discriminative between TB, LTBI and HC in all literature datasets as well as in our dataset. Functional annotation of these genes highlights a possible role for genes involved in calcium signaling and calcium metabolism as biomarkers for active TB. These ten genes were validated by quantitative real-time polymerase chain reaction in an additional cohort of 54 Warao Amerindian children with LTBI, HC and non-TB pneumonia. Decision tree analysis indicated that five of the ten genes were sufficient to classify 78% of the TB cases correctly with no LTBI subjects wrongly classified as TB (100% specificity). Conclusions Our data justify the further exploration of our

  2. Predicting Early Intrahepatic Recurrence of Hepatocellular Carcinoma after Microwave Ablation Using SELDI-TOF Proteomic Signature

    PubMed Central

    Yu, Xiao-ling; Liang, Ping; Dong, Bao-wei; Fan, Jin; Li, Meng; Liu, Fang-yi

    2013-01-01

    Background/Aims Despite great progress in the treatment of hepatocellular carcinoma (HCC) over the last-decade, intrahepatic recurrence is still the most frequent serious adverse event after all the treatments including microwave ablation. This study aimed to predict early recurrence of HCC after microwave ablation using serum proteomic signature. Methods After curative microwave ablation of HCC, 86 patients were followed-up for 1 year. Serum samples were collected before microwave ablation. The mass spectra of proteins were generated using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). Serum samples from 50 patients were randomly selected as a training set and for biomarkers discovery and model development. The remaining serum samples were categorized for validation of the algorithm. Results According to preablation serum protein profiling obtained from the 50 HCC samples in the training set, nine significant differentially-expressed proteins were detected in the serum samples between recurrent and non-recurrent patients. Decision classification tree combined with three candidate proteins with m/z values of 7787, 6858 and 6646 was produced using Biomarker Patterns Software with sensitivity of 85.7% and specificity of 88.9% in the training set. When the SELDI marker pattern was tested with the blinded testing set, it yielded a sensitivity of 80.0%, a specificity of 88.5% and a positive predictive value of 86.1%. Conclusions Differentially-expressed protein peaks in preablation serum screened by SELDI are associated with prognosis of HCC. The decision classification tree is a potential tool in predicting early intrahepatic recurrence in HCC patients after microwave ablation. PMID:24349287

  3. Predicting early intrahepatic recurrence of hepatocellular carcinoma after microwave ablation using SELDI-TOF proteomic signature.

    PubMed

    Cao, Xiao-lin; Li, Hua; Yu, Xiao-ling; Liang, Ping; Dong, Bao-wei; Fan, Jin; Li, Meng; Liu, Fang-yi

    2013-01-01

    Despite great progress in the treatment of hepatocellular carcinoma (HCC) over the last-decade, intrahepatic recurrence is still the most frequent serious adverse event after all the treatments including microwave ablation. This study aimed to predict early recurrence of HCC after microwave ablation using serum proteomic signature. After curative microwave ablation of HCC, 86 patients were followed-up for 1 year. Serum samples were collected before microwave ablation. The mass spectra of proteins were generated using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). Serum samples from 50 patients were randomly selected as a training set and for biomarkers discovery and model development. The remaining serum samples were categorized for validation of the algorithm. According to preablation serum protein profiling obtained from the 50 HCC samples in the training set, nine significant differentially-expressed proteins were detected in the serum samples between recurrent and non-recurrent patients. Decision classification tree combined with three candidate proteins with m/z values of 7787, 6858 and 6646 was produced using Biomarker Patterns Software with sensitivity of 85.7% and specificity of 88.9% in the training set. When the SELDI marker pattern was tested with the blinded testing set, it yielded a sensitivity of 80.0%, a specificity of 88.5% and a positive predictive value of 86.1%. Differentially-expressed protein peaks in preablation serum screened by SELDI are associated with prognosis of HCC. The decision classification tree is a potential tool in predicting early intrahepatic recurrence in HCC patients after microwave ablation.

  4. Predictive Outcomes for HER2-enriched Cancer Using Growth and Metastasis Signatures Driven By SPARC.

    PubMed

    Güttlein, Leandro N; Benedetti, Lorena G; Fresno, Cristóbal; Spallanzani, Raúl G; Mansilla, Sabrina F; Rotondaro, Cecilia; Raffo Iraolagoitia, Ximena L; Salvatierra, Edgardo; Bravo, Alicia I; Fernández, Elmer A; Gottifredi, Vanesa; Zwirner, Norberto W; Llera, Andrea S; Podhajcer, Osvaldo L

    2017-03-01

    Understanding the mechanism of metastatic dissemination is crucial for the rational design of novel therapeutics. The secreted protein acidic and rich in cysteine (SPARC) is a matricellular glycoprotein which has been extensively associated with human breast cancer aggressiveness although the underlying mechanisms are still unclear. Here, shRNA-mediated SPARC knockdown greatly reduced primary tumor growth and completely abolished lung colonization of murine 4T1 and LM3 breast malignant cells implanted in syngeneic BALB/c mice. A comprehensive study including global transcriptomic analysis followed by biological validations confirmed that SPARC induces primary tumor growth by enhancing cell cycle and by promoting a COX-2-mediated expansion of myeloid-derived suppressor cells (MDSC). The role of SPARC in metastasis involved a COX-2-independent enhancement of cell disengagement from the primary tumor and adherence to the lungs that fostered metastasis implantation. Interestingly, SPARC-driven gene expression signatures obtained from these murine models predicted the clinical outcome of patients with HER2-enriched breast cancer subtypes. In total, the results reveal that SPARC and its downstream effectors are attractive targets for antimetastatic therapies in breast cancer.Implications: These findings shed light on the prometastatic role of SPARC, a key protein expressed by breast cancer cells and surrounding stroma, with important consequences for disease outcome. Mol Cancer Res; 15(3); 304-16. ©2016 AACR. ©2016 American Association for Cancer Research.

  5. Using glint to perform geometric signature prediction and pose estimation

    NASA Astrophysics Data System (ADS)

    Paulson, Christopher; Zelnio, Edmund; Gorham, LeRoy; Wu, Dapeng

    2012-05-01

    We consider two problems in this paper. The rst problem is to construct a dictionary of elements without using synthetic data or a subset of the data collection; the second problem is to estimate the orientation of the vehicle, independent of the elevation angle. These problems are important to the SAR community because it will alleviate the cost to create the dictionary and reduce the number of elements in the dictionary needed for classication. In order to accomplish these tasks, we utilize the glint phenomenology, which is usually viewed as a hindrance in most algorithms but is valuable information in our research. One way to capitalize on the glint information is to predict the location of the int by using geometry of the single and double bounce phenomenology. After qualitative examination of the results, we were able to deduce that the geometry information was sucient for accurately predicting the location of the glint. Another way that we exploited the glint characteristics was by using it to extract the angle feature which we will use to do the pose estimation. Using this technique we were able to predict the cardinal heading of the vehicle within +/-2° with 96:6% having 0° error. Now this research will have an impact on the classication of SAR images because the geometric prediction will reduce the cost and time to develop and maintain the database for SAR ATR systems and the pose estimation will reduce the computational time and improve accuracy of vehicle classication.

  6. Improved signature prediction through coupling of ShipIR and CFD

    NASA Astrophysics Data System (ADS)

    Vaitekunas, David A.; Sideroff, Chris; Moussa, Christine

    2011-05-01

    Most existing platform signature models use semi-empirical correlations to predict flow convection on internal and external surfaces, a key element in the prediction of accurate skin signature. Although these convection algorithms are capable of predicting bulk heat transfer coefficients between each surface and the designated flow region, they are not capable of capturing local effects such as flow stagnation, flow separation, and flow history. Most computational fluid dynamics (CFD) codes lack the ability to predict changes in background solar and thermal irradiation with the environment and sun location, nor do they include multi-bounce radiative surface exchanges by default in their solvers. Existing interfaces between CFD and signature prediction typically involve a one-directional mapping of CFD predicted temperatures to the signature model. This paper describes a new functional interface between the NATO-standard ship signature model (ShipIR) and the ANSYS Fluent model, where a bi-directional mapping is used to transfer the thermal radiation predictions from ShipIR to Fluent, and after re-iteration of the CFD solution, transfer the wall and fluid temperatures back to ShipIR for further refinement of local-area heat transfer coefficients, and re-iteration of the ShipIR thermal solution. Both models converge to an RMS difference of 0.3 °C within a few successive iterations (5-6). This new functional interface is described through a detailed thermal/IR simulation of an unclassified research vessel, the Canadian Forces Auxiliary Vessel (CFAV) Quest. Future efforts to validate this new modelling approach using shipboard measurements are also discussed.

  7. ToxCast: Developing Predictive Signatures of Chemically Induced Toxicity (Developing Predictive Bioactivity Signatures from ToxCasts HTS Data)

    EPA Science Inventory

    ToxCast, the United States Environmental Protection Agency’s chemical prioritization research program, is developing methods for utilizing computational chemistry, bioactivity profiling and toxicogenomic data to predict potential for toxicity and prioritize limited testing resour...

  8. ToxCast: Developing Predictive Signatures of Chemically Induced Toxicity (Developing Predictive Bioactivity Signatures from ToxCasts HTS Data)

    EPA Science Inventory

    ToxCast, the United States Environmental Protection Agency’s chemical prioritization research program, is developing methods for utilizing computational chemistry, bioactivity profiling and toxicogenomic data to predict potential for toxicity and prioritize limited testing resour...

  9. A Semi-Supervised Approach for Refining Transcriptional Signatures of Drug Response and Repositioning Predictions

    PubMed Central

    Iorio, Francesco; Shrestha, Roshan L.; Levin, Nicolas; Boilot, Viviane; Garnett, Mathew J.; Saez-Rodriguez, Julio; Draviam, Viji M.

    2015-01-01

    We present a novel strategy to identify drug-repositioning opportunities. The starting point of our method is the generation of a signature summarising the consensual transcriptional response of multiple human cell lines to a compound of interest (namely the seed compound). This signature can be derived from data in existing databases, such as the connectivity-map, and it is used at first instance to query a network interlinking all the connectivity-map compounds, based on the similarity of their transcriptional responses. This provides a drug neighbourhood, composed of compounds predicted to share some effects with the seed one. The original signature is then refined by systematically reducing its overlap with the transcriptional responses induced by drugs in this neighbourhood that are known to share a secondary effect with the seed compound. Finally, the drug network is queried again with the resulting refined signatures and the whole process is carried on for a number of iterations. Drugs in the final refined neighbourhood are then predicted to exert the principal mode of action of the seed compound. We illustrate our approach using paclitaxel (a microtubule stabilising agent) as seed compound. Our method predicts that glipizide and splitomicin perturb microtubule function in human cells: a result that could not be obtained through standard signature matching methods. In agreement, we find that glipizide and splitomicin reduce interphase microtubule growth rates and transiently increase the percentage of mitotic cells–consistent with our prediction. Finally, we validated the refined signatures of paclitaxel response by mining a large drug screening dataset, showing that human cancer cell lines whose basal transcriptional profile is anti-correlated to them are significantly more sensitive to paclitaxel and docetaxel. PMID:26452147

  10. Computational prediction of miRNAs and their targets in Phaseolus vulgaris using simple sequence repeat signatures.

    PubMed

    Nithin, Chandran; Patwa, Nisha; Thomas, Amal; Bahadur, Ranjit Prasad; Basak, Jolly

    2015-06-12

    MicroRNAs (miRNAs) are endogenous, noncoding, short RNAs directly involved in regulating gene expression at the post-transcriptional level. In spite of immense importance, limited information of P. vulgaris miRNAs and their expression patterns prompted us to identify new miRNAs in P. vulgaris by computational methods. Besides conventional approaches, we have used the simple sequence repeat (SSR) signatures as one of the prediction parameter. Moreover, for all other parameters including normalized Shannon entropy, normalized base pairing index and normalized base-pair distance, instead of taking a fixed cut-off value, we have used 99% probability range derived from the available data. We have identified 208 mature miRNAs in P. vulgaris belonging to 118 families, of which 201 are novel. 97 of the predicted miRNAs in P. vulgaris were validated with the sequencing data obtained from the small RNA sequencing of P. vulgaris. Randomly selected predicted miRNAs were also validated using qRT-PCR. A total of 1305 target sequences were identified for 130 predicted miRNAs. Using 80% sequence identity cut-off, proteins coded by 563 targets were identified. The computational method developed in this study was also validated by predicting 229 miRNAs of A. thaliana and 462 miRNAs of G. max, of which 213 for A. thaliana and 397 for G. max are existing in miRBase 20. There is no universal SSR that is conserved among all precursors of Viridiplantae, but conserved SSR exists within a miRNA family and is used as a signature in our prediction method. Prediction of known miRNAs of A. thaliana and G. max validates the accuracy of our method. Our findings will contribute to the present knowledge of miRNAs and their targets in P. vulgaris. This computational method can be applied to any species of Viridiplantae for the successful prediction of miRNAs and their targets.

  11. MicroRNA Expression Signatures During Malignant Progression From Barrett's Esophagus.

    PubMed

    Bansal, Ajay; Gupta, Vijayalaxmi; Wang, Kenneth

    2016-06-01

    The rapid increase and poor survival of esophageal adenocarcinoma (EAC) have led to significant efforts to promote early detection. Given that the premalignant lesion of Barrett's esophagus (BE) is the major known risk factor for EAC, multiple investigators have studied biomarker signatures that can predict malignant progression of BE to EAC. MicroRNAs, a novel class of gene regulators, are small non-coding RNAs and have been associated with carcinogenesis. MicroRNAs are ideal biomarkers because of their remarkable stability in fixed tissues, a common method for collection of clinical specimens, and in blood either within exosomes or as microRNA-protein complexes. Multiple studies show potential of microRNAs as tissue and blood biomarkers for diagnosis and prognosis of EAC but the results need confirmation in prospective studies. Although head-to-head comparisons are lacking, microRNA panels require less genes than messenger RNA panels for diagnosis of EAC in BE. MicroRNA diagnostic panels will need to be compared for accuracy against global measures of genome instability that were recently shown to be good predictors of progression but require sophisticated analytic techniques. Early studies on blood microRNA panels are promising but have found microRNA markers to be inconsistent among studies. MicroRNA expression in blood is different between various microRNA sub-compartments such as exosomes and microRNA-protein complexes and could affect blood microRNA measurements. Further standardization is needed to yield consistent results. We have summarized the current understanding of the tissue and blood microRNA signatures that may predict the development and progression of EAC.

  12. Species-Specific Predictive Signatures of Developmental Toxicity Using the ToxCast Chemical Library

    EPA Science Inventory

    EPA’s ToxCastTM project is profiling the in vitro bioactivity of chemicals to generate predictive signatures that correlate with observed in vivo toxicity. In vitro profiling methods from ToxCast data consist of over 600 high-throughput screening (HTS) and high-content screening ...

  13. Species-Specific Predictive Signatures of Developmental Toxicity Using the ToxCast Chemical Library

    EPA Science Inventory

    EPA’s ToxCastTM project is profiling the in vitro bioactivity of chemicals to generate predictive signatures that correlate with observed in vivo toxicity. In vitro profiling methods from ToxCast data consist of over 600 high-throughput screening (HTS) and high-content screening ...

  14. A distinct metabolic signature predicts development of fasting plasma glucose

    PubMed Central

    2012-01-01

    Background High blood glucose and diabetes are amongst the conditions causing the greatest losses in years of healthy life worldwide. Therefore, numerous studies aim to identify reliable risk markers for development of impaired glucose metabolism and type 2 diabetes. However, the molecular basis of impaired glucose metabolism is so far insufficiently understood. The development of so called 'omics' approaches in the recent years promises to identify molecular markers and to further understand the molecular basis of impaired glucose metabolism and type 2 diabetes. Although univariate statistical approaches are often applied, we demonstrate here that the application of multivariate statistical approaches is highly recommended to fully capture the complexity of data gained using high-throughput methods. Methods We took blood plasma samples from 172 subjects who participated in the prospective Metabolic Syndrome Berlin Potsdam follow-up study (MESY-BEPO Follow-up). We analysed these samples using Gas Chromatography coupled with Mass Spectrometry (GC-MS), and measured 286 metabolites. Furthermore, fasting glucose levels were measured using standard methods at baseline, and after an average of six years. We did correlation analysis and built linear regression models as well as Random Forest regression models to identify metabolites that predict the development of fasting glucose in our cohort. Results We found a metabolic pattern consisting of nine metabolites that predicted fasting glucose development with an accuracy of 0.47 in tenfold cross-validation using Random Forest regression. We also showed that adding established risk markers did not improve the model accuracy. However, external validation is eventually desirable. Although not all metabolites belonging to the final pattern are identified yet, the pattern directs attention to amino acid metabolism, energy metabolism and redox homeostasis. Conclusions We demonstrate that metabolites identified using a high

  15. A distinct metabolic signature predicts development of fasting plasma glucose.

    PubMed

    Hische, Manuela; Larhlimi, Abdelhalim; Schwarz, Franziska; Fischer-Rosinský, Antje; Bobbert, Thomas; Assmann, Anke; Catchpole, Gareth S; Pfeiffer, Andreas Fh; Willmitzer, Lothar; Selbig, Joachim; Spranger, Joachim

    2012-02-02

    High blood glucose and diabetes are amongst the conditions causing the greatest losses in years of healthy life worldwide. Therefore, numerous studies aim to identify reliable risk markers for development of impaired glucose metabolism and type 2 diabetes. However, the molecular basis of impaired glucose metabolism is so far insufficiently understood. The development of so called 'omics' approaches in the recent years promises to identify molecular markers and to further understand the molecular basis of impaired glucose metabolism and type 2 diabetes. Although univariate statistical approaches are often applied, we demonstrate here that the application of multivariate statistical approaches is highly recommended to fully capture the complexity of data gained using high-throughput methods. We took blood plasma samples from 172 subjects who participated in the prospective Metabolic Syndrome Berlin Potsdam follow-up study (MESY-BEPO Follow-up). We analysed these samples using Gas Chromatography coupled with Mass Spectrometry (GC-MS), and measured 286 metabolites. Furthermore, fasting glucose levels were measured using standard methods at baseline, and after an average of six years. We did correlation analysis and built linear regression models as well as Random Forest regression models to identify metabolites that predict the development of fasting glucose in our cohort. We found a metabolic pattern consisting of nine metabolites that predicted fasting glucose development with an accuracy of 0.47 in tenfold cross-validation using Random Forest regression. We also showed that adding established risk markers did not improve the model accuracy. However, external validation is eventually desirable. Although not all metabolites belonging to the final pattern are identified yet, the pattern directs attention to amino acid metabolism, energy metabolism and redox homeostasis. We demonstrate that metabolites identified using a high-throughput method (GC-MS) perform well in

  16. 76 FR 62000 - Express Mail Domestic Postage Refund Policy and Waiver of Signature

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-10-06

    ... 111 Express Mail Domestic Postage Refund Policy and Waiver of Signature AGENCY: Postal Service TM... States Postal Service, Domestic Mail Manual (DMM ) throughout various sections to modify the policy for filing claims for domestic Express Mail refunds from 90 days to 30 days after the date of mailing, and...

  17. A signature microRNA expression profile for the cellular response to thermal stress

    NASA Astrophysics Data System (ADS)

    Wilmink, Gerald J.; Roth, Caleb C.; Ketchum, Norma; Ibey, Bennett L.; Waterworth, Angela; Suarez, Maria; Roach, William P.

    2009-02-01

    Recently, an extensive layer of intra-cellular signals was discovered that was previously undetected by genetic radar. It is now known that this layer consists primarily of a class of short noncoding RNA species that are referred to as microRNAs (miRNAs). MiRNAs regulate protein synthesis at the post-transcriptional level, and studies have shown that they are involved in many fundamental cellular processes. In this study, we hypothesized that miRNAs may be involved in cellular stress response mechanisms, and that cells exposed to thermal stress may exhibit a signature miRNA expression profile indicative of their functional involvement in such mechanisms. To test our hypothesis, human dermal fibroblasts were exposed to an established hyperthermic protocol, and the ensuing miRNA expression levels were evaluated 4 hr post-exposure using microRNA microarray gene chips. The microarray data shows that 123 miRNAs were differentially expressed in cells exposed to thermal stress. We collectively refer to these miRNAs as thermalregulated microRNAs (TRMs). Since miRNA research is in its infancy, it is interesting to note that only 27 of the 123 TRMs are currently annotated in the Sanger miRNA registry. Prior to publication, we plan to submit the remaining novel 96 miRNA gene sequences for proper naming. Computational and thermodynamic modeling algorithms were employed to identify putative mRNA targets for the TRMs, and these studies predict that TRMs regulate the mRNA expression of various proteins that are involved in the cellular stress response. Future empirical studies will be conducted to validate these theoretical predictions, and to further examine the specific role that TRMs play in the cellular stress response.

  18. HVPG signature: A prognostic and predictive tool in hepatocellular carcinoma

    PubMed Central

    Xiang, Yi; Chen, Jinjun; Zhao, Jianbo; Li, Jing; Qi, Fu-Zhen; Xu, Yong

    2016-01-01

    Hepatic venous pressure gradient (HVPG) measurement provides independent prognostic value in patients with cirrhosis, and the prognostic and predictive role of HVPG in hepatocellular carcinoma (HCC) also has been explored. The management of HCC is limited to the European Association for the Study of the Liver (EASL) and American Association for the Study of Liver Diseases (AASLD) guidelines that consider that HVPG≥10 mmHg to be a contraindication for hepatic resection (HR), otherwise other treatment modalities are recommended. Current studies show that a raised HVPG diagnosed directly or indirectly leads to a negative prognosis of patients with HCC and cirrhosis, but HVPG greater than 10 mmHg should not be regarded as an absolute contraindication for HR. Selecting direct or surrogate measurement of HVPG is still under debate. Only several studies reported the impact of HVPG in negative prognosis of HCC patients after liver transplantation (LT) and the value of HVPG in the prediction of HCC development, which need to be further validated. PMID:27566593

  19. Oxidative stress/reactive metabolite gene expression signature in rat liver detects idiosyncratic hepatotoxicants

    SciTech Connect

    Leone, Angelique; Nie, Alex; Brandon Parker, J.; Sawant, Sharmilee; Piechta, Leigh-Anne; Kelley, Michael F. Mark Kao, L.; Jim Proctor, S.; Verheyen, Geert; Johnson, Mark D.; Lord, Peter G.; McMillian, Michael K.

    2014-03-15

    Previously we reported a gene expression signature in rat liver for detecting a specific type of oxidative stress (OS) related to reactive metabolites (RM). High doses of the drugs disulfiram, ethinyl estradiol and nimesulide were used with another dozen paradigm OS/RM compounds, and three other drugs flutamide, phenacetin and sulindac were identified by this signature. In a second study, antiepileptic drugs were compared for covalent binding and their effects on OS/RM; felbamate, carbamazepine, and phenobarbital produced robust OS/RM gene expression. In the present study, liver RNA samples from drug-treated rats from more recent experiments were examined for statistical fit to the OS/RM signature. Of all 97 drugs examined, in addition to the nine drugs noted above, 19 more were identified as OS/RM-producing compounds—chlorpromazine, clozapine, cyproterone acetate, dantrolene, dipyridamole, glibenclamide, isoniazid, ketoconazole, methapyrilene, naltrexone, nifedipine, sulfamethoxazole, tamoxifen, coumarin, ritonavir, amitriptyline, valproic acid, enalapril, and chloramphenicol. Importantly, all of the OS/RM drugs listed above have been linked to idiosyncratic hepatotoxicity, excepting chloramphenicol, which does not have a package label for hepatotoxicity, but does have a black box warning for idiosyncratic bone marrow suppression. Most of these drugs are not acutely toxic in the rat. The OS/RM signature should be useful to avoid idiosyncratic hepatotoxicity of drug candidates. - Highlights: • 28 of 97 drugs gave a positive OS/RM gene expression signature in rat liver. • The specificity of the signature for human idiosyncratic hepatotoxicants was 98%. • The sensitivity of the signature for human idiosyncratic hepatotoxicants was 75%. • The signature can help eliminate hepatotoxicants from drug development.

  20. Prediction of the TNT signature from buried UXO/landmines

    SciTech Connect

    Webb, S.W.; Phelan, J.M.; Finsterle, S.A.; Pruess, K.

    1998-06-01

    The detection and removal of buried unexploded ordnance (UXO) and landmines is one of the most important problems facing the world today. Numerous detection strategies are being developed, including infrared, electrical conductivity, ground-penetrating radar, and chemical sensors. Chemical sensors rely on the detection of TNT molecules, which are transported from buried UXO/landmines by advection and diffusion in the soil. As part of this effort, numerical models are being developed to predict TNT transport in soils including the effect of precipitation and evaporation. Modifications will be made to TOUGH2 for application to the TNT chemical sensing problem. Understanding the fate and transport of TNT in the soil will affect the design, performance and operation of chemical sensors by indicating preferred sensing strategies.

  1. An Expression Signature as an Aid to the Histologic Classification of Non-Small Cell Lung Cancer.

    PubMed

    Girard, Luc; Rodriguez-Canales, Jaime; Behrens, Carmen; Thompson, Debrah M; Botros, Ihab W; Tang, Hao; Xie, Yang; Rekhtman, Natasha; Travis, William D; Wistuba, Ignacio I; Minna, John D; Gazdar, Adi F

    2016-10-01

    Most non-small cell lung cancers (NSCLC) are now diagnosed from small specimens, and classification using standard pathology methods can be difficult. This is of clinical relevance as many therapy regimens and clinical trials are histology dependent. The purpose of this study was to develop an mRNA expression signature as an adjunct test for routine histopathologic classification of NSCLCs. A microarray dataset of resected adenocarcinomas (ADC) and squamous cell carcinomas (SCC) was used as the learning set for an ADC-SCC signature. The Cancer Genome Atlas (TCGA) lung RNAseq dataset was used for validation. Another microarray dataset of ADCs and matched nonmalignant lung was used as the learning set for a tumor versus nonmalignant signature. The classifiers were selected as the most differentially expressed genes and sample classification was determined by a nearest distance approach. We developed a 62-gene expression signature that contained many genes used in immunostains for NSCLC typing. It includes 42 genes that distinguish ADC from SCC and 20 genes differentiating nonmalignant lung from lung cancer. Testing of the TCGA and other public datasets resulted in high prediction accuracies (93%-95%). In addition, a prediction score was derived that correlates both with histologic grading and prognosis. We developed a practical version of the Classifier using the HTG EdgeSeq nuclease protection-based technology in combination with next-generation sequencing that can be applied to formalin-fixed paraffin-embedded (FFPE) tissues and small biopsies. Our RNA classifier provides an objective, quantitative method to aid in the pathologic diagnosis of lung cancer. Clin Cancer Res; 22(19); 4880-9. ©2016 AACR. ©2016 American Association for Cancer Research.

  2. A trichostatin A expression signature identified by TempO-Seq targeted whole transcriptome profiling.

    PubMed

    Yeakley, Joanne M; Shepard, Peter J; Goyena, Diana E; VanSteenhouse, Harper C; McComb, Joel D; Seligmann, Bruce E

    2017-01-01

    The use of gene expression signatures to classify compounds, identify efficacy or toxicity, and differentiate close analogs relies on the sensitivity of the method to identify modulated genes. We used a novel ligation-based targeted whole transcriptome expression profiling assay, TempO-Seq®, to determine whether previously unreported compound-responsive genes could be identified and incorporated into a broad but specific compound signature. TempO-Seq exhibits 99.6% specificity, single cell sensitivity, and excellent correlation with fold differences measured by RNA-Seq (R2 = 0.9) for 20,629 targets. Unlike many expression assays, TempO-Seq does not require RNA purification, cDNA synthesis, or capture of targeted RNA, and lacks a 3' end bias. To investigate the sensitivity of the TempO-Seq assay to identify significantly modulated compound-responsive genes, we derived whole transcriptome profiles from MCF-7 cells treated with the histone deacetylase inhibitor Trichostatin A (TSA) and identified more than 9,000 differentially expressed genes. The TSA profile for MCF-7 cells overlapped those for HL-60 and PC-3 cells in the Connectivity Map (cMAP) database, suggesting a common TSA-specific expression profile independent of baseline gene expression. A 43-gene cell-independent TSA signature was extracted from cMAP and confirmed in TempO-Seq MCF-7 data. Additional genes that were not previously reported to be TSA responsive in the cMAP database were also identified. TSA treatment of 5 cell types revealed 1,136 differentially expressed genes in common, including 785 genes not previously reported to be TSA responsive. We conclude that TSA induces a specific expression signature that is consistent across widely different cell types, that this signature contains genes not previously associated with TSA responses, and that TempO-Seq provides the sensitive differential expression detection needed to define such compound-specific, cell-independent, changes in expression.

  3. Gene Expression Deconvolution for Uncovering Molecular Signatures in Response to Therapy in Juvenile Idiopathic Arthritis

    PubMed Central

    Rosenberg, Alan M.; Yeung, Rae S. M.; Morris, Quaid

    2016-01-01

    Gene expression-based signatures help identify pathways relevant to diseases and treatments, but are challenging to construct when there is a diversity of disease mechanisms and treatments in patients with complex diseases. To overcome this challenge, we present a new application of an in silico gene expression deconvolution method, ISOpure-S1, and apply it to identify a common gene expression signature corresponding to response to treatment in 33 juvenile idiopathic arthritis (JIA) patients. Using pre- and post-treatment gene expression profiles only, we found a gene expression signature that significantly correlated with a reduction in the number of joints with active arthritis, a measure of clinical outcome (Spearman rho = 0.44, p = 0.040, Bonferroni correction). This signature may be associated with a decrease in T-cells, monocytes, neutrophils and platelets. The products of most differentially expressed genes include known biomarkers for JIA such as major histocompatibility complexes and interleukins, as well as novel biomarkers including α-defensins. This method is readily applicable to expression datasets of other complex diseases to uncover shared mechanistic patterns in heterogeneous samples. PMID:27244050

  4. Direct modeling parameter signature analysis and failure mode prediction of physical systems using hybrid computer optimization

    NASA Technical Reports Server (NTRS)

    Drake, R. L.; Duvoisin, P. F.; Asthana, A.; Mather, T. W.

    1971-01-01

    High speed automated identification and design of dynamic systems, both linear and nonlinear, are discussed. Special emphasis is placed on developing hardware and techniques which are applicable to practical problems. The basic modeling experiment and new results are described. Using the improvements developed successful identification of several systems, including a physical example as well as simulated systems, was obtained. The advantages of parameter signature analysis over signal signature analysis in go-no go testing of operational systems were demonstrated. The feasibility of using these ideas in failure mode prediction in operating systems was also investigated. An improved digital controlled nonlinear function generator was developed, de-bugged, and completely documented.

  5. Use of mRNA expression signatures to discover small molecule inhibitors of skeletal muscle atrophy

    PubMed Central

    Adams, Christopher M.; Ebert, Scott M.; Dyle, Michael C.

    2017-01-01

    Purpose of review Here, we discuss a recently developed experimental strategy for discovering small molecules with potential to prevent and treat skeletal muscle atrophy. Recent findings Muscle atrophy involves and requires widespread changes in skeletal muscle gene expression, which generate complex but measurable patterns of positive and negative changes in skeletal muscle mRNA levels (a.k.a. mRNA expression signatures of muscle atrophy). Many bioactive small molecules generate their own characteristic mRNA expression signatures, and by identifying small molecules whose signatures approximate mirror images of muscle atrophy signatures, one may identify small molecules with potential to prevent and/or reverse muscle atrophy. Unlike a conventional drug discovery approach, this strategy does not rely on a predefined molecular target but rather exploits the complexity of muscle atrophy to identify small molecules that counter the entire spectrum of pathological changes in atrophic muscle. We discuss how this strategy has been used to identify two natural compounds, ursolic acid and tomatidine, that reduce muscle atrophy and improve skeletal muscle function. Summary Discovery strategies based on mRNA expression signatures can elucidate new approaches for preserving and restoring muscle mass and function. PMID:25807353

  6. Gene Expression Signatures of Energetic Acclimatisation in the Reef Building Coral Acropora millepora

    PubMed Central

    Andreakis, Nikos; Ulstrup, Karin E.; Matz, Mikhail V.

    2013-01-01

    Background Understanding the mechanisms by which natural populations cope with environmental stress is paramount to predict their persistence in the face of escalating anthropogenic impacts. Reef-building corals are increasingly exposed to local and global stressors that alter nutritional status causing reduced fitness and mortality, however, these responses can vary considerably across species and populations. Methodology/Principal Findings We compare the expression of 22 coral host genes in individuals from an inshore and an offshore reef location using quantitative Reverse Transcription-PCR (qRT-PCR) over the course of 26 days following translocation into a shaded, filtered seawater environment. Declines in lipid content and PSII activity of the algal endosymbionts (Symbiodinium ITS-1 type C2) over the course of the experiment indicated that heterotrophic uptake and photosynthesis were limited, creating nutritional deprivation conditions. Regulation of coral host genes involved in metabolism, CO2 transport and oxidative stress could be detected already after five days, whereas PSII activity took twice as long to respond. Opposing expression trajectories of Tgl, which releases fatty acids from the triacylglycerol storage, and Dgat1, which catalyses the formation of triglycerides, indicate that the decline in lipid content can be attributed, at least in part, by mobilisation of triacylglycerol stores. Corals from the inshore location had initially higher lipid content and showed consistently elevated expression levels of two genes involved in metabolism (aldehyde dehydrogenase) and calcification (carbonic anhydrase). Conclusions/Significance Coral host gene expression adjusts rapidly upon change in nutritional conditions, and therefore can serve as an early signature of imminent coral stress. Consistent gene expression differences between populations indicate that corals acclimatize and/or adapt to local environments. Our results set the stage for analysis of

  7. Gene expression signatures of energetic acclimatisation in the reef building coral Acropora millepora.

    PubMed

    Bay, Line K; Guérécheau, Aurélie; Andreakis, Nikos; Ulstrup, Karin E; Matz, Mikhail V

    2013-01-01

    Understanding the mechanisms by which natural populations cope with environmental stress is paramount to predict their persistence in the face of escalating anthropogenic impacts. Reef-building corals are increasingly exposed to local and global stressors that alter nutritional status causing reduced fitness and mortality, however, these responses can vary considerably across species and populations. We compare the expression of 22 coral host genes in individuals from an inshore and an offshore reef location using quantitative Reverse Transcription-PCR (qRT-PCR) over the course of 26 days following translocation into a shaded, filtered seawater environment. Declines in lipid content and PSII activity of the algal endosymbionts (Symbiodinium ITS-1 type C2) over the course of the experiment indicated that heterotrophic uptake and photosynthesis were limited, creating nutritional deprivation conditions. Regulation of coral host genes involved in metabolism, CO2 transport and oxidative stress could be detected already after five days, whereas PSII activity took twice as long to respond. Opposing expression trajectories of Tgl, which releases fatty acids from the triacylglycerol storage, and Dgat1, which catalyses the formation of triglycerides, indicate that the decline in lipid content can be attributed, at least in part, by mobilisation of triacylglycerol stores. Corals from the inshore location had initially higher lipid content and showed consistently elevated expression levels of two genes involved in metabolism (aldehyde dehydrogenase) and calcification (carbonic anhydrase). Coral host gene expression adjusts rapidly upon change in nutritional conditions, and therefore can serve as an early signature of imminent coral stress. Consistent gene expression differences between populations indicate that corals acclimatize and/or adapt to local environments. Our results set the stage for analysis of these processes in natural coral populations, to better understand the

  8. Characterising Cytokine Gene Expression Signatures in Patients with Severe Sepsis

    PubMed Central

    Grealy, Robert; White, Mary; Stordeur, Patrick; Kelleher, Dermot; Doherty, Derek G.; McManus, Ross; Ryan, Thomas

    2013-01-01

    Introduction. Severe sepsis in humans may be related to an underlying profound immune suppressive state. We investigated the link between gene expression of immune regulatory cytokines and the range of illness severity in patients with infection and severe sepsis. Methods. A prospective observational study included 54 ICU patients with severe sepsis, 53 patients with infection without organ failure, and 20 healthy controls. Gene expression in peripheral blood mononuclear cells (PBMC) was measured using real-time polymerase chain reaction. Results. Infection differed from health by decreased expression of the IL2, and IL23 and greater expression of IL10 and IL27. Severe sepsis differed from infection by having decreased IL7, IL23, IFNγ, and TNFα gene expression. An algorithm utilising mRNA copy number for TNFα, IFNγ, IL7, IL10, and IL23 accurately distinguished sepsis from severe sepsis with a receiver operator characteristic value of 0.88. Gene expression was similar with gram-positive and gram-negative infection and was similar following medical and surgical severe sepsis. Severity of organ failure was associated with serum IL6 protein levels but not with any index of cytokine gene expression in PBMCs. Conclusions. Immune regulatory cytokine gene expression in PBMC provides a robust method of modelling patients' response to infection. PMID:23935244

  9. A 4-miRNA signature predicts the therapeutic outcome of glioblastoma

    PubMed Central

    Niyazi, Maximilian; Pitea, Adriana; Mittelbronn, Michel; Steinbach, Joachim; Sticht, Carsten; Zehentmayr, Franz; Piehlmaier, Daniel; Zitzelsberger, Horst; Ganswindt, Ute; Rödel, Claus; Lauber, Kirsten; Belka, Claus; Unger, Kristian

    2016-01-01

    Multimodal therapy of glioblastoma (GBM) reveals inter-individual variability in terms of treatment outcome. Here, we examined whether a miRNA signature can be defined for the a priori identification of patients with particularly poor prognosis. FFPE sections from 36 GBM patients along with overall survival follow-up were collected retrospectively and subjected to miRNA signature identification from microarray data. A risk score based on the expression of the signature miRNAs and cox-proportional hazard coefficients was calculated for each patient followed by validation in a matched GBM subset of TCGA. Genes potentially regulated by the signature miRNAs were identified by a correlation approach followed by pathway analysis. A prognostic 4-miRNA signature, independent of MGMT promoter methylation, age, and sex, was identified and a risk score was assigned to each patient that allowed defining two groups significantly differing in prognosis (p-value: 0.0001, median survival: 10.6 months and 15.1 months, hazard ratio = 3.8). The signature was technically validated by qRT-PCR and independently validated in an age- and sex-matched subset of standard-of-care treated patients of the TCGA GBM cohort (n=58). Pathway analysis suggested tumorigenesis-associated processes such as immune response, extracellular matrix organization, axon guidance, signalling by NGF, GPCR and Wnt. Here, we describe the identification and independent validation of a 4-miRNA signature that allows stratification of GBM patients into different prognostic groups in combination with one defined threshold and set of coefficients that could be utilized as diagnostic tool to identify GBM patients for improved and/or alternative treatment approaches. PMID:27302927

  10. Quantitative analysis of competition in posttranscriptional regulation reveals a novel signature in target expression variation.

    PubMed

    Klironomos, Filippos D; Berg, Johannes

    2013-02-19

    When small RNAs are loaded onto Argonaute proteins they can form the RNA-induced silencing complexes (RISCs), which mediate RNA interference (RNAi). RISC-formation is dependent on a shared pool of Argonaute proteins and RISC-loading factors, and is susceptible to competition among small RNAs. We present a mathematical model that aims to understand how small RNA competition for RISC-formation affects target gene repression. We discuss that small RNA activity is limited by RISC-formation, RISC-degradation, and the availability of Argonautes. We show that different competition conditions for RISC-loading result in different signatures of RNAi determined also by the amount of RISC-recycling taking place. In particular, we find that the small RNAs, although less efficient at RISC-formation, can perform in the low RISC-recycling range as well as their more effective counterparts. Additionally, we predict that under conditions of low RISC-loading efficiency and high RISC-recycling, the variation in target levels increases linearly with the target transcription rate. Furthermore, we show that RISC-recycling determines the effect that Argonaute scarcity conditions have on target expression variation. Our observations, taken together, offer a framework of predictions that can be used to infer from data the particular characteristics of underlying RNAi activity.

  11. A new molecular signature method for prediction of driver cancer pathways from transcriptional data.

    PubMed

    Rykunov, Dmitry; Beckmann, Noam D; Li, Hui; Uzilov, Andrew; Schadt, Eric E; Reva, Boris

    2016-06-20

    Assigning cancer patients to the most effective treatments requires an understanding of the molecular basis of their disease. While DNA-based molecular profiling approaches have flourished over the past several years to transform our understanding of driver pathways across a broad range of tumors, a systematic characterization of key driver pathways based on RNA data has not been undertaken. Here we introduce a new approach for predicting the status of driver cancer pathways based on signature functions derived from RNA sequencing data. To identify the driver cancer pathways of interest, we mined DNA variant data from TCGA and nominated driver alterations in seven major cancer pathways in breast, ovarian and colon cancer tumors. The activation status of these driver pathways were then characterized using RNA sequencing data by constructing classification signature functions in training datasets and then testing the accuracy of the signatures in test datasets. The signature functions differentiate well tumors with nominated pathway activation from tumors with no signs of activation: average AUC equals to 0.83. Our results confirm that driver genomic alterations are distinctively displayed at the transcriptional level and that the transcriptional signatures can generally provide an alternative to DNA sequencing methods in detecting specific driver pathways.

  12. A new molecular signature method for prediction of driver cancer pathways from transcriptional data

    PubMed Central

    Rykunov, Dmitry; Beckmann, Noam D.; Li, Hui; Uzilov, Andrew; Schadt, Eric E.; Reva, Boris

    2016-01-01

    Assigning cancer patients to the most effective treatments requires an understanding of the molecular basis of their disease. While DNA-based molecular profiling approaches have flourished over the past several years to transform our understanding of driver pathways across a broad range of tumors, a systematic characterization of key driver pathways based on RNA data has not been undertaken. Here we introduce a new approach for predicting the status of driver cancer pathways based on signature functions derived from RNA sequencing data. To identify the driver cancer pathways of interest, we mined DNA variant data from TCGA and nominated driver alterations in seven major cancer pathways in breast, ovarian and colon cancer tumors. The activation status of these driver pathways were then characterized using RNA sequencing data by constructing classification signature functions in training datasets and then testing the accuracy of the signatures in test datasets. The signature functions differentiate well tumors with nominated pathway activation from tumors with no signs of activation: average AUC equals to 0.83. Our results confirm that driver genomic alterations are distinctively displayed at the transcriptional level and that the transcriptional signatures can generally provide an alternative to DNA sequencing methods in detecting specific driver pathways. PMID:27098033

  13. A simple and robust method for connecting small-molecule drugs using gene-expression signatures.

    PubMed

    Zhang, Shu-Dong; Gant, Timothy W

    2008-06-02

    Interaction of a drug or chemical with a biological system can result in a gene-expression profile or signature characteristic of the event. Using a suitably robust algorithm these signatures can potentially be used to connect molecules with similar pharmacological or toxicological properties by gene expression profile. Lamb et al first proposed the Connectivity Map [Lamb et al (2006), Science 313, 1929-1935] to make successful connections among small molecules, genes, and diseases using genomic signatures. Here we have built on the principles of the Connectivity Map to present a simpler and more robust method for the construction of reference gene-expression profiles and for the connection scoring scheme, which importantly allows the valuation of statistical significance of all the connections observed. We tested the new method with two randomly generated gene signatures and three experimentally derived gene signatures (for HDAC inhibitors, estrogens, and immunosuppressive drugs, respectively). Our testing with this method indicates that it achieves a higher level of specificity and sensitivity and so advances the original method. The method presented here not only offers more principled statistical procedures for testing connections, but more importantly it provides effective safeguard against false connections at the same time achieving increased sensitivity. With its robust performance, the method has potential use in the drug development pipeline for the early recognition of pharmacological and toxicological properties in chemicals and new drug candidates, and also more broadly in other 'omics sciences.

  14. A four-gene signature from NCI-60 cell line for survival prediction in non-small cell lung cancer.

    PubMed

    Hsu, Yi-Chiung; Yuan, Shinsheng; Chen, Hsuan-Yu; Yu, Sung-Liang; Liu, Chia-Hsin; Hsu, Pin-Yen; Wu, Guani; Lin, Chia-Hung; Chang, Gee-Chen; Li, Ker-Chau; Yang, Pan-Chyr

    2009-12-01

    Metastasis is the main cause of mortality in non-small cell lung cancer (NSCLC) patients. Genes that can discriminate the invasion ability of cancer cells may become useful candidates for clinical outcome prediction. We identify invasion-associated genes through computational and laboratorial approach that supported this idea in NSCLC. We first conducted invasion assay to characterize the invasion abilities of NCI-60 lung cancer cell lines. We then systematically exploited NCI-60 microarray databases to identify invasion-associated genes that showed differential expression between the high and the low invasion cell line groups. Furthermore, using the microarray data of Duke lung cancer cohort (GSE 3141), invasion-associated genes with good survival prediction potentials were obtained. Finally, we validated the findings by conducting quantitative PCR assay on an in-house collected patient group (n = 69) and by using microarray data from two public western cohorts (n = 257 and 186). The invasion-associated four-gene signature (ANKRD49, LPHN1, RABAC1, and EGLN2) had significant prediction in three validation cohorts (P = 0.0184, 0.002, and 0.017, log-rank test). Moreover, we showed that four-gene signature was an independent prognostic factor (hazard ratio, 2.354, 1.480, and 1.670; P = 0.028, 0.014, and 0.033), independent of other clinical covariates, such as age, gender, and stage. The invasion-associated four-gene signature derived from NCI-60 lung cancer cell lines had good survival prediction power for NSCLC patients.

  15. Polycomb repressive complex 2 epigenomic signature defines age-associated hypermethylation and gene expression changes

    PubMed Central

    Dozmorov, Mikhail G

    2015-01-01

    Although age-associated gene expression and methylation changes have been reported throughout the literature, the unifying epigenomic principles of aging remain poorly understood. Recent explosion in availability and resolution of functional/regulatory genome annotation data (epigenomic data), such as that provided by the ENCODE and Roadmap Epigenomics projects, provides an opportunity for the identification of epigenomic mechanisms potentially altered by age-associated differentially methylated regions (aDMRs) and regulatory signatures in the promoters of age-associated genes (aGENs). In this study we found that aDMRs and aGENs identified in multiple independent studies share a common Polycomb Repressive Complex 2 signature marked by EZH2, SUZ12, CTCF binding sites, repressive H3K27me3, and activating H3K4me1 histone modification marks, and a “poised promoter” chromatin state. This signature is depleted in RNA Polymerase II-associated transcription factor binding sites, activating H3K79me2, H3K36me3, H3K27ac marks, and an “active promoter” chromatin state. The PRC2 signature was shown to be generally stable across cell types. When considering the directionality of methylation changes, we found the PRC2 signature to be associated with aDMRs hypermethylated with age, while hypomethylated aDMRs were associated with enhancers. In contrast, aGENs were associated with the PRC2 signature independently of the directionality of gene expression changes. In this study we demonstrate that the PRC2 signature is the common epigenomic context of genomic regions associated with hypermethylation and gene expression changes in aging. PMID:25880792

  16. Expression Signatures of Long Noncoding RNAs in Adolescent Idiopathic Scoliosis

    PubMed Central

    Liu, Xiao-Yang; Wang, Liang; Yu, Bin; Zhuang, Qian-yu; Wang, Yi-Peng

    2015-01-01

    Purpose. Adolescent idiopathic scoliosis (AIS), the most common pediatric spinal deformity, is considered a complex genetic disease. Causing genes and pathogenesis of AIS are still unclear. This study was designed to identify differentially expressed long noncoding RNAs (lncRNAs) involving the pathogenesis of AIS. Methods. We first performed comprehensive screening of lncRNA and mRNA in AIS patients and healthy children using Agilent human lncRNA + mRNA Array V3.0 microarray. LncRNAs expression in different AIS patients was further evaluated using quantitative PCR. Results. A total of 139 lncRNAs and 546 mRNAs were differentially expressed between AIS patients and healthy control. GO and Pathway analysis showed that these mRNAs might be involved in bone mineralization, neuromuscular junction, skeletal system morphogenesis, nucleotide and nucleic acid metabolism, and regulation of signal pathway. Four lncRNAs (ENST00000440778.1, ENST00000602322.1, ENST00000414894.1, and TCONS_00028768) were differentially expressed between different patients when grouped according to age, height, classification, severity of scoliosis, and Risser grade. Conclusions. This study demonstrates the abnormal expression of lncRNAs and mRNAs in AIS, and the expression of some lncRNAs was related to clinical features. This study is helpful for further understanding of lncRNAs in pathogenesis, treatment, and prognosis of AIS. PMID:26421281

  17. Interferon gene expression signature in rheumatoid arthritis neutrophils correlates with a good response to TNFi therapy.

    PubMed

    Wright, Helen L; Thomas, Huw B; Moots, Robert J; Edwards, Steven W

    2015-01-01

    The aim of this study was to use whole transcriptome sequencing (RNA-Seq) of RA neutrophils to identify pre-therapy gene expression signatures that correlate with disease activity or response to TNF inhibitor (TNFi) therapy. Neutrophils were isolated from the venous blood of RA patients (n = 20) pre-TNFi therapy and from healthy controls (n = 6). RNA was poly(A) selected and sequenced on the Illumina HiSeq 2000 platform. Reads were mapped to the human genome (hg19) using TopHat and differential expression analysis was carried out using edgeR (5% false discovery rate). Signalling pathway analysis was carried out using Ingenuity Pathway Analysis (IPA) software. IFN signalling was confirmed by western blotting for phosphorylated signal transducer and activator of transcription (STAT) proteins. Response to TNFi was measured at 12 weeks using change in the 28-item DAS (DAS28). Pathway analysis with IPA predicted activation of IFN signalling in RA neutrophils, identifying 178 IFN-response genes regulated by IFN-α, IFN-β or IFN-γ (P < 0.01). IPA also predicted activation of STAT1, STAT2 and STAT3 transcription factors in RA neutrophils (P < 0.01), which was confirmed by western blotting. Expression of IFN-response genes was heterogeneous and patients could be categorized as IFN-high or IFN-low. Patients in the IFN-high group achieved a better response to TNFi therapy [ΔDAS28, P = 0.05, odds ratio (OR) 1.4 (95% CI 1.005, 1.950)] than patients in the IFN-low group. The level of expression of IFN-response genes (IFN score) predicted a good response [European League Against Rheumatism (EULAR) criteria] to TNFi using receiver operating characteristic curve analysis (area under the curve 0.76). IFN-response genes are significantly up-regulated in RA neutrophils compared with healthy controls. Higher IFN-response gene expression in RA neutrophils correlates with a good response to TNFi therapy. © The Author 2014. Published by Oxford University Press on behalf of the British

  18. Clinically Viable Gene Expression Assays with Potential for Predicting Benefit from MEK Inhibitors.

    PubMed

    Brant, Roz; Sharpe, Alan; Liptrot, Tom; Dry, Jonathan R; Harrington, Elizabeth A; Barrett, J Carl; Whalley, Nicky; Womack, Christopher; Smith, Paul; Hodgson, Darren R

    2017-03-15

    Purpose: To develop a clinically viable gene expression assay to measure RAS/RAF/MEK/ERK (RAS-ERK) pathway output suitable for hypothesis testing in non-small cell lung cancer (NSCLC) clinical studies.Experimental Design: A published MEK functional activation signature (MEK signature) that measures RAS-ERK functional output was optimized for NSCLC in silico NanoString assays were developed for the NSCLC optimized MEK signature and the 147-gene RAS signature. First, platform transfer from Affymetrix to NanoString, and signature modulation following treatment with KRAS siRNA and MEK inhibitor, were investigated in cell lines. Second, the association of the signatures with KRAS mutation status, dynamic range, technical reproducibility, and spatial and temporal variation was investigated in NSCLC formalin-fixed paraffin-embedded tissue (FFPET) samples.Results: We observed a strong cross-platform correlation and modulation of signatures in vitro Technical and biological replicates showed consistent signature scores that were robust to variation in input total RNA; conservation of scores between primary and metastatic tumor was statistically significant. There were statistically significant associations between high MEK (P = 0.028) and RAS (P = 0.003) signature scores and KRAS mutation in 50 NSCLC samples. The signatures identify overlapping but distinct candidate patient populations from each other and from KRAS mutation testing.Conclusions: We developed a technically and biologically robust NanoString gene expression assay of MEK pathway output, compatible with the quantities of FFPET routinely available. The gene signatures identified a different patient population for MEK inhibitor treatment compared with KRAS mutation testing. The predictive power of the MEK signature should be studied further in clinical trials. Clin Cancer Res; 23(6); 1471-80. ©2016 AACRSee related commentary by Xue and Lito, p. 1365.

  19. Identifying a gene expression signature of cluster headache in blood

    PubMed Central

    Eising, Else; Pelzer, Nadine; Vijfhuizen, Lisanne S.; Vries, Boukje de; Ferrari, Michel D.; ‘t Hoen, Peter A. C.; Terwindt, Gisela M.; van den Maagdenberg, Arn M. J. M.

    2017-01-01

    Cluster headache is a relatively rare headache disorder, typically characterized by multiple daily, short-lasting attacks of excruciating, unilateral (peri-)orbital or temporal pain associated with autonomic symptoms and restlessness. To better understand the pathophysiology of cluster headache, we used RNA sequencing to identify differentially expressed genes and pathways in whole blood of patients with episodic (n = 19) or chronic (n = 20) cluster headache in comparison with headache-free controls (n = 20). Gene expression data were analysed by gene and by module of co-expressed genes with particular attention to previously implicated disease pathways including hypocretin dysregulation. Only moderate gene expression differences were identified and no associations were found with previously reported pathogenic mechanisms. At the level of functional gene sets, associations were observed for genes involved in several brain-related mechanisms such as GABA receptor function and voltage-gated channels. In addition, genes and modules of co-expressed genes showed a role for intracellular signalling cascades, mitochondria and inflammation. Although larger study samples may be required to identify the full range of involved pathways, these results indicate a role for mitochondria, intracellular signalling and inflammation in cluster headache. PMID:28074859

  20. Prognostic stratification improvement by integrating ID1/ID3/IGJ gene expression signature and immunophenotypic profile in adult patients with B-ALL.

    PubMed

    Cruz-Rodriguez, Nataly; Combita, Alba L; Enciso, Leonardo J; Raney, Lauren F; Pinzon, Paula L; Lozano, Olga C; Campos, Alba M; Peñaloza, Niyireth; Solano, Julio; Herrera, Maria V; Zabaleta, Jovanny; Quijano, Sandra

    2017-02-28

    Survival of adults with B-Acute Lymphoblastic Leukemia requires accurate risk stratification of patients in order to provide the appropriate therapy. Contemporary techniques, using clinical and cytogenetic variables are incomplete for prognosis prediction. To improve the classification of adult patients diagnosed with B-ALL into prognosis groups, two strategies were examined and combined: the expression of the ID1/ID3/IGJ gene signature by RT-PCR and the immunophenotypic profile of 19 markers proposed in the EuroFlow protocol by Flow Cytometry in bone marrow samples. Both techniques were correlated to stratify patients into prognostic groups. An inverse relationship between survival and expression of the three-genes signature was observed and an immunophenotypic profile associated with clinical outcome was identified. Markers CD10 and CD20 were correlated with simultaneous overexpression of ID1, ID3 and IGJ. Patients with simultaneous expression of the poor prognosis gene signature and overexpression of CD10 or CD20, had worse Event Free Survival and Overall Survival than patients who had either the poor prognosis gene expression signature or only CD20 or CD10 overexpressed. By utilizing the combined evaluation of these two immunophenotypic markers along with the poor prognosis gene expression signature, the risk stratification can be significantly strengthened. Further studies including a large number of patients are needed to confirm these findings.

  1. A PRIM approach to predictive-signature development for patient stratification.

    PubMed

    Chen, Gong; Zhong, Hua; Belousov, Anton; Devanarayan, Viswanath

    2015-01-30

    Patients often respond differently to a treatment because of individual heterogeneity. Failures of clinical trials can be substantially reduced if, prior to an investigational treatment, patients are stratified into responders and nonresponders based on biological or demographic characteristics. These characteristics are captured by a predictive signature. In this paper, we propose a procedure to search for predictive signatures based on the approach of patient rule induction method. Specifically, we discuss selection of a proper objective function for the search, present its algorithm, and describe a resampling scheme that can enhance search performance. Through simulations, we characterize conditions under which the procedure works well. To demonstrate practical uses of the procedure, we apply it to two real-world data sets. We also compare the results with those obtained from a recent regression-based approach, Adaptive Index Models, and discuss their respective advantages. In this study, we focus on oncology applications with survival responses.

  2. A micro-RNA expression signature for human NAFLD progression.

    PubMed

    Guo, Yan; Xiong, Yanhua; Sheng, Quanghu; Zhao, Shilin; Wattacheril, Julia; Flynn, Charles Robb

    2016-10-01

    The spectrum of nonalcoholic fatty liver disease (NAFLD) describes disease conditions deteriorating from nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH) to cirrhosis (CIR) to hepatocellular carcinoma (HCC). From a molecular and biochemical perspective, our understanding of the etiology of this disease is limited by the broad spectrum of disease presentations, the lack of a thorough understanding of the factors contributing to disease susceptibility, and ethical concerns related to repeat sampling of the liver. To better understand the factors associated with disease progression, we investigated by next-generation RNA sequencing the altered expression of microRNAs (miRNAs) in liver biopsies of class III obese subjects (body mass index ≥40 kg/m(2)) biopsied at the time of elective bariatric surgery. Clinical characteristics and unbiased RNA expression profiles for 233 miRs, 313 transfer RNAs (tRNAs), and 392 miscellaneous small RNAs (snoRNAs, snRNAs, rRNAs) were compared among 36 liver biopsy specimens stratified by disease severity. The abundances of 3 miRNAs that were found to be differentially regulated (miR-301a-3p and miR-34a-5p increased and miR-375 decreased) with disease progression were validated by RT-PCR. No tRNAs or miscellaneous RNAs were found to be associated with disease severity. Similar patterns of increased miR-301a and decreased miR-375 expression were observed in 134 hepatocellular carcinoma (HCC) samples deposited in The Cancer Genome Atlas (TCGA). Our analytical results suggest that NAFLD severity is associated with a specific pattern of altered hepatic microRNA expression that may drive the hallmark of this disorder: altered lipid and carbohydrate metabolism. The three identified miRNAs can potentially be used as biomarkers to access the severity of NAFLD. The persistence of this miRNA expression pattern in an external validation cohort of HCC samples suggests that specific microRNA expression patterns may permit and

  3. An Epigenetic Signature for Monoallelic Olfactory Receptor Expression

    PubMed Central

    Magklara, Angeliki; Yen, Angela; Colquitt, Bradley M.; Clowney, E. Josephine; Allen, William; Markenscoff-Papadimitriou, Eirene; Evans, Zoe A.; Kheradpour, Pouya; Mountoufaris, George; Carey, Catriona; Barnea, Gilad; Kellis, Manolis; Lomvardas, Stavros

    2011-01-01

    SUMMARY Constitutive heterochromatin is traditionally viewed as the static form of heterochromatin that silences pericentromeric and telomeric repeats in a cell cycle and differentiation independent manner. Here, we show that in the mouse olfactory epithelium, olfactory receptor (OR) genes are marked, in a highly dynamic fashion, with the molecular hallmarks of constitutive heterochromatin, H3K9me3 and H4K20me3. The cell-type and developmentally dependent deposition of these marks along the OR clusters is, most likely, reversed during the process of OR choice to allow for monogenic and monoallelic OR expression. In contrast to the current view of OR choice, our data suggest that OR silencing takes place before OR expression, indicating that it is not the product of an OR-elicited feedback signal. This suggests a new role for chromatin-mediated silencing as the molecular foundation upon which singular and stochastic selection can be applied. PMID:21529909

  4. A Simple and Efficient Computational Approach to Chafed Cable Time-Domain Reflectometry Signature Prediction

    NASA Technical Reports Server (NTRS)

    Kowalski, Marc Edward

    2009-01-01

    A method for the prediction of time-domain signatures of chafed coaxial cables is presented. The method is quasi-static in nature, and is thus efficient enough to be included in inference and inversion routines. Unlike previous models proposed, no restriction on the geometry or size of the chafe is required in the present approach. The model is validated and its speed is illustrated via comparison to simulations from a commercial, three-dimensional electromagnetic simulator.

  5. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma

    PubMed Central

    Coroller, Thibaud P.; Grossmann, Patrick; Hou, Ying; Rios Velazquez, Emmanuel; Leijenaar, Ralph T.H.; Hermann, Gretchen; Lambin, Philippe; Haibe-Kains, Benjamin; Mak, Raymond H.; Aerts, Hugo J.W.L.

    2015-01-01

    Background and Purpose Radiomics provides opportunities to quantify the tumor phenotype non-invasively by applying a large number of quantitative imaging features. This study evaluates computed-tomography (CT) radiomic features for their capability to predict distant metastasis (DM) for lung adenocarcinoma patients. Material and Methods We included two datasets: 98 patients for discovery and 84 for validation. The phenotype of the primary tumor was quantified on pre-treatment CT-scans using 635 radiomic features. Univariate and multivariate analysis was performed to evaluate radiomics performance using the concordance index (CI). Results Thirty-five radiomic features were found to be prognostic (CI > 0.60, FDR < 5%) for DM and twelve for survival. It is noteworthy that tumor volume was only moderately prognostic for DM (CI=0.55, p-value=2.77 × 10−5) in the discovery cohort. A radiomic-signature had strong power for predicting DM in the independent validation dataset (CI=0.61, p-value=1.79 ×10−17). Adding this radiomic-signature to a clinical model resulted in a significant improvement of predicting DM in the validation dataset (p-value=1.56 × 10−11). Conclusions Although only basic metrics are routinely quantified, this study shows that radiomic features capturing detailed information of the tumor phenotype can be used as a prognostic biomarker for clinically-relevant factors such as DM. Moreover, the radiomic-signature provided additional information to clinical data. PMID:25746350

  6. Computing molecular signatures as optima of a bi-objective function: method and application to prediction in oncogenomics.

    PubMed

    Gardeux, Vincent; Chelouah, Rachid; Wanderley, Maria F Barbosa; Siarry, Patrick; Braga, Antônio P; Reyal, Fabien; Rouzier, Roman; Pusztai, Lajos; Natowicz, René

    2015-01-01

    Filter feature selection methods compute molecular signatures by selecting subsets of genes in the ranking of a valuation function. The motivations of the valuation functions choice are almost always clearly stated, but those for selecting the genes according to their ranking are hardly ever explicit. We addressed the computation of molecular signatures by searching the optima of a bi-objective function whose solution space was the set of all possible molecular signatures, ie, the set of subsets of genes. The two objectives were the size of the signature-to be minimized-and the interclass distance induced by the signature-to be maximized-. We showed that: 1) the convex combination of the two objectives had exactly n optimal non empty signatures where n was the number of genes, 2) the n optimal signatures were nested, and 3) the optimal signature of size k was the subset of k top ranked genes that contributed the most to the interclass distance. We applied our feature selection method on five public datasets in oncology, and assessed the prediction performances of the optimal signatures as input to the diagonal linear discriminant analysis (DLDA) classifier. They were at the same level or better than the best-reported ones. The predictions were robust, and the signatures were almost always significantly smaller. We studied in more details the performances of our predictive modeling on two breast cancer datasets to predict the response to a preoperative chemotherapy: the performances were higher than the previously reported ones, the signatures were three times smaller (11 versus 30 gene signatures), and the genes member of the signature were known to be involved in the response to chemotherapy. Defining molecular signatures as the optima of a bi-objective function that combined the signature size and the interclass distance was well founded and efficient for prediction in oncogenomics. The complexity of the computation was very low because the optimal signatures

  7. Hyperspectral reflectance signature protocol for predicting subsurface bottom reflectance in water: in-situ and analytical methods

    NASA Astrophysics Data System (ADS)

    Bostater, Charles R.; Rotkiske, Tyler; Oney, Taylor

    2016-10-01

    In-situ measurement of bottom reflectance signatures and bottom features in water are used to test an analytical based irradiance model protocol. Comparisons between predicted and measured bottom reflectance signatures are obtained using measured hyperspectral remote sensing reflectance signatures, water depth and water column constituent concentrations. Analytical solutions and algorithms are used to generate synthetic signatures of different bottom types. The analytical methodology used to simulated bottom reflectance contains offset and bias that can be corrected using spectral window based corrections. Example results are demonstrated for application to coral species, submerged aquatic vegetation and a sand bottom type. Spectral windows are identified for predicting the above bottom types. Sensitivity analysis of predicted bottom reflectance signatures is conducted by varying water depth, chlorophyll, dissolved organic matter and total suspended mater concentrations. The protocol can be applied to shallow subsurface geospatial mapping using sensor based water surface reflectance based upon an analytical model solution derived from primitive radiative transfer theory.

  8. Brain Gene Expression Signatures From Cerebrospinal Fluid Exosome RNA Profiling

    NASA Technical Reports Server (NTRS)

    Zanello, S. B.; Stevens, B.; Calvillo, E.; Tang, R.; Gutierrez Flores, B.; Hu, L.; Skog, J.; Bershad, E.

    2016-01-01

    While the Visual Impairment and Intracranial Pressure (VIIP) syndrome observations have focused on ocular symptoms, spaceflight has been also associated with a number of other performance and neurologic signs, such as headaches, cognitive changes, vertigo, nausea, sleep/circadian disruption and mood alterations, which, albeit likely multifactorial, can also result from elevation of intracranial pressure (ICP). We therefore hypothesize that these various symptoms are caused by disturbances in the neurophysiology of the brain structures and are correlated with molecular markers in the cerebrospinal fluid (CSF) as indicators of neurophysiological changes. Exosomes are 30-200 nm microvesicles shed into all biofluids, including blood, urine, and CSF, carrying a highly rich source of intact protein and RNA cargo. Exosomes have been identified in human CSF, and their proteome and RNA pool is a potential new reservoir for biomarker discovery in neurological disorders. The purpose of this study is to investigate changes in brain gene expression via exosome analysis in patients suffering from ICP elevation of varied severity (idiopathic intracranial hypertension -IIH), a condition which shares some of the neuroophthalmological features of VIIP, as a first step toward obtaining evidence suggesting that cognitive function and ICP levels can be correlated with biomarkers in the CSF. Our preliminary work, reported last year, validated the exosomal technology applicable to CSF analysis and demonstrated that it was possible to obtain gene expression evidence of inflammation processes in traumatic brain injury patients. We are now recruiting patients with suspected IIH requiring lumbar puncture at Baylor College of Medicine. Both CSF (5 ml) and human plasma (10 ml) are being collected in order to compare the pattern of differentially expressed genes observed in CSF and in blood. Since blood is much more accessible than CSF, we would like to determine whether plasma biomarkers for

  9. The in vivo Gene Expression Signature of Oxidative Stress

    PubMed Central

    Han, Eun-Soo; Muller, Florian L.; Perez, Viviana; Qi, Wenbo; Liang, Huiyun; Xi, Liang; Fu, Chunxiao; Doyle, Erin; Hickey, Morgen; Cornell, John; Epstein, Charles J.; Roberts, L. Jackson; Van Remmen, Holly; Richardson, Arlan

    2008-01-01

    How higher organisms respond to elevated oxidative stress in vivo is poorly understood. Therefore, we measured oxidative stress parameters and gene expression alterations (Affymetrix arrays) in the liver caused by elevated reactive oxygen species induced in vivo by diquat or by genetic ablation of the major antioxidant enzymes, CuZn-Superoxide Dismutase (Sod1) and Glutathione Peroxidase-1 (Gpx1). Diquat (50 mg/kg) treatment resulted in a significant increase in oxidative damage within 3 to 6 hours in wild type mice without any lethality. In contrast, treating Sod1−/− or Gpx1−/− mice with a similar concentration of diquat resulted in a significant increase in oxidative damage within an hour of treatment and was lethal, i.e., these mice are extremely sensitive to the oxidative stress generated by diquat. The expression response to elevated oxidative stress in vivo does not involve an upregulation of classical antioxidant genes, though long-term oxidative stress in the Sod1−/− mice leads to a significant upregulation of thiol antioxidants (e.g., Mt1, Srxn1, Gclc, Txnrd1), which appears to be mediated by the redox-sensitive transcription factor, Nrf2. The main finding of our study is that the common response to elevated oxidative stress, with diquat treatment in wild type, Gpx1−/−, Sod1−/− mice and in untreated Sod1−/− mice, is an upregulation of p53 target genes (p21, Gdf15, Plk3, Atf3, Trp53inp1, Ddit4, Gadd45a, Btg2, Ndrg1). A retrospective comparison with previous studies shows that induction of these p53-target genes is a conserved expression response to oxidative stress, in vivo and in vitro, in different species and different cells/organs. PMID:18445702

  10. Gene Expression Signature-Based Screening Identifies New Broadly Effective Influenza A Antivirals

    PubMed Central

    Josset, Laurence; Textoris, Julien; Loriod, Béatrice; Ferraris, Olivier; Moules, Vincent; Lina, Bruno; N'Guyen, Catherine; Diaz, Jean-Jacques; Rosa-Calatrava, Manuel

    2010-01-01

    Classical antiviral therapies target viral proteins and are consequently subject to resistance. To counteract this limitation, alternative strategies have been developed that target cellular factors. We hypothesized that such an approach could also be useful to identify broad-spectrum antivirals. The influenza A virus was used as a model for its viral diversity and because of the need to develop therapies against unpredictable viruses as recently underlined by the H1N1 pandemic. We proposed to identify a gene-expression signature associated with infection by different influenza A virus subtypes which would allow the identification of potential antiviral drugs with a broad anti-influenza spectrum of activity. We analyzed the cellular gene expression response to infection with five different human and avian influenza A virus strains and identified 300 genes as differentially expressed between infected and non-infected samples. The most 20 dysregulated genes were used to screen the connectivity map, a database of drug-associated gene expression profiles. Candidate antivirals were then identified by their inverse correlation to the query signature. We hypothesized that such molecules would induce an unfavorable cellular environment for influenza virus replication. Eight potential antivirals including ribavirin were identified and their effects were tested in vitro on five influenza A strains. Six of the molecules inhibited influenza viral growth. The new pandemic H1N1 virus, which was not used to define the gene expression signature of infection, was inhibited by five out of the eight identified molecules, demonstrating that this strategy could contribute to identifying new broad anti-influenza agents acting on cellular gene expression. The identified infection signature genes, the expression of which are modified upon infection, could encode cellular proteins involved in the viral life cycle. This is the first study showing that gene expression-based screening can be

  11. Digital Gene Expression Signatures for Maize Development1[W][OA

    PubMed Central

    Eveland, Andrea L.; Satoh-Nagasawa, Namiko; Goldshmidt, Alexander; Meyer, Sandra; Beatty, Mary; Sakai, Hajime; Ware, Doreen; Jackson, David

    2010-01-01

    Genome-wide expression signatures detect specific perturbations in developmental programs and contribute to functional resolution of key regulatory networks. In maize (Zea mays) inflorescences, mutations in the RAMOSA (RA) genes affect the determinacy of axillary meristems and thus alter branching patterns, an important agronomic trait. In this work, we developed and tested a framework for analysis of tag-based, digital gene expression profiles using Illumina’s high-throughput sequencing technology and the newly assembled B73 maize reference genome. We also used a mutation in the RA3 gene to identify putative expression signatures specific to stem cell fate in axillary meristem determinacy. The RA3 gene encodes a trehalose-6-phosphate phosphatase and may act at the interface between developmental and metabolic processes. Deep sequencing of digital gene expression libraries, representing three biological replicate ear samples from wild-type and ra3 plants, generated 27 million 20- to 21-nucleotide reads with frequencies spanning 4 orders of magnitude. Unique sequence tags were anchored to 3′-ends of individual transcripts by DpnII and NlaIII digests, which were multiplexed during sequencing. We mapped 86% of nonredundant signature tags to the maize genome, which associated with 37,117 gene models and unannotated regions of expression. In total, 66% of genes were detected by at least nine reads in immature maize ears. We used comparative genomics to leverage existing information from Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa) in functional analyses of differentially expressed maize genes. Results from this study provide a basis for the analysis of short-read expression data in maize and resolved specific expression signatures that will help define mechanisms of action for the RA3 gene. PMID:20833728

  12. Functional correlations of pathogenesis-driven gene expression signatures in tuberculosis.

    PubMed

    Maertzdorf, Jeroen; Ota, Martin; Repsilber, Dirk; Mollenkopf, Hans J; Weiner, January; Hill, Philip C; Kaufmann, Stefan H E

    2011-01-01

    Tuberculosis remains a major health threat and its control depends on improved measures of prevention, diagnosis and treatment. Biosignatures can play a significant role in the development of novel intervention measures against TB and blood transcriptional profiling is increasingly exploited for their rational design. Such profiles also reveal fundamental biological mechanisms associated with the pathology of the disease. We have compared whole blood gene expression in TB patients, as well as in healthy infected and uninfected individuals in a cohort in The Gambia, West Africa and validated previously identified signatures showing high similarities of expression profiles among different cohorts. In this study, we applied a unique combination of classical gene expression analysis with pathway and functional association analysis integrated with intra-individual expression correlations. These analyses were employed for identification of new disease-associated gene signatures, identifying a network of Fc gamma receptor 1 signaling with correlating transcriptional activity as hallmark of gene expression in TB. Remarkable similarities to characteristic signatures in the autoimmune disease systemic lupus erythematosus (SLE) were observed. Functional gene clusters of immunoregulatory interactions involving the JAK-STAT pathway; sensing of microbial patterns by Toll-like receptors and IFN-signaling provide detailed insights into the dysregulation of critical immune processes in TB, involving active expression of both pro-inflammatory and immunoregulatory systems. We conclude that transcriptomics (i) provides a robust system for identification and validation of biosignatures for TB and (ii) application of integrated analysis tools yields novel insights into functional networks underlying TB pathogenesis.

  13. Identification of a RNA-Seq Based 8-Long Non-Coding RNA Signature Predicting Survival in Esophageal Cancer

    PubMed Central

    Fan, Qiaowei; Liu, Bingrong

    2016-01-01

    Background Accumulating evidence suggests the involvement of long non-coding RNAs (lncRNAs) as oncogenic or tumor suppressive regulators in the development of various cancers. In the present study, we aimed to identify a lncRNA signature based on RNA sequencing (RNA-seq) data to predict survival in esophageal cancer. Material/Methods The RNA-seq lncRNA expression data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNAs were screened out between esophageal cancer and normal tissues. Univariate and multivariate Cox regression analysis were performed to establish a lncRNA-related prognostic model. Receiver operating characteristic (ROC) analysis was conducted to test the sensitivity and specificity of the model. GO (gene ontology) functional and KEGG pathway enrichment analyses were performed for mRNAs co-expressed with the lncRNAs to explore the potential functions of the prognostic lncRNAs. Results A total of 265 differentially expressed lncRNAs were identified between esophageal cancer and normal tissues. After univariate and multivariate Cox regression analysis, eight lncRNAs (GS1-600G8.5, LINC00365, CTD-2357A8.3, RP11-705O24.1, LINC01554, RP1-90J4.1, RP11-327J17.1, and LINC00176) were finally screened out to establish a predictive model by which patients could be classified into high-risk and low-risk groups with significantly different overall survival. Further analysis indicated independent prognostic capability of the 8-lncRNA signature from other clinicopathological factors. ROC curve analysis demonstrated good performance of the 8-lncRNA signature. Functional enrichment analysis showed that the prognostic lncRNAs were mainly associated with esophageal cancer related biological processes such as regulation of glucose metabolic process and amino acid and lipids metabolism. Conclusions Our study developed a novel candidate model providing additional and more powerful prognostic information

  14. Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus.

    PubMed

    Baechler, Emily C; Batliwalla, Franak M; Karypis, George; Gaffney, Patrick M; Ortmann, Ward A; Espe, Karl J; Shark, Katherine B; Grande, William J; Hughes, Karis M; Kapur, Vivek; Gregersen, Peter K; Behrens, Timothy W

    2003-03-04

    Systemic lupus erythematosus (SLE) is a complex, inflammatory autoimmune disease that affects multiple organ systems. We used global gene expression profiling of peripheral blood mononuclear cells to identify distinct patterns of gene expression that distinguish most SLE patients from healthy controls. Strikingly, about half of the patients studied showed dysregulated expression of genes in the IFN pathway. Furthermore, this IFN gene expression "signature" served as a marker for more severe disease involving the kidneys, hematopoetic cells, and/or the central nervous system. These results provide insights into the genetic pathways underlying SLE, and identify a subgroup of patients who may benefit from therapies targeting the IFN pathway.

  15. Identification of a five B cell-associated gene prognostic and predictive signature for advanced glioma patients harboring immunosuppressive subtype preference

    PubMed Central

    Wang, Haoyuan; Song, Sonya Wei

    2016-01-01

    High grade gliomas contribute to most brain tumor mortality. A few studies reported that the immune system affected glioma development, and immune biomarkers helped understand the disease and formulate effective immunotherapy for patients. Currently, no B lymphocyte-based prognostic signature was reported in gliomas. By applying 78 B cell lineage-specific genes, we conducted a whole-genome gene expression analysis in 782 high grade gliomas derived from three independent datasets by Cox regression analysis and risk score method for signature identification, and then used Gene Ontology, Gene Set Enrichment Analysis, and other statistical methods for functional annotations of the signature-defined differences. We developed a five B cell-associated gene signature for prognosis of high grade glioma patients, which is independent of clinicopathological and genetic features. The signature identified high risk patients suitable for chemoradiotherapy, whereas low risk patients should rule out chemotherapy with radiotherapy only. We found that tumors of TCGA Mesenchymal subtype and wild type IDH1 were preferentially stratified to the high risk group, which bore strong immunosuppressive microenvironment, while tumors of TCGA Proneural subtype and mutated IDH1 were significantly accumulated to the low risk group, which exhibited less immunosuppressive state. The five B cell-associated gene signature predicts poor survival of high risk patients bearing strong immunosuppression and helps select optimal therapeutic regimens for glioma patients. PMID:27738332

  16. Inferring pathway crosstalk networks using gene set co-expression signatures.

    PubMed

    Wang, Ting; Gu, Jin; Yuan, Jun; Tao, Ran; Li, Yanda; Li, Shao

    2013-07-01

    Constructing molecular interaction networks in cells is important for understanding the underlying mechanisms of biological processes. Except for single gene analysis, several gene set-based methods have been proposed to infer pathway crosstalk by analyzing large-scale gene expression data. But most of them take all pathway genes as a whole to infer the crosstalk. Biological evidence suggests that the pathway crosstalk usually occurs between some subsets rather than the whole sets of pathway genes. In this study, we propose a novel method, sGSCA (signature-based gene set co-expression analysis) which can use the co-expression correlations between subsets of pathway genes to infer the pathway crosstalk networks. The method applies sparse canonical correlation analysis (sCCA) to measure the pathway level co-expression and simultaneously obtain the subsets or signature genes that contribute to the co-expression of pathways. On simulated datasets, sGSCA can efficiently detect pathway crosstalk and the corresponding highly correlated signature genes. We applied sGSCA to two cancer gene expression datasets (one for hepatocellular cancer and the other for lung cancer). In the inferred networks, we found several important pathway crosstalks related to the cancers. The identified signature genes also show high enrichment for the cancer related genes. sGSCA can infer pathway crosstalk networks using large-scale gene expression data, and should be a useful tool for systematically studying the molecular mechanisms of complex diseases on both pathway and gene levels at the same time.

  17. Expression profiling elucidates a molecular gene signature for pulmonary hypertension in sarcoidosis

    PubMed Central

    Singla, Sunit; Zhou, Tong; Javaid, Kamran; Abbasi, Taimur; Casanova, Nancy; Zhang, Wei; Ma, Shwu-Fan; Wade, Michael S.; Noth, Imre; Sweiss, Nadera J.; Garcia, Joe G. N.

    2016-01-01

    Abstract Pulmonary hypertension (PH), when it complicates sarcoidosis, carries a poor prognosis, in part because it is difficult to detect early in patients with worsening respiratory symptoms. Pathogenesis of sarcoidosis occurs via incompletely characterized mechanisms that are distinct from the mechanisms of pulmonary vascular remodeling well known to occur in conjunction with other chronic lung diseases. To address the need for a biomarker to aid in early detection as well as the gap in knowledge regarding the mechanisms of PH in sarcoidosis, we used genome-wide peripheral blood gene expression analysis and identified an 18-gene signature capable of distinguishing sarcoidosis patients with PH (n = 8), sarcoidosis patients without PH (n = 17), and healthy controls (n = 45). The discriminative accuracy of this 18-gene signature was 100% in separating sarcoidosis patients with PH from those without it. If validated in a large replicate cohort, this signature could potentially be used as a diagnostic molecular biomarker for sarcoidosis-associated PH. PMID:28090288

  18. A Comprehensive Gene Expression Meta-analysis Identifies Novel Immune Signatures in Rheumatoid Arthritis Patients

    PubMed Central

    Afroz, Sumbul; Giddaluru, Jeevan; Vishwakarma, Sandeep; Naz, Saima; Khan, Aleem Ahmed; Khan, Nooruddin

    2017-01-01

    Rheumatoid arthritis (RA), a symmetric polyarticular arthritis, has long been feared as one of the most disabling forms of arthritis. Identification of gene signatures associated with RA onset and progression would lead toward development of novel diagnostics and therapeutic interventions. This study was undertaken to identify unique gene signatures of RA patients through large-scale meta-profiling of a diverse collection of gene expression data sets. We carried out a meta-analysis of 8 publicly available RA patients’ (107 RA patients and 76 healthy controls) gene expression data sets and further validated a few meta-signatures in RA patients through quantitative real-time PCR (RT-qPCR). We identified a robust meta-profile comprising 33 differentially expressed genes, which were consistently and significantly expressed across all the data sets. Our meta-analysis unearthed upregulation of a few novel gene signatures including PLCG2, HLA-DOB, HLA-F, EIF4E2, and CYFIP2, which were validated in peripheral blood mononuclear cell samples of RA patients. Further, functional and pathway enrichment analysis reveals perturbation of several meta-genes involved in signaling pathways pertaining to inflammation, antigen presentation, hypoxia, and apoptosis during RA. Additionally, PLCG2 (phospholipase Cγ2) popped out as a novel meta-gene involved in most of the pathways relevant to RA including inflammasome activation, platelet aggregation, and activation, thereby suggesting PLCG2 as a potential therapeutic target for controlling excessive inflammation during RA. In conclusion, these findings highlight the utility of meta-analysis approach in identifying novel gene signatures that might provide mechanistic insights into disease onset, progression and possibly lead toward the development of better diagnostic and therapeutic interventions against RA. PMID:28210261

  19. A Comprehensive Gene Expression Meta-analysis Identifies Novel Immune Signatures in Rheumatoid Arthritis Patients.

    PubMed

    Afroz, Sumbul; Giddaluru, Jeevan; Vishwakarma, Sandeep; Naz, Saima; Khan, Aleem Ahmed; Khan, Nooruddin

    2017-01-01

    Rheumatoid arthritis (RA), a symmetric polyarticular arthritis, has long been feared as one of the most disabling forms of arthritis. Identification of gene signatures associated with RA onset and progression would lead toward development of novel diagnostics and therapeutic interventions. This study was undertaken to identify unique gene signatures of RA patients through large-scale meta-profiling of a diverse collection of gene expression data sets. We carried out a meta-analysis of 8 publicly available RA patients' (107 RA patients and 76 healthy controls) gene expression data sets and further validated a few meta-signatures in RA patients through quantitative real-time PCR (RT-qPCR). We identified a robust meta-profile comprising 33 differentially expressed genes, which were consistently and significantly expressed across all the data sets. Our meta-analysis unearthed upregulation of a few novel gene signatures including PLCG2, HLA-DOB, HLA-F, EIF4E2, and CYFIP2, which were validated in peripheral blood mononuclear cell samples of RA patients. Further, functional and pathway enrichment analysis reveals perturbation of several meta-genes involved in signaling pathways pertaining to inflammation, antigen presentation, hypoxia, and apoptosis during RA. Additionally, PLCG2 (phospholipase Cγ2) popped out as a novel meta-gene involved in most of the pathways relevant to RA including inflammasome activation, platelet aggregation, and activation, thereby suggesting PLCG2 as a potential therapeutic target for controlling excessive inflammation during RA. In conclusion, these findings highlight the utility of meta-analysis approach in identifying novel gene signatures that might provide mechanistic insights into disease onset, progression and possibly lead toward the development of better diagnostic and therapeutic interventions against RA.

  20. Predicting Zoonotic Risk of Influenza A Viruses from Host Tropism Protein Signature Using Random Forest

    PubMed Central

    Eng, Christine L. P.; Tong, Joo Chuan; Tan, Tin Wee

    2017-01-01

    Influenza A viruses remain a significant health problem, especially when a novel subtype emerges from the avian population to cause severe outbreaks in humans. Zoonotic viruses arise from the animal population as a result of mutations and reassortments, giving rise to novel strains with the capability to evade the host species barrier and cause human infections. Despite progress in understanding interspecies transmission of influenza viruses, we are no closer to predicting zoonotic strains that can lead to an outbreak. We have previously discovered distinct host tropism protein signatures of avian, human and zoonotic influenza strains obtained from host tropism predictions on individual protein sequences. Here, we apply machine learning approaches on the signatures to build a computational model capable of predicting zoonotic strains. The zoonotic strain prediction model can classify avian, human or zoonotic strains with high accuracy, as well as providing an estimated zoonotic risk. This would therefore allow us to quickly determine if an influenza virus strain has the potential to be zoonotic using only protein sequences. The swift identification of potential zoonotic strains in the animal population using the zoonotic strain prediction model could provide us with an early indication of an imminent influenza outbreak. PMID:28587080

  1. Gene-expression signatures of Atlantic salmon’s plastic life cycle

    PubMed Central

    Aubin-Horth, Nadia; Letcher, Benjamin H.; Hofmann, Hans A.

    2009-01-01

    How genomic expression differs as a function of life history variation is largely unknown. Atlantic salmon exhibits extreme alternative life histories. We defined the gene-expression signatures of wild-caught salmon at two different life stages by comparing the brain expression profiles of mature sneaker males and immature males, and early migrants and late migrants. In addition to life-stage-specific signatures, we discovered a surprisingly large gene set that was differentially regulated - at similar magnitudes, yet in opposite direction - in both life history transitions. We suggest that this co-variation is not a consequence of many independent cellular and molecular switches in the same direction but rather represents the molecular equivalent of a physiological shift orchestrated by one or very few master regulators. PMID:19401203

  2. A Circulating microRNA Signature Predicts Age-Based Development of Lymphoma

    PubMed Central

    Beheshti, Afshin; Vanderburg, Charles; McDonald, J. Tyson; Ramkumar, Charusheila; Kadungure, Tatenda; Zhang, Hong; Gartenhaus, Ronald B.; Evens, Andrew M.

    2017-01-01

    Extensive epidemiological data have demonstrated an exponential rise in the incidence of non-Hodgkin lymphoma (NHL) that is associated with increasing age. The molecular etiology of this remains largely unknown, which impacts the effectiveness of treatment for patients. We proposed that age-dependent circulating microRNA (miRNA) signatures in the host influence diffuse large B cell lymphoma (DLBCL) development. Our objective was to examine tumor development in an age-based DLBCL system using an inventive systems biology approach. We harnessed a novel murine model of spontaneous DLBCL initiation (Smurf2-deficient) at two age groups: 3 and 15 months old. All Smurf2-deficient mice develop visible DLBCL tumor starting at 15 months of age. Total miRNA was isolated from serum, bone marrow and spleen and were collected for all age groups for Smurf2-deficient mice and age-matched wild-type C57BL/6 mice. Using systems biology techniques, we identified a list of 10 circulating miRNAs being regulated in both the spleen and bone marrow that were present in DLBCL forming mice starting at 3 months of age that were not present in the control mice. Furthermore, this miRNA signature was found to occur circulating in the blood and it strongly impacted JUN and MYC oncogenic signaling. In addition, quantification of the miRNA signature was performed via Droplet Digital PCR technology. It was discovered that a key miRNA signature circulates throughout a host prior to the formation of a tumor starting at 3 months old, which becomes further modulated by age and yielded calculation of a ‘carcinogenic risk score’. This novel age-based circulating miRNA signature may potentially be leveraged as a DLBCL risk profile at a young age to predict future lymphoma development or disease progression as well as for potential innovative miRNA-based targeted therapeutic strategies in lymphoma. PMID:28107482

  3. Bioinformatic identification of prognostic signature defined by copy number alteration and expression of CCNE1 in non-muscle invasive bladder cancer

    PubMed Central

    Song, Bic-Na; Kim, Seon-Kyu; Chu, In-Sun

    2017-01-01

    Non-muscle invasive bladder cancer (NMIBC) patients frequently fail to respond to treatment and experience disease progression because of their clinical and biological diversity. In this study, we identify a prognostic molecular signature for predicting the heterogeneity of NMIBC by using an integrative analysis of copy number and gene expression data. We analyzed the copy number and gene expression profiles of 404 patients with bladder cancer obtained from The Cancer Genome Atlas (TCGA) consortium. Of the 14 molecules with significant copy number alterations that were previously reported, 13 were significantly correlated with copy number and expression changes. Prognostic gene sets based on the 13 genes were developed, and their prognostic values were verified in three independent patient cohorts (n=501). Among them, a signature of CCNE1 and its coexpressed genes was significantly associated with disease progression and validated in the independent cohorts. The CCNE1 signature was an independent risk factor based on the result of a multivariate analysis (hazard ratio=6.849, 95% confidence interval=1.613–29.092, P=0.009). Finally, gene network and upstream regulator analyses revealed that NMIBC progression is potentially mediated by CCND1-CCNE1-SP1 pathways. The prognostic molecular signature defined by copy number and expression changes of CCNE1 suggests a novel diagnostic tool for predicting the likelihood of NMIBC progression. PMID:28082741

  4. Development of an experimental capability to validate infrared signature predictions of installed aircraft exhaust systems

    NASA Astrophysics Data System (ADS)

    Rooks, Steve; Fair, Martin L.; Smith, Anthony G.; Chettle, Nicholas

    2002-08-01

    As methods continue to develop for predicting infrared signatures for complex propulsion systems, the need to validate such methods and, indeed to gain confidence in new designs grows. Within Dstl, work to develop static engine test rigs has been carried out. These rigs allow aspects of infrared signature such as plume mixing, cavity emissions, surface impingement and subsequent treatment, obscuration and nozzle shaping to be studied. However, there is a growing need for data, which is more closely related to actual flight conditions. Full flight measurements are prohibitively expensive and often out of the question when a range of geometries are to be studied. Wind tunnel tests can also be difficult because of the quantity of power required for the free stream flow and the need to produce realistic hot gas. This paper describes the work that has been carried out to produce a cost effective free stream measurement capability, which makes use of existing static engine facilities. By bleeding engine compressor flows and exhaust flows, a reduced scale system has been created which allows the simulation of infrared propulsion issues at free stream Mach numbers of up to 0.5. The data obtained with this system has been used to validate the prediction methods for 3D-exhaust plume and afterbody infrared signature.

  5. Modelling complex features from histone modification signatures using genetic algorithm for the prediction of enhancer region.

    PubMed

    Lee, Nung Kion; Fong, Pui Kwan; Abdullah, Mohd Tajuddin

    2014-01-01

    Using Genetic Algorithm, this paper presents a modelling method to generate novel logical-based features from DNA sequences enriched with H3K4mel histone signatures. Current histone signature is mostly represented using k-mers content features incapable of representing all the possible complex interactions of various DNA segments. The main contributions are, among others: (a) demonstrating that there are complex interactions among sequence segments in the histone regions; (b) developing a parse tree representation of the logical complex features. The proposed novel feature is compared to the k-mers content features using datasets from the mouse (mm9) genome. Evaluation results show that the new feature improves the prediction performance as shown by f-measure for all datasets tested. Also, it is discovered that tree-based features generated from a single chromosome can be generalized to predict histone marks in other chromosomes not used in the training. These findings have a great impact on feature design considerations for histone signatures as well as other classifier design features.

  6. Protein Expression Signatures for Inhibition of Epidermal Growth Factor Receptor-mediated Signaling*

    PubMed Central

    Myers, Matthew V.; Manning, H. Charles; Coffey, Robert J.; Liebler, Daniel C.

    2012-01-01

    Analysis of cellular signaling networks typically involves targeted measurements of phosphorylated protein intermediates. However, phosphoproteomic analyses usually require affinity enrichment of phosphopeptides and can be complicated by artifactual changes in phosphorylation caused by uncontrolled preanalytical variables, particularly in the analysis of tissue specimens. We asked whether changes in protein expression, which are more stable and easily analyzed, could reflect network stimulation and inhibition. We employed this approach to analyze stimulation and inhibition of the epidermal growth factor receptor (EGFR) by EGF and selective EGFR inhibitors. Shotgun analysis of proteomes from proliferating A431 cells, EGF-stimulated cells, and cells co-treated with the EGFR inhibitors cetuximab or gefitinib identified groups of differentially expressed proteins. Comparisons of these protein groups identified 13 proteins whose EGF-induced expression changes were reversed by both EGFR inhibitors. Targeted multiple reaction monitoring analysis verified differential expression of 12 of these proteins, which comprise a candidate EGFR inhibition signature. We then tested these 12 proteins by multiple reaction monitoring analysis in three other models: 1) a comparison of DiFi (EGFR inhibitor-sensitive) and HCT116 (EGFR-insensitive) cell lines, 2) in formalin-fixed, paraffin-embedded mouse xenograft DiFi and HCT116 tumors, and 3) in tissue biopsies from a patient with the gastric hyperproliferative disorder Ménétrier's disease who was treated with cetuximab. Of the proteins in the candidate signature, a core group, including c-Jun, Jagged-1, and Claudin 4, were decreased by EGFR inhibitors in all three models. Although the goal of these studies was not to validate a clinically useful EGFR inhibition signature, the results confirm the hypothesis that clinically used EGFR inhibitors generate characteristic protein expression changes. This work further outlines a prototypical

  7. Gene signatures of drug resistance predict patient survival in colorectal cancer

    PubMed Central

    Zheng, Y; Zhou, J; Tong, Y

    2015-01-01

    Different combinations of 5-fluorouracil (5-FU), oxaliplatin, irinotecan and other newly developed agents have been used to treat colorectal cancer. Despite the advent of new treatment regimens, the 5-year survival rate for metastatic colorectal cancer remains low (~10%). Knowing the drug sensitivity of a given tumor for a particular agent could significantly impact decision making and treatment planning. Biomarkers are proven to be successful in characterizing patients into different response groups. Using survival prediction analysis, we have identified three independent gene signatures, which are associated with sensitivity of colorectal cancer cells to 5-FU, oxaliplatin or irinotecan. On the basis of the three gene signatures, three score systems were developed to stratify patients from sensitive to resistance. These score systems exhibited robustness in stratify patients in two independent clinical studies. Patients with high scores in all three drugs exhibited the lowest survival. PMID:25179828

  8. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes

    PubMed Central

    Varshney, Arushi; Scott, Laura J.; Welch, Ryan P.; Erdos, Michael R.; Chines, Peter S.; Narisu, Narisu; Albanus, Ricardo D’O.; Orchard, Peter; Wolford, Brooke N.; Kursawe, Romy; Vadlamudi, Swarooparani; Cannon, Maren E.; Didion, John P.; Hensley, John; Kirilusha, Anthony; Bonnycastle, Lori L.; Taylor, D. Leland; Watanabe, Richard; Mohlke, Karen L.; Boehnke, Michael; Collins, Francis S.; Parker, Stephen C. J.; Stitzel, Michael L.

    2017-01-01

    Genome-wide association studies (GWAS) have identified >100 independent SNPs that modulate the risk of type 2 diabetes (T2D) and related traits. However, the pathogenic mechanisms of most of these SNPs remain elusive. Here, we examined genomic, epigenomic, and transcriptomic profiles in human pancreatic islets to understand the links between genetic variation, chromatin landscape, and gene expression in the context of T2D. We first integrated genome and transcriptome variation across 112 islet samples to produce dense cis-expression quantitative trait loci (cis-eQTL) maps. Additional integration with chromatin-state maps for islets and other diverse tissue types revealed that cis-eQTLs for islet-specific genes are specifically and significantly enriched in islet stretch enhancers. High-resolution chromatin accessibility profiling using assay for transposase-accessible chromatin sequencing (ATAC-seq) in two islet samples enabled us to identify specific transcription factor (TF) footprints embedded in active regulatory elements, which are highly enriched for islet cis-eQTL. Aggregate allelic bias signatures in TF footprints enabled us de novo to reconstruct TF binding affinities genetically, which support the high-quality nature of the TF footprint predictions. Interestingly, we found that T2D GWAS loci were strikingly and specifically enriched in islet Regulatory Factor X (RFX) footprints. Remarkably, within and across independent loci, T2D risk alleles that overlap with RFX footprints uniformly disrupt the RFX motifs at high-information content positions. Together, these results suggest that common regulatory variations have shaped islet TF footprints and the transcriptome and that a confluent RFX regulatory grammar plays a significant role in the genetic component of T2D predisposition. PMID:28193859

  9. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes.

    PubMed

    Varshney, Arushi; Scott, Laura J; Welch, Ryan P; Erdos, Michael R; Chines, Peter S; Narisu, Narisu; Albanus, Ricardo D'O; Orchard, Peter; Wolford, Brooke N; Kursawe, Romy; Vadlamudi, Swarooparani; Cannon, Maren E; Didion, John P; Hensley, John; Kirilusha, Anthony; Bonnycastle, Lori L; Taylor, D Leland; Watanabe, Richard; Mohlke, Karen L; Boehnke, Michael; Collins, Francis S; Parker, Stephen C J; Stitzel, Michael L

    2017-02-28

    Genome-wide association studies (GWAS) have identified >100 independent SNPs that modulate the risk of type 2 diabetes (T2D) and related traits. However, the pathogenic mechanisms of most of these SNPs remain elusive. Here, we examined genomic, epigenomic, and transcriptomic profiles in human pancreatic islets to understand the links between genetic variation, chromatin landscape, and gene expression in the context of T2D. We first integrated genome and transcriptome variation across 112 islet samples to produce dense cis-expression quantitative trait loci (cis-eQTL) maps. Additional integration with chromatin-state maps for islets and other diverse tissue types revealed that cis-eQTLs for islet-specific genes are specifically and significantly enriched in islet stretch enhancers. High-resolution chromatin accessibility profiling using assay for transposase-accessible chromatin sequencing (ATAC-seq) in two islet samples enabled us to identify specific transcription factor (TF) footprints embedded in active regulatory elements, which are highly enriched for islet cis-eQTL. Aggregate allelic bias signatures in TF footprints enabled us de novo to reconstruct TF binding affinities genetically, which support the high-quality nature of the TF footprint predictions. Interestingly, we found that T2D GWAS loci were strikingly and specifically enriched in islet Regulatory Factor X (RFX) footprints. Remarkably, within and across independent loci, T2D risk alleles that overlap with RFX footprints uniformly disrupt the RFX motifs at high-information content positions. Together, these results suggest that common regulatory variations have shaped islet TF footprints and the transcriptome and that a confluent RFX regulatory grammar plays a significant role in the genetic component of T2D predisposition.

  10. Effects Of Hydrothermal Alteration On Magnetic Properties And Magnetic Signatures - Implications For Predictive Magnetic Exploration Models

    NASA Astrophysics Data System (ADS)

    Clark, D.

    2012-12-01

    Magnetics is the most widely used geophysical method in hard rock exploration and magnetic surveys are an integral part of exploration programs for many types of mineral deposit, including porphyry Cu, intrusive-related gold, volcanic-hosted epithermal Au, IOCG, VMS, and Ni sulfide deposits. However, the magnetic signatures of ore deposits and their associated mineralized systems are extremely variable and exploration that is based simply on searching for signatures that resemble those of known deposits and systems is rarely successful. Predictive magnetic exploration models are based upon well-established geological models, combined with magnetic property measurements and geological information from well-studied deposits, and guided by magnetic petrological understanding of the processes that create, destroy and modify magnetic minerals in rocks. These models are designed to guide exploration by predicting magnetic signatures that are appropriate to specific geological settings, taking into account factors such as tectonic province; protolith composition; post-formation tilting/faulting/ burial/ exhumation and partial erosion; and metamorphism. Patterns of zoned hydrothermal alteration are important indicators of potentially mineralized systems and, if properly interpreted, can provided vectors to ore. Magnetic signatures associated with these patterns at a range of scales can provide valuable information on prospectivity and can guide drilling, provided they are correctly interpreted in geological terms. This presentation reviews effects of the important types of hydrothermal alteration on magnetic properties within mineralized systems, with particular reference to porphyry copper and IOCG deposits. For example, an unmodified gold-rich porphyry copper system, emplaced into mafic-intermediate volcanic host rocks (such as Bajo de la Alumbrera, Argentina) exhibits an inner potassic zone that is strongly mineralized and magnetite-rich, which is surrounded by an outer

  11. An independent validation of a gene expression signature to differentiate malignant melanoma from benign melanocytic nevi

    PubMed Central

    Flake, Darl D.; Busam, Klaus; Cockerell, Clay; Helm, Klaus; McNiff, Jennifer; Reed, Jon; Tschen, Jaime; Kim, Jinah; Barnhill, Raymond; Elenitsas, Rosalie; Prieto, Victor G.; Nelson, Jonathan; Kimbrell, Hillary; Kolquist, Kathryn A.; Brown, Krystal L.; Warf, M. Bryan; Roa, Benjamin B.; Wenstrup, Richard J.

    2016-01-01

    BACKGROUND Recently, a 23‐gene signature was developed to produce a melanoma diagnostic score capable of differentiating malignant and benign melanocytic lesions. The primary objective of this study was to independently assess the ability of the gene signature to differentiate melanoma from benign nevi in clinically relevant lesions. METHODS A set of 1400 melanocytic lesions was selected from samples prospectively submitted for gene expression testing at a clinical laboratory. Each sample was tested and subjected to an independent histopathologic evaluation by 3 experienced dermatopathologists. A primary diagnosis (benign or malignant) was assigned to each sample, and diagnostic concordance among the 3 dermatopathologists was required for inclusion in analyses. The sensitivity and specificity of the score in differentiating benign and malignant melanocytic lesions were calculated to assess the association between the score and the pathologic diagnosis. RESULTS The gene expression signature differentiated benign nevi from malignant melanoma with a sensitivity of 91.5% and a specificity of 92.5%. CONCLUSIONS These results reflect the performance of the gene signature in a diverse array of samples encountered in routine clinical practice. Cancer 2017;123:617–628. © 2016 American Cancer Society. PMID:27768230

  12. Immune signature of metastatic breast cancer: Identifying predictive markers of immunotherapy response.

    PubMed

    Kim, Ji-Yeon; Lee, Eunjin; Park, Kyunghee; Park, Woong-Yang; Jung, Hae Hyun; Ahn, Jin Seok; Im, Young-Hyuck; Park, Yeon Hee

    2017-07-18

    In breast cancer (BC), up to 10-20% patients were known to have clinical benefit with immune checkpoint inhibitors, and biomarkers are needed for optimal use of this multi-potential therapeutic strategy. Accordingly, we conducted an experiment to identify expression of genes associated with immune checkpoints that represent potential targets of cancer immunotherapy. We performed whole-transcriptome sequencing and whole-exome sequencing using 37 refractory BC specimens. In the immune pathway gene set expression analysis, we found that HER2 expression and previous taxane treatment were positively correlated with high expression of immune gene set expression (p = 0.070 and 0.008, respectively). The nine genes associated with immune checkpoints - PDCD1(PD-1), CD274(PD-L1), CD276(B7-H3), CTLA-4, IDO1, LAG3, VTCN1, HAVCR2, and TNFRSF4(OX40) - interacted with each other. In addition, HER2 expression also affected the expression levels of these genes (p = 0.044). Lastly, expression of immune checkpoint genes and tissue-infiltrating lymphocytes were positively correlated in metastatic BCs (p < 0.001). In conclusion, we suggest that HER2 expression and previous taxane treatment are potential surrogate markers for high expression of immune checkpoint genes and immune pathway gene sets. Further study of the BC immune signature with large-scale, translational data sets is warranted.

  13. Gene expression signatures but not cell cycle checkpoint functions distinguish AT carriers from normal individuals

    PubMed Central

    Zhang, Liwen; Simpson, Dennis A.; Innes, Cynthia L.; Chou, Jeff; Bushel, Pierre R.; Paules, Richard S.; Kaufmann, William K.

    2013-01-01

    Ataxia telangiectasia (AT) is a rare autosomal recessive disease caused by mutations in the ataxia telangiectasia-mutated gene (ATM). AT carriers with one mutant ATM allele are usually not severely affected although they carry an increased risk of developing cancer. There has not been an easy and reliable diagnostic method to identify AT carriers. Cell cycle checkpoint functions upon ionizing radiation (IR)-induced DNA damage and gene expression signatures were analyzed in the current study to test for differential responses in human lymphoblastoid cell lines with different ATM genotypes. While both dose- and time-dependent G1 and G2 checkpoint functions were highly attenuated in ATM−/− cell lines, these functions were preserved in ATM+/− cell lines equivalent to ATM+/+ cell lines. However, gene expression signatures at both baseline (consisting of 203 probes) and post-IR treatment (consisting of 126 probes) were able to distinguish ATM+/− cell lines from ATM+/+ and ATM−/− cell lines. Gene ontology (GO) and pathway analysis of the genes in the baseline signature indicate that ATM function-related categories, DNA metabolism, cell cycle, cell death control, and the p53 signaling pathway, were overrepresented. The same analyses of the genes in the IR-responsive signature revealed that biological categories including response to DNA damage stimulus, p53 signaling, and cell cycle pathways were overrepresented, which again confirmed involvement of ATM functions. The results indicate that AT carriers who have unaffected G1 and G2 checkpoint functions can be distinguished from normal individuals and AT patients by expression signatures of genes related to ATM functions. PMID:23943852

  14. Computing Molecular Signatures as Optima of a Bi-Objective Function: Method and Application to Prediction in Oncogenomics

    PubMed Central

    Gardeux, Vincent; Chelouah, Rachid; Wanderley, Maria F Barbosa; Siarry, Patrick; Braga, Antônio P; Reyal, Fabien; Rouzier, Roman; Pusztai, Lajos; Natowicz, René

    2015-01-01

    BACKGROUND Filter feature selection methods compute molecular signatures by selecting subsets of genes in the ranking of a valuation function. The motivations of the valuation functions choice are almost always clearly stated, but those for selecting the genes according to their ranking are hardly ever explicit. METHOD We addressed the computation of molecular signatures by searching the optima of a bi-objective function whose solution space was the set of all possible molecular signatures, ie, the set of subsets of genes. The two objectives were the size of the signature–to be minimized–and the interclass distance induced by the signature–to be maximized–. RESULTS We showed that: 1) the convex combination of the two objectives had exactly n optimal non empty signatures where n was the number of genes, 2) the n optimal signatures were nested, and 3) the optimal signature of size k was the subset of k top ranked genes that contributed the most to the interclass distance. We applied our feature selection method on five public datasets in oncology, and assessed the prediction performances of the optimal signatures as input to the diagonal linear discriminant analysis (DLDA) classifier. They were at the same level or better than the best-reported ones. The predictions were robust, and the signatures were almost always significantly smaller. We studied in more details the performances of our predictive modeling on two breast cancer datasets to predict the response to a preoperative chemotherapy: the performances were higher than the previously reported ones, the signatures were three times smaller (11 versus 30 gene signatures), and the genes member of the signature were known to be involved in the response to chemotherapy. CONCLUSIONS Defining molecular signatures as the optima of a bi-objective function that combined the signature size and the interclass distance was well founded and efficient for prediction in oncogenomics. The complexity of the computation

  15. Predictability of Expressed Career Intent.

    ERIC Educational Resources Information Center

    Shenk, Faye

    An historical study of officer input from the various Air Force commissioning programs was initiated in 1963. The study was designed to determine the predictability of an Air Force officer's career decision and to evaluate relationships between career intent and demographic, environmental, and attitudinal factors. Career-intention information for…

  16. Genome-Wide Prediction and Validation of Intergenic Enhancers in Arabidopsis Using Open Chromatin Signatures[OPEN

    PubMed Central

    Zhu, Bo; Zhang, Wenli; Jiang, Jiming

    2015-01-01

    Enhancers are important regulators of gene expression in eukaryotes. Enhancers function independently of their distance and orientation to the promoters of target genes. Thus, enhancers have been difficult to identify. Only a few enhancers, especially distant intergenic enhancers, have been identified in plants. We developed an enhancer prediction system based exclusively on the DNase I hypersensitive sites (DHSs) in the Arabidopsis thaliana genome. A set of 10,044 DHSs located in intergenic regions, which are away from any gene promoters, were predicted to be putative enhancers. We examined the functions of 14 predicted enhancers using the β-glucuronidase gene reporter. Ten of the 14 (71%) candidates were validated by the reporter assay. We also designed 10 constructs using intergenic sequences that are not associated with DHSs, and none of these constructs showed enhancer activities in reporter assays. In addition, the tissue specificity of the putative enhancers can be precisely predicted based on DNase I hypersensitivity data sets developed from different plant tissues. These results suggest that the open chromatin signature-based enhancer prediction system developed in Arabidopsis may serve as a universal system for enhancer identification in plants. PMID:26373455

  17. Identification of Single- and Multiple-Class Specific Signature Genes from Gene Expression Profiles by Group Marker Index

    PubMed Central

    Tsai, Yu-Shuen; Aguan, Kripamoy; Pal, Nikhil R.; Chung, I-Fang

    2011-01-01

    Informative genes from microarray data can be used to construct prediction model and investigate biological mechanisms. Differentially expressed genes, the main targets of most gene selection methods, can be classified as single- and multiple-class specific signature genes. Here, we present a novel gene selection algorithm based on a Group Marker Index (GMI), which is intuitive, of low-computational complexity, and efficient in identification of both types of genes. Most gene selection methods identify only single-class specific signature genes and cannot identify multiple-class specific signature genes easily. Our algorithm can detect de novo certain conditions of multiple-class specificity of a gene and makes use of a novel non-parametric indicator to assess the discrimination ability between classes. Our method is effective even when the sample size is small as well as when the class sizes are significantly different. To compare the effectiveness and robustness we formulate an intuitive template-based method and use four well-known datasets. We demonstrate that our algorithm outperforms the template-based method in difficult cases with unbalanced distribution. Moreover, the multiple-class specific genes are good biomarkers and play important roles in biological pathways. Our literature survey supports that the proposed method identifies unique multiple-class specific marker genes (not reported earlier to be related to cancer) in the Central Nervous System data. It also discovers unique biomarkers indicating the intrinsic difference between subtypes of lung cancer. We also associate the pathway information with the multiple-class specific signature genes and cross-reference to published studies. We find that the identified genes participate in the pathways directly involved in cancer development in leukemia data. Our method gives a promising way to find genes that can involve in pathways of multiple diseases and hence opens up the possibility of using an existing

  18. Identification of single- and multiple-class specific signature genes from gene expression profiles by group marker index.

    PubMed

    Tsai, Yu-Shuen; Aguan, Kripamoy; Pal, Nikhil R; Chung, I-Fang

    2011-01-01

    Informative genes from microarray data can be used to construct prediction model and investigate biological mechanisms. Differentially expressed genes, the main targets of most gene selection methods, can be classified as single- and multiple-class specific signature genes. Here, we present a novel gene selection algorithm based on a Group Marker Index (GMI), which is intuitive, of low-computational complexity, and efficient in identification of both types of genes. Most gene selection methods identify only single-class specific signature genes and cannot identify multiple-class specific signature genes easily. Our algorithm can detect de novo certain conditions of multiple-class specificity of a gene and makes use of a novel non-parametric indicator to assess the discrimination ability between classes. Our method is effective even when the sample size is small as well as when the class sizes are significantly different. To compare the effectiveness and robustness we formulate an intuitive template-based method and use four well-known datasets. We demonstrate that our algorithm outperforms the template-based method in difficult cases with unbalanced distribution. Moreover, the multiple-class specific genes are good biomarkers and play important roles in biological pathways. Our literature survey supports that the proposed method identifies unique multiple-class specific marker genes (not reported earlier to be related to cancer) in the Central Nervous System data. It also discovers unique biomarkers indicating the intrinsic difference between subtypes of lung cancer. We also associate the pathway information with the multiple-class specific signature genes and cross-reference to published studies. We find that the identified genes participate in the pathways directly involved in cancer development in leukemia data. Our method gives a promising way to find genes that can involve in pathways of multiple diseases and hence opens up the possibility of using an existing

  19. Predicting Photometric and Spectroscopic Signatures of Rings Around Transiting Extrasolar Planets

    NASA Astrophysics Data System (ADS)

    Ohta, Yasuhiro; Taruya, Atsushi; Suto, Yasushi

    2009-01-01

    We present theoretical predictions for the photometric and spectroscopic signatures of rings around transiting extrasolar planets. On the basis of a general formulation for the transiting signature in the stellar light curve and the velocity anomaly due to the Rossiter effect, we compute the expected signals analytically for a face-on ring system, and numerically for more general configurations. We study the detectability of a ring around a transiting planet located at a = 3 AU for a variety of obliquity and azimuthal angles, and find that it is possible to detect the ring signature both photometrically and spectroscopically unless the ring is almost edge-on (i.e., the obliquity angle, θ, of the ring is much less than unity). We also consider the detectability of planetary rings around a close-in planet, HD 209458b (θ ≈ 90° - i orbit≈ 3fdg32), and Saturn (θ ≈ 26fdg7) as illustrative examples. While the former is difficult to detect with the current precision (photometric precision of 10-4 and radial velocity precision of 1 m s-1), a marginal detection of the latter is possible photometrically. If the future precision of the radial velocity measurement goes below 0.1 m s-1, they will even be detectable from ground-based spectroscopic observations.

  20. Association of a cytarabine chemosensitivity related gene expression signature with survival in cytogenetically normal acute myeloid leukemia

    PubMed Central

    Yan, Han; Wen, Lu; Tan, Dan; Xie, Pan; Pang, Feng-mei; Zhou, Hong-hao; Zhang, Wei; Liu, Zhao-qian; Tang, Jie; Li, Xi; Chen, Xiao-ping

    2017-01-01

    The prognosis of cytogenetically normal acute myeloid leukemia (CN-AML) varies greatly among patients. Achievement of complete remission (CR) after chemotherapy is indispensable for a better prognosis. To develop a gene signature predicting overall survival (OS) in CN-AML, we performed data mining procedure based on whole genome expression data of both blood cancer cell lines and AML patients from open access database. A gene expression signature including 42 probes was derived. These probes were significantly associated with both cytarabine half maximal inhibitory concentration values in blood cancer cell lines and OS in CN-AML patients. By using cox regression analysis and linear regression analysis, a chemo-sensitive score calculated algorithm based on mRNA expression levels of the 42 probes was established. The scores were associated with OS in both the training sample (p=5.13 × 10−4, HR=2.040, 95% CI: 1.364-3.051) and the validation sample (p=0.002, HR=2.528, 95% CI: 1.393-4.591) of the GSE12417 dataset from Gene Expression Omnibus. In The Cancer Genome Atlas (TCGA) CN-AML patients, higher scores were found to be associated with both worse OS (p=0.013, HR=2.442, 95% CI: 1.205-4.950) and DFS (p=0.015, HR=2.376, 95% CI: 1.181-4.779). Results of gene ontology (GO) analysis showed that all the significant GO Terms were correlated with cellular component of mitochondrion. In summary, a novel gene set that could predict prognosis of CN-AML was identified presently, which provided a new way to identify genes impacting AML chemo-sensitivity and prognosis. PMID:27903973

  1. pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures

    PubMed Central

    2015-01-01

    Drug development has a high attrition rate, with poor pharmacokinetic and safety properties a significant hurdle. Computational approaches may help minimize these risks. We have developed a novel approach (pkCSM) which uses graph-based signatures to develop predictive models of central ADMET properties for drug development. pkCSM performs as well or better than current methods. A freely accessible web server (http://structure.bioc.cam.ac.uk/pkcsm), which retains no information submitted to it, provides an integrated platform to rapidly evaluate pharmacokinetic and toxicity properties. PMID:25860834

  2. Expression signatures of long non-coding RNAs in early brain injury following experimental subarachnoid hemorrhage.

    PubMed

    Zheng, Bingjie; Liu, Huailei; Wang, Ruke; Xu, Shancai; Liu, Yaohua; Wang, Kaikai; Hou, Xu; Shen, Chen; Wu, Jianing; Chen, Xin; Wu, Pei; Zhang, Guang; Ji, Zhiyong; Wang, Hongyu; Xiao, Yao; Han, Jianyi; Shi, Huaizhang; Zhao, Shiguang

    2015-07-01

    Subarachnoid hemorrhage (SAH) is an important cause of mortality in stroke patients. Long non-coding RNAs (LncRNAs) have important functions in brain disease, however their expression profiles in SAH remain to be elucidated. The present study aimed to investigate the expression signatures of LncRNAs and mRNAs in early brain injury (EBI) following SAH in a rat model. Male Wistar rats were randomly divided into an SAH group and a sham operation group. The expression signatures of the LncRNAs and mRNAs in the temporal lobe cortex were investigated using a rat LncRNAs array following experimental SAH. The results revealed that there were 144 downregulated and 64 upregulated LncRNAs and 181 downregulated and 221 upregulated mRNAs following SAH. Additionally, two upregulated (BC092207, MRuc008hvl) and three downregulated (XR_006756, MRAK038897, MRAK017168) LncRNAs were confirmed using reverse transcription quantitative polymerase chain reaction. The differentially expressed mRNAs were further analyzed using the Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The pathway analysis results provided by the KEGG database indicated that eight pathways associated with inflammation were involved in EBI following SAH. In conclusion, these results demonstrated that the expression profiles of the LncRNAs and mRNAs were significantly different between the SAH-induced EBI group and the sham operation group. These differently expressed LncRNAs may be important in EBI following SAH.

  3. Distinct microRNA expression signatures in human right atrial and ventricular myocardium.

    PubMed

    Zhang, Yangyang; Wang, Xiaowei; Xu, Xiaohan; Wang, Jun; Liu, Xiang; Chen, Yijiang

    2012-12-01

    Human atrial and ventricular myocardium has distinct structure and physiology. MicroRNAs (miRNAs) are the central players in the regulation of gene expression, participating in many physiological processes. A comprehensive knowledge of miRNA expression in the human heart is essential for the understanding of myocardial function. The aim of this study was to compare the miRNA signature in human right atrial and ventricular myocardium. Agilent human miRNA arrays were used to indicate the miRNA expression signatures of the right atrial (n = 8) and ventricular (n = 9) myocardium of healthy individuals. Quantitative reverse transcription-polymerase chain reactions (qRT-PCRs) were used to validate the array results. DIANA-mirPath was used to incorporate the miRNAs into pathways. MiRNA arrays showed that 169 miRNAs were expressed at different levels in human right atrial and ventricular myocardium. The unsupervised hierarchical clustering analysis based on the 169 dysregulated miRNAs showed that miRNA expression categorized two well-defined clusters that corresponded to human right atrial and ventricular myocardium. The qRT-PCR results correlated well with the microarray data. Bioinformatic analysis indicated the potential miRNA targets and molecular pathways. This study indicates that distinct miRNA expression signatures in human right atrial and ventricular myocardium. The findings provide a novel understanding of the molecular differences between human atrial and ventricular myocardium and may establish a framework for an anatomically detailed evaluation of cardiac function regulation.

  4. A combined oncogenic pathway signature of BRAF, KRAS and PI3KCA mutation improves colorectal cancer classification and cetuximab treatment prediction

    PubMed Central

    Tian, Sun; Simon, Iris; Moreno, Victor; Roepman, Paul; Tabernero, Josep; Snel, Mireille; van't Veer, Laura; Salazar, Ramon; Bernards, Rene

    2013-01-01

    Objective To develop gene expression profiles that characterise KRAS-, BRAF- or PIK3CA-activated- tumours, and to explore whether these profiles might be helpful in predicting the response to the epidermal growth factor receptor (EGFR) pathway inhibitors better than mutation status alone. Design Fresh frozen tumour samples from 381 colorectal cancer (CRC) patients were collected and mutations in KRAS, BRAF and PIK3CA were assessed. Using microarray data, three individual oncogenic and a combined model were developed and validated in an independent set of 80 CRC patients, and in a dataset from metastatic CRC patients treated with cetuximab. Results 175 tumours (45.9%) harboured oncogenic mutations in KRAS (30.2%), BRAF (11.0%) and PIK3CA (11.5%). Activating mutation signatures for KRAS (75 genes), for BRAF (58 genes,) and for PIK3CA (49 genes) were developed. The development of a combined oncogenic pathway signature-classified tumours as ‘activated oncogenic’, or as ‘wildtype-like’ with a sensitivity of 90.3% and a specificity of 61.7%. The identified signature revealed other mechanisms that can activate ERK/MAPK pathway in KRAS, BRAF and PIK3CA wildtype patients. The combined signature is associated with response to cetuximab treatment in patients with metastatic CRC (HR 2.51, p<0.0009). Conclusion A combined oncogenic pathway signature allows the identification of patients with an active EGFR-signalling pathway that could benefit from downstream pathway inhibition. PMID:22798500

  5. A combined oncogenic pathway signature of BRAF, KRAS and PI3KCA mutation improves colorectal cancer classification and cetuximab treatment prediction.

    PubMed

    Tian, Sun; Simon, Iris; Moreno, Victor; Roepman, Paul; Tabernero, Josep; Snel, Mireille; van't Veer, Laura; Salazar, Ramon; Bernards, Rene; Capella, Gabriel

    2013-04-01

    To develop gene expression profiles that characterise KRAS-, BRAF- or PIK3CA-activated- tumours, and to explore whether these profiles might be helpful in predicting the response to the epidermal growth factor receptor (EGFR) pathway inhibitors better than mutation status alone. Fresh frozen tumour samples from 381 colorectal cancer (CRC) patients were collected and mutations in KRAS, BRAF and PIK3CA were assessed. Using microarray data, three individual oncogenic and a combined model were developed and validated in an independent set of 80 CRC patients, and in a dataset from metastatic CRC patients treated with cetuximab. 175 tumours (45.9%) harboured oncogenic mutations in KRAS (30.2%), BRAF (11.0%) and PIK3CA (11.5%). Activating mutation signatures for KRAS (75 genes), for BRAF (58 genes,) and for PIK3CA (49 genes) were developed. The development of a combined oncogenic pathway signature-classified tumours as 'activated oncogenic', or as 'wildtype-like' with a sensitivity of 90.3% and a specificity of 61.7%. The identified signature revealed other mechanisms that can activate ERK/MAPK pathway in KRAS, BRAF and PIK3CA wildtype patients. The combined signature is associated with response to cetuximab treatment in patients with metastatic CRC (HR 2.51, p<0.0009). A combined oncogenic pathway signature allows the identification of patients with an active EGFR-signalling pathway that could benefit from downstream pathway inhibition.

  6. Common disease signatures from gene expression analysis in Huntington's disease human blood and brain.

    PubMed

    Mina, Eleni; van Roon-Mom, Willeke; Hettne, Kristina; van Zwet, Erik; Goeman, Jelle; Neri, Christian; A C 't Hoen, Peter; Mons, Barend; Roos, Marco

    2016-08-01

    Huntington's disease (HD) is a devastating brain disorder with no effective treatment or cure available. The scarcity of brain tissue makes it hard to study changes in the brain and impossible to perform longitudinal studies. However, peripheral pathology in HD suggests that it is possible to study the disease using peripheral tissue as a monitoring tool for disease progression and/or efficacy of novel therapies. In this study, we investigated if blood can be used to monitor disease severity and progression in brain. Since previous attempts using only gene expression proved unsuccessful, we compared blood and brain Huntington's disease signatures in a functional context. Microarray HD gene expression profiles from three brain regions were compared to the transcriptome of HD blood generated by next generation sequencing. The comparison was performed with a combination of weighted gene co-expression network analysis and literature based functional analysis (Concept Profile Analysis). Uniquely, our comparison of blood and brain datasets was not based on (the very limited) gene overlap but on the similarity between the gene annotations in four different semantic categories: "biological process", "cellular component", "molecular function" and "disease or syndrome". We identified signatures in HD blood reflecting a broad pathophysiological spectrum, including alterations in the immune response, sphingolipid biosynthetic processes, lipid transport, cell signaling, protein modification, spliceosome, RNA splicing, vesicle transport, cell signaling and synaptic transmission. Part of this spectrum was reminiscent of the brain pathology. The HD signatures in caudate nucleus and BA4 exhibited the highest similarity with blood, irrespective of the category of semantic annotations used. BA9 exhibited an intermediate similarity, while cerebellum had the least similarity. We present two signatures that were shared between blood and brain: immune response and spinocerebellar ataxias

  7. Predictive value of Sp1/Sp3/FLIP signature for prostate cancer recurrence.

    PubMed

    Bedolla, Roble G; Gong, Jingjing; Prihoda, Thomas J; Yeh, I-Tien; Thompson, Ian M; Ghosh, Rita; Kumar, Addanki P

    2012-01-01

    Prediction of prostate cancer prognosis is challenging and predictive biomarkers of recurrence remain elusive. Although prostate specific antigen (PSA) has high sensitivity (90%) at a PSA level of 4.0 ng/mL, its low specificity leads to many false positive results and considerable overtreatment of patients and its performance at lower ranges is poor. Given the histopathological and molecular heterogeneity of prostate cancer, we propose that a panel of markers will be a better tool than a single marker. We tested a panel of markers composed of the anti-apoptotic protein FLIP and its transcriptional regulators Sp1 and Sp3 using prostate tissues from 64 patients with recurrent and non-recurrent cancer who underwent radical prostatectomy as primary treatment for prostate cancer and were followed with PSA measurements for at least 5 years. Immunohistochemical staining for Sp1, Sp3, and FLIP was performed on these tissues and scored based on the proportion and intensity of staining. The predictive value of the FLIP/Sp1/Sp3 signature for clinical outcome (recurrence vs. non-recurrence) was explored with logistic regression, and combinations of FLIP/Sp1/Sp3 and Gleason score were analyzed with a stepwise (backward and forward) logistic model. The discrimination of the markers was identified by sensitivity-specificity analysis and the diagnostic value of FLIP/Sp1/Sp3 was determined using area under the curve (AUC) for receiver operator characteristic curves. The AUCs for FLIP, Sp1, Sp3, and Gleason score for predicting PSA failure and non-failure were 0.71, 0.66, 0.68, and 0.76, respectively. However, this increased to 0.93 when combined. Thus, the "biomarker signature" of FLIP/Sp1/Sp3 combined with Gleason score predicted disease recurrence and stratified patients who are likely to benefit from more aggressive treatment.

  8. Epithelial cells captured from ductal carcinoma in situ reveal a gene expression signature associated with progression to invasive breast cancer

    PubMed Central

    Abuázar, Carolina Sens; de Toledo Osorio, Cynthia Aparecida Bueno; Pinilla, Mabel Gigliola; da Silva, Sabrina Daniela; Camargo, Anamaria Aranha; Silva, Wilson Araujo; e Ferreira, Elisa Napolitano; Brentani, Helena Paula; Carraro, Dirce Maria

    2016-01-01

    Breast cancer biomarkers that can precisely predict the risk of progression of non-invasive ductal carcinoma in situ (DCIS) lesions to invasive disease are lacking. The identification of molecular alterations that occur during the invasion process is crucial for the discovery of drivers of transition to invasive disease and, consequently, biomarkers with clinical utility. In this study, we explored differences in gene expression in mammary epithelial cells before and after the morphological manifestation of invasion, i.e., early and late stages, respectively. In the early stage, epithelial cells were captured from both pre-invasive lesions with distinct malignant potential [pure DCIS as well as the in situ component that co-exists with invasive breast carcinoma lesions (DCIS-IBC)]; in the late stage, epithelial cells were captured from the two distinct morphological components of the same sample (in situ and invasive components). Candidate genes were identified using cDNA microarray and rapid subtractive hybridization (RaSH) cDNA libraries and validated by RT-qPCR assay using new samples from each group. These analyses revealed 26 genes, including 20 from the early and 6 from the late stage. The expression profile based on the 20 genes, marked by a preferential decrease in expression level towards invasive phenotype, discriminated the majority of DCIS samples. Thus, this study revealed a gene expression signature with the potential to predict DCIS progression and, consequently, provides opportunities to tailor treatments for DCIS patients. PMID:27708222

  9. Validation Study: Response-Predictive Gene Expression Profiling of Glioma Progenitor Cells In Vitro.

    PubMed

    Moeckel, Sylvia; Vollmann-Zwerenz, Arabel; Proescholdt, Martin; Brawanski, Alexander; Riemenschneider, Markus J; Bogdahn, Ulrich; Bosserhoff, Anja-Katrin; Spang, Rainer; Hau, Peter

    2016-01-01

    In a previous publication we introduced a novel approach to identify genes that hold predictive information about treatment outcome. A linear regression model was fitted by using the least angle regression algorithm (LARS) with the expression profiles of a construction set of 18 glioma progenitor cells enhanced for brain tumor initiating cells (BTIC) before and after in vitro treatment with the tyrosine kinase inhibitor Sunitinib. Profiles from treated progenitor cells allowed predicting therapy-induced impairment of proliferation in vitro. Prediction performance was validated in leave one out cross validation. In this study, we used an additional validation set of 18 serum-free short-term treated in vitro cell cultures to test the predictive properties of the signature in an independent cohort. We assessed proliferation rates together with transcriptome-wide expression profiles after Sunitinib treatment of each individual cell culture, following the methods of the previous publication. We confirmed treatment-induced expression changes in our validation set, but our signature failed to predict proliferation inhibition. Neither re-calculation of the combined dataset with all 36 BTIC cultures nor separation of samples into TCGA subclasses did generate a proliferation prediction. Although the gene signature published from our construction set exhibited good prediction accuracy in cross validation, we were not able to validate the signature in an independent validation data set. Reasons could be regression to the mean, the moderate numbers of samples, or too low differences in the response to proliferation inhibition in the validation set. At this stage and based on the presented results, we conclude that the signature does not warrant further developmental steps towards clinical application.

  10. A 16 Yin Yang gene expression ratio signature for ER+/node- breast cancer.

    PubMed

    Xu, Wayne; Jia, Gaofeng; Cai, Nianguang; Huang, Shujun; Davie, James R; Pitz, Marshall; Banerji, Shantanu; Murphy, Leigh

    2017-03-15

    Breast cancer is one of the leading causes of cancer death in women. It is a complex and heterogeneous disease with different clinical outcomes. Stratifying patients into subgroups with different outcomes could help guide clinical decision making. In this study, we used two opposing groups of genes, Yin and Yang, to develop a prognostic expression ratio signature. Using the METABRIC cohort we identified a16-gene signature capable of stratifying breast cancer patients into four risk levels with intention that low-risk patients would not undergo adjuvant systemic therapy, intermediate-low-risk patients will be treated with hormonal therapy only, and intermediate-high- and high-risk groups will be treated by chemotherapy in addition to the hormonal therapy. The 16-gene signature for four risk level stratifications of breast cancer patients has been validated using 14 independent datasets. Notably, the low-risk group (n = 51) of 205 estrogen receptor-positive and node negative (ER+/node-) patients from three different datasets who had not had any systemic adjuvant therapy had 100% 15-year disease-specific survival rate. The Concordance Index of YMR for ER+/node negative patients is close to the commercially available signatures. However, YMR showed more significance (HR = 3.7, p = 8.7e-12) in stratifying ER+/node- subgroup than OncotypeDx (HR = 2.7, p = 1.3e-7), MammaPrint (HR = 2.5, p = 5.8e-7), rorS (HR = 2.4, p = 1.4e-6), and NPI (HR = 2.6, p = 1.2e-6). YMR signature may be developed as a clinical tool to select a subgroup of low-risk ER+/node- patients who do not require any adjuvant hormonal therapy (AHT). © 2016 UICC.

  11. Computational analysis of expression of human embryonic stem cell-associated signatures in tumors

    PubMed Central

    2011-01-01

    Background The cancer stem cell model has been proposed based on the linkage between human embryonic stem cells and human cancer cells. However, the evidences supporting the cancer stem cell model remain to be collected. In this study, we extensively examined the expression of human embryonic stem cell-associated signatures including core genes, transcription factors, pathways and microRNAs in various cancers using the computational biology approach. Results We used the class comparison analysis and survival analysis algorithms to identify differentially expressed genes and their associated transcription factors, pathways and microRNAs among normal vs. tumor or good prognosis vs. poor prognosis phenotypes classes based on numerous human cancer gene expression data. We found that most of the human embryonic stem cell- associated signatures were frequently identified in the analysis, suggesting a strong linkage between human embryonic stem cells and cancer cells. Conclusions The present study revealed the close linkage between the human embryonic stem cell associated gene expression profiles and cancer-associated gene expression profiles, and therefore offered an indirect support for the cancer stem cell theory. However, many interest issues remain to be addressed further. PMID:22041030

  12. Immune signatures and disorder-specific patterns in a cross-disorder gene expression analysis

    PubMed Central

    de Jong, Simone; Newhouse, Stephen J.; Patel, Hamel; Lee, Sanghyuck; Dempster, David; Curtis, Charles; Paya-Cano, Jose; Murphy, Declan; Wilson, C. Ellie; Horder, Jamie; Mendez, M. Andreina; Asherson, Philip; Rivera, Margarita; Costello, Helen; Maltezos, Stefanos; Whitwell, Susannah; Pitts, Mark; Tye, Charlotte; Ashwood, Karen L.; Bolton, Patrick; Curran, Sarah; McGuffin, Peter; Dobson, Richard; Breen, Gerome

    2016-01-01

    Background Recent studies point to overlap between neuropsychiatric disorders in symptomatology and genetic aetiology. Aims To systematically investigate genomics overlap between childhood and adult attention-deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and major depressive disorder (MDD). Method Analysis of whole-genome blood gene expression and genetic risk scores of 318 individuals. Participants included individuals affected with adult ADHD (n = 93), childhood ADHD (n = 17), MDD (n = 63), ASD (n = 51), childhood dual diagnosis of ADHD–ASD (n = 16) and healthy controls (n = 78). Results Weighted gene co-expression analysis results reveal disorder-specific signatures for childhood ADHD and MDD, and also highlight two immune-related gene co-expression modules correlating inversely with MDD and adult ADHD disease status. We find no significant relationship between polygenic risk scores and gene expression signatures. Conclusions Our results reveal disorder overlap and specificity at the genetic and gene expression level. They suggest new pathways contributing to distinct pathophysiology in psychiatric disorders and shed light on potential shared genomic risk factors. PMID:27151072

  13. Evaluation of data discretization methods to derive platform independent isoform expression signatures for multi-class tumor subtyping

    PubMed Central

    2015-01-01

    Background Many supervised learning algorithms have been applied in deriving gene signatures for patient stratification from gene expression data. However, transferring the multi-gene signatures from one analytical platform to another without loss of classification accuracy is a major challenge. Here, we compared three unsupervised data discretization methods--Equal-width binning, Equal-frequency binning, and k-means clustering--in accurately classifying the four known subtypes of glioblastoma multiforme (GBM) when the classification algorithms were trained on the isoform-level gene expression profiles from exon-array platform and tested on the corresponding profiles from RNA-seq data. Results We applied an integrated machine learning framework that involves three sequential steps; feature selection, data discretization, and classification. For models trained and tested on exon-array data, the addition of data discretization step led to robust and accurate predictive models with fewer number of variables in the final models. For models trained on exon-array data and tested on RNA-seq data, the addition of data discretization step dramatically improved the classification accuracies with Equal-frequency binning showing the highest improvement with more than 90% accuracies for all the models with features chosen by Random Forest based feature selection. Overall, SVM classifier coupled with Equal-frequency binning achieved the best accuracy (> 95%). Without data discretization, however, only 73.6% accuracy was achieved at most. Conclusions The classification algorithms, trained and tested on data from the same platform, yielded similar accuracies in predicting the four GBM subgroups. However, when dealing with cross-platform data, from exon-array to RNA-seq, the classifiers yielded stable models with highest classification accuracies on data transformed by Equal frequency binning. The approach presented here is generally applicable to other cancer types for

  14. Evaluation of data discretization methods to derive platform independent isoform expression signatures for multi-class tumor subtyping.

    PubMed

    Jung, Segun; Bi, Yingtao; Davuluri, Ramana V

    2015-01-01

    Many supervised learning algorithms have been applied in deriving gene signatures for patient stratification from gene expression data. However, transferring the multi-gene signatures from one analytical platform to another without loss of classification accuracy is a major challenge. Here, we compared three unsupervised data discretization methods--Equal-width binning, Equal-frequency binning, and k-means clustering--in accurately classifying the four known subtypes of glioblastoma multiforme (GBM) when the classification algorithms were trained on the isoform-level gene expression profiles from exon-array platform and tested on the corresponding profiles from RNA-seq data. We applied an integrated machine learning framework that involves three sequential steps; feature selection, data discretization, and classification. For models trained and tested on exon-array data, the addition of data discretization step led to robust and accurate predictive models with fewer number of variables in the final models. For models trained on exon-array data and tested on RNA-seq data, the addition of data discretization step dramatically improved the classification accuracies with Equal-frequency binning showing the highest improvement with more than 90% accuracies for all the models with features chosen by Random Forest based feature selection. Overall, SVM classifier coupled with Equal-frequency binning achieved the best accuracy (> 95%). Without data discretization, however, only 73.6% accuracy was achieved at most. The classification algorithms, trained and tested on data from the same platform, yielded similar accuracies in predicting the four GBM subgroups. However, when dealing with cross-platform data, from exon-array to RNA-seq, the classifiers yielded stable models with highest classification accuracies on data transformed by Equal frequency binning. The approach presented here is generally applicable to other cancer types for classification and identification of

  15. A gene expression signature for recent onset rheumatoid arthritis in peripheral blood mononuclear cells

    PubMed Central

    Olsen, N; Sokka, T; Seehorn, C; Kraft, B; Maas, K; Moore, J; Aune, T

    2004-01-01

    Background: In previous studies the presence of a distinct gene expression pattern has been shown in peripheral blood cells from patients with autoimmune disease. Objective: To determine whether other specific signatures might be used to identify subsets of these autoimmune diseases and whether gene expression patterns in early disease might identify pathogenetic factors. Methods: Peripheral blood mononuclear cells were acquired from patients with rheumatoid arthritis (RA) and analysed by microarrays containing over 4300 named human genes. Patients with RA for <2 years were compared with subjects with longstanding RA (average duration 10 years) and with patients with other immune or autoimmune diagnoses. Results: Cluster analyses permitted separation of the patients with early RA (ERA) from those with longstanding disease. Comparison with other patient groups suggested that the ERA signature showed some overlap with that seen in the normal immune response to viral antigen as well as with a subset of patients with systemic lupus erythematosus. Conclusions: The ERA signature may reflect, in part, a response to an unknown infectious agent. Furthermore, shared features with some lupus patients suggest that common aetiological factors and pathogenetic pathways may be involved in these two autoimmune disorders. PMID:15479887

  16. Next-generation sequencing of microRNAs uncovers expression signatures in polarized macrophages.

    PubMed

    Cobos Jiménez, Viviana; Bradley, Edward J; Willemsen, Antonius M; van Kampen, Antoine H C; Baas, Frank; Kootstra, Neeltje A

    2014-02-01

    microRNAs (miRNAs) are small noncoding RNAs that regulate gene expression at a posttranscriptional level and play a crucial role in the development of cells of the immune system. Macrophages are essential for generating inflammatory reactions upon tissue damage and encountering of invading pathogens, yet modulation of their immune responses is critical for maintaining tissue homeostasis. Macrophages can present different phenotypes, depending on the cytokine environment they encounter in the affected tissues. In this study, we have identified expression signatures of miRNAs that are differentially regulated during maturation of monocytes and polarization of macrophages by cytokines. We present a comprehensive characterization of miRNA expression in human monocytes and M1, M2a, and M2c polarized macrophages, using next-generation sequencing. Furthermore, we show that miRNA expression signatures are closely related to the various immune functions of polarized macrophages and therefore are involved in shaping the diverse phenotypes of these cells. The miRNAs identified here serve as markers for identification of inflammatory macrophages involved in the development of immune responses. Our findings contribute to understanding the role of miRNAs in determining the macrophage function in healthy and diseased tissues.

  17. Gene trio signatures as molecular markers to predict response to doxorubicin cyclophosphamide neoadjuvant chemotherapy in breast cancer patients.

    PubMed

    Barros Filho, M C; Katayama, M L H; Brentani, H; Abreu, A P S; Barbosa, E M; Oliveira, C T; Góes, J C S; Brentani, M M; Folgueira, M A A K

    2010-12-01

    In breast cancer patients submitted to neoadjuvant chemotherapy (4 cycles of doxorubicin and cyclophosphamide, AC), expression of groups of three genes (gene trio signatures) could distinguish responsive from non-responsive tumors, as demonstrated by cDNA microarray profiling in a previous study by our group. In the current study, we determined if the expression of the same genes would retain the predictive strength, when analyzed by a more accessible technique (real-time RT-PCR). We evaluated 28 samples already analyzed by cDNA microarray, as a technical validation procedure, and 14 tumors, as an independent biological validation set. All patients received neoadjuvant chemotherapy (4 AC). Among five trio combinations previously identified, defined by nine genes individually investigated (BZRP, CLPTM1, MTSS1, NOTCH1, NUP210, PRSS11, RPL37A, SMYD2, and XLHSRF-1), the most accurate were established by RPL37A, XLHSRF-1 based trios, with NOTCH1 or NUP210. Both trios correctly separated 86% of tumors (87% sensitivity and 80% specificity for predicting response), according to their response to chemotherapy (82% in a leave-one-out cross-validation method). Using the pre-established features obtained by linear discriminant analysis, 71% samples from the biological validation set were also correctly classified by both trios (72% sensitivity; 66% specificity). Furthermore, we explored other gene combinations to achieve a higher accuracy in the technical validation group (as a training set). A new trio, MTSS1, RPL37 and SMYD2, correctly classified 93% of samples from the technical validation group (95% sensitivity and 80% specificity; 86% accuracy by the cross-validation method) and 79% from the biological validation group (72% sensitivity and 100% specificity). Therefore, the combined expression of MTSS1, RPL37 and SMYD2, as evaluated by real-time RT-PCR, is a potential candidate to predict response to neoadjuvant doxorubicin and cyclophosphamide in breast cancer patients.

  18. Alzheimer-signature MRI biomarker predicts AD dementia in cognitively normal adults

    PubMed Central

    Stoub, T.R.; Shah, R.C.; Sperling, R.A.; Killiany, R.J.; Albert, M.S.; Hyman, B.T.; Blacker, D.; deToledo-Morrell, L.

    2011-01-01

    Objective: Since Alzheimer disease (AD) neuropathology is thought to develop years before dementia, it may be possible to detect subtle AD-related atrophy in preclinical AD. Here we hypothesized that the “disease signature” of AD-related cortical thinning, previously identified in patients with mild AD dementia, would be useful as a biomarker to detect anatomic abnormalities consistent with AD in cognitively normal (CN) adults who develop AD dementia after longitudinal follow-up. Methods: We studied 2 independent samples of adults who were CN when scanned. In sample 1, 8 individuals developing AD dementia (CN-AD converters) after an average of 11.1 years were compared to 25 individuals who remained CN (CN-stable). In sample 2, 7 CN-AD converters (average follow-up 7.1 years) were compared to 25 CN-stable individuals. Results: AD-signature cortical thinning in CN-AD converters in both samples was remarkably similar, about 0.2 mm (p < 0.05). Despite this small absolute difference, Cohen d effect sizes for these differences were very large (>1). Of the 11 CN individuals with baseline low AD-signature thickness (≥1 SD below cohort mean), 55% developed AD dementia over nearly the next decade, while none of the 9 high AD-signature thickness individuals (≥1 SD above mean) developed dementia. This marker predicted time to diagnosis of dementia (hazard ratio = 3.4, p < 0.0005); 1 SD of thinning increased dementia risk by 3.4. Conclusions: By focusing on cortical regions known to be affected in AD dementia, subtle but reliable atrophy is identifiable in asymptomatic individuals nearly a decade before dementia, making this measure a potentially important imaging biomarker of early neurodegeneration. PMID:21490323

  19. Multivariate morphological brain signatures predict chronic abdominal pain patients from healthy control subjects

    PubMed Central

    Labus, Jennifer S.; Van Horn, John D.; Gupta, Arpana; Alaverdyan, Mher; Torgerson, Carinna; Ashe-McNalley, Cody; Irimia, Andrei; Hong, Jui-Yang; Naliboff, Bruce; Tillisch, Kirsten; Mayer, Emeran A.

    2015-01-01

    Irritable bowel syndrome (IBS) is the most common chronic visceral pain disorder. The pathophysiology of IBS is incompletely understood, however evidence strongly suggests dysregulation of the brain-gut axis. The aim of this study was to apply multivariate pattern analysis to identify an IBS-related morphometric brain signature which could serve as a central biological marker and provide new mechanistic insights into the pathophysiology of IBS. Parcellation of 165 cortical and subcortical regions was performed using Freesurfer and the Destrieux and Harvard-Oxford atlases. Volume, mean curvature, surface area and cortical thickness were calculated for each region. Sparse partial least squares-discriminant analysis was applied to develop a diagnostic model using a training set of 160 females (80 healthy controls, 80 IBS). Predictive accuracy was assessed in an age matched holdout test set of 52 females (26 health controls, 26 IBS). A two-component classification algorithm comprised of the morphometry of 1) primary somato-sensory and motor regions, and 2) multimodal network regions, explained 36% of the variance. Overall predictive accuracy of the classification algorithm was 70%. Small effect size associations were observed between the somatosensory and motor signature and non-gastrointestinal somatic symptoms. The findings demonstrate the predictive accuracy of a classification algorithm based solely on regional brain morphometry is not sufficient but they do provide support for the utility of multivariate pattern analysis for identifying meaningful neurobiological markers in IBS. Perspective This article presents the development, optimization, and testing of a classification algorithm for discriminating female IBS patients from healthy controls using only brain morphometry data. The results provide support for utility of multivariate pattern analysis for identifying meaningful neurobiological markers in IBS. PMID:25906347

  20. A Predictive Metabolic Signature for the Transition From Gestational Diabetes Mellitus to Type 2 Diabetes.

    PubMed

    Allalou, Amina; Nalla, Amarnadh; Prentice, Kacey J; Liu, Ying; Zhang, Ming; Dai, Feihan F; Ning, Xian; Osborne, Lucy R; Cox, Brian J; Gunderson, Erica P; Wheeler, Michael B

    2016-09-01

    Gestational diabetes mellitus (GDM) affects 3-14% of pregnancies, with 20-50% of these women progressing to type 2 diabetes (T2D) within 5 years. This study sought to develop a metabolomics signature to predict the transition from GDM to T2D. A prospective cohort of 1,035 women with GDM pregnancy were enrolled at 6-9 weeks postpartum (baseline) and were screened for T2D annually for 2 years. Of 1,010 women without T2D at baseline, 113 progressed to T2D within 2 years. T2D developed in another 17 women between 2 and 4 years. A nested case-control design used 122 incident case patients matched to non-case patients by age, prepregnancy BMI, and race/ethnicity. We conducted metabolomics with baseline fasting plasma and identified 21 metabolites that significantly differed by incident T2D status. Machine learning optimization resulted in a decision tree modeling that predicted T2D incidence with a discriminative power of 83.0% in the training set and 76.9% in an independent testing set, which is far superior to measuring fasting plasma glucose levels alone. The American Diabetes Association recommends T2D screening in the early postpartum period via oral glucose tolerance testing after GDM, which is a time-consuming and inconvenient procedure. Our metabolomics signature predicted T2D incidence from a single fasting blood sample. This study represents the first metabolomics study of the transition from GDM to T2D validated in an independent testing set, facilitating early interventions. © 2016 by the American Diabetes Association.

  1. Common and specific signatures of gene expression and protein-protein interactions in autoimmune diseases.

    PubMed

    Tuller, T; Atar, S; Ruppin, E; Gurevich, M; Achiron, A

    2013-03-01

    different subsignaling pathways. Analyses of the expression levels of dozens of genes and the protein-protein interactions among them demonstrated that CD and UC have relatively similar gene expression signatures, whereas the gene expression signatures of T1D and JRA relatively differ from the signatures of the other autoimmune diseases. These diseases are the only ones activated via the Fcɛ pathway. The relevant genes and pathways reported in this study are discussed at length, and may be helpful in the diagnoses and understanding of autoimmunity and/or specific autoimmune diseases.

  2. Predicting signatures of anisotropic resonance energy transfer in dye-functionalized nanoparticles.

    PubMed

    Gil, Gabriel; Corni, Stefano; Delgado, Alain; Bertoni, Andrea; Goldoni, Guido

    2016-11-13

    Resonance energy transfer (RET) is an inherently anisotropic process. Even the simplest, well-known Förster theory, based on the transition dipole-dipole coupling, implicitly incorporates the anisotropic character of RET. In this theoretical work, we study possible signatures of the fundamental anisotropic character of RET in hybrid nanomaterials composed of a semiconductor nanoparticle (NP) decorated with molecular dyes. In particular, by means of a realistic kinetic model, we show that the analysis of the dye photoluminescence difference for orthogonal input polarizations reveals the anisotropic character of the dye-NP RET which arises from the intrinsic anisotropy of the NP lattice. In a prototypical core/shell wurtzite CdSe/ZnS NP functionalized with cyanine dyes (Cy3B), this difference is predicted to be as large as 75% and it is strongly dependent in amplitude and sign on the dye-NP distance. We account for all the possible RET processes within the system, together with competing decay pathways in the separate segments. In addition, we show that the anisotropic signature of RET is persistent up to a large number of dyes per NP.

  3. KIT mutations confer a distinct gene expression signature in core binding factor leukaemia.

    PubMed

    Lück, Sonja C; Russ, Annika C; Du, Juan; Gaidzik, Verena; Schlenk, Richard F; Pollack, Jonathan R; Döhner, Konstanze; Döhner, Hartmut; Bullinger, Lars

    2010-03-01

    Core binding factor (CBF) leukaemias, characterized by either inv(16)(p13.1q22) or t(8;21)(q22;q22), constitute acute myeloid leukaemia (AML) subgroups with favourable prognosis. However, 40-50% of patients relapse, emphasizing the need for risk-adapted treatment approaches. In this regard, studying secondary genetic aberrations, such as mutations of the KIT gene, is of great interest, particularly as they can be targeted by receptor tyrosine kinase inhibitors (TKI). However, so far little is known about the biology underlying KIT-mutated CBF leukaemias. We analysed gene expression profiles of 83 CBF AML cases with known KIT mutation status in order to gain novel insights in KIT-mutated CBF pathogenesis. KIT-mutated cases were characterized by deregulation of genes belonging to the NFkB signalling complex suggesting impaired control of apoptosis. Notably, a subgroup of KIT wildtype cases was also characterized by the KIT mutation signature due to yet unknown aberrations. Our data suggest that this CBF leukaemia subgroup might profit from TKI therapy, however, the relevance of the KIT mutation-associated signature remains to be validated prior to clinical implementation. Nevertheless, the existence of such a signature supports the notion of relevant biological differences in CBF leukaemia and might serve as diagnostic tool in the future.

  4. Semantic Signature: Comparative Interpretation of Gene Expression on a Semantic Space.

    PubMed

    Kim, Jihun; Kim, Keewon; Kim, Ju Han

    2016-01-01

    Background. Interpretation of microarray data remains challenging because biological meaning should be extracted from enormous numeric matrices and be presented explicitly. Moreover, huge public repositories of microarray dataset are ready to be exploited for comparative analysis. This study aimed to provide a platform where essential implication of a microarray experiment could be visually expressed and various microarray datasets could be intuitively compared. Results. On the semantic space, gene sets from Molecular Signature Database (MSigDB) were plotted as landmarks and their relative distances were calculated by Lin's semantic similarity measure. By formal concept analysis, a microarray dataset was transformed into a concept lattice with gene clusters as objects and Gene Ontology terms as attributes. Concepts of a lattice were located on the semantic space reflecting semantic distance from landmarks and edges between concepts were drawn; consequently, a specific geographic pattern could be observed from a microarray dataset. We termed a distinctive geography shared by microarray datasets of the same category as "semantic signature." Conclusions. "Semantic space," a map of biological entities, could serve as a universal platform for comparative microarray analysis. When microarray data were displayed on the semantic space as concept lattices, "semantic signature," characteristic geography for a microarray experiment, could be discovered.

  5. Next generation sequencing-based expression profiling identifies signatures from benign stromal proliferations that define stromal components of breast cancer.

    PubMed

    Guo, Xiangqian; Zhu, Shirley X; Brunner, Alayne L; van de Rijn, Matt; West, Robert B

    2013-12-17

    Multiple studies have shown that the tumor microenvironment (TME) of carcinomas can play an important role in the initiation, progression, and metastasis of cancer. Here we test the hypothesis that specific benign fibrous soft tissue tumor gene expression profiles may represent distinct stromal fibroblastic reaction types that occur in different breast cancers. The discovered stromal profiles could classify breast cancer based on the type of stromal reaction patterns in the TME. Next generation sequencing-based gene expression profiling (3SEQ) was performed on formalin fixed, paraffin embedded (FFPE) samples of 10 types of fibrous soft tissue tumors. We determined the extent to which these signatures could identify distinct subsets of breast cancers in four publicly available breast cancer datasets. A total of 53 fibrous tumors were sequenced by 3SEQ with an average of 29 million reads per sample. Both the gene signatures derived from elastofibroma (EF) and fibroma of tendon sheath (FOTS) demonstrated robust outcome results for survival in the four breast cancer datasets. The breast cancers positive for the EF signature (20-33% of the cohort) demonstrated significantly better outcome for survival. In contrast, the FOTS signature-positive breast cancers (11-35% of the cohort) had a worse outcome. We defined and validated two new stromal signatures in breast cancer (EF and FOTS), which are significantly associated with prognosis. Our group has previously identified novel cancer stromal gene expression signatures associated with outcome differences in breast cancer by gene expression profiling of three soft tissue tumors, desmoid-type fibromatosis (DTF), solitary fibrous tumor (SFT), and tenosynovial giant cell tumor (TGCT/CSF1), as surrogates for stromal expression patterns. By combining the stromal signatures of EF and FOTS, with our previously identified DTF and TGCT/CSF1 signatures we can now characterize clinically relevant stromal expression profiles in the TME

  6. Survival of patients with gastrointestinal cancers can be predicted by a surrogate microRNA signature for cancer stem-like cells marked by DCLK1 kinase

    PubMed Central

    Weygant, Nathaniel; Ge, Yang; Qu, Dongfeng; Kaddis, John S.; Berry, William L.; May, Randal; Chandrakesan, Parthasarathy; Bannerman-Menson, Edwin; Vega, Kenneth J.; Tomasek, James J.; Bronze, Michael S.; An, Guangyu; Houchen, Courtney W.

    2016-01-01

    DCLK1 is a gastrointestinal (GI) tuft cell kinase that has been investigated as a biomarker of cancer stem-like cells in colon and pancreatic cancers. However, its utility as a biomarker may be limited in principle by signal instability and dilution in heterogeneous tumors, where the proliferation of diverse tumor cell lineages obscures the direct measurement of DCLK1 activity. To address this issue, we explored the definition of a microRNA signature as a surrogate biomarker for DCLK1 in cancer stem-like cells. Utilizing RNA/miRNA sequencing datasets from the Cancer Genome Atlas, we identified a surrogate 15-miRNA expression signature for DCLK1 activity across several GI cancers, including colon, pancreatic and stomach cancers. Notably, Cox regression and Kaplan-Meier analysis demonstrated that this signature could predict the survival of patients with these cancers. Moreover, we identified patient subgroups that predicted the clinical utility of this DCLK1 surrogate biomarker. Our findings greatly strengthen the clinical significance for DCLK1 expression across GI cancers. Further, they provide an initial guidepost toward the development of improved prognostic biomarkers or companion biomarkers for DCLK1-targeted therapies to eradicate cancer stem-like cells in these malignancies. PMID:27287716

  7. Ontology based molecular signatures for immune cell types via gene expression analysis.

    PubMed

    Meehan, Terrence F; Vasilevsky, Nicole A; Mungall, Christopher J; Dougall, David S; Haendel, Melissa A; Blake, Judith A; Diehl, Alexander D

    2013-08-30

    New technologies are focusing on characterizing cell types to better understand their heterogeneity. With large volumes of cellular data being generated, innovative methods are needed to structure the resulting data analyses. Here, we describe an 'Ontologically BAsed Molecular Signature' (OBAMS) method that identifies novel cellular biomarkers and infers biological functions as characteristics of particular cell types. This method finds molecular signatures for immune cell types based on mapping biological samples to the Cell Ontology (CL) and navigating the space of all possible pairwise comparisons between cell types to find genes whose expression is core to a particular cell type's identity. We illustrate this ontological approach by evaluating expression data available from the Immunological Genome project (IGP) to identify unique biomarkers of mature B cell subtypes. We find that using OBAMS, candidate biomarkers can be identified at every strata of cellular identity from broad classifications to very granular. Furthermore, we show that Gene Ontology can be used to cluster cell types by shared biological processes in order to find candidate genes responsible for somatic hypermutation in germinal center B cells. Moreover, through in silico experiments based on this approach, we have identified genes sets that represent genes overexpressed in germinal center B cells and identify genes uniquely expressed in these B cells compared to other B cell types. This work demonstrates the utility of incorporating structured ontological knowledge into biological data analysis - providing a new method for defining novel biomarkers and providing an opportunity for new biological insights.

  8. A common molecular signature in ASD gene expression: following Root 66 to autism.

    PubMed

    Diaz-Beltran, L; Esteban, F J; Wall, D P

    2016-01-05

    Several gene expression experiments on autism spectrum disorders have been conducted using both blood and brain tissue. Individually, these studies have advanced our understanding of the molecular systems involved in the molecular pathology of autism and have formed the bases of ongoing work to build autism biomarkers. In this study, we conducted an integrated systems biology analysis of 9 independent gene expression experiments covering 657 autism, 9 mental retardation and developmental delay and 566 control samples to determine if a common signature exists and to test whether regulatory patterns in the brain relevant to autism can also be detected in blood. We constructed a matrix of differentially expressed genes from these experiments and used a Jaccard coefficient to create a gene-based phylogeny, validated by bootstrap. As expected, experiments and tissue types clustered together with high statistical confidence. However, we discovered a statistically significant subgrouping of 3 blood and 2 brain data sets from 3 different experiments rooted by a highly correlated regulatory pattern of 66 genes. This Root 66 appeared to be non-random and of potential etiologic relevance to autism, given their enriched roles in neurological processes key for normal brain growth and function, learning and memory, neurodegeneration, social behavior and cognition. Our results suggest that there is a detectable autism signature in the blood that may be a molecular echo of autism-related dysregulation in the brain.

  9. An immune-inflammation gene expression signature in prostate tumors of smokers

    PubMed Central

    Yi, Ming; Tang, Wei; Luo, Jun; Dorsey, Tiffany H.; Stagliano, Katherine E.; Gillespie, John W.; Hudson, Robert S.; Terunuma, Atsushi; Shoe, Jennifer L.; Haines, Diana C.; Yfantis, Harris G.; Han, Misop; Martin, Damali N.; Jordan, Symone V.; Borin, James F.; Naslund, Michael J.; Alexander, Richard B.; Stephens, Robert M.; Loffredo, Christopher A.; Lee, Dong H.; Putluri, Nagireddy; Sreekumar, Arun; Hurwitz, Arthur A.; Ambs, Stefan

    2016-01-01

    Smokers develop metastatic prostate cancer more frequently than nonsmokers, suggesting that a tobacco-derived factor is driving metastatic progression. To identify smoking-induced alterations in human prostate cancer, we analyzed gene and protein expression patterns in tumors collected from current, past, and never smokers. By this route, we elucidated a distinct pattern of molecular alterations characterized by an immune and inflammation signature in tumors from current smokers that were either attenuated or absent in past and never smokers. Specifically, this signature included elevated immunoglobulin expression by tumor-infiltrating B cells, NF-κB activation, and increased chemokine expression. In an alternate approach to characterize smoking-induced oncogenic alterations, we also explored the effects of nicotine in human prostate cancer cells and prostate cancer-prone TRAMP mice. These investigations showed that nicotine increased glutamine consumption and invasiveness of cancer cells in vitro and accelerated metastatic progression in tumor-bearing TRAMP mice. Overall, our findings suggested that nicotine was sufficient to induce a phenotype resembling the epidemiology of smoking-associated prostate cancer progression, illuminating a novel candidate driver underlying metastatic prostate cancer in current smokers. PMID:26719530

  10. An Immune-Inflammation Gene Expression Signature in Prostate Tumors of Smokers.

    PubMed

    Prueitt, Robyn L; Wallace, Tiffany A; Glynn, Sharon A; Yi, Ming; Tang, Wei; Luo, Jun; Dorsey, Tiffany H; Stagliano, Katherine E; Gillespie, John W; Hudson, Robert S; Terunuma, Atsushi; Shoe, Jennifer L; Haines, Diana C; Yfantis, Harris G; Han, Misop; Martin, Damali N; Jordan, Symone V; Borin, James F; Naslund, Michael J; Alexander, Richard B; Stephens, Robert M; Loffredo, Christopher A; Lee, Dong H; Putluri, Nagireddy; Sreekumar, Arun; Hurwitz, Arthur A; Ambs, Stefan

    2016-03-01

    Smokers develop metastatic prostate cancer more frequently than nonsmokers, suggesting that a tobacco-derived factor is driving metastatic progression. To identify smoking-induced alterations in human prostate cancer, we analyzed gene and protein expression patterns in tumors collected from current, past, and never smokers. By this route, we elucidated a distinct pattern of molecular alterations characterized by an immune and inflammation signature in tumors from current smokers that were either attenuated or absent in past and never smokers. Specifically, this signature included elevated immunoglobulin expression by tumor-infiltrating B cells, NF-κB activation, and increased chemokine expression. In an alternate approach to characterize smoking-induced oncogenic alterations, we also explored the effects of nicotine in human prostate cancer cells and prostate cancer-prone TRAMP mice. These investigations showed that nicotine increased glutamine consumption and invasiveness of cancer cells in vitro and accelerated metastatic progression in tumor-bearing TRAMP mice. Overall, our findings suggest that nicotine is sufficient to induce a phenotype resembling the epidemiology of smoking-associated prostate cancer progression, illuminating a novel candidate driver underlying metastatic prostate cancer in current smokers. ©2015 American Association for Cancer Research.

  11. Gene Expression Profiling Identifies IRF4-Associated Molecular Signatures in Hematological Malignancies

    PubMed Central

    Wang, Ling; Yao, Zhi Q.; Moorman, Jonathan P.; Xu, Yanji; Ning, Shunbin

    2014-01-01

    The lymphocyte-specific transcription factor Interferon (IFN) Regulatory Factor 4 (IRF4) is implicated in certain types of lymphoid and myeloid malignancies. However, the molecular mechanisms underlying its interactions with these malignancies are largely unknown. In this study, we have first profiled molecular signatures associated with IRF4 expression in associated cancers, by analyzing existing gene expression profiling datasets. Our results show that IRF4 is overexpressed in melanoma, in addition to previously reported contexts including leukemia, myeloma, and lymphoma, and that IRF4 is associated with a unique gene expression pattern in each context. A pool of important genes involved in B-cell development, oncogenesis, cell cycle regulation, and cell death including BATF, LIMD1, CFLAR, PIM2, and CCND2 are common signatures associated with IRF4 in non-Hodgkin B cell lymphomas. We confirmed the correlation of IRF4 with LIMD1 and CFLAR in a panel of cell lines derived from lymphomas. Moreover, we profiled the IRF4 transcriptome in the context of EBV latent infection, and confirmed several genes including IFI27, IFI44, GBP1, and ARHGAP18, as well as CFLAR as novel targets for IRF4. These results provide valuable information for understanding the IRF4 regulatory network, and improve our knowledge of the unique roles of IRF4 in different hematological malignancies. PMID:25207815

  12. Identification of Aging-Associated Gene Expression Signatures That Precede Intestinal Tumorigenesis

    PubMed Central

    Okuchi, Yoshihisa; Imajo, Masamichi; Mizuno, Rei; Kamioka, Yuji; Miyoshi, Hiroyuki; Taketo, Makoto Mark; Nagayama, Satoshi; Sakai, Yoshiharu; Matsuda, Michiyuki

    2016-01-01

    Aging-associated alterations of cellular functions have been implicated in various disorders including cancers. Due to difficulties in identifying aging cells in living tissues, most studies have focused on aging-associated changes in whole tissues or certain cell pools. Thus, it remains unclear what kinds of alterations accumulate in each cell during aging. While analyzing several mouse lines expressing fluorescent proteins (FPs), we found that expression of FPs is gradually silenced in the intestinal epithelium during aging in units of single crypt composed of clonal stem cell progeny. The cells with low FP expression retained the wild-type Apc allele and the tissues composed of them did not exhibit any histological abnormality. Notably, the silencing of FPs was also observed in intestinal adenomas and the surrounding normal mucosae of Apc-mutant mice, and mediated by DNA methylation of the upstream promoter. Our genome-wide analysis then showed that the silencing of FPs reflects specific gene expression alterations during aging, and that these alterations occur in not only mouse adenomas but also human sporadic and hereditary (familial adenomatous polyposis) adenomas. Importantly, pharmacological inhibition of DNA methylation, which suppresses adenoma development in Apc-mutant mice, reverted the aging-associated silencing of FPs and gene expression alterations. These results identify aging-associated gene expression signatures that are heterogeneously induced by DNA methylation and precede intestinal tumorigenesis triggered by Apc inactivation, and suggest that pharmacological inhibition of the signature genes could be a novel strategy for the prevention and treatment of intestinal tumors. PMID:27589228

  13. A DNA Hypomethylation Signature Predicts Antitumor Activity of LSD1 Inhibitors in SCLC.

    PubMed

    Mohammad, Helai P; Smitheman, Kimberly N; Kamat, Chandrashekhar D; Soong, David; Federowicz, Kelly E; Van Aller, Glenn S; Schneck, Jess L; Carson, Jeffrey D; Liu, Yan; Butticello, Michael; Bonnette, William G; Gorman, Shelby A; Degenhardt, Yan; Bai, Yuchen; McCabe, Michael T; Pappalardi, Melissa B; Kasparec, Jiri; Tian, Xinrong; McNulty, Kenneth C; Rouse, Meagan; McDevitt, Patrick; Ho, Thau; Crouthamel, Michelle; Hart, Timothy K; Concha, Nestor O; McHugh, Charles F; Miller, William H; Dhanak, Dashyant; Tummino, Peter J; Carpenter, Christopher L; Johnson, Neil W; Hann, Christine L; Kruger, Ryan G

    2015-07-13

    Epigenetic dysregulation has emerged as an important mechanism in cancer. Alterations in epigenetic machinery have become a major focus for targeted therapies. The current report describes the discovery and biological activity of a cyclopropylamine containing inhibitor of Lysine Demethylase 1 (LSD1), GSK2879552. This small molecule is a potent, selective, orally bioavailable, mechanism-based irreversible inactivator of LSD1. A proliferation screen of cell lines representing a number of tumor types indicated that small cell lung carcinoma (SCLC) is sensitive to LSD1 inhibition. The subset of SCLC lines and primary samples that undergo growth inhibition in response to GSK2879552 exhibit DNA hypomethylation of a signature set of probes, suggesting this may be used as a predictive biomarker of activity.

  14. Gut Microbiota Signatures Predict Host and Microbiota Responses to Dietary Interventions in Obese Individuals

    PubMed Central

    Korpela, Katri; Flint, Harry J.; Johnstone, Alexandra M.; Lappi, Jenni; Poutanen, Kaisa; Dewulf, Evelyne; Delzenne, Nathalie; de Vos, Willem M.; Salonen, Anne

    2014-01-01

    Background Interactions between the diet and intestinal microbiota play a role in health and disease, including obesity and related metabolic complications. There is great interest to use dietary means to manipulate the microbiota to promote health. Currently, the impact of dietary change on the microbiota and the host metabolism is poorly predictable and highly individual. We propose that the responsiveness of the gut microbiota may depend on its composition, and associate with metabolic changes in the host. Methodology Our study involved three independent cohorts of obese adults (n = 78) from Belgium, Finland, and Britain, participating in different dietary interventions aiming to improve metabolic health. We used a phylogenetic microarray for comprehensive fecal microbiota analysis at baseline and after the intervention. Blood cholesterol, insulin and inflammation markers were analyzed as indicators of host response. The data were divided into four training set – test set pairs; each intervention acted both as a part of a training set and as an independent test set. We used linear models to predict the responsiveness of the microbiota and the host, and logistic regression to predict responder vs. non-responder status, or increase vs. decrease of the health parameters. Principal Findings Our models, based on the abundance of several, mainly Firmicute species at baseline, predicted the responsiveness of the microbiota (AUC  =  0.77–1; predicted vs. observed correlation  =  0.67–0.88). Many of the predictive taxa showed a non-linear relationship with the responsiveness. The microbiota response associated with the change in serum cholesterol levels with an AUC of 0.96, highlighting the involvement of the intestinal microbiota in metabolic health. Conclusion This proof-of-principle study introduces the first potential microbial biomarkers for dietary responsiveness in obese individuals with impaired metabolic health, and reveals the potential of

  15. 4-protein signature predicting tamoxifen treatment outcome in recurrent breast cancer.

    PubMed

    De Marchi, Tommaso; Liu, Ning Qing; Stingl, Cristoph; Timmermans, Mieke A; Smid, Marcel; Look, Maxime P; Tjoa, Mila; Braakman, Rene B H; Opdam, Mark; Linn, Sabine C; Sweep, Fred C G J; Span, Paul N; Kliffen, Mike; Luider, Theo M; Foekens, John A; Martens, John W M; Umar, Arzu

    2016-01-01

    Estrogen receptor (ER) positive tumors represent the majority of breast malignancies, and are effectively treated with hormonal therapies, such as tamoxifen. However, in the recurrent disease resistance to tamoxifen therapy is common and a major cause of death. In recent years, in-depth proteome analyses have enabled identification of clinically useful biomarkers, particularly, when heterogeneity in complex tumor tissue was reduced using laser capture microdissection (LCM). In the current study, we performed high resolution proteomic analysis on two cohorts of ER positive breast tumors derived from patients who either manifested good or poor outcome to tamoxifen treatment upon recurrence. A total of 112 fresh frozen tumors were collected from multiple medical centers and divided into two sets: an in-house training and a multi-center test set. Epithelial tumor cells were enriched with LCM and analyzed by nano-LC Orbitrap mass spectrometry (MS), which yielded >3000 and >4000 quantified proteins in the training and test sets, respectively. Raw data are available via ProteomeXchange with identifiers PXD000484 and PXD000485. Statistical analysis showed differential abundance of 99 proteins, of which a subset of 4 proteins was selected through a multivariate step-down to develop a predictor for tamoxifen treatment outcome. The 4-protein signature significantly predicted poor outcome patients in the test set, independent of predictive histopathological characteristics (hazard ratio [HR] = 2.17; 95% confidence interval [CI] = 1.15 to 4.17; multivariate Cox regression p value = 0.017). Immunohistochemical (IHC) staining of PDCD4, one of the signature proteins, on an independent set of formalin-fixed paraffin-embedded tumor tissues provided and independent technical validation (HR = 0.72; 95% CI = 0.57 to 0.92; multivariate Cox regression p value = 0.009). We hereby report the first validated protein predictor for tamoxifen treatment outcome in recurrent ER-positive breast

  16. EnzML: multi-label prediction of enzyme classes using InterPro signatures

    PubMed Central

    2012-01-01

    Background Manual annotation of enzymatic functions cannot keep up with automatic genome sequencing. In this work we explore the capacity of InterPro sequence signatures to automatically predict enzymatic function. Results We present EnzML, a multi-label classification method that can efficiently account also for proteins with multiple enzymatic functions: 50,000 in UniProt. EnzML was evaluated using a standard set of 300,747 proteins for which the manually curated Swiss-Prot and KEGG databases have agreeing Enzyme Commission (EC) annotations. EnzML achieved more than 98% subset accuracy (exact match of all correct Enzyme Commission classes of a protein) for the entire dataset and between 87 and 97% subset accuracy in reannotating eight entire proteomes: human, mouse, rat, mouse-ear cress, fruit fly, the S. pombe yeast, the E. coli bacterium and the M. jannaschii archaebacterium. To understand the role played by the dataset size, we compared the cross-evaluation results of smaller datasets, either constructed at random or from specific taxonomic domains such as archaea, bacteria, fungi, invertebrates, plants and vertebrates. The results were confirmed even when the redundancy in the dataset was reduced using UniRef100, UniRef90 or UniRef50 clusters. Conclusions InterPro signatures are a compact and powerful attribute space for the prediction of enzymatic function. This representation makes multi-label machine learning feasible in reasonable time (30 minutes to train on 300,747 instances with 10,852 attributes and 2,201 class values) using the Mulan Binary Relevance Nearest Neighbours algorithm implementation (BR-kNN). PMID:22533924

  17. MARQ: an online tool to mine GEO for experiments with similar or opposite gene expression signatures.

    PubMed

    Vazquez, Miguel; Nogales-Cadenas, Ruben; Arroyo, Javier; Botías, Pedro; García, Raul; Carazo, Jose M; Tirado, Francisco; Pascual-Montano, Alberto; Carmona-Saez, Pedro

    2010-07-01

    The enormous amount of data available in public gene expression repositories such as Gene Expression Omnibus (GEO) offers an inestimable resource to explore gene expression programs across several organisms and conditions. This information can be used to discover experiments that induce similar or opposite gene expression patterns to a given query, which in turn may lead to the discovery of new relationships among diseases, drugs or pathways, as well as the generation of new hypotheses. In this work, we present MARQ, a web-based application that allows researchers to compare a query set of genes, e.g. a set of over- and under-expressed genes, against a signature database built from GEO datasets for different organisms and platforms. MARQ offers an easy-to-use and integrated environment to mine GEO, in order to identify conditions that induce similar or opposite gene expression patterns to a given experimental condition. MARQ also includes additional functionalities for the exploration of the results, including a meta-analysis pipeline to find genes that are differentially expressed across different experiments. The application is freely available at http://marq.dacya.ucm.es.

  18. MicroRNA Expression Signature Is Altered in the Cardiac Remodeling Induced by High Fat Diets.

    PubMed

    Guedes, Elaine Castilho; França, Gustavo Starvaggi; Lino, Caroline Antunes; Koyama, Fernanda Christtanini; Moreira, Luana do Nascimento; Alexandre, Juliana Gomes; Barreto-Chaves, Maria Luiza M; Galante, Pedro Alexandre Favoretto; Diniz, Gabriela Placoná

    2016-08-01

    Recent studies have revealed the involvement of microRNAs (miRNAs) in the control of cardiac hypertrophy and myocardial function. In addition, several reports have demonstrated that high fat (HF) diet induces cardiac hypertrophy and remodeling. In the current study, we investigated the effect of diets containing different percentages of fat on the cardiac miRNA expression signature. To address this question, male C57Bl/6 mice were fed with a low fat (LF) diet or two HF diets, containing 45 kcal% fat (HF45%) and 60 kcal% fat (HF60%) for 10 and 20 weeks. HF60% diet promoted an increase on body weight, fasting glycemia, insulin, leptin, total cholesterol, triglycerides, and induced glucose intolerance. HF feeding promoted cardiac remodeling, as evidenced by increased cardiomyocyte transverse diameter and interstitial fibrosis. RNA sequencing analysis demonstrated that HF feeding induced distinct miRNA expression patterns in the heart. HF45% diet for 10 and 20 weeks changed the abundance of 64 and 26 miRNAs in the heart, respectively. On the other hand, HF60% diet for 10 and 20 weeks altered the abundance of 27 and 88 miRNAs in the heart, respectively. Bioinformatics analysis indicated that insulin signaling pathway was overrepresented in response to HF diet. An inverse correlation was observed between cardiac levels of GLUT4 and miRNA-29c. Similarly, we found an inverse correlation between expression of GSK3β and the expression of miRNA-21a-3p, miRNA-29c-3p, miRNA-144-3p, and miRNA-195a-3p. In addition, miRNA-1 overexpression prevented cardiomyocyte hypertrophy. Taken together, our results revealed differentially expressed miRNA signatures in the heart in response to different HF diets. J. Cell. Physiol. 231: 1771-1783, 2016. © 2015 Wiley Periodicals, Inc.

  19. Embryonic stem cell gene expression signatures in the canine mammary tumor: a bioinformatics approach.

    PubMed

    Zamani-Ahmadmahmudi, Mohamad

    2016-08-01

    Canine breast cancer was considered as an ideal model of comparative oncology for the human breast cancer, as there is significant overlap between biological and clinical characteristics of the human and canine breast cancer. We attempt to clarify expression profile of the embryonic stem cell (ES) gene signatures in canine breast cancer. Using microarray datasets (GSE22516 and GSE20718), expression of the three major ES gene signatures (modules or gene-sets), including Myc, ESC-like, and PRC modules, was primarily analyzed through Gene-Set Enrichment Analysis (GSEA) method in tumor and healthy datasets. For confirmation of the primary results, an additional 13 ES gene-sets which were categorized into four groups including ES expressed (ES exp1 and ES exp2), NOS targets (Nanog targets, Oct4 targets, Sox2 targets, NOS targets, and NOS TFs), Polycomb targets (Suz12 targets, Eed targets, H3K27 bound, and PRC2 targets), and Myc targets (Myc targets1, and Myc targets2) were tested in the tumor and healthy datasets. Our results revealed that there is a valuable overlap between canine and human breast cancer ES gene-sets expression profile, where Myc and ESC-like modules were up-regulated and PRC module was down-regulated in metastatic canine mammary gland tumors. Further analysis of the secondary gene-sets indicated overexpression of the ES expressed, NOS targets (Nanog targets, Oct4 targets, Sox2 targets, and NOS targets), and Myc targets and underexpression of the Polycomb targets in metastatic canine breast cancer.

  20. Altered peritumoral microRNA expression predicts head and neck cancer patients with a high risk of recurrence.

    PubMed

    Ganci, Federica; Sacconi, Andrea; Manciocco, Valentina; Covello, Renato; Benevolo, Maria; Rollo, Francesca; Strano, Sabrina; Valsoni, Sara; Bicciato, Silvio; Spriano, Giuseppe; Muti, Paola; Fontemaggi, Giulia; Blandino, Giovanni

    2017-10-01

    Head and neck squamous cell carcinoma is typically characterized by a high incidence of local recurrences. It has been extensively shown that mucosa from head and neck squamous cell carcinoma patients carries both genetic and gene expression alterations, which are mostly attributable to major etiologic agents of head and neck squamous cell carcinoma. We previously identified a signature of microRNAs (miRNAs) whose high expression in tumors is predictive of recurrence. Here, we investigated whether the deregulation of miRNA expression in the tumor-surrounding mucosa is correlated to disease recurrence. Specifically, comparing the miRNA expression in matched tumoral, peritumoral, and normal tissues collected from head and neck squamous cell carcinoma patients, we identified 35 miRNAs that are deregulated in both tumoral and peritumoral tissues as compared with normal matched samples. Four of these composed a miRNA signature that predicts head and neck squamous cell carcinoma local recurrence independently from prognostic clinical variables. The predictive power of the miRNA signature increased when using the expression levels derived from both the peritumoral and the tumoral tissues. The expression signal of the miRNAs composing the predictive signature correlated with the transcriptional levels of genes mostly associated with proliferation. Our results show that expression of miRNAs in tumor-surrounding mucosa may strongly contribute to the identification of head and neck squamous cell carcinoma patients at high risk of local recurrence.

  1. Predictive Value of Sp1/Sp3/FLIP Signature for Prostate Cancer Recurrence

    PubMed Central

    Bedolla, Roble G.; Gong, Jingjing; Prihoda, Thomas J.; Yeh, I-Tien; Thompson, Ian M.; Ghosh, Rita; Kumar, Addanki P.

    2012-01-01

    Prediction of prostate cancer prognosis is challenging and predictive biomarkers of recurrence remain elusive. Although prostate specific antigen (PSA) has high sensitivity (90%) at a PSA level of 4.0 ng/mL, its low specificity leads to many false positive results and considerable overtreatment of patients and its performance at lower ranges is poor. Given the histopathological and molecular heterogeneity of prostate cancer, we propose that a panel of markers will be a better tool than a single marker. We tested a panel of markers composed of the anti-apoptotic protein FLIP and its transcriptional regulators Sp1 and Sp3 using prostate tissues from 64 patients with recurrent and non-recurrent cancer who underwent radical prostatectomy as primary treatment for prostate cancer and were followed with PSA measurements for at least 5 years. Immunohistochemical staining for Sp1, Sp3, and FLIP was performed on these tissues and scored based on the proportion and intensity of staining. The predictive value of the FLIP/Sp1/Sp3 signature for clinical outcome (recurrence vs. non-recurrence) was explored with logistic regression, and combinations of FLIP/Sp1/Sp3 and Gleason score were analyzed with a stepwise (backward and forward) logistic model. The discrimination of the markers was identified by sensitivity-specificity analysis and the diagnostic value of FLIP/Sp1/Sp3 was determined using area under the curve (AUC) for receiver operator characteristic curves. The AUCs for FLIP, Sp1, Sp3, and Gleason score for predicting PSA failure and non-failure were 0.71, 0.66, 0.68, and 0.76, respectively. However, this increased to 0.93 when combined. Thus, the “biomarker signature” of FLIP/Sp1/Sp3 combined with Gleason score predicted disease recurrence and stratified patients who are likely to benefit from more aggressive treatment. PMID:23028678

  2. Gene expression signatures in chronic and aggressive periodontitis: a pilot study.

    PubMed

    Papapanou, Panos N; Abron, Armin; Verbitsky, Miguel; Picolos, Doros; Yang, Jun; Qin, Jie; Fine, James B; Pavlidis, Paul

    2004-06-01

    This pilot study examined gene expression signatures in pathological gingival tissues of subjects with chronic or aggressive periodontitis, and explored whether new subclasses of periodontitis can be identified based on gene expression profiles. A total of 14 patients, seven with chronic and seven with aggressive periodontitis, were examined with respect to clinical periodontal status, composition of subgingival bacterial plaque assessed by checkerboard hybridizations, and levels of serum IgG antibodies to periodontal bacteria assayed by checkerboard immunoblotting. In addition, at least two pathological pockets/patient were biopsied, processed for RNA extraction, amplification and labeling, and used to study gene expression using Affymetrix U-133 A arrays. Based on a total of 35 microarrays, no significantly different gene expression profiles appeared to emerge between chronic and aggressive periodontitis. However, a de novo grouping of the 14 subjects into two fairly robust clusters was possible based on similarities in gene expression. These two groups had similar clinical periodontal status and subgingival bacterial profiles, but differed significantly with respect to serum IgG levels against the important periodontal pathogens Porphyromonas gingivalis, Tannerella forsythensis and Campylobacter rectus. These early data point to the usefulness of gene expression profiling techniques in the identification of subclasses of periodontitis with common pathobiology.

  3. Genome-wide analysis of microRNA and mRNA expression signatures in cancer

    PubMed Central

    Li, Ming-hui; Fu, Sheng-bo; Xiao, Hua-sheng

    2015-01-01

    Cancer is an extremely diverse and complex disease that results from various genetic and epigenetic changes such as DNA copy-number variations, mutations, and aberrant mRNA and/or protein expression caused by abnormal transcriptional regulation. The expression profiles of certain microRNAs (miRNAs) and messenger RNAs (mRNAs) are closely related to cancer progression stages. In the past few decades, DNA microarray and next-generation sequencing techniques have been widely applied to identify miRNA and mRNA signatures for cancers on a genome-wide scale and have provided meaningful insights into cancer diagnosis, prognosis and personalized medicine. In this review, we summarize the progress in genome-wide analysis of miRNAs and mRNAs as cancer biomarkers, highlighting their diagnostic and prognostic roles. PMID:26299954

  4. A gene expression biomarker accurately predicts estrogen ...

    EPA Pesticide Factsheets

    The EPA’s vision for the Endocrine Disruptor Screening Program (EDSP) in the 21st Century (EDSP21) includes utilization of high-throughput screening (HTS) assays coupled with computational modeling to prioritize chemicals with the goal of eventually replacing current Tier 1 screening tests. The ToxCast program currently includes 18 HTS in vitro assays that evaluate the ability of chemicals to modulate estrogen receptor α (ERα), an important endocrine target. We propose microarray-based gene expression profiling as a complementary approach to predict ERα modulation and have developed computational methods to identify ERα modulators in an existing database of whole-genome microarray data. The ERα biomarker consisted of 46 ERα-regulated genes with consistent expression patterns across 7 known ER agonists and 3 known ER antagonists. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression data sets from experiments in MCF-7 cells. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% or 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) OECD ER reference chemicals including “very weak” agonists and replicated predictions based on 18 in vitro ER-associated HTS assays. For 114 chemicals present in both the HTS data and the MCF-7 c

  5. An Approach for Assessing the Signature Quality of Various Chemical Assays when Predicting the Culture Media Used to Grow Microorganisms

    SciTech Connect

    Holmes, Aimee E.; Sego, Landon H.; Webb-Robertson, Bobbie-Jo M.; Kreuzer, Helen W.; Anderson, Richard M.; Unwin, Stephen D.; Weimar, Mark R.; Tardiff, Mark F.; Corley, Courtney D.

    2013-02-01

    We demonstrate an approach for assessing the quality of a signature system designed to predict the culture medium used to grow a microorganism. The system was comprised of four chemical assays designed to identify various ingredients that could be used to produce the culture medium. The analytical measurements resulting from any combination of these four assays can be used in a Bayesian network to predict the probabilities that the microorganism was grown using one of eleven culture media. We evaluated combinations of the signature system by removing one or more of the assays from the Bayes network. We measured and compared the quality of the various Bayes nets in terms of fidelity, cost, risk, and utility, a method we refer to as Signature Quality Metrics

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

    PubMed Central

    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.

    2016-01-01

    Background 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. Patients and methods An open-label prospective phase II trial (‘PREDICT’) in patients with MAGE-A3-positive unresectable stage IIIB-C/IV-M1a melanoma. Results 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. Conclusion 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. PMID:27502712

  7. Visualization and analysis for multidimensional gene expressions signature of cigarette smoking

    NASA Astrophysics Data System (ADS)

    Wang, Changbo; Xiao, Zhao; Zhang, Tianlun; Cui, Jin; Pang, Chenming

    2011-11-01

    Biologists often use gene chip to get massive experimental data in the field of bioscience and chemical sciences. Facing a large amount of experimental data, researchers often need to find out a few interesting data or simple regulations. This paper presents a set of methods to visualize and analyze the data for gene expression signatures of people who smoke. We use the latest research data from National Center for Biotechnology Information. Totally, there are more than 400 thousand expressions data. Using these data, we can use parallel coordinates method to visualize the different gene expressions between smokers and nonsmokers and we can distinguish non-smokers, former smokers and current smokers by using the different colors. It can be easy to find out which gene is more important during the lung cancer angiogenesis in the smoking people. In another way, we can use a hierarchical model to visualize the inner relation of different genes. The location of the nodes shows different expression moment and the distance to the root shows the sequence of the expression. We can use the ring layout to represent all the nodes, and connect the different nodes which are related with color lines. Combined with the parallel coordinates method, the visualization result show the important genes and some inner relation obviously, which is useful for examination and prevention of lung cancer.

  8. An atlas of human gene expression from massively parallel signature sequencing (MPSS)

    PubMed Central

    Jongeneel, C. Victor; Delorenzi, Mauro; Iseli, Christian; Zhou, Daixing; Haudenschild, Christian D.; Khrebtukova, Irina; Kuznetsov, Dmitry; Stevenson, Brian J.; Strausberg, Robert L.; Simpson, Andrew J.G.; Vasicek, Thomas J.

    2005-01-01

    We have used massively parallel signature sequencing (MPSS) to sample the transcriptomes of 32 normal human tissues to an unprecedented depth, thus documenting the patterns of expression of almost 20,000 genes with high sensitivity and specificity. The data confirm the widely held belief that differences in gene expression between cell and tissue types are largely determined by transcripts derived from a limited number of tissue-specific genes, rather than by combinations of more promiscuously expressed genes. Expression of a little more than half of all known human genes seems to account for both the common requirements and the specific functions of the tissues sampled. A classification of tissues based on patterns of gene expression largely reproduces classifications based on anatomical and biochemical properties. The unbiased sampling of the human transcriptome achieved by MPSS supports the idea that most human genes have been mapped, if not functionally characterized. This data set should prove useful for the identification of tissue-specific genes, for the study of global changes induced by pathological conditions, and for the definition of a minimal set of genes necessary for basic cell maintenance. The data are available on the Web at http://mpss.licr.org and http://sgb.lynxgen.com. PMID:15998913

  9. Effector CD4+ T cell expression signatures and immune-mediated disease associated genes.

    PubMed

    Zhang, Wei; Ferguson, John; Ng, Sok Meng; Hui, Ken; Goh, Gerald; Lin, Aiping; Esplugues, Enric; Flavell, Richard A; Abraham, Clara; Zhao, Hongyu; Cho, Judy H

    2012-01-01

    Genome-wide association studies (GWAS) in immune-mediated diseases have identified over 150 associated genomic loci. Many of these loci play a role in T cell responses, and regulation of T cell differentiation plays a critical role in immune-mediated diseases; however, the relationship between implicated disease loci and T cell differentiation is incompletely understood. To further address this relationship, we examined differential gene expression in naïve human CD4+ T cells, as well as in in vitro differentiated Th1, memory Th17-negative and Th17-enriched CD4+ T cells subsets using microarray and RNASeq. We observed a marked enrichment for increased expression in memory CD4+ compared to naïve CD4+ T cells of genes contained among immune-mediated disease loci. Within memory T cells, expression of disease-associated genes was typically increased in Th17-enriched compared to Th17-negative cells. Utilizing RNASeq and promoter methylation studies, we identified a differential regulation pattern for genes solely expressed in Th17 cells (IL17A and CCL20) compared to genes expressed in both Th17 and Th1 cells (IL23R and IL12RB2), where high levels of promoter methylation are correlated to near zero RNASeq levels for IL17A and CCL20. These findings have implications for human Th17 celI plasticity and for the regulation of Th17-Th1 expression signatures. Importantly, utilizing RNASeq we found an abundant isoform of IL23R terminating before the transmembrane domain that was enriched in Th17 cells. In addition to molecular resolution, we find that RNASeq provides significantly improved power to define differential gene expression and identify alternative gene variants relative to microarray analysis. The comprehensive integration of differential gene expression between cell subsets with disease-association signals, and functional pathways provides insight into disease pathogenesis.

  10. Comparison of galectin expression signatures in rejected and accepted murine corneal allografts.

    PubMed

    Sugaya, Satoshi; Chen, Wei-Sheng; Cao, Zhiyi; Kenyon, Kenneth R; Yamaguchi, Takefumi; Omoto, Masashiro; Hamrah, Pedram; Panjwani, Noorjahan

    2015-06-01

    Although members of the galectin family of carbohydrate-binding proteins are thought to play a role in the immune response and regulation of allograft survival, little is known about the galectin expression signature in failed corneal grafts. The aim of this study was to compare the galectin expression pattern in accepted and rejected murine corneal allografts. Using BALB/c mice as recipients and C57BL/6 mice as donors, a total of 57 transplants were successfully performed. One week after transplantation, the grafts were scored for opacity by slit-lamp microscopy. Opacity scores of 3+ or greater on postoperative week 4 were considered rejected. Grafted corneas were harvested on postoperative week 4, and their galectin expressions were analyzed by Western blot and immunofluorescence staining. As determined by the Western blot analyses, galectins-1, 3, 7, 8 and 9 were expressed in normal corneas. Although in both accepted and rejected grafts, expression levels of the 5 lectins were upregulated compared with normal corneas, there were distinct differences in the expression levels of galectins-8 and 9 between accepted and rejected grafts, as both the Western blot and immunofluorescence staining revealed that galectin-8 is upregulated, whereas galectin-9 is downregulated in the rejected grafts compared with the accepted grafts. Our findings that corneal allograft rejection is associated with increased galectin-8 expression and reduced galectin-9 expression, support the hypothesis that galectin-8 may reduce graft survival, whereas galectin-9 may promote graft survival. As a potential therapeutic intervention, inhibition of galectin-8 and/or treatment with exogenous galectin-9 may enhance corneal allograft survival rates.

  11. Comparison of galectin expression signatures in rejected and accepted murine corneal allografts

    PubMed Central

    Kenyon, Kenneth R; Yamaguchi, Takefumi; Omoto, Masashiro; Hamrah, Pedram; Panjwani, Noorjahan

    2015-01-01

    Purpose Although members of the galectin family of carbohydrate-binding proteins are thought to play a role in the immune response and regulation of allograft survival, little is known about the galectin expression signature in failed corneal grafts. The goal of this study is to compare the galectin expression pattern in accepted and rejected murine corneal allografts. Method Using BALB/c mice as recipients and C57BL/6 mice as donors, a total of 57 transplants were successfully performed. One week after transplantation, the grafts were scored for opacity by slit-lamp microscopy. Opacity scores of 3+ or greater on postoperative week 4 were considered rejected. Grafted corneas were harvested on postoperative week 4, and their galectin expression was analyzed by Western blot and immunofluorescence staining. Result As determined by Western blot analyses, galectins-1, -3, -7, -8 and -9 were expressed in normal corneas. Although in both accepted and rejected grafts, expression levels of the five lectins were upregulated compared to normal corneas, there were distinct differences in the expression levels of galectins-8 and -9 between accepted and rejected grafts, as both Western blot and immunofluorescence staining revealed galectin-8 is upregulated, whereas galectin-9 is downregulated in rejected grafts compared to accepted grafts. Conclusion Our findings that corneal allograft rejection is associated with an increased galectin-8 expression and a reduced galectin-9 expression, support the hypothesis that galectin-8 may reduce graft survival, whereas galectin-9 may promote graft survival. As a potential therapeutic intervention, inhibition of galectin-8 and/or treatment with exogenous galectin-9 may enhance corneal allograft survival rates. PMID:25961492

  12. ColoLipidGene: signature of lipid metabolism-related genes to predict prognosis in stage-II colon cancer patients

    PubMed Central

    Vargas, Teodoro; Moreno-Rubio, Juan; Herranz, Jesús; Cejas, Paloma; Molina, Susana; González-Vallinas, Margarita; Mendiola, Marta; Burgos, Emilio; Aguayo, Cristina; Custodio, Ana B.; Machado, Isidro; Ramos, David; Gironella, Meritxell; Espinosa-Salinas, Isabel; Ramos, Ricardo; Martín-Hernández, Roberto; Risueño, Alberto; De Las Rivas, Javier; Reglero, Guillermo; Yaya, Ricardo; Fernández-Martos, Carlos; Aparicio, Jorge; Maurel, Joan; Feliu, Jaime; de Molina, Ana Ramírez

    2015-01-01

    Lipid metabolism plays an essential role in carcinogenesis due to the requirements of tumoral cells to sustain increased structural, energetic and biosynthetic precursor demands for cell proliferation. We investigated the association between expression of lipid metabolism-related genes and clinical outcome in intermediate-stage colon cancer patients with the aim of identifying a metabolic profile associated with greater malignancy and increased risk of relapse. Expression profile of 70 lipid metabolism-related genes was determined in 77 patients with stage II colon cancer. Cox regression analyses using c-index methodology was applied to identify a metabolic-related signature associated to prognosis. The metabolic signature was further confirmed in two independent validation sets of 120 patients and additionally, in a group of 264 patients from a public database. The combined analysis of these 4 genes, ABCA1, ACSL1, AGPAT1 and SCD, constitutes a metabolic-signature (ColoLipidGene) able to accurately stratify stage II colon cancer patients with 5-fold higher risk of relapse with strong statistical power in the four independent groups of patients. The identification of a group of 4 genes that predict survival in intermediate-stage colon cancer patients allows delineation of a high-risk group that may benefit from adjuvant therapy, and avoids the toxic and unnecessary chemotherapy in patients classified as low-risk group. PMID:25749516

  13. Genetic and epigenetic regulation and expression signatures of glutathione S-transferases in developing mouse liver.

    PubMed

    Cui, Julia Yue; Choudhuri, Supratim; Knight, Tamara R; Klaassen, Curtis D

    2010-07-01

    The hepatic glutathione S-transferases (Gsts) are critical phase II enzymes in protecting cellular macromolecules against electrophiles and oxidative stress. Little is known about the ontogeny of Gsts and the underlying regulatory mechanisms during liver development. Therefore, in this study, the ontogeny and the regulatory mechanisms of 19 known Gst isoforms were investigated in mouse liver from 2 days before birth to postnatal day 45. With the exception of Gstm5 and MGst2 that showed a progressive decline in postnatal messenger RNA (mRNA) expression, most other Gst isoforms showed a progressive increase in postnatal mRNA expression. Two-way hierarchical clustering revealed three distinct expression patterns of these Gsts isoforms: perinatal, adolescent, and adult enriched. The expression signatures of certain Gst isoforms showed positive association with the ontogeny of critical xenobiotic-sensing transcription factors, including aryl hydrocarbon receptor, pregnane X receptor (PXR), constitutive androstane receptor, peroxisome proliferator-activated receptor alpha, and NF-E2-related factor-2. Specifically, genome-wide chromatin immunoprecipitation coupled with the next generation sequencing technology (ChIP-Seq) revealed direct PXR-binding sites to the Gsta, Gstm, Gstt, and Gstp polycistron clusters as well as to the Mgst1 gene locus. Chromatin immunoprecipitation-on-chip analysis demonstrated that DNA methylation and histone H3K27-trimethylation (H3K27me3), two-gene expression-suppressing epigenetic marks, were consistently low around the Gstz1 gene locus. In contrast, enrichment of histone H3K4-dimethylation (H3K4me2), a hallmark for gene activation, increased 60% around the Gstz1 gene locus from prenatal to the young adult period. Regression analysis revealed a strong correlation between the enrichment of H3K4me2 and Gstz1 mRNA expression (r = 0.76). In conclusion, this study characterized three distinct ontogenic expression signatures of the 19 Gst isoforms

  14. MAOA expression predicts vulnerability for alcohol use.

    PubMed

    Cervera-Juanes, R; Wilhem, L J; Park, B; Lee, R; Locke, J; Helms, C; Gonzales, S; Wand, G; Jones, S R; Grant, K A; Ferguson, B

    2016-04-01

    The role of the monoamines dopamine (DA) and serotonin (5HT) and the monoamine-metabolizing enzyme monoamine oxidase A (MAOA) have been repeatedly implicated in studies of alcohol use and dependence. Genetic investigations of MAOA have yielded conflicting associations between a common polymorphism (MAOA-LPR) and risk for alcohol abuse. The present study provides direct comparison of tissue-specific MAOA expression and the level of alcohol consumption. We analyzed rhesus macaque MAOA (rhMAOA) expression in blood from males before and after 12 months of alcohol self-administration. In addition, nucleus accumbens core (NAc core) and cerebrospinal fluid (CSF) were collected from alcohol access and control (no alcohol access) subjects at the 12-month time point for comparison. The rhMAOA expression level in the blood of alcohol-naive subjects was negatively correlated with subsequent alcohol consumption level. The mRNA expression was independent of rhMAOA-LPR genotype and global promoter methylation. After 12 months of alcohol use, blood rhMAOA expression had decreased in an alcohol dose-dependent manner. Also after 12 months, rhMAOA expression in the NAc core was significantly lower in the heavy drinkers, as compared with control subjects. The CSF measured higher levels of DA and lower DOPAC/DA ratios among the heavy drinkers at the same time point. These results provide novel evidence that blood MAOA expression predicts alcohol consumption and that heavy alcohol use is linked to low MAOA expression in both the blood and NAc core. Together, the findings suggest a mechanistic link between dampened MAOA expression, elevated DA and alcohol abuse.

  15. MAOA EXPRESSION PREDICTS VULNERABILITY FOR ALCOHOL USE

    PubMed Central

    Cervera-Juanes, Rita; Wilhem, Larry J.; Park, Byung; Lee, Richard; Locke, Jason; Helms, Christa; Gonzales, Steven; Wand, Gary; Jones, Sara R.; Grant, Kathleen A.; Ferguson, Betsy

    2015-01-01

    The role of the monoamines dopamine (DA) and serotonin (5HT) and the monoamine-metabolizing enzyme monoamine oxidase A (MAOA) have been repeatedly implicated in studies of alcohol use and dependence. Genetic investigations of MAOA have yielded conflicting associations between a common polymorphism (MAOA-LPR) and risk for alcohol abuse. The present study provides direct comparison of tissue-specific MAOA expression and the level of alcohol consumption. We analyzed rhesus macaque MAOA (rhMAOA) expression in blood from males before and after 12-months of alcohol self-administration. In addition, nucleus accumbens core (NAc core) and cerebrospinal fluid (CSF) were collected from alcohol-access and control (no alcohol access) subjects at the 12-month time point for comparison. The rhMAOA expression level in the blood of alcohol-naïve subjects was negatively correlated with subsequent alcohol consumption level. The mRNA expression was independent of rhMAOA-LPR genotype and global promoter methylation. After 12 months of alcohol use, blood rhMAOA expression had decreased in an alcohol dose-dependent manner. Also after 12 months, rhMAOA expression in the NAc core was significantly lower in the heavy drinkers, as compared to control subjects. The CSF measured higher levels of DA and lower DOPAC/DA ratios amongst the heavy drinkers at the same time point. These results provide novel evidence that blood MAOA expression predicts alcohol consumption and that heavy alcohol use is linked to low MAOA expression in both the blood and NAc core. Together, the findings suggest a mechanistic link between dampened MAOA expression, elevated DA and alcohol abuse. PMID:26148813

  16. Airway basal cells of healthy smokers express an embryonic stem cell signature relevant to lung cancer.

    PubMed

    Shaykhiev, Renat; Wang, Rui; Zwick, Rachel K; Hackett, Neil R; Leung, Roland; Moore, Malcolm A S; Sima, Camelia S; Chao, Ion Wa; Downey, Robert J; Strulovici-Barel, Yael; Salit, Jacqueline; Crystal, Ronald G

    2013-09-01

    Activation of the human embryonic stem cell (hESC) signature genes has been observed in various epithelial cancers. In this study, we found that the hESC signature is selectively induced in the airway basal stem/progenitor cell population of healthy smokers (BC-S), with a pattern similar to that activated in all major types of human lung cancer. We further identified a subset of 6 BC-S hESC genes, whose coherent overexpression in lung adenocarcinoma (AdCa) was associated with reduced lung function, poorer differentiation grade, more advanced tumor stage, remarkably shorter survival, and higher frequency of TP53 mutations. BC-S shared with hESC and a considerable subset of lung carcinomas a common TP53 inactivation molecular pattern which strongly correlated with the BC-S hESC gene expression. These data provide transcriptome-based evidence that smoking-induced reprogramming of airway BC toward the hESC-like phenotype might represent a common early molecular event in the development of aggressive lung carcinomas in humans.

  17. Genetic risk profiling and gene signature modeling to predict risk of complications after IPAA.

    PubMed

    Sehgal, Rishabh; Berg, Arthur; Polinski, Joseph I; Hegarty, John P; Lin, Zhenwu; McKenna, Kevin J; Stewart, David B; Poritz, Lisa S; Koltun, Walter A

    2012-03-01

    Severe pouchitis and Crohn's disease-like complications are 2 adverse postoperative complications that confound the success of the IPAA in patients with ulcerative colitis. To date, approximately 83 single nucleotide polymorphisms within 55 genes have been associated with IBD. The aim of this study was to identify single-nucleotide polymorphisms that correlate with complications after IPAA that could be utilized in a gene signature fashion to predict postoperative complications and aid in preoperative surgical decision making. One hundred forty-two IPAA patients were retrospectively classified as "asymptomatic" (n = 104, defined as no Crohn's disease-like complications or severe pouchitis for at least 2 years after IPAA) and compared with a "severe pouchitis" group (n = 12, ≥ 4 episodes pouchitis per year for 2 years including the need for long-term therapy to maintain remission) and a "Crohn's disease-like" group (n = 26, presence of fistulae, pouch inlet stricture, proximal small-bowel disease, or pouch granulomata, occurring at least 6 months after surgery). Genotyping for 83 single-nucleotide polymorphisms previously associated with Crohn's disease and/or ulcerative colitis was performed on a customized Illumina genotyping platform. The top 2 single-nucleotide polymorphisms statistically identified as being independently associated with each of Crohn's disease-like and severe pouchitis were used in a multivariate logistic regression model. These single-nucleotide polymorphisms were then used to create probability equations to predict overall chance of a positive or negative outcome for that complication. The top 2 single-nucleotide polymorphisms for Crohn's disease-like complications were in the 10q21 locus and the gene for PTGER4 (p = 0.006 and 0.007), whereas for severe pouchitis it was NOD2 and TNFSF15 (p = 0.003 and 0.011). Probability equations suggested that the risk of these 2 complications greatly increased with increasing number of risk alleles

  18. A germline predictive signature of response to platinum chemotherapy in esophageal cancer.

    PubMed

    Rumiato, Enrica; Boldrin, Elisa; Malacrida, Sandro; Battaglia, Giorgio; Bocus, Paolo; Castoro, Carlo; Cagol, Matteo; Chiarion-Sileni, Vanna; Ruol, Alberto; Amadori, Alberto; Saggioro, Daniela

    2016-05-01

    Platinum-based neoadjuvant therapy is the standard treatment for esophageal cancer (EC). At present, no reliable response markers exist, and patient therapeutic outcome is variable and very often unpredictable. The aim of this study was to understand the contribution of host constitutive DNA polymorphisms in discriminating between responder and nonresponder patients. DNA collected from 120 EC patients treated with platinum-based neoadjuvant chemotherapy was analyzed using drug metabolism enzymes and transporters (DMET) array platform that interrogates polymorphisms in 225 genes of drug metabolism and disposition. Four gene variants of DNA repair machinery, 2 in ERCC1 (rs11615; rs3212986), and 2 in XPD (rs1799793; rs13181) were also studied. Association analysis was performed with pTest software and corrected by permutation test. Predictive models of response were created using the receiver-operating characteristics curve approach and adjusted by the bootstrap procedure. Sixteen single nucleotide polymorphisms (SNPs) of the DMET array resulted significantly associated with either good or poor response; no association was found for the 4 variants mapping in DNA repair genes. The predictive power of 5 DMET SNPs mapping in ABCC2, ABCC3, CYP2A6, PPARG, and SLC7A8 genes was greater than that of clinical factors alone (area under the curve [AUC] = 0.74 vs 0.62). Interestingly, their combination with the clinical variables significantly increased the predictivity of the model (AUC = 0.78 vs 0.62, P = 0.0016). In conclusion, we identified a genetic signature of response to platinum-based neoadjuvant chemotherapy in EC patients. Our results also disclose the potential benefit of combining genetic and clinical variables for personalized EC management.

  19. RNA Sequencing Reveals that Kaposi Sarcoma-Associated Herpesvirus Infection Mimics Hypoxia Gene Expression Signature

    PubMed Central

    Viollet, Coralie; Davis, David A.; Tekeste, Shewit S.; Reczko, Martin; Pezzella, Francesco; Ragoussis, Jiannis

    2017-01-01

    Kaposi sarcoma-associated herpesvirus (KSHV) causes several tumors and hyperproliferative disorders. Hypoxia and hypoxia-inducible factors (HIFs) activate latent and lytic KSHV genes, and several KSHV proteins increase the cellular levels of HIF. Here, we used RNA sequencing, qRT-PCR, Taqman assays, and pathway analysis to explore the miRNA and mRNA response of uninfected and KSHV-infected cells to hypoxia, to compare this with the genetic changes seen in chronic latent KSHV infection, and to explore the degree to which hypoxia and KSHV infection interact in modulating mRNA and miRNA expression. We found that the gene expression signatures for KSHV infection and hypoxia have a 34% overlap. Moreover, there were considerable similarities between the genes up-regulated by hypoxia in uninfected (SLK) and in KSHV-infected (SLKK) cells. hsa-miR-210, a HIF-target known to have pro-angiogenic and anti-apoptotic properties, was significantly up-regulated by both KSHV infection and hypoxia using Taqman assays. Interestingly, expression of KSHV-encoded miRNAs was not affected by hypoxia. These results demonstrate that KSHV harnesses a part of the hypoxic cellular response and that a substantial portion of hypoxia-induced changes in cellular gene expression are induced by KSHV infection. Therefore, targeting hypoxic pathways may be a useful way to develop therapeutic strategies for KSHV-related diseases. PMID:28046107

  20. Gene-expression signatures differ between different clinical forms of familial hemophagocytic lymphohistiocytosis

    PubMed Central

    Nestheide, Shawnagay V.; Barnes, Michael G.; Villanueva, Joyce; Zhang, Kejian; Grom, Alexei A.; Filipovich, Alexandra H.

    2013-01-01

    We performed gene-expression profiling of PBMCs obtained from patients with familial hemophagocytic lymphohistiocytosis (FHL) to screen for biologic correlates with the genetic and/or clinical forms of this disease. Unsupervised hierarchical clustering of 167 differentially expressed probe sets, representing 143 genes, identified 3 groups of patients corresponding to the genetic forms and clinical presentations of the disease. Two clusters of up- and down-regulated genes separated patients with perforin-deficient FHL from those with unidentified genetic cause(s) of the disease. The clusterscomprised genes involved in defense/immune responses, apoptosis, zinc homeostasis, and systemic inflammation. Unsupervised hierarchical clustering partitioned patients with unknown genetic cause(s) of FHL into 2 well-distinguished subgroups. Patterns of up- and down-regulated genes separated patients with “late-onset” and “relapsing” forms of FHL from patients with an “early onset and rapidly evolving” form of the disease. A cluster was identified in patients with “late onset and relapsing” form of FHL related to B- and T-cell differentiation/survival, T-cell activation, and vesicular transport. The resulting data suggest that unique gene-expression signatures can distinguish between genetic and clinical subtypes of FHL. These differentially expressed genes may represent biomarkers that can be used as predictors of disease progression. PMID:23264592

  1. RNA Sequencing Reveals that Kaposi Sarcoma-Associated Herpesvirus Infection Mimics Hypoxia Gene Expression Signature.

    PubMed

    Viollet, Coralie; Davis, David A; Tekeste, Shewit S; Reczko, Martin; Ziegelbauer, Joseph M; Pezzella, Francesco; Ragoussis, Jiannis; Yarchoan, Robert

    2017-01-01

    Kaposi sarcoma-associated herpesvirus (KSHV) causes several tumors and hyperproliferative disorders. Hypoxia and hypoxia-inducible factors (HIFs) activate latent and lytic KSHV genes, and several KSHV proteins increase the cellular levels of HIF. Here, we used RNA sequencing, qRT-PCR, Taqman assays, and pathway analysis to explore the miRNA and mRNA response of uninfected and KSHV-infected cells to hypoxia, to compare this with the genetic changes seen in chronic latent KSHV infection, and to explore the degree to which hypoxia and KSHV infection interact in modulating mRNA and miRNA expression. We found that the gene expression signatures for KSHV infection and hypoxia have a 34% overlap. Moreover, there were considerable similarities between the genes up-regulated by hypoxia in uninfected (SLK) and in KSHV-infected (SLKK) cells. hsa-miR-210, a HIF-target known to have pro-angiogenic and anti-apoptotic properties, was significantly up-regulated by both KSHV infection and hypoxia using Taqman assays. Interestingly, expression of KSHV-encoded miRNAs was not affected by hypoxia. These results demonstrate that KSHV harnesses a part of the hypoxic cellular response and that a substantial portion of hypoxia-induced changes in cellular gene expression are induced by KSHV infection. Therefore, targeting hypoxic pathways may be a useful way to develop therapeutic strategies for KSHV-related diseases.

  2. Gene Expression Music Algorithm-Based Characterization of the Ewing Sarcoma Stem Cell Signature

    PubMed Central

    2016-01-01

    Gene Expression Music Algorithm (GEMusicA) is a method for the transformation of DNA microarray data into melodies that can be used for the characterization of differentially expressed genes. Using this method we compared gene expression profiles from endothelial cells (EC), hematopoietic stem cells, neuronal stem cells, embryonic stem cells (ESC), and mesenchymal stem cells (MSC) and defined a set of genes that can discriminate between the different stem cell types. We analyzed the behavior of public microarray data sets from Ewing sarcoma (“Ewing family tumors,” EFT) cell lines and biopsies in GEMusicA after prefiltering DNA microarray data for the probe sets from the stem cell signature. Our results demonstrate that individual Ewing sarcoma cell lines have a high similarity to ESC or EC. Ewing sarcoma cell lines with inhibited Ewing sarcoma breakpoint region 1-Friend leukemia virus integration 1 (EWSR1-FLI1) oncogene retained the similarity to ESC and EC. However, correlation coefficients between GEMusicA-processed expression data between EFT and ESC decreased whereas correlation coefficients between EFT and EC as well as between EFT and MSC increased after knockdown of EWSR1-FLI1. Our data support the concept of EFT being derived from cells with features of embryonic and endothelial cells. PMID:27446218

  3. Transcriptome-Level Signatures in Gene Expression and Gene Expression Variability during Bacterial Adaptive Evolution

    PubMed Central

    Erickson, Keesha E.; Otoupal, Peter B.

    2017-01-01

    ABSTRACT Antibiotic-resistant bacteria are an increasingly serious public health concern, as strains emerge that demonstrate resistance to almost all available treatments. One factor that contributes to the crisis is the adaptive ability of bacteria, which exhibit remarkable phenotypic and gene expression heterogeneity in order to gain a survival advantage in damaging environments. This high degree of variability in gene expression across biological populations makes it a challenging task to identify key regulators of bacterial adaptation. Here, we research the regulation of adaptive resistance by investigating transcriptome profiles of Escherichia coli upon adaptation to disparate toxins, including antibiotics and biofuels. We locate potential target genes via conventional gene expression analysis as well as using a new analysis technique examining differential gene expression variability. By investigating trends across the diverse adaptation conditions, we identify a focused set of genes with conserved behavior, including those involved in cell motility, metabolism, membrane structure, and transport, and several genes of unknown function. To validate the biological relevance of the observed changes, we synthetically perturb gene expression using clustered regularly interspaced short palindromic repeat (CRISPR)-dCas9. Manipulation of select genes in combination with antibiotic treatment promotes adaptive resistance as demonstrated by an increased degree of antibiotic tolerance and heterogeneity in MICs. We study the mechanisms by which identified genes influence adaptation and find that select differentially variable genes have the potential to impact metabolic rates, mutation rates, and motility. Overall, this work provides evidence for a complex nongenetic response, encompassing shifts in gene expression and gene expression variability, which underlies adaptive resistance. IMPORTANCE Even initially sensitive bacteria can rapidly thwart antibiotic treatment

  4. Transcriptome-Level Signatures in Gene Expression and Gene Expression Variability during Bacterial Adaptive Evolution.

    PubMed

    Erickson, Keesha E; Otoupal, Peter B; Chatterjee, Anushree

    2017-01-01

    Antibiotic-resistant bacteria are an increasingly serious public health concern, as strains emerge that demonstrate resistance to almost all available treatments. One factor that contributes to the crisis is the adaptive ability of bacteria, which exhibit remarkable phenotypic and gene expression heterogeneity in order to gain a survival advantage in damaging environments. This high degree of variability in gene expression across biological populations makes it a challenging task to identify key regulators of bacterial adaptation. Here, we research the regulation of adaptive resistance by investigating transcriptome profiles of Escherichia coli upon adaptation to disparate toxins, including antibiotics and biofuels. We locate potential target genes via conventional gene expression analysis as well as using a new analysis technique examining differential gene expression variability. By investigating trends across the diverse adaptation conditions, we identify a focused set of genes with conserved behavior, including those involved in cell motility, metabolism, membrane structure, and transport, and several genes of unknown function. To validate the biological relevance of the observed changes, we synthetically perturb gene expression using clustered regularly interspaced short palindromic repeat (CRISPR)-dCas9. Manipulation of select genes in combination with antibiotic treatment promotes adaptive resistance as demonstrated by an increased degree of antibiotic tolerance and heterogeneity in MICs. We study the mechanisms by which identified genes influence adaptation and find that select differentially variable genes have the potential to impact metabolic rates, mutation rates, and motility. Overall, this work provides evidence for a complex nongenetic response, encompassing shifts in gene expression and gene expression variability, which underlies adaptive resistance. IMPORTANCE Even initially sensitive bacteria can rapidly thwart antibiotic treatment through stress

  5. Prediction of reversible disulfide based on features from local structural signatures.

    PubMed

    Sun, Ming-An; Wang, Yejun; Zhang, Qing; Xia, Yiji; Ge, Wei; Guo, Dianjing

    2017-04-04

    Disulfide bonds are traditionally considered to play only structural roles. In recent years, increasing evidence suggests that the disulfide proteome is made up of structural disulfides and reversible disulfides. Unlike structural disulfides, reversible disulfides are usually of important functional roles and may serve as redox switches. Interestingly, only specific disulfide bonds are reversible while others are not. However, whether reversible disulfides can be predicted based on structural information remains largely unknown. In this study, two datasets with both types of disulfides were compiled using independent approaches. By comparison of various features extracted from the local structural signatures, we identified several features that differ significantly between reversible and structural disulfides, including disulfide bond length, along with the number, amino acid composition, secondary structure and physical-chemical properties of surrounding amino acids. A SVM-based classifier was developed for predicting reversible disulfides. RESULTS: By 10-fold cross-validation, the model achieved accuracy of 0.750, sensitivity of 0.352, specificity of 0.953, MCC of 0.405 and AUC of 0.751 using the RevSS_PDB dataset. The robustness was further validated by using RevSS_RedoxDB as independent testing dataset. This model was applied to proteins with known structures in the PDB database. The results show that one third of the predicted reversible disulfide containing proteins are well-known redox enzymes, while the remaining are non-enzyme proteins. Given that reversible disulfides are frequently reported from functionally important non-enzyme proteins such as transcription factors, the predictions may provide valuable candidates of novel reversible disulfides for further experimental investigation. This study provides the first comparative analysis between the reversible and the structural disulfides. Distinct features remarkably different between these two

  6. A host-based RT-PCR gene expression signature to identify acute respiratory viral infection.

    PubMed

    Zaas, Aimee K; Burke, Thomas; Chen, Minhua; McClain, Micah; Nicholson, Bradly; Veldman, Timothy; Tsalik, Ephraim L; Fowler, Vance; Rivers, Emanuel P; Otero, Ronny; Kingsmore, Stephen F; Voora, Deepak; Lucas, Joseph; Hero, Alfred O; Carin, Lawrence; Woods, Christopher W; Ginsburg, Geoffrey S

    2013-09-18

    Improved ways to diagnose acute respiratory viral infections could decrease inappropriate antibacterial use and serve as a vital triage mechanism in the event of a potential viral pandemic. Measurement of the host response to infection is an alternative to pathogen-based diagnostic testing and may improve diagnostic accuracy. We have developed a host-based assay with a reverse transcription polymerase chain reaction (RT-PCR) TaqMan low-density array (TLDA) platform for classifying respiratory viral infection. We developed the assay using two cohorts experimentally infected with influenza A H3N2/Wisconsin or influenza A H1N1/Brisbane, and validated the assay in a sample of adults presenting to the emergency department with fever (n = 102) and in healthy volunteers (n = 41). Peripheral blood RNA samples were obtained from individuals who underwent experimental viral challenge or who presented to the emergency department and had microbiologically proven viral respiratory infection or systemic bacterial infection. The selected gene set on the RT-PCR TLDA assay classified participants with experimentally induced influenza H3N2 and H1N1 infection with 100 and 87% accuracy, respectively. We validated this host gene expression signature in a cohort of 102 individuals arriving at the emergency department. The sensitivity of the RT-PCR test was 89% [95% confidence interval (CI), 72 to 98%], and the specificity was 94% (95% CI, 86 to 99%). These results show that RT-PCR-based detection of a host gene expression signature can classify individuals with respiratory viral infection and sets the stage for prospective evaluation of this diagnostic approach in a clinical setting.

  7. Meta-Analysis of Gene Expression Signatures Defining the Epithelial to Mesenchymal Transition during Cancer Progression

    PubMed Central

    Gröger, Christian J.; Grubinger, Markus; Waldhör, Thomas; Vierlinger, Klemens; Mikulits, Wolfgang

    2012-01-01

    The epithelial to mesenchymal transition (EMT) represents a crucial event during cancer progression and dissemination. EMT is the conversion of carcinoma cells from an epithelial to a mesenchymal phenotype that associates with a higher cell motility as well as enhanced chemoresistance and cancer stemness. Notably, EMT has been increasingly recognized as an early event of metastasis. Numerous gene expression studies (GES) have been conducted to obtain transcriptome signatures and marker genes to understand the regulatory mechanisms underlying EMT. Yet, no meta-analysis considering the multitude of GES of EMT has been performed to comprehensively elaborate the core genes in this process. Here we report the meta-analysis of 18 independent and published GES of EMT which focused on different cell types and treatment modalities. Computational analysis revealed clustering of GES according to the type of treatment rather than to cell type. GES of EMT induced via transforming growth factor-β and tumor necrosis factor-α treatment yielded uniformly defined clusters while GES of models with alternative EMT induction clustered in a more complex fashion. In addition, we identified those up- and downregulated genes which were shared between the multitude of GES. This core gene list includes well known EMT markers as well as novel genes so far not described in this process. Furthermore, several genes of the EMT-core gene list significantly correlated with impaired pathological complete response in breast cancer patients. In conclusion, this meta-analysis provides a comprehensive survey of available EMT expression signatures and shows fundamental insights into the mechanisms that are governing carcinoma progression. PMID:23251436

  8. Numerical Predictions of Sonic Boom Signatures for a Straight Line Segmented Leading Edge Model

    NASA Technical Reports Server (NTRS)

    Elmiligui, Alaa A.; Wilcox, Floyd J.; Cliff, Susan; Thomas, Scott

    2012-01-01

    A sonic boom wind tunnel test was conducted on a straight-line segmented leading edge (SLSLE) model in the NASA Langley 4- by 4- Foot Unitary Plan Wind Tunnel (UPWT). The purpose of the test was to determine whether accurate sonic boom measurements could be obtained while continuously moving the SLSLE model past a conical pressure probe. Sonic boom signatures were also obtained using the conventional move-pause data acquisition method for comparison. The continuous data acquisition approach allows for accurate signatures approximately 15 times faster than a move-pause technique. These successful results provide an incentive for future testing with greatly increased efficiency using the continuous model translation technique with the single probe to measure sonic boom signatures. Two widely used NASA codes, USM3D (Navier-Stokes) and CART3D-AERO (Euler, adjoint-based adaptive mesh), were used to compute off-body sonic boom pressure signatures of the SLSLE model at several different altitudes below the model at Mach 2.0. The computed pressure signatures compared well with wind tunnel data. The effect of the different altitude for signature extraction was evaluated by extrapolating the near field signatures to the ground and comparing pressure signatures and sonic boom loudness levels.

  9. Glycan-related gene expression signatures in breast cancer subtypes; relation to survival.

    PubMed

    Potapenko, Ivan O; Lüders, Torben; Russnes, Hege G; Helland, Åslaug; Sørlie, Therese; Kristensen, Vessela N; Nord, Silje; Lingjærde, Ole C; Børresen-Dale, Anne-Lise; Haakensen, Vilde D

    2015-04-01

    Alterations in glycan structures are early signs of malignancy and have recently been proposed to be in part a driving force behind malignant transformation. Here, we explore whether differences in expression of genes related to the process of glycosylation exist between breast carcinoma subtypes - and look for their association to clinical parameters. Five expression datasets of 454 invasive breast carcinomas, 31 ductal carcinomas in situ (DCIS), and 79 non-malignant breast tissue samples were analysed. Results were validated in 1960 breast carcinomas. 419 genes encoding glycosylation-related proteins were selected. The DCIS samples appeared expression-wise similar to carcinomas, showing altered gene expression related to glycosaminoglycans (GAGs) and N-glycans when compared to non-malignant samples. In-situ lesions with different aggressiveness potentials demonstrated changes in glycosaminoglycan sulfation and adhesion proteins. Subtype-specific expression patterns revealed down-regulation of genes encoding glycan-binding proteins in the luminal A and B subtypes. Clustering basal-like samples using a consensus list of genes differentially expressed across discovery datasets produced two clusters with significantly differing prognosis in the validation dataset. Finally, our analyses suggest that glycolipids may play an important role in carcinogenesis of breast tumors - as demonstrated by association of B3GNT5 and UGCG genes to patient survival. In conclusion, most glycan-specific changes occur early in the carcinogenic process. We have identified glycan-related alterations specific to breast cancer subtypes including a prognostic signature for two basal-like subgroups. Future research in this area may potentially lead to markers for better prognostication and treatment stratification of breast cancer patients.

  10. A Meta-analysis of Gene Expression Signatures of Blood Pressure and Hypertension

    PubMed Central

    Chen, Brian H.; Liu, Chunyu; Joehanes, Roby; Johnson, Andrew D.; Yao, Chen; Ying, Sai-xia; Courchesne, Paul; Milani, Lili; Raghavachari, Nalini; Wang, Richard; Liu, Poching; Reinmaa, Eva; Dehghan, Abbas; Hofman, Albert; Uitterlinden, André G.; Hernandez, Dena G.; Bandinelli, Stefania; Singleton, Andrew; Melzer, David; Metspalu, Andres; Carstensen, Maren; Grallert, Harald; Herder, Christian; Meitinger, Thomas; Peters, Annette; Roden, Michael; Waldenberger, Melanie; Dörr, Marcus; Felix, Stephan B.; Zeller, Tanja; Vasan, Ramachandran; O'Donnell, Christopher J.; Munson, Peter J.; Yang, Xia; Prokisch, Holger; Völker, Uwe; van Meurs, Joyce B. J.; Ferrucci, Luigi; Levy, Daniel

    2015-01-01

    Genome-wide association studies (GWAS) have uncovered numerous genetic variants (SNPs) that are associated with blood pressure (BP). Genetic variants may lead to BP changes by acting on intermediate molecular phenotypes such as coded protein sequence or gene expression, which in turn affect BP variability. Therefore, characterizing genes whose expression is associated with BP may reveal cellular processes involved in BP regulation and uncover how transcripts mediate genetic and environmental effects on BP variability. A meta-analysis of results from six studies of global gene expression profiles of BP and hypertension in whole blood was performed in 7017 individuals who were not receiving antihypertensive drug treatment. We identified 34 genes that were differentially expressed in relation to BP (Bonferroni-corrected p<0.05). Among these genes, FOS and PTGS2 have been previously reported to be involved in BP-related processes; the others are novel. The top BP signature genes in aggregate explain 5%–9% of inter-individual variance in BP. Of note, rs3184504 in SH2B3, which was also reported in GWAS to be associated with BP, was found to be a trans regulator of the expression of 6 of the transcripts we found to be associated with BP (FOS, MYADM, PP1R15A, TAGAP, S100A10, and FGBP2). Gene set enrichment analysis suggested that the BP-related global gene expression changes include genes involved in inflammatory response and apoptosis pathways. Our study provides new insights into molecular mechanisms underlying BP regulation, and suggests novel transcriptomic markers for the treatment and prevention of hypertension. PMID:25785607

  11. The conformational signature of arrestin3 predicts its trafficking and signaling functions

    PubMed Central

    Lee, Mi-Hye; Appleton, Kathryn M.; Strungs, Erik G.; Kwon, Joshua Y.; Morinelli, Thomas A.; Peterson, Yuri K.; Laporte, Stephane A.; Luttrell, Louis M.

    2016-01-01

    Arrestins are cytosolic proteins that regulate G protein-coupled receptor (GPCR) desensitization, internalization, trafficking, and signaling1,2. Arrestin recruitment uncouples GPCRs from heterotrimeric G proteins, and targets them for internalization via clathrin-coated pits3,4. Arrestins also function as ligand-regulated scaffolds that recruit multiple non-G protein effectors into GPCR-based ‘signalsomes’5,6. While the dominant function(s) of arrestins vary between receptors, the mechanism whereby different GPCRs specify divergent arrestin functions is not understood. Using a panel of intramolecular FlAsH-BRET reporters7 to monitor conformational changes in arrestin3, we show here that GPCRs impose distinctive arrestin ‘conformational signatures’ that reflect the stability of the receptor-arrestin complex and role of arrestin3 in activating or dampening downstream signaling events. The predictive value of these signatures extends to structurally distinct ligands activating the same GPCR, such that the innate properties of the ligand are reflected as changes in arrestin3 conformation. Our findings demonstrate that information about ligand-receptor conformation is encoded within the population average arrestin3 conformation, and provide insight into how different GPCRs can use a common effector for different purposes. This approach may have application in the characterization and development of functionally selective GPCR ligands8,9 and in identifying factors that dictate arrestin conformation and function. PMID:27007854

  12. Aging-dependent alterations in gene expression and a mitochondrial signature of responsiveness to human influenza vaccination.

    PubMed

    Thakar, Juilee; Mohanty, Subhasis; West, A Phillip; Joshi, Samit R; Ueda, Ikuyo; Wilson, Jean; Meng, Hailong; Blevins, Tamara P; Tsang, Sui; Trentalange, Mark; Siconolfi, Barbara; Park, Koonam; Gill, Thomas M; Belshe, Robert B; Kaech, Susan M; Shadel, Gerald S; Kleinstein, Steven H; Shaw, Albert C

    2015-01-01

    To elucidate gene expression pathways underlying age-associated impairment in influenza vaccine response, we screened young (age 21-30) and older (age≥65) adults receiving influenza vaccine in two consecutive seasons and identified those with strong or absent response to vaccine, including a subset of older adults meeting criteria for frailty. PBMCs obtained prior to vaccination (Day 0) and at day 2 or 4, day 7 and day 28 post-vaccine were subjected to gene expression microarray analysis. We defined a response signature and also detected induction of a type I interferon response at day 2 and a plasma cell signature at day 7 post-vaccine in young responders. The response signature was dysregulated in older adults, with the plasma cell signature induced at day 2, and was never induced in frail subjects (who were all non-responders). We also identified a mitochondrial signature in young vaccine responders containing genes mediating mitochondrial biogenesis and oxidative phosphorylation that was consistent in two different vaccine seasons and verified by analyses of mitochondrial content and protein expression. These results represent the first genome-wide transcriptional profiling analysis of age-associated dynamics following influenza vaccination, and implicate changes in mitochondrial biogenesis and function as a critical factor in human vaccine responsiveness.

  13. A surprising cross-species conservation in the genomic landscape of mouse and human oral cancer identifies a transcriptional signature predicting metastatic disease

    PubMed Central

    Onken, Michael D.; Winkler, Ashley E.; Kanchi, Krishna-Latha; Chalivendra, Varun; Law, Jonathan H.; Rickert, Charles G.; Kallogjeri, Dorina; Judd, Nancy P.; Dunn, Gavin P.; Piccirillo, Jay F.; Lewis, James S.; Mardis, Elaine R.; Uppaluri, Ravindra

    2014-01-01

    Purpose Improved understanding of the molecular basis underlying oral squamous cell carcinoma (OSCC) aggressive growth has significant clinical implications. Herein, cross-species genomic comparison of carcinogen-induced murine and human OSCCs with indolent or metastatic growth yielded results with surprising translational relevance. Experimental Design Murine OSCC cell lines were subjected to next-generation sequencing (NGS) to define their mutational landscape, to define novel candidate cancer genes and to assess for parallels with known drivers in human OSCC. Expression arrays identified a mouse metastasis signature and we assessed its representation in 4 independent human datasets comprising 324 patients using weighted voting and Gene Set Enrichment Analysis (GSEA). Kaplan-Meier analysis and multivariate Cox proportional hazards modeling were used to stratify outcomes. A qRT-PCR assay based on the mouse signature coupled to a machine-learning algorithm was developed and used to stratify an independent set of 31 patients with respect to metastatic lymphadenopathy. Results NGS revealed conservation of human driver pathway mutations in mouse OSCC including in Trp53, MAPK, PI3K, NOTCH, JAK/STAT and FAT1–4. Moreover, comparative analysis between The Cancer Genome Atlas (TCGA) and mouse samples defined AKAP9, MED12L and MYH6 as novel putative cancer genes. Expression analysis identified a transcriptional signature predicting aggressiveness and clinical outcomes, which were validated in 4 independent human OSCC datasets. Finally, we harnessed the translational potential of this signature by creating a clinically feasible assay that stratified OSCC patients with a 93.5% accuracy. Conclusions These data demonstrate surprising cross-species genomic conservation that has translational relevance for human oral squamous cell cancer. PMID:24668645

  14. Expression profiling feline peripheral blood monocytes identifies a transcriptional signature associated with type two diabetes mellitus.

    PubMed

    O'Leary, Caroline A; Sedhom, Mamdouh; Reeve-Johnson, Mia; Mallyon, John; Irvine, Katharine M

    2017-04-01

    Diabetes mellitus is a common disease of cats and is similar to type 2 diabetes (T2D) in humans, especially with respect to the role of obesity-induced insulin resistance, glucose toxicity, decreased number of pancreatic β-cells and pancreatic amyloid deposition. Cats have thus been proposed as a valuable translational model of T2D. In humans, inflammation associated with adipose tissue is believed to be central to T2D development, and peripheral blood monocytes (PBM) are important in the inflammatory cascade which leads to insulin resistance and β-cell failure. PBM may thus provide a useful window to study the pathogenesis of diabetes mellitus in cats, however feline monocytes are poorly characterised. In this study, we used the Affymetrix Feline 1.0ST array to profile peripheral blood monocytes from 3 domestic cats with T2D and 3 cats with normal glucose tolerance. Feline monocytes were enriched for genes expressed in human monocytes, and, despite heterogeneous gene expression, we identified a T2D-associated expression signature associated with cell cycle perturbations, DNA repair and the unfolded protein response, oxidative phosphorylation and inflammatory responses. Our data provide novel insights into the feline monocyte transcriptome, and support the hypothesis that inflammatory monocytes contribute to T2D pathogenesis in cats as well as in humans. Copyright © 2017 Elsevier B.V. All rights reserved.

  15. A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen.

    PubMed

    Ma, Xiao-Jun; Wang, Zuncai; Ryan, Paula D; Isakoff, Steven J; Barmettler, Anne; Fuller, Andrew; Muir, Beth; Mohapatra, Gayatry; Salunga, Ranelle; Tuggle, J Todd; Tran, Yen; Tran, Diem; Tassin, Ana; Amon, Paul; Wang, Wilson; Wang, Wei; Enright, Edward; Stecker, Kimberly; Estepa-Sabal, Eden; Smith, Barbara; Younger, Jerry; Balis, Ulysses; Michaelson, James; Bhan, Atul; Habin, Karleen; Baer, Thomas M; Brugge, Joan; Haber, Daniel A; Erlander, Mark G; Sgroi, Dennis C

    2004-06-01

    Tamoxifen significantly reduces tumor recurrence in certain patients with early-stage estrogen receptor-positive breast cancer, but markers predictive of treatment failure have not been identified. Here, we generated gene expression profiles of hormone receptor-positive primary breast cancers in a set of 60 patients treated with adjuvant tamoxifen monotherapy. An expression signature predictive of disease-free survival was reduced to a two-gene ratio, HOXB13 versus IL17BR, which outperformed existing biomarkers. Ectopic expression of HOXB13 in MCF10A breast epithelial cells enhances motility and invasion in vitro, and its expression is increased in both preinvasive and invasive primary breast cancer. The HOXB13:IL17BR expression ratio may be useful for identifying patients appropriate for alternative therapeutic regimens in early-stage breast cancer.

  16. A three ion channel genes-based signature predicts prognosis of primary glioblastoma patients and reveals a chemotherapy sensitive subtype

    PubMed Central

    Liu, Xiu; Yan, Xiao-Yan; Wang, Wen; Wu, Fan; Liang, Ting-Yu; Yang, Fan; Hu, Hui-Min; Mao, Heng-Xu; Liu, Yan-Wei; Zhang, Shi-Zhong

    2016-01-01

    Increasing evidence suggests that ion channels not only regulate electric signaling in excitable cells but also play important roles in the development of brain tumor. However, the roles of ion channels in glioma remain controversial. In the present study, we systematically analyzed the expression patterns of ion channel genes in a cohort of Chinese patients with glioma using RNAseq expression profiling. First, a molecular signature comprising three ion channel genes (KCNN4, KCNB1 and KCNJ10) was identified using Univariate Cox regression and two-tailed student's t test conducted in overall survival (OS) and gene expression. We assigned a risk score based on three ion channel genes to each primary Glioblastoma multiforme (pGBM) patient. We demonstrated that pGBM patients who had a high risk of unfavorable outcome were sensitive to chemotherapy. Next, we screened the three ion genes-based signature in different molecular glioma subtypes. The signature showed a Mesenchymal subtype and wild-type IDH1 preference. Gene ontology (GO) analysis for the functional annotation of the signature showed that patients with high-risk scores tended to exhibit the increased expression of proteins associated with apoptosis, immune response, cell adhesion and motion and vasculature development. Gene Set Enrichment Analysis (GSEA) results showed that pathways associated with negative regulation of programmed cell death, cell proliferation and locomotory behavior were highly expressed in the high-risk group. These results suggest that ion channel gene expression could improve the subtype classification in gliomas at the molecular level. The findings in the present study have been validated in two independent cohorts. PMID:27713134

  17. Gene Expression Signature Analysis Identifies Vorinostat as a Candidate Therapy for Gastric Cancer

    PubMed Central

    Choi, Woonyoung; Park, Yun-Yong; Kim, KyoungHyun; Kim, Sang-Bae; Lee, Ju-Seog; Mills, Gordon B.; Cho, Jae Yong

    2011-01-01

    Background Gastric cancer continues to be one of the deadliest cancers in the world and therefore identification of new drugs targeting this type of cancer is thus of significant importance. The purpose of this study was to identify and validate a therapeutic agent which might improve the outcomes for gastric cancer patients in the future. Methodology/Principal Findings Using microarray technology, we generated a gene expression profile of human gastric cancer–specific genes from human gastric cancer tissue samples. We used this profile in the Broad Institute's Connectivity Map analysis to identify candidate therapeutic compounds for gastric cancer. We found the histone deacetylase inhibitor vorinostat as the lead compound and thus a potential therapeutic drug for gastric cancer. Vorinostat induced both apoptosis and autophagy in gastric cancer cell lines. Pharmacological and genetic inhibition of autophagy however, increased the therapeutic efficacy of vorinostat, indicating that a combination of vorinostat with autophagy inhibitors may therapeutically be more beneficial. Moreover, gene expression analysis of gastric cancer identified a collection of genes (ITGB5, TYMS, MYB, APOC1, CBX5, PLA2G2A, and KIF20A) whose expression was elevated in gastric tumor tissue and downregulated more than 2-fold by vorinostat treatment in gastric cancer cell lines. In contrast, SCGB2A1, TCN1, CFD, APLP1, and NQO1 manifested a reversed pattern. Conclusions/Significance We showed that analysis of gene expression signature may represent an emerging approach to discover therapeutic agents for gastric cancer, such as vorinostat. The observation of altered gene expression after vorinostat treatment may provide the clue to identify the molecular mechanism of vorinostat and those patients likely to benefit from vorinostat treatment. PMID:21931799

  18. Gene expression signatures in tree shrew choroid in response to three myopiagenic conditions

    PubMed Central

    He, Li; Frost, Michael R.; Siegwart, John T.; Norton, Thomas T.

    2014-01-01

    We examined gene expression in tree shrew choroid in response to three different myopiagenic conditions: minus lens (ML) wear, form deprivation (FD), and continuous darkness (DK). Four groups of tree shrews (n = 7 per group) were used. Starting 24 days after normal eye opening (days of visual experience [DVE]), the ML group wore a monocular −5 D lens for 2 days. The FD group wore a monocular translucent diffuser for 2 days. The DK group experienced continuous darkness binocularly for 11 days, starting at 17 DVE. An age-matched normal group was examined at 26 DVE. Quantitative PCR was used to measure the relative (treated eye vs. control eye) differences in mRNA levels in the choroid for 77 candidate genes. Small myopic changes were observed in the treated eyes (relative to the control eyes) of the ML group (−1.0 ± 0.2 D; mean ± SEM) and FD group (−1.9 ± 0.2 D). A larger myopia developed in the DK group (−4.4 ± 1.0 D) relative to Normal eyes (both groups, mean of right and left eyes). In the ML group, 28 genes showed significant differential mRNA expression; eighteen were down-regulated. A very similar pattern occurred in the FD group; twenty-seven of the same genes were similarly regulated, along with five additional genes. Fewer expression differences in the DK group were significant compared to normal or the control eyes of the ML and FD groups, but the pattern was similar to that of the ML and FD differential expression patterns. These data suggest that, at the level of the choroid, the gene expression signatures produced by “GO” emmetropization signals are highly similar despite the different visual conditions. PMID:25072854

  19. A five-miRNA signature with prognostic and predictive value for MGMT promoter-methylated glioblastoma patients.

    PubMed

    Cheng, Wen; Ren, Xiufang; Cai, Jinquan; Zhang, Chuanbao; Li, Mingyang; Wang, Kuanyu; Liu, Yang; Han, Sheng; Wu, Anhua

    2015-10-06

    Although O(6)-methylguanine DNA methyltransferase (MGMT) promoter methylation status is an important marker for glioblastoma multiforme (GBM), there is considerable variability in the clinical outcome of patients with similar methylation profiles. The present study aimed to refine the prognostic and predictive value of MGMT promoter status in GBM by identifying a micro (mi)RNA risk signature. Data from The Cancer Genome Atlas was used for this study, with MGMT promoter-methylated samples randomly divided into training and internal validation sets. Data from The Chinese Glioma Genome Atlas was used for independent validation. A five miRNA-based risk signature was established for MGMT promoter-methylated GBM to distinguish cases as high- or low-risk with distinct prognoses, which was confirmed using internal and external validation sets. Importantly, the prognostic value of the signature was significant in different cohorts stratified by clinicopathologic factors and alkylating chemotherapy, and a multivariate Cox analysis found it to be an independent prognostic marker along with age and chemotherapy. Based on these three factors, we developed a quantitative model with greater accuracy for predicting the 1-year survival of patients with MGMT promoter-methylated GBM. These results indicate that the five-miRNA signature is an independent risk predictor for GBM with MGMT promoter methylation and can be used to identify patients at high risk of unfavorable outcome and resistant to alkylating chemotherapy, underscoring its potential for personalized GBM management.

  20. Prediction of Chemical Respiratory Sensitizers Using GARD, a Novel In Vitro Assay Based on a Genomic Biomarker Signature

    PubMed Central

    Albrekt, Ann-Sofie; Borrebaeck, Carl A. K.; Lindstedt, Malin

    2015-01-01

    Background Repeated exposure to certain low molecular weight (LMW) chemical compounds may result in development of allergic reactions in the skin or in the respiratory tract. In most cases, a certain LMW compound selectively sensitize the skin, giving rise to allergic contact dermatitis (ACD), or the respiratory tract, giving rise to occupational asthma (OA). To limit occurrence of allergic diseases, efforts are currently being made to develop predictive assays that accurately identify chemicals capable of inducing such reactions. However, while a few promising methods for prediction of skin sensitization have been described, to date no validated method, in vitro or in vivo, exists that is able to accurately classify chemicals as respiratory sensitizers. Results Recently, we presented the in vitro based Genomic Allergen Rapid Detection (GARD) assay as a novel testing strategy for classification of skin sensitizing chemicals based on measurement of a genomic biomarker signature. We have expanded the applicability domain of the GARD assay to classify also respiratory sensitizers by identifying a separate biomarker signature containing 389 differentially regulated genes for respiratory sensitizers in comparison to non-respiratory sensitizers. By using an independent data set in combination with supervised machine learning, we validated the assay, showing that the identified genomic biomarker is able to accurately classify respiratory sensitizers. Conclusions We have identified a genomic biomarker signature for classification of respiratory sensitizers. Combining this newly identified biomarker signature with our previously identified biomarker signature for classification of skin sensitizers, we have developed a novel in vitro testing strategy with a potent ability to predict both skin and respiratory sensitization in the same sample. PMID:25760038

  1. Importance of Correlation between Gene Expression Levels: Application to the Type I Interferon Signature in Rheumatoid Arthritis

    PubMed Central

    Reynier, Frédéric; Petit, Fabien; Paye, Malick; Turrel-Davin, Fanny; Imbert, Pierre-Emmanuel; Hot, Arnaud; Mougin, Bruno; Miossec, Pierre

    2011-01-01

    Background The analysis of gene expression data shows that many genes display similarity in their expression profiles suggesting some co-regulation. Here, we investigated the co-expression patterns in gene expression data and proposed a correlation-based research method to stratify individuals. Methodology/Principal Findings Using blood from rheumatoid arthritis (RA) patients, we investigated the gene expression profiles from whole blood using Affymetrix microarray technology. Co-expressed genes were analyzed by a biclustering method, followed by gene ontology analysis of the relevant biclusters. Taking the type I interferon (IFN) pathway as an example, a classification algorithm was developed from the 102 RA patients and extended to 10 systemic lupus erythematosus (SLE) patients and 100 healthy volunteers to further characterize individuals. We developed a correlation-based algorithm referred to as Classification Algorithm Based on a Biological Signature (CABS), an alternative to other approaches focused specifically on the expression levels. This algorithm applied to the expression of 35 IFN-related genes showed that the IFN signature presented a heterogeneous expression between RA, SLE and healthy controls which could reflect the level of global IFN signature activation. Moreover, the monitoring of the IFN-related genes during the anti-TNF treatment identified changes in type I IFN gene activity induced in RA patients. Conclusions In conclusion, we have proposed an original method to analyze genes sharing an expression pattern and a biological function showing that the activation levels of a biological signature could be characterized by its overall state of correlation. PMID:22043277

  2. Gene expression signatures in tree shrew choroid during lens-induced myopia and recovery

    PubMed Central

    He, Li; Frost, Michael R.; Siegwart, John T.; Norton, Thomas T.

    2014-01-01

    ), 4 genes were significantly down-regulated and 18 genes were significantly up-regulated. Thirteen genes showed bi-directional regulation in GO vs. STOP. The pattern of differential gene expression in STOP was very different from that in GO or in STAY. Significant regulation was observed in genes involved in signaling as well as extracellular matrix turnover. These data support an active role for the choroid in the signaling cascade from retina to sclera. Distinctly different treated eye vs. control eye mRNA signatures are present in the choroid in the GO, STAY, and STOP conditions. The STAY signature, present after full compensation has occurred and the GO visual stimulus is no longer present, may participate in maintaining an elongated globe. The 13 genes with bi-directional expression differences in GO and STOP responded in a sign of defocus-dependent manner. Taken together, these data further suggest that a network of choroidal gene expression changes generate the signal that alters scleral fibroblast gene expression and axial elongation rate. PMID:24742494

  3. Expression signature as a biomarker for prenatal diagnosis of trisomy 21.

    PubMed

    Volk, Marija; Maver, Aleš; Lovrečić, Luca; Juvan, Peter; Peterlin, Borut

    2013-01-01

    A universal biomarker panel with the potential to predict high-risk pregnancies or adverse pregnancy outcome does not exist. Transcriptome analysis is a powerful tool to capture differentially expressed genes (DEG), which can be used as biomarker-diagnostic-predictive tool for various conditions in prenatal setting. In search of biomarker set for predicting high-risk pregnancies, we performed global expression profiling to find DEG in Ts21. Subsequently, we performed targeted validation and diagnostic performance evaluation on a larger group of case and control samples. Initially, transcriptomic profiles of 10 cultivated amniocyte samples with Ts21 and 9 with normal euploid constitution were determined using expression microarrays. Datasets from Ts21 transcriptomic studies from GEO repository were incorporated. DEG were discovered using linear regression modelling and validated using RT-PCR quantification on an independent sample of 16 cases with Ts21 and 32 controls. The classification performance of Ts21 status based on expression profiling was performed using supervised machine learning algorithm and evaluated using a leave-one-out cross validation approach. Global gene expression profiling has revealed significant expression changes between normal and Ts21 samples, which in combination with data from previously performed Ts21 transcriptomic studies, were used to generate a multi-gene biomarker for Ts21, comprising of 9 gene expression profiles. In addition to biomarker's high performance in discriminating samples from global expression profiling, we were also able to show its discriminatory performance on a larger sample set 2, validated using RT-PCR experiment (AUC=0.97), while its performance on data from previously published studies reached discriminatory AUC values of 1.00. Our results show that transcriptomic changes might potentially be used to discriminate trisomy of chromosome 21 in the prenatal setting. As expressional alterations reflect both, causal

  4. Lupus anti-ribosomal P autoantibody proteomes express convergent biclonal signatures.

    PubMed

    Al Kindi, M A; Colella, A D; Beroukas, D; Chataway, T K; Gordon, T P

    2016-04-01

    Lupus-specific anti-ribosomal P (anti-Rib-P) autoantibodies have been implicated in the pathogenesis of neurological complications in systemic lupus erythematosus (SLE). The aim of the present study was to determine variable (V)-region signatures of secreted autoantibody proteomes specific for the Rib-P heterocomplex and investigate the molecular basis of the reported cross-reactivity with Sm autoantigen. Anti-Rib-P immunoglobulins (IgGs) were purified from six anti-Rib-P-positive sera by elution from enzyme-linked immunosorbent assay (ELISA) plates coated with either native Rib-P proteins or an 11-amino acid peptide (11-C peptide) representing the conserved COOH-terminal P epitope. Rib-P- and 11-C peptide-specific IgGs were analysed for heavy (H) and light (L) chain clonality and V-region expression using an electrophoretic and de-novo and database-driven mass spectrometric sequencing workflow. Purified anti-Rib-P and anti-SmD IgGs were tested for cross-reactivity on ELISA and their proteome data sets analysed for shared clonotypes. Anti-Rib-P autoantibody proteomes were IgG1 kappa-restricted and comprised two public clonotypes defined by unique H/L chain pairings. The major clonotypic population was specific for the common COOH-terminal epitope, while the second shared the same pairing signature as a recently reported anti-SmD clonotype, accounting for two-way immunoassay cross-reactivity between these lupus autoantibodies. Sequence convergence of anti-Rib-P proteomes suggests common molecular pathways of autoantibody production and identifies stereotyped clonal populations that are thought to play a pathogenic role in neuropsychiatric lupus. Shared clonotypic structures for anti-Rib-P and anti-Sm responses suggest a common B cell clonal origin for subsets of these lupus-specific autoantibodies.

  5. Predicting metastasized seminoma using gene expression.

    PubMed

    Ruf, Christian G; Linbecker, Michael; Port, Matthias; Riecke, Armin; Schmelz, Hans U; Wagner, Walter; Meineke, Victor; Abend, Michael

    2012-07-01

    Treatment options for testis cancer depend on the histological subtype as well as on the clinical stage. An accurate staging is essential for correct treatment. The 'golden standard' for staging purposes is CT, but occult metastasis cannot be detected with this method. Currently, parameters such as primary tumour size, vessel invasion or invasion of the rete testis are used for predicting occult metastasis. Last year the association of these parameters with metastasis could not be validated in a new independent cohort. Gene expression analysis in testis cancer allowed discrimination between the different histological subtypes (seminoma and non-seminoma) as well as testis cancer and normal testis tissue. In a two-stage study design we (i) screened the whole genome (using human whole genome microarrays) for candidate genes associated with the metastatic stage in seminoma and (ii) validated and quantified gene expression of our candidate genes (real-time quantitative polymerase chain reaction) on another independent group. Gene expression measurements of two of our candidate genes (dopamine receptor D1 [DRD1] and family with sequence similarity 71, member F2 [FAM71F2]) examined in primary testis cancers made it possible to discriminate the metastasis status in seminoma. The discriminative ability of the genes exceeded the predictive significance of currently used histological/pathological parameters. Based on gene expression analysis the present study provides suggestions for improved individual decision making either in favour of early adjuvant therapy or increased surveillance. To evaluate the usefulness of gene expression profiling for predicting metastatic status in testicular seminoma at the time of first diagnosis compared with established clinical and pathological parameters. Total RNA was isolated from testicular tumours of metastasized patients (12 patients, clinical stage IIa-III), non-metastasized patients (40, clinical stage I) and adjacent 'normal' tissue

  6. Colon Cancer Cells Gene Expression Signature As Response to 5- Fluorouracil, Oxaliplatin, and Folinic Acid Treatment

    PubMed Central

    Negrei, Carolina; Hudita, Ariana; Ginghina, Octav; Galateanu, Bianca; Voicu, Sorina Nicoleta; Stan, Miriana; Costache, Marieta; Fenga, Concettina; Drakoulis, Nikolaos; Tsatsakis, Aristidis M.

    2016-01-01

    5-FU cytotoxicity mechanism has been assigned both to the miss-incorporation of fluoronucleotides into RNA and DNA and to the inhibition of thymidylate synthase. 5-FU is one of the most widely used chemotherapeutic drugs, although it has severe side effects that may vary between patients. Pharmacogenetic studies related to 5-FU have been traditionally focused on the rate-limiting catabolic enzyme, dihydropyrimidine dehydrogenase that breaks 80–85% of 5-FU into its inactive metabolite. Choosing the right dosing scheme and chemotherapy strategy for each individual patient remains challenging for personalized chemotherapy management. In the general effort toward reduction of colorectal cancer mortality, in vitro screening studies play a very important role. To accelerate translation research, increasing interest has been focused on using in vivo-like models such as three-dimensional spheroids. The development of higher throughput assays to quantify phenotypic changes in spheroids is an active research area. Consequently, in this study we used the microarray technology to reveal the HT-29 colorectal adenocarcinoma cells gene expression signature as response to 5-FU/OXP/FA treatment in a state of the art 3D culture system. We report here an increased reactive oxygen species production under treatment, correlated with a decrease in cell viability and proliferation potential. With respect to the HT-29 cells gene expression under the treatment with 5-FU/OXP/FA, we found 15.247 genes that were significantly differentially expressed (p < 0.05) with a fold change higher that two-fold. Among these, 7136 genes were upregulated and 8111 genes were downregulated under experimental conditions as compared to untreated cells. The most relevant and statistic significant (p < 0.01) pathways in the experiment are associated with the genes that displayed significant differential expression and are related to intracellular signaling, oxidative stress, apoptosis, and cancer. PMID

  7. Characterization and Interlaboratory Comparison of a Gene Expression Signature for Differentiating Genotoxic Mechanisms

    PubMed Central

    Ellinger-Ziegelbauer, Heidrun; Fostel, Jennifer M.; Aruga, Chinami; Bauer, Daniel; Boitier, Eric; Deng, Shibing; Dickinson, Donna; Le Fevre, Anne-Celine; Fornace, Albert J.; Grenet, Olivier; Gu, Yizhong; Hoflack, Jean-Christophe; Shiiyama, Masako; Smith, Roger; Snyder, Ronald D.; Spire, Catherine; Tanaka, Gotaro; Aubrecht, Jiri

    2009-01-01

    The genotoxicity testing battery is highly sensitive for detection of chemical carcinogens. However, it features a low specificity and provides only limited mechanistic information required for risk assessment of positive findings. This is especially important in case of positive findings in the in vitro chromosome damage assays, because chromosome damage may be also induced secondarily to cell death. An increasing body of evidence indicates that toxicogenomic analysis of cellular stress responses provides an insight into mechanisms of action of genotoxicants. To evaluate the utility of such a toxicogenomic analysis we evaluated gene expression profiles of TK6 cells treated with four model genotoxic agents using a targeted high density real-time PCR approach in a multilaboratory project coordinated by the Health and Environmental Sciences Institute Committee on the Application of Genomics in Mechanism-based Risk Assessment. We show that this gene profiling technology produced reproducible data across laboratories allowing us to conclude that expression analysis of a relevant gene set is capable of distinguishing compounds that cause DNA adducts or double strand breaks from those that interfere with mitotic spindle function or that cause chromosome damage as a consequence of cytotoxicity. Furthermore, our data suggest that the gene expression profiles at early time points are most likely to provide information relevant to mechanisms of genotoxic damage and that larger gene expression arrays will likely provide richer information for differentiating molecular mechanisms of action of genotoxicants. Although more compounds need to be tested to identify a robust molecular signature, this study confirms the potential of toxicogenomic analysis for investigation of genotoxic mechanisms. PMID:19465456

  8. Gene expression signature of DMBA-induced hamster buccal pouch carcinomas: modulation by chlorophyllin and ellagic acid.

    PubMed

    Vidya Priyadarsini, Ramamurthi; Kumar, Neeraj; Khan, Imran; Thiyagarajan, Paranthaman; Kondaiah, Paturu; Nagini, Siddavaram

    2012-01-01

    Chlorophyllin (CHL), a water-soluble, semi-synthetic derivative of chlorophyll and ellagic acid (EA), a naturally occurring polyphenolic compound in berries, grapes, and nuts have been reported to exert anticancer effects in various human cancer cell lines and in animal tumour models. The present study was undertaken to examine the mechanism underlying chemoprevention and changes in gene expression pattern induced by dietary supplementation of chlorophyllin and ellagic acid in the 7,12-dimethylbenz[a]anthracene (DMBA)-induced hamster buccal pouch (HBP) carcinogenesis model by whole genome profiling using pangenomic microarrays. In hamsters painted with DMBA, the expression of 1,700 genes was found to be altered significantly relative to control. Dietary supplementation of chlorophyllin and ellagic acid modulated the expression profiles of 104 and 37 genes respectively. Microarray analysis also revealed changes in the expression of TGFβ receptors, NF-κB, cyclin D1, and matrix metalloproteinases (MMPs) that may play a crucial role in the transformation of the normal buccal pouch to a malignant phenotype. This gene expression signature was altered on treatment with chlorophyllin and ellagic acid. Our study has also revealed patterns of gene expression signature specific for chlorophyllin and ellagic acid exposure. Thus dietary chlorophyllin and ellagic acid that can reverse gene expression signature associated with carcinogenesis are novel candidates for cancer prevention and therapy.

  9. Stem cells and germ cells: microRNA and gene expression signatures.

    PubMed

    Dyce, Paul William; Toms, Derek; Li, Julang

    2010-04-01

    The study of primordial germ cell development in vivo is hampered by their low numbers and inaccessibility. Recent research has shown the ability of embryonic and adult stem cells to differentiate into primordial germ cells and more mature gametes and this generation of germ cells in vitro may be an attractive model for their study. One of the biggest challenges facing in vitro differentiation of stem cells into primordial germ cells is the lack of markers to clearly distinguish the two. As both cell types originate early in embryonic development they share many pluripotent markers such as OCT4, VASA, FRAGILIS, and NANOG. Genome wide microarray profiling has been used to identify transcriptome patterns unique to primordial germ cells. A more thorough analysis of the temporal and quantitative expression of a panel of genes may be more robust in distinguishing these two cell populations. MicroRNAs, short RNA molecules that have been shown to regulate translation through interactions with mRNA transcripts, have also recently come under investigation for the role they may play in pluripotency. Attempts to elucidate key microRNAs responsible for both stem cell and primordial germ cell characteristics have recently been undertaken. Unique microRNAs, either individually or as global profiles, may also help to distinguish differentiated primordial germ cells from stem cells in vitro. This review will examine gene expression and microRNA signatures in stem cells and germ cells as ways to distinguish these closely related cell types.

  10. Prostate cancer gene expression signature of patients with high body mass index

    PubMed Central

    Sharad, Shashwat; Srivastava, Anjali; Ravulapalli, Suma; Parker, Patrick; Chen, Yongmei; Li, Hua; Petrovics, Gyorgy; Dobi, Albert

    2010-01-01

    The goal of this study was to evaluate prostate cancer gene expression signatures associated with elevated body mass index (BMI). Global gene expression profiles of prostate tumor cells and matching normal epithelial cells were compared between patients with features of normal- and high BMI at the time of radical prostatectomy. Knowledge-based analyses revealed an association of high BMI with altered levels of lipid metabolism and cholesterol homeostasis genes, such as stearoyl-CoA desaturase 1 (SCD1) and insulin-induced gene 1 (INSIG1), respectively, in prostate tumor cells. These genes were connected to known pathways of tumorigenesis revealed by the v-maf (musculoaponeurotic fibrosarcoma) oncogene homolog (MAF), notch receptor ligand, jagged 1 (JAG1), and the alanyl aminopeptidase (ANPEP/CD13) genes. This study highlighted that SCD1, a known target of statins, may play a mechanistic role in the recently noted beneficial effects of statin treatment in reducing biochemical recurrence of prostate cancer. An additional finding of our study is that some of the obesity related genes were upregulated in tumor-matched normal cells within the high BMI group, when compared to normal cells within the normal BMI cohort. PMID:21060327

  11. Genomic Signatures Predict Poor Outcome in Undifferentiated Pleomorphic Sarcomas and Leiomyosarcomas

    PubMed Central

    Silveira, Sara Martoreli; Villacis, Rolando Andre Rios; Marchi, Fabio Albuquerque; Barros Filho, Mateus de Camargo; Drigo, Sandra Aparecida; Neto, Cristovam Scapulatempo; Lopes, Ademar; da Cunha, Isabela Werneck; Rogatto, Silvia Regina

    2013-01-01

    Undifferentiated high-grade pleomorphic sarcomas (UPSs) display aggressive clinical behavior and frequently develop local recurrence and distant metastasis. Because these sarcomas often share similar morphological patterns with other tumors, particularly leiomyosarcomas (LMSs), classification by exclusion is frequently used. In this study, array-based comparative genomic hybridization (array CGH) was used to analyze 20 UPS and 17 LMS samples from untreated patients. The LMS samples presented a lower frequency of genomic alterations compared with the UPS samples. The most frequently altered UPS regions involved gains at 20q13.33 and 7q22.1 and losses at 3p26.3. Gains at 8q24.3 and 19q13.12 and losses at 9p21.3 were frequently detected in the LMS samples. Of these regions, gains at 1q21.3, 11q12.2-q12.3, 16p11.2, and 19q13.12 were significantly associated with reduced overall survival times in LMS patients. A multivariate analysis revealed that gains at 1q21.3 were an independent prognostic marker of shorter survival times in LMS patients (HR = 13.76; P = 0.019). Although the copy number profiles of the UPS and LMS samples could not be distinguished using unsupervised hierarchical clustering analysis, one of the three clusters presented cases associated with poor prognostic outcome (P = 0.022). A relative copy number analysis for the ARNT, SLC27A3, and PBXIP1 genes was performed using quantitative real-time PCR in 11 LMS and 16 UPS samples. Gains at 1q21-q22 were observed in both tumor types, particularly in the UPS samples. These findings provide strong evidence for the existence of a genomic signature to predict poor outcome in a subset of UPS and LMS patients. PMID:23825676

  12. Signatures of MRI-driven Turbulence in Protoplanetary Disks: Predictions for ALMA Observations

    NASA Astrophysics Data System (ADS)

    Simon, Jacob B.; Hughes, A. Meredith; Flaherty, Kevin M.; Bai, Xue-Ning; Armitage, Philip J.

    2015-08-01

    Spatially resolved observations of molecular line emission have the potential to yield unique constraints on the nature of turbulence within protoplanetary disks. Using a combination of local non-ideal magnetohydrodynamics (MHD) simulations and radiative transfer calculations, tailored to properties of the disk around HD 163296, we assess the ability of ALMA to detect turbulence driven by the magnetorotational instability (MRI). Our local simulations show that the MRI produces small-scale turbulent velocity fluctuations that increase in strength with height above the mid-plane. For a set of simulations at different disk radii, we fit a Maxwell-Boltzmann distribution to the turbulent velocity and construct a turbulent broadening parameter as a function of radius and height. We input this broadening into radiative transfer calculations to quantify observational signatures of MRI-driven disk turbulence. We find that the ratio of the peak line flux to the flux at line center is a robust diagnostic of turbulence that is only mildly degenerate with systematic uncertainties in disk temperature. For the CO(3-2) line, which we expect to probe the most magnetically active slice of the disk column, variations in the predicted peak-to-trough ratio between our most and least turbulent models span a range of approximately 15%. Additional independent constraints can be derived from the morphology of spatially resolved line profiles, and we estimate the resolution required to detect turbulence on different spatial scales. We discuss the role of lower optical depth molecular tracers, which trace regions closer to the disk mid-plane where velocities in MRI-driven models are systematically lower.

  13. SIGNATURES OF MRI-DRIVEN TURBULENCE IN PROTOPLANETARY DISKS: PREDICTIONS FOR ALMA OBSERVATIONS

    SciTech Connect

    Simon, Jacob B.; Hughes, A. Meredith; Flaherty, Kevin M.; Bai, Xue-Ning; Armitage, Philip J.

    2015-08-01

    Spatially resolved observations of molecular line emission have the potential to yield unique constraints on the nature of turbulence within protoplanetary disks. Using a combination of local non-ideal magnetohydrodynamics (MHD) simulations and radiative transfer calculations, tailored to properties of the disk around HD 163296, we assess the ability of ALMA to detect turbulence driven by the magnetorotational instability (MRI). Our local simulations show that the MRI produces small-scale turbulent velocity fluctuations that increase in strength with height above the mid-plane. For a set of simulations at different disk radii, we fit a Maxwell–Boltzmann distribution to the turbulent velocity and construct a turbulent broadening parameter as a function of radius and height. We input this broadening into radiative transfer calculations to quantify observational signatures of MRI-driven disk turbulence. We find that the ratio of the peak line flux to the flux at line center is a robust diagnostic of turbulence that is only mildly degenerate with systematic uncertainties in disk temperature. For the CO(3–2) line, which we expect to probe the most magnetically active slice of the disk column, variations in the predicted peak-to-trough ratio between our most and least turbulent models span a range of approximately 15%. Additional independent constraints can be derived from the morphology of spatially resolved line profiles, and we estimate the resolution required to detect turbulence on different spatial scales. We discuss the role of lower optical depth molecular tracers, which trace regions closer to the disk mid-plane where velocities in MRI-driven models are systematically lower.

  14. Gene Expression Patterns of Dengue Virus-Infected Children from Nicaragua Reveal a Distinct Signature of Increased Metabolism

    PubMed Central

    Loke, P'ng; Hammond, Samantha N.; Leung, Jacqueline M.; Kim, Charles C.; Batra, Sajeev; Rocha, Crisanta; Balmaseda, Angel; Harris, Eva

    2010-01-01

    Background Infection with dengue viruses (DENV) leads to a spectrum of disease outcomes. The pathophysiology of severe versus non-severe manifestations of DENV infection may be driven by host responses, which could be reflected in the transcriptional profiles of peripheral blood immune cells. Methodology/Principal Findings We conducted genome-wide microarray analysis of whole blood RNA from 34 DENV-infected children in Nicaragua collected on days 3–6 of illness, with different disease manifestations. Gene expression analysis identified genes that are differentially regulated between clinical subgroups. The most striking transcriptional differences were observed between dengue patients with and without shock, especially in the expression of mitochondrial ribosomal proteins associated with protein biosynthesis. In the dengue hemorrhagic fever patients, one subset of differentially expressed genes encode neutrophil-derived anti-microbial peptides associated with innate immunity. By performing a meta-analysis of our dataset in conjunction with previously published datasets, we confirmed that DENV infection in vivo is associated with large changes to protein and nucleic acid metabolism. Additionally, whereas in vitro infection leads to an increased interferon signature, this was not consistently observed from in vivo patient samples, suggesting that the interferon response in vivo is relatively transient and was no longer observed by days 3–6 of illness. Conclusions/Significance These data highlight important differences between different manifestations of severity during DENV infection as well as identify some commonalities. Compilation of larger datasets in the future across multiple studies, as we have initiated in this report, may well lead to better prediction of disease manifestation via a systems biology approach. PMID:20559541

  15. High BAALC expression associates with other molecular prognostic markers, poor outcome, and a distinct gene-expression signature in cytogenetically normal patients younger than 60 years with acute myeloid leukemia: a Cancer and Leukemia Group B (CALGB) study.

    PubMed

    Langer, Christian; Radmacher, Michael D; Ruppert, Amy S; Whitman, Susan P; Paschka, Peter; Mrózek, Krzysztof; Baldus, Claudia D; Vukosavljevic, Tamara; Liu, Chang-Gong; Ross, Mary E; Powell, Bayard L; de la Chapelle, Albert; Kolitz, Jonathan E; Larson, Richard A; Marcucci, Guido; Bloomfield, Clara D

    2008-06-01

    BAALC expression is considered an independent prognostic factor in cytogenetically normal acute myeloid leukemia (CN-AML), but has yet to be investigated together with multiple other established prognostic molecular markers in CN-AML. We analyzed BAALC expression in 172 primary CN-AML patients younger than 60 years of age, treated similarly on CALGB protocols. High BAALC expression was associated with FLT3-ITD (P = .04), wild-type NPM1 (P < .001), mutated CEBPA (P = .003), MLL-PTD (P = .009), absent FLT3-TKD (P = .005), and high ERG expression (P = .05). In multivariable analysis, high BAALC expression independently predicted lower complete remission rates (P = .04) when adjusting for ERG expression and age, and shorter survival (P = .04) when adjusting for FLT3-ITD, NPM1, CEBPA, and white blood cell count. A gene-expression signature of 312 probe sets differentiating high from low BAALC expressers was identified. High BAALC expression was associated with overexpression of genes involved in drug resistance (MDR1) and stem cell markers (CD133, CD34, KIT). Global microRNA-expression analysis did not reveal significant differences between BAALC expression groups. However, an analysis of microRNAs that putatively target BAALC revealed a potentially interesting inverse association between expression of miR-148a and BAALC. We conclude that high BAALC expression is an independent adverse prognostic factor and is associated with a specific gene-expression profile.

  16. EMERGE: a flexible modelling framework to predict genomic regulatory elements from genomic signatures

    PubMed Central

    van Duijvenboden, Karel; de Boer, Bouke A.; Capon, Nicolas; Ruijter, Jan M.; Christoffels, Vincent M.

    2016-01-01

    Regulatory DNA elements, short genomic segments that regulate gene expression, have been implicated in developmental disorders and human disease. Despite this clinical urgency, only a small fraction of the regulatory DNA repertoire has been confirmed through reporter gene assays. The overall success rate of functional validation of candidate regulatory elements is low. Moreover, the number and diversity of datasets from which putative regulatory elements can be identified is large and rapidly increasing. We generated a flexible and user-friendly tool to integrate the information from different types of genomic datasets, e.g. ATAC-seq, ChIP-seq, conservation, aiming to increase the ease and success rate of functional prediction. To this end, we developed the EMERGE program that merges all datasets that the user considers informative and uses a logistic regression framework, based on validated functional elements, to set optimal weights to these datasets. ROC curve analysis shows that a combination of datasets leads to improved prediction of tissue-specific enhancers in human, mouse and Drosophila genomes. Functional assays based on this prediction can be expected to have substantially higher success rates. The resulting integrated signal for prediction of functional elements can be plotted in a build-in genome browser or exported for further analysis. PMID:26531828

  17. A genomic biomarker signature can predict skin sensitizers using a cell-based in vitro alternative to animal tests.

    PubMed

    Johansson, Henrik; Lindstedt, Malin; Albrekt, Ann-Sofie; Borrebaeck, Carl A K

    2011-08-08

    Allergic contact dermatitis is an inflammatory skin disease that affects a significant proportion of the population. This disease is caused by an adverse immune response towards chemical haptens, and leads to a substantial economic burden for society. Current test of sensitizing chemicals rely on animal experimentation. New legislations on the registration and use of chemicals within pharmaceutical and cosmetic industries have stimulated significant research efforts to develop alternative, human cell-based assays for the prediction of sensitization. The aim is to replace animal experiments with in vitro tests displaying a higher predictive power. We have developed a novel cell-based assay for the prediction of sensitizing chemicals. By analyzing the transcriptome of the human cell line MUTZ-3 after 24 h stimulation, using 20 different sensitizing chemicals, 20 non-sensitizing chemicals and vehicle controls, we have identified a biomarker signature of 200 genes with potent discriminatory ability. Using a Support Vector Machine for supervised classification, the prediction performance of the assay revealed an area under the ROC curve of 0.98. In addition, categorizing the chemicals according to the LLNA assay, this gene signature could also predict sensitizing potency. The identified markers are involved in biological pathways with immunological relevant functions, which can shed light on the process of human sensitization. A gene signature predicting sensitization, using a human cell line in vitro, has been identified. This simple and robust cell-based assay has the potential to completely replace or drastically reduce the utilization of test systems based on experimental animals. Being based on human biology, the assay is proposed to be more accurate for predicting sensitization in humans, than the traditional animal-based tests.

  18. A genomic biomarker signature can predict skin sensitizers using a cell-based in vitro alternative to animal tests

    PubMed Central

    2011-01-01

    Background Allergic contact dermatitis is an inflammatory skin disease that affects a significant proportion of the population. This disease is caused by an adverse immune response towards chemical haptens, and leads to a substantial economic burden for society. Current test of sensitizing chemicals rely on animal experimentation. New legislations on the registration and use of chemicals within pharmaceutical and cosmetic industries have stimulated significant research efforts to develop alternative, human cell-based assays for the prediction of sensitization. The aim is to replace animal experiments with in vitro tests displaying a higher predictive power. Results We have developed a novel cell-based assay for the prediction of sensitizing chemicals. By analyzing the transcriptome of the human cell line MUTZ-3 after 24 h stimulation, using 20 different sensitizing chemicals, 20 non-sensitizing chemicals and vehicle controls, we have identified a biomarker signature of 200 genes with potent discriminatory ability. Using a Support Vector Machine for supervised classification, the prediction performance of the assay revealed an area under the ROC curve of 0.98. In addition, categorizing the chemicals according to the LLNA assay, this gene signature could also predict sensitizing potency. The identified markers are involved in biological pathways with immunological relevant functions, which can shed light on the process of human sensitization. Conclusions A gene signature predicting sensitization, using a human cell line in vitro, has been identified. This simple and robust cell-based assay has the potential to completely replace or drastically reduce the utilization of test systems based on experimental animals. Being based on human biology, the assay is proposed to be more accurate for predicting sensitization in humans, than the traditional animal-based tests. PMID:21824406

  19. Gene-expression signature of tumor recurrence in patients with stage II and III colon cancer treated with 5'fluoruracil-based adjuvant chemotherapy.

    PubMed

    Giráldez, María Dolores; Lozano, Juan José; Cuatrecasas, Míriam; Alonso-Espinaco, Virginia; Maurel, Joan; Mármol, Maribel; Hörndler, Carlos; Ortego, Javier; Alonso, Vicente; Escudero, Pilar; Ramírez, Gina; Petry, Christoph; Lasalvia, Luis; Bohmann, Kerstin; Wirtz, Ralph; Mira, Aurea; Castells, Antoni

    2013-03-01

    Although receiving adjuvant chemotherapy after radical surgery, a disappointing proportion of patients with colorectal cancer will develop tumor recurrence. Probability of relapse is currently predicted from pathological staging, there being a need for additional markers to further select high-risk patients. This study was aimed to identify a gene-expression signature to predict tumor recurrence in patients with Stages II and III colon cancer treated with 5'fluoruracil (5FU)-based adjuvant chemotherapy. Two-hundred and twenty-eight patients diagnosed with Stages II-III colon cancer and treated with surgical resection and 5FU-based adjuvant chemotherapy were included. RNA was extracted from formalin-fixed, paraffin-embedded tissue samples and expression of 27 selected candidate genes was analyzed by RT-qPCR. A tumor recurrence predicting model, including clinico-pathological variables and gene-expression profiling, was developed by Cox regression analysis and validated by bootstrapping. The regression analysis identified tumor stage and S100A2 and S100A10 gene expression as independently associated with tumor recurrence. The risk score derived from this model was able to discriminate two groups with a highly significant different probability of tumor recurrence (HR, 2.75; 95%CI, 1.71-4.39; p = 0.0001), which it was maintained when patients were stratified according to tumor stage. The algorithm was also able to distinguish two groups with different overall survival (HR, 2.68; 95%CI, 1.12-6.42; p = 0.03). Identification of a new gene-expression signature associated with a high probability of tumor recurrence in patients with Stages II and III colon cancer receiving adjuvant 5FU-based chemotherapy, and its combination in a robust, easy-to-use and reliable algorithm may contribute to tailor treatment and surveillance strategies.

  20. Ontology based molecular signatures for immune cell types via gene expression analysis

    PubMed Central

    2013-01-01

    Background New technologies are focusing on characterizing cell types to better understand their heterogeneity. With large volumes of cellular data being generated, innovative methods are needed to structure the resulting data analyses. Here, we describe an ‘Ontologically BAsed Molecular Signature’ (OBAMS) method that identifies novel cellular biomarkers and infers biological functions as characteristics of particular cell types. This method finds molecular signatures for immune cell types based on mapping biological samples to the Cell Ontology (CL) and navigating the space of all possible pairwise comparisons between cell types to find genes whose expression is core to a particular cell type’s identity. Results We illustrate this ontological approach by evaluating expression data available from the Immunological Genome project (IGP) to identify unique biomarkers of mature B cell subtypes. We find that using OBAMS, candidate biomarkers can be identified at every strata of cellular identity from broad classifications to very granular. Furthermore, we show that Gene Ontology can be used to cluster cell types by shared biological processes in order to find candidate genes responsible for somatic hypermutation in germinal center B cells. Moreover, through in silico experiments based on this approach, we have identified genes sets that represent genes overexpressed in germinal center B cells and identify genes uniquely expressed in these B cells compared to other B cell types. Conclusions This work demonstrates the utility of incorporating structured ontological knowledge into biological data analysis – providing a new method for defining novel biomarkers and providing an opportunity for new biological insights. PMID:24004649

  1. Baseline Gene Expression Signatures in Monocytes from Multiple Sclerosis Patients Treated with Interferon-beta

    PubMed Central

    Bustamante, Marta F.; Nurtdinov, Ramil N.; Río, Jordi; Montalban, Xavier; Comabella, Manuel

    2013-01-01

    Background A relatively large proportion of relapsing-remitting multiple sclerosis (RRMS) patients do not respond to interferon-beta (IFNb) treatment. In previous studies with peripheral blood mononuclear cells (PBMC), we identified a subgroup of IFNb non-responders that was characterized by a baseline over-expression of type I IFN inducible genes. Additional mechanistic experiments carried out in IFNb non-responders suggested a selective alteration of the type I IFN signaling pathway in the population of blood monocytes. Here, we aimed (i) to investigate whether the type I IFN signaling pathway is up-regulated in isolated monocytes from IFNb non-responders at baseline; and (ii) to search for additional biological pathways in this cell population that may be implicated in the response to IFNb treatment. Methods Twenty RRMS patients classified according to their clinical response to IFNb treatment and 10 healthy controls were included in the study. Monocytes were purified from PBMC obtained before treatment by cell sorting and the gene expression profiling was determined with oligonucleotide microarrays. Results and discussion Purified monocytes from IFNb non-responders were characterized by an over-expression of type I IFN responsive genes, which confirms the type I IFN signature in monocytes suggested from previous studies. Other relevant signaling pathways that were up-regulated in IFNb non-responders were related with the mitochondrial function and processes such as protein synthesis and antigen presentation, and together with the type I IFN signaling pathway, may also be playing roles in the response to IFNb. PMID:23637780

  2. MicroRNA expression profiling and DNA methylation signature for deregulated microRNA in cutaneous T-cell lymphoma.

    PubMed

    Sandoval, Juan; Díaz-Lagares, Angel; Salgado, Rocío; Servitje, Octavio; Climent, Fina; Ortiz-Romero, Pablo L; Pérez-Ferriols, Amparo; Garcia-Muret, Maria P; Estrach, Teresa; Garcia, Mar; Nonell, Lara; Esteller, Manel; Pujol, Ramon M; Espinet, Blanca; Gallardo, Fernando

    2015-04-01

    MicroRNAs usually regulate gene expression negatively, and aberrant expression has been involved in the development of several types of cancers. Microarray profiling of microRNA expression was performed to define a microRNA signature in a series of mycosis fungoides tumor stage (MFt, n=21) and CD30+ primary cutaneous anaplastic large cell lymphoma (CD30+ cALCL, n=11) samples in comparison with inflammatory dermatoses (ID, n=5). Supervised clustering confirmed a distinctive microRNA profile for cutaneous T-cell lymphoma (CTCL) with respect to ID. A 40 microRNA signature was found in MFt including upregulated onco-microRNAs (miR-146a, miR-142-3p/5p, miR-21, miR-181a/b, and miR-155) and downregulated tumor-suppressor microRNAs (miR-200ab/429 cluster, miR-10b, miR-193b, miR-141/200c, and miR-23b/27b). Regarding CD30+ cALCL, 39 differentially expressed microRNAs were identified. Particularly, overexpression of miR-155, miR-21, or miR-142-3p/5p and downregulation of the miR-141/200c clusters were observed. DNA methylation in microRNA gene promoters, as expression regulatory mechanism for deregulated microRNAs, was analyzed using Infinium 450K array and approximately one-third of the differentially expressed microRNAs showed significant DNA methylation differences. Two different microRNA methylation signatures for MFt and CD30+ cALCL were found. Correlation analysis showed an inverse relationship for microRNA promoter methylation and microRNA expression. These results reveal a subgroup-specific epigenetically regulated microRNA signatures for MFt and CD30+ cALCL patients.

  3. Focused and Steady-State Characteristics of Shaped Sonic Boom Signatures: Prediction and Analysis

    NASA Technical Reports Server (NTRS)

    Maglieri, Domenic J.; Bobbitt, Percy J.; Massey, Steven J.; Plotkin, Kenneth J.; Kandil, Osama A.; Zheng, Xudong

    2011-01-01

    The objective of this study is to examine the effect of flight, at off-design conditions, on the propagated sonic boom pressure signatures of a small "low-boom" supersonic aircraft. The amplification, or focusing, of the low magnitude "shaped" signatures produced by maneuvers such as the accelerations from transonic to supersonic speeds, climbs, turns, pull-up and pushovers is the concern. To analyze these effects, new and/or improved theoretical tools have been developed, in addition to the use of existing methodology. Several shaped signatures are considered in the application of these tools to the study of selected maneuvers and off-design conditions. The results of these applications are reported in this paper as well as the details of the new analytical tools. Finally, the magnitude of the focused boom problem for "low boom" supersonic aircraft designs has been more accurately quantified and potential "mitigations" suggested. In general, "shaped boom" signatures, designed for cruise flight, such as asymmetric and symmetric flat-top and initial-shock ramp waveforms retain their basic shape during transition flight. Complex and asymmetric and symmetric initial shock ramp waveforms provide lower magnitude focus boom levels than N-waves or asymmetric and symmetric flat-top signatures.

  4. Hepatocellular carcinoma associated microRNA expression signature: integrated bioinformatics analysis, experimental validation and clinical significance

    PubMed Central

    Shi, Ke-Qing; Lin, Zhuo; Chen, Xiang-Jian; Song, Mei; Wang, Yu-Qun; Cai, Yi-Jing; Yang, Nai-Bing; Zheng, Ming-Hua; Dong, Jin-Zhong; Zhang, Lei; Chen, Yong-Ping

    2015-01-01

    microRNA (miRNA) expression profiles varied greatly among current studies due to different technological platforms and small sample size. Systematic and integrative analysis of published datesets that compared the miRNA expression profiles between hepatocellular carcinoma (HCC) tissue and paired adjacent noncancerous liver tissue was performed to determine candidate HCC associated miRNAs. Moreover, we further validated the confirmed miRNAs in a clinical setting using qRT-PCR and Tumor Cancer Genome Atlas (TCGA) dataset. A miRNA integrated-signature of 5 upregulated and 8 downregulated miRNAs was identified from 26 published datesets in HCC using robust rank aggregation method. qRT-PCR demonstrated that miR-93-5p, miR-224-5p, miR-221-3p and miR-21-5p was increased, whereas the expression of miR-214-3p, miR-199a-3p, miR-195-5p, miR-150-5p and miR-145-5p was decreased in the HCC tissues, which was also validated on TCGA dataset. A miRNA based score using LASSO regression model provided a high accuracy for identifying HCC tissue (AUC = 0.982): HCC risk score = 0.180E_miR-221 + 0.0262E_miR-21 - 0.007E_miR-223 - 0.185E_miR-130a. E_miR-n = Log 2 (expression of microRNA n). Furthermore, expression of 5 miRNAs (miR-222, miR-221, miR-21 miR-214 and miR-130a) correlated with pathological tumor grade. Cox regression analysis showed that miR-21 was related with 3-year survival (hazard ratio [HR]: 1.509, 95%CI: 1.079–2.112, P = 0.016) and 5-year survival (HR: 1.416, 95%CI: 1.057–1.897, P = 0.020). However, none of the deregulated miRNAs was related with microscopic vascular invasion. This study provides a basis for further clinical application of miRNAs in HCC. PMID:26231037

  5. Hepatocellular carcinoma associated microRNA expression signature: integrated bioinformatics analysis, experimental validation and clinical significance.

    PubMed

    Shi, Ke-Qing; Lin, Zhuo; Chen, Xiang-Jian; Song, Mei; Wang, Yu-Qun; Cai, Yi-Jing; Yang, Nai-Bing; Zheng, Ming-Hua; Dong, Jin-Zhong; Zhang, Lei; Chen, Yong-Ping

    2015-09-22

    microRNA (miRNA) expression profiles varied greatly among current studies due to different technological platforms and small sample size. Systematic and integrative analysis of published datesets that compared the miRNA expression profiles between hepatocellular carcinoma (HCC) tissue and paired adjacent noncancerous liver tissue was performed to determine candidate HCC associated miRNAs. Moreover, we further validated the confirmed miRNAs in a clinical setting using qRT-PCR and Tumor Cancer Genome Atlas (TCGA) dataset. A miRNA integrated-signature of 5 upregulated and 8 downregulated miRNAs was identified from 26 published datesets in HCC using robust rank aggregation method. qRT-PCR demonstrated that miR-93-5p, miR-224-5p, miR-221-3p and miR-21-5p was increased, whereas the expression of miR-214-3p, miR-199a-3p, miR-195-5p, miR-150-5p and miR-145-5p was decreased in the HCC tissues, which was also validated on TCGA dataset. A miRNA based score using LASSO regression model provided a high accuracy for identifying HCC tissue (AUC = 0.982): HCC risk score = 0.180E_miR-221 + 0.0262E_miR-21 - 0.007E_miR-223 - 0.185E_miR-130a. E_miR-n = Log 2 (expression of microRNA n). Furthermore, expression of 5 miRNAs (miR-222, miR-221, miR-21 miR-214 and miR-130a) correlated with pathological tumor grade. Cox regression analysis showed that miR-21 was related with 3-year survival (hazard ratio [HR]: 1.509, 95%CI: 1.079-2.112, P = 0.016) and 5-year survival (HR: 1.416, 95%CI: 1.057-1.897, P = 0.020). However, none of the deregulated miRNAs was related with microscopic vascular invasion. This study provides a basis for further clinical application of miRNAs in HCC.

  6. Increased angiogenesis is associated with a 32-gene expression signature and 6p21 amplification in aggressive endometrial cancer

    PubMed Central

    Wik, Elisabeth; Mannelqvist, Monica; Kusonmano, Kanthida; Knutsvik, Gøril; Haldorsen, Ingfrid; Trovik, Jone; Øyan, Anne M.; Kalland, Karl-H.; Staff, Anne Cathrine; Salvesen, Helga B.; Akslen, Lars A.

    2015-01-01

    Background Angiogenesis is a hallmark of cancer. The aim of this study was to explore whether microvessel proliferation is associated with gene expression profiles or copy number alterations in endometrial cancer. Methods A prospective series of endometrial carcinomas was studied for angiogenesis markers, gene expression profiles, and gene copy number data. For validation, an independent series of endometrial carcinomas as well as an external cohort of endometrial cancer patients were examined by gene expression microarrays. Results Increased microvessel proliferation (MVP) was associated with aggressive tumor features and reduced survival, and a 32-gene expression signature was found to separate tumors with high versus low MVP. An increased 32-gene signature score was confirmed to associate with high-grade tumor features and reduced survival by independent cohorts. Copy number studies revealed that amplification of the 6p21 region was significantly associated with MVP, a high 32-gene score, as well as reduced survival. Conclusion Increased MVP was significantly associated with aggressive endometrial cancer and reduced survival. Integrated analyses demonstrated significant associations between increased vascular proliferation, amplification of the 6p21 region, VEGF-A mRNA expression, and the 32-gene angiogenesis signature. Our findings indicate amplification of 6p21 as a possible driver of tumor vascular proliferation in endometrial cancer. PMID:25860936

  7. A gene expression signature of confinement in peripheral blood of red wolves (Canis rufus).

    PubMed

    Kennerly, Erin; Ballmann, Anne; Martin, Stanton; Wolfinger, Russ; Gregory, Simon; Stoskopf, Michael; Gibson, Greg

    2008-06-01

    The stresses that animals experience as a result of modification of their ecological circumstances induce physiological changes that leave a signature in profiles of gene expression. We illustrate this concept in a comparison of free range and confined North American red wolves (Canis rufus). Transcription profiling of peripheral blood samples from 13 red wolf individuals in the Alligator River region of North Carolina revealed a strong signal of differentiation. Four hundred eighty-two out of 2980 transcripts detected on Illumina HumanRef8 oligonucleotide bead arrays were found to differentiate free range and confined wolves at a false discovery rate of 12.8% and P < 0.05. Over-representation of genes in focal adhesion, insulin signalling, proteasomal, and tryptophan metabolism pathways suggests the activation of pro-inflammatory and stress responses in confined animals. Consequently, characterization of differential transcript abundance in an accessible tissue such as peripheral blood identifies biomarkers that could be useful in animal management practices and for evaluating the impact of habitat changes on population health, particularly as attention turns to the impact of climate change on physiology and in turn species distributions.

  8. A Two-Gene Signature, SKI and SLAMF1, Predicts Time-to-Treatment in Previously Untreated Patients with Chronic Lymphocytic Leukemia

    PubMed Central

    Schweighofer, Carmen D.; Coombes, Kevin R.; Barron, Lynn L.; Diao, Lixia; Newman, Rachel J.; Ferrajoli, Alessandra; O'Brien, Susan; Wierda, William G.; Luthra, Rajyalakshmi; Medeiros, L. Jeffrey; Keating, Michael J.; Abruzzo, Lynne V.

    2011-01-01

    We developed and validated a two-gene signature that predicts prognosis in previously-untreated chronic lymphocytic leukemia (CLL) patients. Using a 65 sample training set, from a cohort of 131 patients, we identified the best clinical models to predict time-to-treatment (TTT) and overall survival (OS). To identify individual genes or combinations in the training set with expression related to prognosis, we cross-validated univariate and multivariate models to predict TTT. We identified four gene sets (5, 6, 12, or 13 genes) to construct multivariate prognostic models. By optimizing each gene set on the training set, we constructed 11 models to predict the time from diagnosis to treatment. Each model also predicted OS and added value to the best clinical models. To determine which contributed the most value when added to clinical variables, we applied the Akaike Information Criterion. Two genes were consistently retained in the models with clinical variables: SKI (v-SKI avian sarcoma viral oncogene homolog) and SLAMF1 (signaling lymphocytic activation molecule family member 1; CD150). We optimized a two-gene model and validated it on an independent test set of 66 samples. This two-gene model predicted prognosis better on the test set than any of the known predictors, including ZAP70 and serum β2-microglobulin. PMID:22194822

  9. Long non-coding RNAs as novel expression signatures modulate DNA damage and repair in cadmium toxicology

    PubMed Central

    Zhou, Zhiheng; Liu, Haibai; Wang, Caixia; Lu, Qian; Huang, Qinhai; Zheng, Chanjiao; Lei, Yixiong

    2015-01-01

    Increasing evidence suggests that long non-coding RNAs (lncRNAs) are involved in a variety of physiological and pathophysiological processes. Our study was to investigate whether lncRNAs as novel expression signatures are able to modulate DNA damage and repair in cadmium(Cd) toxicity. There were aberrant expression profiles of lncRNAs in 35th Cd-induced cells as compared to untreated 16HBE cells. siRNA-mediated knockdown of ENST00000414355 inhibited the growth of DNA-damaged cells and decreased the expressions of DNA-damage related genes (ATM, ATR and ATRIP), while increased the expressions of DNA-repair related genes (DDB1, DDB2, OGG1, ERCC1, MSH2, RAD50, XRCC1 and BARD1). Cadmium increased ENST00000414355 expression in the lung of Cd-exposed rats in a dose-dependent manner. A significant positive correlation was observed between blood ENST00000414355 expression and urinary/blood Cd concentrations, and there were significant correlations of lncRNA-ENST00000414355 expression with the expressions of target genes in the lung of Cd-exposed rats and the blood of Cd exposed workers. These results indicate that some lncRNAs are aberrantly expressed in Cd-treated 16HBE cells. lncRNA-ENST00000414355 may serve as a signature for DNA damage and repair related to the epigenetic mechanisms underlying the cadmium toxicity and become a novel biomarker of cadmium toxicity. PMID:26472689

  10. Long non-coding RNAs as novel expression signatures modulate DNA damage and repair in cadmium toxicology

    NASA Astrophysics Data System (ADS)

    Zhou, Zhiheng; Liu, Haibai; Wang, Caixia; Lu, Qian; Huang, Qinhai; Zheng, Chanjiao; Lei, Yixiong

    2015-10-01

    Increasing evidence suggests that long non-coding RNAs (lncRNAs) are involved in a variety of physiological and pathophysiological processes. Our study was to investigate whether lncRNAs as novel expression signatures are able to modulate DNA damage and repair in cadmium(Cd) toxicity. There were aberrant expression profiles of lncRNAs in 35th Cd-induced cells as compared to untreated 16HBE cells. siRNA-mediated knockdown of ENST00000414355 inhibited the growth of DNA-damaged cells and decreased the expressions of DNA-damage related genes (ATM, ATR and ATRIP), while increased the expressions of DNA-repair related genes (DDB1, DDB2, OGG1, ERCC1, MSH2, RAD50, XRCC1 and BARD1). Cadmium increased ENST00000414355 expression in the lung of Cd-exposed rats in a dose-dependent manner. A significant positive correlation was observed between blood ENST00000414355 expression and urinary/blood Cd concentrations, and there were significant correlations of lncRNA-ENST00000414355 expression with the expressions of target genes in the lung of Cd-exposed rats and the blood of Cd exposed workers. These results indicate that some lncRNAs are aberrantly expressed in Cd-treated 16HBE cells. lncRNA-ENST00000414355 may serve as a signature for DNA damage and repair related to the epigenetic mechanisms underlying the cadmium toxicity and become a novel biomarker of cadmium toxicity.

  11. Discriminative local subspaces in gene expression data for effective gene function prediction

    PubMed Central

    Gutiérrez, Rodrigo A.; Soto, Alvaro

    2012-01-01

    Motivation: Massive amounts of genome-wide gene expression data have become available, motivating the development of computational approaches that leverage this information to predict gene function. Among successful approaches, supervised machine learning methods, such as Support Vector Machines (SVMs), have shown superior prediction accuracy. However, these methods lack the simple biological intuition provided by co-expression networks (CNs), limiting their practical usefulness. Results: In this work, we present Discriminative Local Subspaces (DLS), a novel method that combines supervised machine learning and co-expression techniques with the goal of systematically predict genes involved in specific biological processes of interest. Unlike traditional CNs, DLS uses the knowledge available in Gene Ontology (GO) to generate informative training sets that guide the discovery of expression signatures: expression patterns that are discriminative for genes involved in the biological process of interest. By linking genes co-expressed with these signatures, DLS is able to construct a discriminative CN that links both, known and previously uncharacterized genes, for the selected biological process. This article focuses on the algorithm behind DLS and shows its predictive power using an Arabidopsis thaliana dataset and a representative set of 101 GO terms from the Biological Process Ontology. Our results show that DLS has a superior average accuracy than both SVMs and CNs. Thus, DLS is able to provide the prediction accuracy of supervised learning methods while maintaining the intuitive understanding of CNs. Availability: A MATLAB® implementation of DLS is available at http://virtualplant.bio.puc.cl/cgi-bin/Lab/tools.cgi Contact: tfpuelma@uc.cl Supplementary Information: Supplementary data are available at http://bioinformatics.mpimp-golm.mpg.de/. PMID:22820203

  12. Predicted and observed magnetic signatures of martian (de)magnetized impact craters

    NASA Astrophysics Data System (ADS)

    Langlais, Benoit; Thébault, Erwan

    2011-04-01

    The current morphology of the martian lithospheric magnetic field results from magnetization and demagnetization processes, both of which shaped the planet. The largest martian impact craters, Hellas, Argyre, Isidis and Utopia, are not associated with intense magnetic fields at spacecraft altitude. This is usually interpreted as locally non- or de-magnetized areas, as large impactors may have reset the magnetization of the pre-impact material. We study the effects of impacts on the magnetic field. First, a careful analysis is performed to compute the impact demagnetization effects. We assume that the pre-impact lithosphere acquired its magnetization while cooling in the presence of a global, centered and mainly dipolar magnetic field, and that the subsequent demagnetization is restricted to the excavation area created by large craters, between 50- and 500-km diameter. Depth-to-diameter ratio of the transient craters is set to 0.1, consistent with observed telluric bodies. Associated magnetic field is computed between 100- and 500-km altitude. For a single-impact event, the maximum magnetic field anomaly associated with a crater located over the magnetic pole is maximum above the crater. A 200-km diameter crater presents a close-to-1-nT magnetic field anomaly at 400-km altitude, while a 100-km diameter crater has a similar signature at 200-km altitude. Second, we statistically study the 400-km altitude Mars Global Surveyor magnetic measurements modelled locally over the visible impact craters. This approach offers a local estimate of the confidence to which the magnetic field can be computed from real measurements. We conclude that currently craters down to a diameter of 200 km can be characterized. There is a slight anti-correlation of -0.23 between magnetic field intensity and impact crater diameters, although we show that this result may be fortuitous. A complete low-altitude magnetic field mapping is needed. New data will allow predicted weak anomalies above

  13. A gene expression signature shared by human mature oocytes and embryonic stem cells

    PubMed Central

    Assou, Said; Cerecedo, Doris; Tondeur, Sylvie; Pantesco, Véronique; Hovatta, Outi; Klein, Bernard; Hamamah, Samir; De Vos, John

    2009-01-01

    Background The first week of human pre-embryo development is characterized by the induction of totipotency and then pluripotency. The understanding of this delicate process will have far reaching implication for in vitro fertilization and regenerative medicine. Human mature MII oocytes and embryonic stem (ES) cells are both able to achieve the feat of cell reprogramming towards pluripotency, either by somatic cell nuclear transfer or by cell fusion, respectively. Comparison of the transcriptome of these two cell types may highlight genes that are involved in pluripotency initiation. Results Based on a microarray compendium of 205 samples, we compared the gene expression profile of mature MII oocytes and human ES cells (hESC) to that of somatic tissues. We identified a common oocyte/hESC gene expression profile, which included a strong cell cycle signature, genes associated with pluripotency such as LIN28 and TDGF1, a large chromatin remodelling network (TOP2A, DNMT3B, JARID2, SMARCA5, CBX1, CBX5), 18 different zinc finger transcription factors, including ZNF84, and several still poorly annotated genes such as KLHL7, MRS2, or the Selenophosphate synthetase 1 (SEPHS1). Interestingly, a large set of genes was also found to code for proteins involved in the ubiquitination and proteasome pathway. Upon hESC differentiation into embryoid bodies, the transcription of this pathway declined. In vitro, we observed a selective sensitivity of hESC to the inhibition of the activity of the proteasome. Conclusion These results shed light on the gene networks that are concurrently overexpressed by the two human cell types with somatic cell reprogramming properties. PMID:19128516

  14. Can specific transcriptional regulators assemble a universal cancer signature?

    NASA Astrophysics Data System (ADS)

    Roy, Janine; Isik, Zerrin; Pilarsky, Christian; Schroeder, Michael

    2013-10-01

    Recently, there is a lot of interest in using biomarker signatures derived from gene expression data to predict cancer progression. We assembled signatures of 25 published datasets covering 13 types of cancers. How do these signatures compare with each other? On one hand signatures answering the same biological question should overlap, whereas signatures predicting different cancer types should differ. On the other hand, there could also be a Universal Cancer Signature that is predictive independently of the cancer type. Initially, we generate signatures for all datasets using classical approaches such as t-test and fold change and then, we explore signatures resulting from a network-based method, that applies the random surfer model of Google's PageRank algorithm. We show that the signatures as published by the authors and the signatures generated with classical methods do not overlap - not even for the same cancer type - whereas the network-based signatures strongly overlap. Selecting 10 out of 37 universal cancer genes gives the optimal prediction for all cancers thus taking a first step towards a Universal Cancer Signature. We furthermore analyze and discuss the involved genes in terms of the Hallmarks of cancer and in particular single out SP1, JUN/FOS and NFKB1 and examine their specific role in cancer progression.

  15. Combining modelling and experimental approaches to explain how calcium signatures are decoded by calmodulin-binding transcription activators (CAMTAs) to produce specific gene expression responses.

    PubMed

    Liu, Junli; Whalley, Helen J; Knight, Marc R

    2015-10-01

    Experimental data show that Arabidopsis thaliana is able to decode different calcium signatures to produce specific gene expression responses. It is also known that calmodulin-binding transcription activators (CAMTAs) have calmodulin (CaM)-binding domains. Therefore, the gene expression responses regulated by CAMTAs respond to calcium signals. However, little is known about how different calcium signatures are decoded by CAMTAs to produce specific gene expression responses. A dynamic model of Ca(2+) -CaM-CAMTA binding and gene expression responses is developed following thermodynamic and kinetic principles. The model is parameterized using experimental data. Then it is used to analyse how different calcium signatures are decoded by CAMTAs to produce specific gene expression responses. Modelling analysis reveals that: calcium signals in the form of cytosolic calcium concentration elevations are nonlinearly amplified by binding of Ca(2+) , CaM and CAMTAs; amplification of Ca(2+) signals enables calcium signatures to be decoded to give specific CAMTA-regulated gene expression responses; gene expression responses to a calcium signature depend upon its history and accumulate all the information during the lifetime of the calcium signature. Information flow from calcium signatures to CAMTA-regulated gene expression responses has been established by combining experimental data with mathematical modelling.

  16. A machine learning approach for identifying amino acid signatures in the HIV env gene predictive of dementia.

    PubMed

    Holman, Alexander G; Gabuzda, Dana

    2012-01-01

    The identification of nucleotide sequence variations in viral pathogens linked to disease and clinical outcomes is important for developing vaccines and therapies. However, identifying these genetic variations in rapidly evolving pathogens adapting to selection pressures unique to each host presents several challenges. Machine learning tools provide new opportunities to address these challenges. In HIV infection, virus replicating within the brain causes HIV-associated dementia (HAD) and milder forms of neurocognitive impairment in 20-30% of patients with unsuppressed viremia. HIV neurotropism is primarily determined by the viral envelope (env) gene. To identify amino acid signatures in the HIV env gene predictive of HAD, we developed a machine learning pipeline using the PART rule-learning algorithm and C4.5 decision tree inducer to train a classifier on a meta-dataset (n = 860 env sequences from 78 patients: 40 HAD, 38 non-HAD). To increase the flexibility and biological relevance of our analysis, we included 4 numeric factors describing amino acid hydrophobicity, polarity, bulkiness, and charge, in addition to amino acid identities. The classifier had 75% predictive accuracy in leave-one-out cross-validation, and identified 5 signatures associated with HAD diagnosis (p<0.05, Fisher's exact test). These HAD signatures were found in the majority of brain sequences from 8 of 10 HAD patients from an independent cohort. Additionally, 2 HAD signatures were validated against env sequences from CSF of a second independent cohort. This analysis provides insight into viral genetic determinants associated with HAD, and develops novel methods for applying machine learning tools to analyze the genetics of rapidly evolving pathogens.

  17. A Machine Learning Approach for Identifying Amino Acid Signatures in the HIV Env Gene Predictive of Dementia

    PubMed Central

    Holman, Alexander G.; Gabuzda, Dana

    2012-01-01

    The identification of nucleotide sequence variations in viral pathogens linked to disease and clinical outcomes is important for developing vaccines and therapies. However, identifying these genetic variations in rapidly evolving pathogens adapting to selection pressures unique to each host presents several challenges. Machine learning tools provide new opportunities to address these challenges. In HIV infection, virus replicating within the brain causes HIV-associated dementia (HAD) and milder forms of neurocognitive impairment in 20–30% of patients with unsuppressed viremia. HIV neurotropism is primarily determined by the viral envelope (env) gene. To identify amino acid signatures in the HIV env gene predictive of HAD, we developed a machine learning pipeline using the PART rule-learning algorithm and C4.5 decision tree inducer to train a classifier on a meta-dataset (n = 860 env sequences from 78 patients: 40 HAD, 38 non-HAD). To increase the flexibility and biological relevance of our analysis, we included 4 numeric factors describing amino acid hydrophobicity, polarity, bulkiness, and charge, in addition to amino acid identities. The classifier had 75% predictive accuracy in leave-one-out cross-validation, and identified 5 signatures associated with HAD diagnosis (p<0.05, Fisher’s exact test). These HAD signatures were found in the majority of brain sequences from 8 of 10 HAD patients from an independent cohort. Additionally, 2 HAD signatures were validated against env sequences from CSF of a second independent cohort. This analysis provides insight into viral genetic determinants associated with HAD, and develops novel methods for applying machine learning tools to analyze the genetics of rapidly evolving pathogens. PMID:23166702

  18. Probing the ToxCastTM Chemical Library for Predictive Signatures of Developmental Toxicity -NLTO Poster

    EPA Science Inventory

    EPA’s ToxCast™ project is profiling the in vitro bioactivity of chemical compounds to assess pathway-level and cell-based signatures that correlate with observed in vivo toxicity. We hypothesize that cell signaling pathways are primary targets for diverse environmental chemicals ...

  19. Probing the ToxCast Chemical Library for Predictive Signatures of Developmental Toxicity

    EPA Science Inventory

    EPA’s ToxCast™ project is profiling the in vitro bioactivity of chemical compounds to assess pathway-level and cell-based signatures that correlate with observed in vivo toxicity. We hypothesize that cell signaling pathways are primary targets for diverse environmental chemicals ...

  20. Probing the ToxCastTM Chemical Library for Predictive Signatures of Developmental Toxicity -NLTO Poster

    EPA Science Inventory

    EPA’s ToxCast™ project is profiling the in vitro bioactivity of chemical compounds to assess pathway-level and cell-based signatures that correlate with observed in vivo toxicity. We hypothesize that cell signaling pathways are primary targets for diverse environmental chemicals ...

  1. Probing the ToxCast Chemical Library for Predictive Signatures of Developmental Toxicity

    EPA Science Inventory

    EPA’s ToxCast™ project is profiling the in vitro bioactivity of chemical compounds to assess pathway-level and cell-based signatures that correlate with observed in vivo toxicity. We hypothesize that cell signaling pathways are primary targets for diverse environmental chemicals ...

  2. KRAS driven expression signature has prognostic power superior to mutation status in non‐small cell lung cancer

    PubMed Central

    Nagy, Ádám; Pongor, Lőrinc Sándor; Szabó, András; Santarpia, Mariacarmela

    2016-01-01

    KRAS is the most frequently mutated oncogene in non‐small cell lung cancer (NSCLC). However, the prognostic role of KRAS mutation status in NSCLC still remains controversial. We hypothesize that the expression changes of genes affected by KRAS mutation status will have the most prominent effect and could be used as a prognostic signature in lung cancer. We divided NSCLC patients with mutation and RNA‐seq data into KRAS mutated and wild type groups. Mann‐Whitney test was used to identify genes showing altered expression between these cohorts. Mean expression of the top five genes was designated as a “transcriptomic fingerprint” of the mutation. We evaluated the effect of this signature on clinical outcome in 2,437 NSCLC patients using univariate and multivariate Cox regression analysis. Mutation of KRAS was most common in adenocarcinoma. Mutation status and KRAS expression were not correlated to prognosis. The transcriptomic fingerprint of KRAS include FOXRED2, KRAS, TOP1, PEX3 and ABL2. The KRAS signature had a high prognostic power. Similar results were achieved when using the second and third set of strongest genes. Moreover, all cutoff values delivered significant prognostic power (p < 0.01). The KRAS signature also remained significant (p < 0.01) in a multivariate analysis including age, gender, smoking history and tumor stage. We generated a “surrogate signature” of KRAS mutation status in NSCLC patients by computationally linking genotype and gene expression. We show that secondary effects of a mutation can have a higher prognostic relevance than the primary genetic alteration itself. PMID:27859136

  3. Cell-type-specific signatures of microRNAs on target mRNA expression.

    PubMed

    Sood, Pranidhi; Krek, Azra; Zavolan, Mihaela; Macino, Giuseppe; Rajewsky, Nikolaus

    2006-02-21

    Although it is known that the human genome contains hundreds of microRNA (miRNA) genes and that each miRNA can regulate a large number of mRNA targets, the overall effect of miRNAs on mRNA tissue profiles has not been systematically elucidated. Here, we show that predicted human mRNA targets of several highly tissue-specific miRNAs are typically expressed in the same tissue as the miRNA but at significantly lower levels than in tissues where the miRNA is not present. Conversely, highly expressed genes are often enriched in mRNAs that do not have the recognition motifs for the miRNAs expressed in these tissues. Together, our data support the hypothesis that miRNA expression broadly contributes to tissue specificity of mRNA expression in many human tissues. Based on these insights, we apply a computational tool to directly correlate 3' UTR motifs with changes in mRNA levels upon miRNA overexpression or knockdown. We show that this tool can identify functionally important 3' UTR motifs without cross-species comparison.

  4. Genomic Signatures in Breast Cancer: Limitations of Available Predictive Data and the Importance of Prognosis.

    PubMed

    Esteva, Francisco J

    2015-06-01

    Several biomarkers and gene mutations in breast cancer have been shown to be predictive, in that they determine which treatments a patient should receive. Ideally, predictive markers would be available that could determine the most appropriate treatment plan based on a patient’s biology. This goal is becoming a reality in some treatment settings and cancer types, with the increasing use of targeted therapies directed against specific molecular abnormalities. Immunohistochemistry (IHC) testing is in standard use for guiding breast cancer therapy. Testing for the estrogen receptor (ER) and progesterone receptor (PR) guides the use of endocrine therapy, and human epidermal growth factor receptor 2 (HER2) testing guides the use of HER2-targeted therapies. Although IHC provides some discrimination among breast cancer subsets and helps identify appropriate therapy, more information can be gained through gene expression analyses. Contemporary multianalyte assays have demonstrated greater precision and reproducibility than seen with IHC-based assays. The most important contribution of multigene assays is the identification of women with ER/PR-positive, HER2-negative, early-stage breast cancer who are at low risk of recurrence and therefore will likely do well with endocrine therapy alone. These patients can be safely spared from the cytotoxic effects of chemotherapy.

  5. A New Gene Expression Signature for Triple Negative Breast Cancer Using Frozen Fresh Tissue before Neoadjuvant Chemotherapy.

    PubMed

    Santuario-Facio, Sandra Karina; Cardona-Huerta, Servando; Perez-Paramo, Yadira Xitlalli; Trevino, Victor; Hernandez-Cabrera, Francisco; Rojas-Martinez, Augusto; Uscanga-Perales, Grecia; Martinez-Rodriguez, Jorge Luis; Martinez-Jacobo, Lizeth; Padilla-Rivas, Gerardo; Muñoz-Maldonado, Gerardo; Gonzalez-Guerrero, Juan Francisco; Valero-Gomez, Javier; Vazquez-Guerrero, Ana Lorena; Martinez-Rodriguez, Herminia Guadalupe; Barboza-Quintana, Alvaro; Barboza-Quintana, Oralia; Garza-Guajardo, Raquel; Ortiz-Lopez, Rocio

    2017-05-04

    Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer tumors. Comparisons between TNBC and non-triple negative breast cancer (nTNBC) may help to differentiate key components involved in TNBC neoplasms. The purpose of the study was to analyze the expression profile of TNBC versus nTNBC tumors in a homogeneous population from northeastern Mexico. A prospective study of 50 patients was conducted (25 TNBC and 25 nTNBC). Clinic parameters were equally distributed for TNBC and nTNBC: age at diagnosis (51 vs 47 years, p=0.1), glucose levels (107 mg/dl vs 104 mg/dl, p=0.64), and body mass index (28 vs 29, p=0.14), respectively. Core biopsies were collected for histopathological diagnosis and gene expression analyses. Total RNA was isolated and expression profiling was performed. 40 genes showed differential expression pattern in TNBC tumors. Among these, 9 over-expressed genes (PRKX/PRKY, UGT8, HMGA1, LPIN1, HAPLN3, and ANKRD11), and one under-expressed (ANX9) gene are involved in general metabolism. Based on this biochemical peculiarity, and the over-expression of BCL11A and FOXC1 (involved in tumor growth and metastasis, respectively) we validated by qPCR the expression profile of 7 genes out of the signature. In this report, a new gene signature for TNBC is proposed. To our knowledge, this is the first TNBC signature which describes genes involved in general metabolism. The findings may be pertinent for Mexican patients and require to be evaluated in further ethnic groups and populations.

  6. Using Chemical-Induced Gene Expression in Cultured Human Cells to Predict Chemical Toxicity.

    PubMed

    Liu, Ruifeng; Yu, Xueping; Wallqvist, Anders

    2016-11-21

    Chemical toxicity is conventionally evaluated in animal models. However, animal models are resource intensive; moreover, they face ethical and scientific challenges because the outcomes obtained by animal testing may not correlate with human responses. To develop an alternative method for assessing chemical toxicity, we investigated the feasibility of using chemical-induced genome-wide expression changes in cultured human cells to predict the potential of a chemical to cause specific organ injuries in humans. We first created signatures of chemical-induced gene expression in a vertebral-cancer of the prostate cell line for ∼15,000 chemicals tested in the US National Institutes of Health Library of Integrated Network-Based Cellular Signatures program. We then used the signatures to create naı̈ve Bayesian prediction models for chemical-induced human liver cholestasis, interstitial nephritis, and long QT syndrome. Detailed cross-validation analyses indicated that the models were robust with respect to false positives and false negatives in the samples we used to train the models and could predict the likelihood that chemicals would cause specific organ injuries. In addition, we performed a literature search for drugs and dietary supplements, not formally categorized as causing organ injuries in humans but predicted by our models to be most likely to do so. We found a high percentage of these compounds associated with case reports of relevant organ injuries, lending support to the idea that in vitro cell-based experiments can be used to predict the toxic potential of chemicals. We believe that this approach, combined with a robust technique to model human exposure to chemicals, may serve as a promising alternative to animal-based chemical toxicity assessment.

  7. Independent prognostic value of BCR-ABL1-like signature and IKZF1 deletion, but not high CRLF2 expression, in children with B-cell precursor ALL

    PubMed Central

    van der Veer, Arian; Waanders, Esmé; Pieters, Rob; Willemse, Marieke E.; Van Reijmersdal, Simon V.; Russell, Lisa J.; Harrison, Christine J.; Evans, William E.; van der Velden, Vincent H. J.; Hoogerbrugge, Peter M.; Van Leeuwen, Frank; Escherich, Gabriele; Horstmann, Martin A.; Mohammadi Khankahdani, Leila; Rizopoulos, Dimitris; De Groot-Kruseman, Hester A.; Sonneveld, Edwin; Kuiper, Roland P.

    2013-01-01

    Most relapses in childhood B-cell precursor acute lymphoblastic leukemia (BCP-ALL) are not predicted using current prognostic features. Here, we determined the co-occurrence and independent prognostic relevance of 3 recently identified prognostic features: BCR-ABL1-like gene signature, deletions in IKZF1, and high CRLF2 messenger RNA expression (CRLF2-high). These features were determined in 4 trials representing 1128 children with ALL: DCOG ALL-8, ALL9, ALL10, and Cooperative ALL (COALL)-97/03. BCR-ABL1-like, IKZF1-deleted, and CRLF2-high cases constitute 33.7% of BCR-ABL1–negative, MLL wild-type BCP-ALL cases, of which BCR-ABL1-like and IKZF1 deletion (co)occurred most frequently. Higher cumulative incidence of relapse was found for BCR-ABL1-like and IKZF1-deleted, but not CRLF2-high, cases relative to remaining BCP-ALL cases, reflecting the observations in each of the cohorts analyzed separately. No relapses occurred among cases with CRLF2-high as single feature, whereas 62.9% of all relapses in BCR-ABL1–negative, MLL wild-type BCP-ALL occurred in cases with BCR-ABL1-like signature and/or IKZF1 deletion. Both the BCR-ABL1-like signature and IKZF1 deletions were prognostic features independent of conventional prognostic markers in a multivariate model, and both remained prognostic among cases with intermediate minimal residual disease. The BCR-ABL1-like signature and an IKZF1 deletion, but not CRLF2-high, are prognostic factors and are clinically of importance to identify high-risk patients who require more intensive and/or alternative therapies. PMID:23974192

  8. Multi-walled carbon nanotube-induced gene signatures in the mouse lung: potential predictive value for human lung cancer risk and prognosis

    PubMed Central

    Guo, Nancy L; Wan, Ying-Wooi; Denvir, James; Porter, Dale W; Pacurari, Maricica; Wolfarth, Michael G; Castranova, Vincent; Qian, Yong

    2012-01-01

    Concerns over the potential for multi-walled carbon nanotubes (MWCNT) to induce lung carcinogenesis have emerged. This study sought to (1) identify gene expression signatures in the mouse lungs following pharyngeal aspiration of well-dispersed MWCNT and (2) determine if these genes were associated with human lung cancer risk and progression. Genome-wide mRNA expression profiles were analyzed in mouse lungs (n=160) exposed to 0, 10, 20, 40, or 80 µg of MWCNT by pharyngeal aspiration at 1, 7, 28, and 56 days post-exposure. By using pairwise-Statistical Analysis of Microarray (SAM) and linear modeling, 24 genes were selected, which have significant changes in at least two time points, have a more than 1.5 fold change at all doses, and are significant in the linear model for the dose or the interaction of time and dose. Additionally, a 38-gene set was identified as related to cancer from 330 genes differentially expressed at day 56 post-exposure in functional pathway analysis. Using the expression profiles of the cancer-related gene set in 8 mice at day 56 post-exposure to 10 µg of MWCNT, a nearest centroid classification accurately predicts human lung cancer survival with a significant hazard ratio in training set (n=256) and test set (n=186). Furthermore, both gene signatures were associated with human lung cancer risk (n=164) with significant odds ratios. These results may lead to development of a surveillance approach for early detection of lung cancer and prognosis associated with MWCNT in the workplace. PMID:22891886

  9. Proteomic analysis of MG132-treated germinating pollen reveals expression signatures associated with proteasome inhibition.

    PubMed

    Vannini, Candida; Bracale, Marcella; Crinelli, Rita; Marconi, Valerio; Campomenosi, Paola; Marsoni, Milena; Scoccianti, Valeria

    2014-01-01

    Chemical inhibition of the proteasome has been previously found to effectively impair pollen germination and tube growth in vitro. However, the mediators of these effects at the molecular level are unknown. By performing 2DE proteomic analysis, 24 differentially expressed protein spots, representing 14 unique candidate proteins, were identified in the pollen of kiwifruit (Actinidia deliciosa) germinated in the presence of the MG132 proteasome inhibitor. qPCR analysis revealed that 11 of these proteins are not up-regulated at the mRNA level, but are most likely stabilized by proteasome inhibition. These differentially expressed proteins are predicted to function in various pathways including energy and lipid metabolism, cell wall synthesis, protein synthesis/degradation and stress responses. In line with this evidence, the MG132-induced changes in the proteome were accompanied by an increase in ATP and ROS content and by an alteration in fatty acid composition.

  10. Comparative expression profiling in grape (Vitis vinifera) berries derived from frequency analysis of ESTs and MPSS signatures

    PubMed Central

    Iandolino, Alberto; Nobuta, Kan; da Silva, Francisco Goes; Cook, Douglas R; Meyers, Blake C

    2008-01-01

    Background Vitis vinifera (V. vinifera) is the primary grape species cultivated for wine production, with an industry valued annually in the billions of dollars worldwide. In order to sustain and increase grape production, it is necessary to understand the genetic makeup of grape species. Here we performed mRNA profiling using Massively Parallel Signature Sequencing (MPSS) and combined it with available Expressed Sequence Tag (EST) data. These tag-based technologies, which do not require a priori knowledge of genomic sequence, are well-suited for transcriptional profiling. The sequence depth of MPSS allowed us to capture and quantify almost all the transcripts at a specific stage in the development of the grape berry. Results The number and relative abundance of transcripts from stage II grape berries was defined using Massively Parallel Signature Sequencing (MPSS). A total of 2,635,293 17-base and 2,259,286 20-base signatures were obtained, representing at least 30,737 and 26,878 distinct sequences. The average normalized abundance per signature was ~49 TPM (Transcripts Per Million). Comparisons of the MPSS signatures with available Vitis species' ESTs and a unigene set demonstrated that 6,430 distinct contigs and 2,190 singletons have a perfect match to at least one MPSS signature. Among the matched sequences, ESTs were identified from tissues other than berries or from berries at different developmental stages. Additional MPSS signatures not matching to known grape ESTs can extend our knowledge of the V. vinifera transcriptome, particularly when these data are used to assist in annotation of whole genome sequences from Vitis vinifera. Conclusion The MPSS data presented here not only achieved a higher level of saturation than previous EST based analyses, but in doing so, expand the known set of transcripts of grape berries during the unique stage in development that immediately precedes the onset of ripening. The MPSS dataset also revealed evidence of antisense

  11. Measurement and Model Predicted Corrosion Related Magnetic Signature: Applied on CFAV Quest

    DTIC Science & Technology

    2010-10-01

    prises en vue de les réduire au minimum. En plus de la peinture anticorrosion, la partie submergée du navire auxiliaire de recherche canadien (NAFC...de la peinture . À titre de comparaison, la signature magnétique totale d’un navire démagnétisé du même tonnage peut avoir une amplitude d’environ 1

  12. Identification of high risk anaplastic gliomas by a diagnostic and prognostic signature derived from mRNA expression profiling.

    PubMed

    Zhang, Chuan-Bao; Zhu, Ping; Yang, Pei; Cai, Jin-Quan; Wang, Zhi-Liang; Li, Qing-Bin; Bao, Zhao-Shi; Zhang, Wei; Jiang, Tao

    2015-11-03

    Anaplastic gliomas are characterized by variable clinical and genetic features, but there are few studies focusing on the substratification of anaplastic gliomas. To identify a more objective and applicable classification of anaplastic gliomas, we analyzed whole genome mRNA expression profiling of four independent datasets. Univariate Cox regression, linear risk score formula and receiver operating characteristic (ROC) curve were applied to derive a gene signature with best prognostic performance. The corresponding clinical and molecular information were further analyzed for interpretation of the different prognosis and the independence of the signature. Gene ontology (GO), Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA) were performed for functional annotation of the differences. We found a three-gene signature, by applying which, the anaplastic gliomas could be divided into low risk and high risk groups. The two groups showed a high concordance with grade II and grade IV gliomas, respectively. The high risk group was more aggressive and complex. The three-gene signature showed diagnostic and prognostic value in anaplastic gliomas.

  13. Development of a Transient Acoustic Boundary Element Method to Predict the Noise Signature of Swimming Fish

    NASA Astrophysics Data System (ADS)

    Wagenhoffer, Nathan; Moored, Keith; Jaworski, Justin

    2015-11-01

    Animals have evolved flexible wings and fins to efficiently and quietly propel themselves through the air and water. The design of quiet and efficient bio-inspired propulsive concepts requires a rapid, unified computational framework that integrates three essential features: the fluid mechanics, the elastic structural response, and the noise generation. This study focuses on the development, validation, and demonstration of a transient, two-dimensional acoustic boundary element solver accelerated by a fast multipole algorithm. The resulting acoustic solver is used to characterize the acoustic signature produced by a vortex street advecting over a NACA 0012 airfoil, which is representative of vortex-body interactions that occur in schools of swimming fish. Both 2S and 2P canonical vortex streets generated by fish are investigated over the range of Strouhal number 0 . 2 < St < 0 . 4 , and the acoustic signature of the airfoil is quantified. This study provides the first estimate of the noise signature of a school of swimming fish. Lehigh University CORE Grant.

  14. From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma

    PubMed Central

    Cai, Xiao-yong; Wen, Dong-yue; Ye, Zhi-hua; Liang, Liang; Zhang, Lu; Wang, Han-lin

    2017-01-01

    Background Liver hepatocellular carcinoma accounts for the overwhelming majority of primary liver cancers and its belated diagnosis and poor prognosis call for novel biomarkers to be discovered, which, in the era of big data, innovative bioinformatics and computational techniques can prove to be highly helpful in. Methods Big data aggregated from The Cancer Genome Atlas and Natural Language Processing were integrated to generate differentially expressed genes. Relevant signaling pathways of differentially expressed genes went through Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes and Panther pathway enrichment analysis and protein-protein interaction network. The pathway ranked high in the enrichment analysis was further investigated, and selected genes with top priority were evaluated and assessed in terms of their diagnostic and prognostic values. Results A list of 389 genes was generated by overlapping genes from The Cancer Genome Atlas and Natural Language Processing. Three pathways demonstrated top priorities, and the one with specific associations with cancers, ‘pathways in cancer,’ was analyzed with its four highlighted genes, namely, BIRC5, E2F1, CCNE1, and CDKN2A, which were validated using Oncomine. The detection pool composed of the four genes presented satisfactory diagnostic power with an outstanding integrated AUC of 0.990 (95% CI [0.982–0.998], P < 0.001, sensitivity: 96.0%, specificity: 96.5%). BIRC5 (P = 0.021) and CCNE1 (P = 0.027) were associated with poor prognosis, while CDKN2A (P = 0.066) and E2F1 (P = 0.088) demonstrated no statistically significant differences. Discussion The study illustrates liver hepatocellular carcinoma gene signatures, related pathways and networks from the perspective of big data, featuring the cancer-specific pathway with priority, ‘pathways in cancer.’ The detection pool of the four highlighted genes, namely BIRC5, E2F1, CCNE1 and CDKN2A, should be further investigated

  15. Immune Signatures Following Single Dose Trastuzumab Predict Pathologic Response to Preoperative Trastuzumab and Chemotherapy in HER2-Positive Early Breast Cancer

    PubMed Central

    Varadan, Vinay; Gilmore, Hannah; Miskimen, Kristy L.S.; Tuck, David; Parsai, Shikha; Awadallah, Amad; Krop, Ian E.; Winer, Eric P.; Bossuyt, Veerle; Somlo, George; Abu-Khalaf, Maysa M.; Fenton, Mary Anne; Sikov, William; Harris, Lyndsay N.

    2017-01-01

    Purpose Recent data suggest that intrinsic subtype and immune cell infiltration may predict response to trastuzumab-based therapy. We studied the interaction between these factors, changes in immune signatures following brief exposure to trastuzumab, and achievement of pathologic complete response (pCR) to subsequent preoperative trastuzumab and chemotherapy in HER2-positive breast cancer. Experimental Design In patients enrolled on two multicenter trials (03-311 and 211B), tumor core biopsies were obtained at baseline and after brief exposure to single-agent trastuzumab or nab-paclitaxel. Gene expression profiles were assessed to assign PAM50 subtypes, measure immune cell activation, and were correlated with response. Results The pCR rate was significantly higher in HER2-enriched tumors in the Discovery, 03-311 (36%, P = 0.043) dataset, as compared with other subtypes, which validated in 211B (50%, P = 0.048). Significant increases in a signature of immune cell admixture (Immune Index) were observed only following brief exposure to trastuzumab in HER2-enriched tumors (Discovery/03-311, P = 0.05; Validation/211B, P = 0.02). Increased Immune Index was predictive of response after brief exposure (03-311, P = 0.03; 211B, P = 0.04), but not at baseline, in addition to increased expression of a CD4+ follicular helper T-cell signature (03-311, P = 0.05; 211B, P = 0.04). Brief exposure to trastuzumab significantly increased gene expression of the T-cell marker PD-1 in HER2-enriched tumors (Discovery/03-311, P = 0.045) and PD-1 positivity by IHC (Validation/211B, P = 0.035). Conclusions Correlations between pCR rates, increases in Immune Index and markers of T-cell activity following brief exposure to trastuzumab in HER2-enriched tumors provide novel insights into the interaction between tumor biology, antitumor immunity, and response to treatment, and suggest potential clinically useful biomarkers in HER2+ breast cancers. PMID:26842237

  16. Gene expression profile predictive of response to chemotherapy in metastatic colorectal cancer.

    PubMed

    Estevez-Garcia, Purificacion; Rivera, Fernando; Molina-Pinelo, Sonia; Benavent, Marta; Gómez, Javier; Limón, Maria Luisa; Pastor, Maria Dolores; Martinez-Perez, Julia; Paz-Ares, Luis; Carnero, Amancio; Garcia-Carbonero, Rocio

    2015-03-20

    Fluoropyrimidine-based chemotherapy (CT) has been the mainstay of care of metastatic colorectal cancer (mCRC) for years. Response rates are only observed, however, in about half of treated patients, and there are no reliable tools to prospectively identify patients more likely to benefit from therapy. The purpose of our study was to identify a gene expression profile predictive of CT response in mCRC. Whole genome expression analyses (Affymetrix GeneChip HG-U133 Plus 2.0) were performed in fresh frozen tumor samples of 37 mCRC patients (training cohort). Differential gene expression profiles among the two study conditions (responders versus non-responders) were assessed using supervised class prediction algorithms. A set of 161 differentially expressed genes in responders (23 patients; 62%) versus non-responders (14 patients; 38%) was selected for further assessment and validation by RT-qPCR (TaqMan Low Density Arrays (TLDA) 7900 HT Micro Fluidic Cards) in an independent multi-institutional cohort (53 mCRC patients). Seven of these genes were confirmed as significant predictors of response. Patients with a favorable predictive signature had significantly greater response rate (58% vs. 13%, p = 0.024), progression-free survival (61% vs. 13% at 1 year, HR = 0.32, p = 0.009) and overall survival (32 vs. 16 months, HR = 0.21, p = 0.003) than patients with an unfavorable gene signature. This is the first study to validate a gene-expression profile predictive of response to CT in mCRC patients. Larger and prospective confirmatory studies are required, however, in order to successfully provide oncologists with adequate tools to optimize treatment selection in routine clinical practice.

  17. Identification of a liver cirrhosis signature in plasma for predicting hepatocellular carcinoma risk in a population-based cohort of hepatitis B carriers.

    PubMed

    Liu, Chia-Chi; Wang, Ya-Hui; Chuang, Eric Y; Tsai, Mong-Hsun; Chuang, Ya-Hui; Lin, Chih-Lin; Liu, Chun-Jen; Hsiao, Bo-Yu; Lin, Shi-Ming; Liu, Li-Yu; Yu, Ming-Whei

    2014-01-01

    Liver cirrhosis is a critical state in the natural course of hepatocellular carcinoma (HCC). We sought to investigate the potential of in-depth proteomics to reveal plasma protein signatures that reflect common networks/pathways of liver cirrhosis, and to determine whether the cirrhosis-related signature in plasma is linked to the development of HCC among hepatitis B virus (HBV) carriers. We first compared plasma protein profiles using a 174-antibody microarray system between three groups of HBV carriers with different Child's grades of cirrhosis, which revealed a panel of 45 differentially expressed proteins with a high accuracy for discriminating Child's B/C. Ingenuity Pathway Analysis identified two main up-regulated networks connecting the 45 proteins that were most enriched for genes in the pathway of hepatic stellate cell activation. A parsimonious subset of 11 pathway-based proteins was then selected for quantification to correlate with HCC risk among 49 HCC cases and 50 controls in a nested case-control study within a 16-yr follow-up cohort of HBV carriers. A high risk score derived from a principal component analysis, which was used to extract the cluster structure of the 11 proteins, was associated with HCC (odds ratio = 4.83, 95% confidence interval: 1.26-18.56) even after adjustment for viral and clinical variables, implying the involvement of a pattern of coordinated proteins. Stepwise logistic regression on the 11 proteins revealed ICAM-2 as an independent predictor for HCC. These findings may give further insight into the pathobiology of hepatocarcinogenesis, allow testing of the cirrhosis-related plasma protein signature as a potential predictive biomarker for HCC.

  18. MicroRNA expression signature of castration-resistant prostate cancer: the microRNA-221/222 cluster functions as a tumour suppressor and disease progression marker

    PubMed Central

    Goto, Yusuke; Kojima, Satoko; Nishikawa, Rika; Kurozumi, Akira; Kato, Mayuko; Enokida, Hideki; Matsushita, Ryosuke; Yamazaki, Kazuto; Ishida, Yasuo; Nakagawa, Masayuki; Naya, Yukio; Ichikawa, Tomohiko; Seki, Naohiko

    2015-01-01

    Background: Our present study of the microRNA (miRNA) expression signature in castration-resistant prostate cancer (CRPC) revealed that the clustered miRNAs microRNA-221 (miR-221) and microRNA-222 (miR-222) are significantly downregulated in cancer tissues. The aim of this study was to investigate the functional roles of miR-221 and miR-222 in prostate cancer (PCa) cells. Methods: A CRPC miRNA signature was constructed by PCR-based array methods. Functional studies of differentially expressed miRNAs were analysed using PCa cells. The association between miRNA expression and overall survival was estimated by the Kaplan–Meier method. In silico database and genome-wide gene expression analyses were performed to identify molecular targets regulated by the miR-221/222 cluster. Results: miR-221 and miR-222 were significantly downregulated in PCa and CRPC specimens. Kaplan–Meier survival curves showed that low expression of miR-222 predicted a short duration of progression to CRPC. Restoration of miR-221 or miR-222 in cancer cells revealed that both miRNAs significantly inhibited cancer cell migration and invasion. Ecm29 was directly regulated by the miR-221/222 cluster in PCa cells. Conclusions: Loss of the tumour-suppressive miR-221/222 cluster enhanced migration and invasion in PCa cells. Our data describing targets regulated by the tumour-suppressive miR-221/222 cluster provide insights into the mechanisms of PCa and CRPC progression. PMID:26325107

  19. Identifying protective host gene expression signatures within the spleen during West Nile virus infection in the collaborative cross model.

    PubMed

    Green, Richard; Wilkins, Courtney; Thomas, Sunil; Sekine, Aimee; Ireton, Renee C; Ferris, Martin T; Hendrick, Duncan M; Voss, Kathleen; de Villena, Fernando Pardo-Manuel; Baric, Ralph; Heise, Mark; Gale, Michael

    2016-12-01

    Flaviviruses are hematophagous arthropod-viruses that pose global challenges to human health. Like Zika virus, West Nile Virus (WNV) is a flavivirus for which no approved vaccine exists [1]. The role host genetics play in early detection and response to WNV still remains largely unexplained. In order to capture the impact of genetic variation on innate immune responses, we studied gene expression following WNV infection using the collaborative cross (CC). The CC is a mouse genetics resource composed of hundreds of independently bred, octo-parental recombinant inbred mouse lines [2]. To accurately capture the host immune gene expression signatures of West Nile infection, we used the nanostring platform to evaluate expression in spleen tissue isolated from CC mice infected with WNV over a time course of 4, 7, and 12 days' post-infection [3]. Nanostring is a non-amplification based digital method to quantitate gene expression that uses color-coded molecular barcodes to detect hundreds of transcripts in a sample. Using this approach, we identified unique gene signatures in spleen tissue at days 4, 7, and 12 following WNV infection, which delineated distinct differences between asymptomatic and symptomatic CC lines. We also identified novel immune genes. Data was deposited into the Gene Expression Omnibus under accession GSE86000.

  20. Gene-expression signature of benign monoclonal gammopathy evident in multiple myeloma is linked to good prognosis

    PubMed Central

    Zhan, Fenghuang; Barlogie, Bart; Arzoumanian, Varant; Huang, Yongsheng; Williams, David R.; Hollmig, Klaus; Pineda-Roman, Mauricio; Tricot, Guido; van Rhee, Frits; Zangari, Maurizio; Dhodapkar, Madhav; Shaughnessy, John D.

    2007-01-01

    Monoclonal gammopathy of undetermined significance (MGUS) can progress to multiple myeloma (MM). Although these diseases share many of the same genetic features, it is still unclear whether global gene-expression profiling might identify prior genomic signatures that distinguish them. Through significance analysis of microarrays, 52 genes involved in important pathways related to cancer were differentially expressed in the plasma cells of healthy subjects (normal plasma-cell [NPC]; n = 22) and patients with stringently defined MGUS/smoldering MM (n = 24) and symptomatic MM (n = 351) (P < .001). Unsupervised hierarchical clustering of 351 patients with MM, 44 with MGUS (24 + 20), and 16 with MM from MGUS created 2 major cluster branches, one containing 82% of the MGUS patients and the other containing 28% of the MM patients, termed MGUS-like MM (MGUS-L MM). Using the same clustering approach on an independent cohort of 214 patients with MM, 27% were found to be MGUS-L. This molecular signature, despite its association with a lower incidence of complete remission (P = .006), was associated with low-risk clinical and molecular features and superior survival (P < .01). The MGUS-L signature was also seen in plasma cells from 15 of 20 patients surviving more than 10 years after autotransplantation. These data provide insight into the molecular mechanisms of plasma-cell dyscrasias. PMID:17023574

  1. Pharmacologic inhibition of RORγt regulates Th17 signature gene expression and suppresses cutaneous inflammation in vivo.

    PubMed

    Skepner, Jill; Ramesh, Radha; Trocha, Mark; Schmidt, Darby; Baloglu, Erkan; Lobera, Mercedes; Carlson, Thaddeus; Hill, Jonathan; Orband-Miller, Lisa A; Barnes, Ashley; Boudjelal, Mohamed; Sundrud, Mark; Ghosh, Shomir; Yang, Jianfei

    2014-03-15

    IL-17-producing CD4(+)Th17 cells, CD8(+)Tc17 cells, and γδ T cells play critical roles in the pathogenesis of autoimmune psoriasis. RORγt is required for the differentiation of Th17 cells and expression of IL-17. In this article, we describe a novel, potent, and selective RORγt inverse agonist (TMP778), and its inactive diastereomer (TMP776). This chemistry, for the first time to our knowledge, provides a unique and powerful set of tools to probe RORγt-dependent functions. TMP778, but not TMP776, blocked human Th17 and Tc17 cell differentiation and also acutely modulated IL-17A production and inflammatory Th17-signature gene expression (Il17a, Il17f, Il22, Il26, Ccr6, and Il23) in mature human Th17 effector/memory T cells. In addition, TMP778, but not TMP776, inhibited IL-17A production in both human and mouse γδ T cells. IL-23-induced IL-17A production was also blocked by TMP778 treatment. In vivo targeting of RORγt in mice via TMP778 administration reduced imiquimod-induced psoriasis-like cutaneous inflammation. Further, TMP778 selectively regulated Th17-signature gene expression in mononuclear cells isolated from both the blood and affected skin of psoriasis patients. In summary, to our knowledge, we are the first to demonstrate that RORγt inverse agonists: 1) inhibit Tc17 cell differentiation, as well as IL-17 production by γδ T cells and CD8(+) Tc17 cells; 2) block imiquimod-induced cutaneous inflammation; 3) inhibit Th17 signature gene expression by cells isolated from psoriatic patient samples; and 4) block IL-23-induced IL-17A expression. Thus, RORγt is a tractable drug target for the treatment of cutaneous inflammatory disorders, which may afford additional therapeutic benefit over existing modalities that target only IL-17A.

  2. A New Gene Expression Signature for Triple-Negative Breast Cancer Using Frozen Fresh Tissue before Neoadjuvant Chemotherapy

    PubMed Central

    Santuario-Facio, Sandra K; Cardona-Huerta, Servando; Perez-Paramo, Yadira X; Trevino, Victor; Hernandez-Cabrera, Francisco; Rojas-Martinez, Augusto; Uscanga-Perales, Grecia; Martinez-Rodriguez, Jorge L; Martinez-Jacobo, Lizeth; Padilla-Rivas, Gerardo; Muñoz-Maldonado, Gerardo; Gonzalez-Guerrero, Juan Francisco; Valero-Gomez, Javier; Vazquez-Guerrero, Ana L; Martinez-Rodriguez, Herminia G; Barboza-Quintana, Alvaro; Barboza-Quintana, Oralia; Garza-Guajardo, Raquel; Ortiz-Lopez, Rocio

    2017-01-01

    Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer tumors. Comparisons between TNBC and non–triple-negative breast cancer (nTNBC) may help to differentiate key components involved in TNBC neoplasms. The purpose of the study was to analyze the expression profile of TNBC versus nTNBC tumors in a homogeneous population from northeastern Mexico. A prospective study of 50 patients (25 TNBC and 25 nTNBC) was conducted. Clinic parameters were equally distributed for TNBC and nTNBC: age at diagnosis (51 versus 47 years, p = 0.1), glucose level (107 mg/dl versus 104 mg/dl, p = 0.64), and body mass index (28 versus 29, p = 0.14). Core biopsies were collected for histopathological diagnosis and gene expression analysis. Total RNA was isolated and expression profiling was performed. Forty genes showed differential expression pattern in TNBC tumors. Among these, nine overexpressed genes (PRKX/PRKY, UGT8, HMGA1, LPIN1, HAPLN3, FAM171A1, BCL141A, FOXC1, and ANKRD11), and one underexpressed gene (ANX9) are involved in general metabolism. Based on this biochemical peculiarity and the overexpression of BCL11A and FOXC1 (involved in tumor growth and metastasis, respectively), we validated by quantitative polymerase chain reaction the expression profiles of seven genes out of the signature. In this report, a new gene signature for TNBC is proposed. To our knowledge, this is the first TNBC signature that describes genes involved in general metabolism. The findings may be pertinent for Mexican patients and require evaluation in other ethnic groups and populations. PMID:28474731

  3. A three-lncRNA signature derived from the Atlas of ncRNA in cancer (TANRIC) database predicts the survival of patients with head and neck squamous cell carcinoma.

    PubMed

    Cao, Wei; Liu, Jian-Nan; Liu, Zeqi; Wang, Xu; Han, Ze-Guang; Ji, Tong; Chen, Wan-Tao; Zou, Xin

    2017-02-01

    Long non-coding RNAs (lncRNAs) have important biological functions and can be used as prognostic biomarkers in cancer. To identify a lncRNA prognostic signature for head and neck squamous cell carcinoma (HNSCC). We analysed RNA-seq data derived from the TANRIC database to identify a lncRNA prognostic signature model using the orthogonal partial least squares discrimination analysis (OPLS-DA) and 1.5-fold expression change criterion methods. The prognosis prediction model based on the lncRNA signatures and clinical parameters were evaluated using the 5-fold cross validation method. A total of 84 out of 3199 lncRNAs were significantly associated with the survival of patients with HNSCC (log-rank test P<0.01). Using the OPLS-DA and 1.5-fold change selection criterion, 5 lncRNAs (KTN1-AS1, LINC00460, GUSBP11, LINC00923 and RP5-894A10.6) were further selected. The prediction power of each combination of the 5 lncRNAs was evaluated through the receiver operating characteristic (ROC) curve and a three-lncRNA panel (KTN1-AS1, LINC00460 and RP5-894A10.6) achieved the highest prognostic prediction power (AUC 0.68, 95% CI 0.60-0.76, P<0.0001) in the cohort. The patients were categorized into high- and low-risk groups based on their three-lncRNA profiles. Patients with high-risk scores had worse overall survival than those with low risk scores in the cohort (log-rank test P=0.0003). Multivariable Cox regression analyses showed that the lncRNA signature and tumour grade were independent prognostic factors for patients with HNSCC. Our findings showed that the three-lncRNA signature might be a novel biomarker for the accurate prognosis prediction of patients with HNSCC. Copyright © 2016 Elsevier Ltd. All rights reserved.

  4. Correlation of p16(INK4A) expression and HPV copy number with cellular FTIR spectroscopic signatures of cervical cancer cells.

    PubMed

    Ostrowska, Kamila M; Garcia, Amaya; Meade, Aidan D; Malkin, Alison; Okewumi, Ifeoluwapo; O'Leary, John J; Martin, Cara; Byrne, Hugh J; Lyng, Fiona M

    2011-04-07

    Cervical cancer, a potentially preventable disease, has its main aetiology in infection by high risk human papillomavirus (HR-HPV). Approaches to improving cervical cancer screening and diagnostic methodologies include molecular biological analysis, targeting of biomarker proteins, but also exploration and implementation of new techniques such as vibrational spectroscopy. This study correlates the biomarker protein p16(INK4A) expression levels dependent on HPV copy number with the infrared absorption spectral signatures of the cervical cancer cell lines, HPV negative C33A, HPV-16 positive SiHa and CaSki and HPV-18 positive HeLa. Confocal fluorescence microscopy demonstrated that p16(INK4A) is expressed in all investigated cell lines in both nuclear and cytoplasmic regions, although predominantly in the cytoplasm. Flow cytometry was used to quantify the p16(INK4A) expression levels and demonstrated a correlation, albeit nonlinear, between the reported number of integrated HPV copies and p16(INK4A) expression levels. CaSki cells were found to have the highest level of expression, HeLa intermediate levels, and SiHa and C33A the lowest levels. FTIR spectra revealed differences in nucleic acid, lipid and protein signatures between the cell lines with varying HPV copy number. Peak intensities exhibited increasing tendency in nucleic acid levels and decreasing tendency in lipid levels with increasing HPV copy number, and although they were found to be nonlinearly correlated with the HPV copy number, their dependence on p16(INK4A) levels was found to be close to linear. Principal Component Analysis (PCA) of the infrared absorption spectra revealed differences between nuclear and cytoplasmic spectroscopic signatures for all cell lines, and furthermore clearly differentiated the groups of spectra representing each cell line. Finally, Partial Least Squares (PLS) analysis was employed to construct a model which can predict the p16(INK4A) expression level based on a spectral

  5. Precise predictions for top-quark-plus-missing-energy signatures at the LHC.

    PubMed

    Boughezal, Radja; Schulze, Markus

    2013-05-10

    We study the pair production of scalar top-quark partners decaying to a top-quark pair plus large missing energy at the LHC, a signature which appears in numerous models that address outstanding problems at the TeV scale. The severe experimental search cuts require a description which combines higher-order corrections to both production and decay dynamics for a realistic final state. We do this at next-to-leading order in QCD. We find large, kinematic-dependent QCD corrections that differ dramatically depending upon the observable under consideration, potentially impacting the search for and interpretation of these states.

  6. Genome-wide gene expression profiling to predict resistance to anthracyclines in breast cancer patients

    PubMed Central

    Haibe-Kains, B.; Desmedt, C.; Di Leo, A.; Azambuja, E.; Larsimont, D.; Selleslags, J.; Delaloge, S.; Duhem, C.; Kains, J.P.; Carly, B.; Maerevoet, M.; Vindevoghel, A.; Rouas, G.; Lallemand, F.; Durbecq, V.; Cardoso, F.; Salgado, R.; Rovere, R.; Bontempi, G.; Michiels, S.; Buyse, M.; Nogaret, J.M.; Qi, Y.; Symmans, F.; Pusztai, L.; D'Hondt, V.; Piccart-Gebhart, M.; Sotiriou, C.

    2013-01-01

    Validated biomarkers predictive of response/resistance to anthracyclines in breast cancer are currently lacking. The neoadjuvant Trial of Principle (TOP) study, in which patients with estrogen receptor (ER)–negative tumors were treated with anthracycline (epirubicin) monotherapy, was specifically designed to evaluate the predictive value of topoisomerase II-alpha (TOP2A) and develop a gene expression signature to identify those patients who do not benefit from anthracyclines. Here we describe in details the contents and quality controls for the gene expression and clinical data associated with the study published by Desmedt and colleagues in the Journal of Clinical Oncology in 2011 (Desmedt et al., 2011). We also provide R code to easily access the data and perform the quality controls and basic analyses relevant to this dataset. PMID:26484051

  7. Peripheral Blood Cell Gene Expression Diagnostic for Identifying Symptomatic Transthyretin Amyloidosis Patients: Male and Female Specific Signatures

    PubMed Central

    Kurian, Sunil M.; Novais, Marta; Whisenant, Thomas; Gelbart, Terri; Buxbaum, Joel N.; Kelly, Jeffery W.; Coelho, Teresa; Salomon, Daniel R.

    2016-01-01

    Background: Early diagnosis of familial transthyretin (TTR) amyloid diseases remains challenging because of variable disease penetrance. Currently, patients must have an amyloid positive tissue biopsy to be eligible for disease-modifying therapies. Endomyocardial biopsies are typically amyloid positive when cardiomyopathy is suspected, but this disease manifestation is generally diagnosed late. Early diagnosis is often difficult because patients exhibit apparent symptoms of polyneuropathy, but have a negative amyloid biopsy. Thus, there is a pressing need for an additional early diagnostic strategy for TTR-aggregation-associated polyneuropathy and cardiomyopathy. Methods and Findings: Global peripheral blood cell mRNA expression profiles from 263 tafamidis-treated and untreated V30M Familiar Amyloid Neuropathy patients, asymptomatic V30M carriers, and healthy, age- and sex-matched controls without TTR mutations were used to differentiate symptomatic from asymptomatic patients. We demonstrate that blood cell gene expression patterns reveal sex-independent, as well as male- and female-specific inflammatory signatures in symptomatic FAP patients, but not in asymptomatic carriers. These signatures differentiated symptomatic patients from asymptomatic V30M carriers with >80% accuracy. There was a global downregulation of the eIF2 pathway and its associated genes in all symptomatic FAP patients. We also demonstrated that the molecular scores based on these signatures significantly trended toward normalized values in an independent cohort of 46 FAP patients after only 3 months of tafamidis treatment. Conclusions: This study identifies novel molecular signatures that differentiate symptomatic FAP patients from asymptomatic V30M carriers as well as affected males and females. We envision using this approach, initially in parallel with amyloid biopsies, to identify individuals who are asymptomatic gene carriers that may convert to FAP patients. Upon further validation

  8. Comparative proteomic analysis of four Bacillus clausii strains: proteomic expression signature distinguishes protein profile of the strains.

    PubMed

    Lippolis, Rosa; Gnoni, Antonio; Abbrescia, Anna; Panelli, Damiano; Maiorano, Stefania; Paternoster, Maria Stefania; Sardanelli, Anna Maria; Papa, Sergio; Gaballo, Antonio

    2011-11-18

    A comparative proteomic approach, using two dimensional gel electrophoresis and mass spectrometry, has been developed to compare and elucidate the differences among the cellular proteomes of four closely related isogenic O/C, SIN, N/R and T, B. clausii strains during both exponential and stationary phases of growth. Image analysis of the electropherograms reveals a high degree of concordance among the four proteomes, some proteins result, however, differently expressed. The proteins spots exhibiting high different expression level were identified, by mass-spectrometry analysis, as alcohol dehydrogenase (ADHA, EC1.2.1.3; ABC0046 isoform) aldehyde dehydrogenase (DHAS, EC 1.2.1.3; ABC0047 isoform) and flagellin-protein of B. clausii KSM-k16. The different expression levels of the two dehydrogenases were confirmed by quantitative RT-PCR and dehydrogenases enzymatic activity. The different patterns of protein expression can be considered as cell proteome signatures of the different strains.

  9. Brief reports: A distinct DNA methylation signature defines breast cancer stem cells and predicts cancer outcome.

    PubMed

    El Helou, Rita; Wicinski, Julien; Guille, Arnaud; Adélaïde, Jose; Finetti, Pascal; Bertucci, François; Chaffanet, Max; Birnbaum, Daniel; Charafe-Jauffret, Emmanuelle; Ginestier, Christophe

    2014-11-01

    Self-renewal and differentiation are two epigenetic programs that regulate stem cells fate. Dysregulation of these two programs leads to the development of cancer stem cells (CSCs). Recent evidence suggests that CSCs are relatively resistant to conventional therapies and responsible for metastasis formation. Deciphering these processes will help understand oncogenesis and allow the development of new targeted therapies. Here, we have used a whole genome promoter microarray to establish the DNA methylation portraits of breast cancer stem cells (bCSCs) and non-bCSCs. A total of 68 differentially methylated regions (DMRs) were more hypomethylated in bCSCs than in non-bCSCs. Using a differentiation assay we demonstrated that DMRs are rapidly hypermethylated within the first 6 hours following induction of CSC differentiation whereas the cells reached the steady-state within 6 days, suggesting that these DMRs are linked to early CSC epigenetic regulation. These DMRs were significantly enriched in genes coding for TGF-β signaling-related proteins. Interestingly, DMRs hypomethylation was correlated to an overexpression of TGF-β signaling genes in a series of 109 breast tumors. Moreover, patients with tumors harboring the bCSC DMRs signature had a worse prognosis than those with non-bCSC DMRs signature. Our results show that bCSCs have a distinct DNA methylation landscape with TGF-β signaling as a key epigenetic regulator of bCSCs differentiation.

  10. Longitudinal Transcriptome Analysis Reveals a Sustained Differential Gene Expression Signature in Patients Treated for Acute Lyme Disease.

    PubMed

    Bouquet, Jerome; Soloski, Mark J; Swei, Andrea; Cheadle, Chris; Federman, Scot; Billaud, Jean-Noel; Rebman, Alison W; Kabre, Beniwende; Halpert, Richard; Boorgula, Meher; Aucott, John N; Chiu, Charles Y

    2016-02-12

    Lyme disease is a tick-borne illness caused by the bacterium Borrelia burgdorferi, and approximately 10 to 20% of patients report persistent symptoms lasting months to years despite appropriate treatment with antibiotics. To gain insights into the molecular basis of acute Lyme disease and the ensuing development of post-treatment symptoms, we conducted a longitudinal transcriptome study of 29 Lyme disease patients (and 13 matched controls) enrolled at the time of diagnosis and followed for up to 6 months. The differential gene expression signature of Lyme disease following the acute phase of infection persisted for at least 3 weeks and had fewer than 44% differentially expressed genes (DEGs) in common with other infectious or noninfectious syndromes. Early Lyme disease prior to antibiotic therapy was characterized by marked upregulation of Toll-like receptor signaling but lack of activation of the inflammatory T-cell apoptotic and B-cell developmental pathways seen in other acute infectious syndromes. Six months after completion of therapy, Lyme disease patients were found to have 31 to 60% of their pathways in common with three different immune-mediated chronic diseases. No differential gene expression signature was observed between Lyme disease patients with resolved illness to those with persistent symptoms at 6 months post-treatment. The identification of a sustained differential gene expression signature in Lyme disease suggests that a panel of selected human host-based biomarkers may address the need for sensitive clinical diagnostics during the "window period" of infection prior to the appearance of a detectable antibody response and may also inform the development of new therapeutic targets. Lyme disease is the most common tick-borne infection in the United States, and some patients report lingering symptoms lasting months to years despite antibiotic treatment. To better understand the role of the human host response in acute Lyme disease and the

  11. Penicillin-Binding Protein Transpeptidase Signatures for Tracking and Predicting β-Lactam Resistance Levels in Streptococcus pneumoniae

    PubMed Central

    Metcalf, Benjamin J.; Chochua, Sopio; Li, Zhongya; Gertz, Robert E.; Walker, Hollis; Hawkins, Paulina A.; Tran, Theresa; Whitney, Cynthia G.; McGee, Lesley; Beall, Bernard W.

    2016-01-01

    ABSTRACT β-Lactam antibiotics are the drugs of choice to treat pneumococcal infections. The spread of β-lactam-resistant pneumococci is a major concern in choosing an effective therapy for patients. Systematically tracking β-lactam resistance could benefit disease surveillance. Here we developed a classification system in which a pneumococcal isolate is assigned to a “PBP type” based on sequence signatures in the transpeptidase domains (TPDs) of the three critical penicillin-binding proteins (PBPs), PBP1a, PBP2b, and PBP2x. We identified 307 unique PBP types from 2,528 invasive pneumococcal isolates, which had known MICs to six β-lactams based on broth microdilution. We found that increased β-lactam MICs strongly correlated with PBP types containing divergent TPD sequences. The PBP type explained 94 to 99% of variation in MICs both before and after accounting for genomic backgrounds defined by multilocus sequence typing, indicating that genomic backgrounds made little independent contribution to β-lactam MICs at the population level. We further developed and evaluated predictive models of MICs based on PBP type. Compared to microdilution MICs, MICs predicted by PBP type showed essential agreement (MICs agree within 1 dilution) of >98%, category agreement (interpretive results agree) of >94%, a major discrepancy (sensitive isolate predicted as resistant) rate of <3%, and a very major discrepancy (resistant isolate predicted as sensitive) rate of <2% for all six β-lactams. Thus, the PBP transpeptidase signatures are robust indicators of MICs to different β-lactam antibiotics in clinical pneumococcal isolates and serve as an accurate alternative to phenotypic susceptibility testing. PMID:27302760

  12. IDH mutation status is associated with a distinct hypoxia/angiogenesis transcriptome signature which is non-invasively predictable with rCBV imaging in human glioma.

    PubMed

    Kickingereder, Philipp; Sahm, Felix; Radbruch, Alexander; Wick, Wolfgang; Heiland, Sabine; Deimling, Andreas von; Bendszus, Martin; Wiestler, Benedikt

    2015-11-05

    The recent identification of IDH mutations in gliomas and several other cancers suggests that this pathway is involved in oncogenesis; however effector functions are complex and yet incompletely understood. To study the regulatory effects of IDH on hypoxia-inducible-factor 1-alpha (HIF1A), a driving force in hypoxia-initiated angiogenesis, we analyzed mRNA expression profiles of 288 glioma patients and show decreased expression of HIF1A targets on a single-gene and pathway level, strong inhibition of upstream regulators such as HIF1A and downstream biological functions such as angio- and vasculogenesis in IDH mutant tumors. Genotype/imaging phenotype correlation analysis with relative cerebral blood volume (rCBV) MRI - a robust and non-invasive estimate of tumor angiogenesis - in 73 treatment-naive patients with low-grade and anaplastic gliomas showed that a one-unit increase in rCBV corresponded to a two-third decrease in the odds for an IDH mutation and correctly predicted IDH mutation status in 88% of patients. Together, these findings (1) show that IDH mutation status is associated with a distinct angiogenesis transcriptome signature which is non-invasively predictable with rCBV imaging and (2) highlight the potential future of radiogenomics (i.e. the correlation between cancer imaging and genomic features) towards a more accurate diagnostic workup of brain tumors.

  13. Longitudinal Transcriptome Analysis Reveals a Sustained Differential Gene Expression Signature in Patients Treated for Acute Lyme Disease

    PubMed Central

    Bouquet, Jerome; Soloski, Mark J.; Swei, Andrea; Cheadle, Chris; Federman, Scot; Billaud, Jean-Noel; Rebman, Alison W.; Kabre, Beniwende; Halpert, Richard; Boorgula, Meher

    2016-01-01

    ABSTRACT Lyme disease is a tick-borne illness caused by the bacterium Borrelia burgdorferi, and approximately 10 to 20% of patients report persistent symptoms lasting months to years despite appropriate treatment with antibiotics. To gain insights into the molecular basis of acute Lyme disease and the ensuing development of post-treatment symptoms, we conducted a longitudinal transcriptome study of 29 Lyme disease patients (and 13 matched controls) enrolled at the time of diagnosis and followed for up to 6 months. The differential gene expression signature of Lyme disease following the acute phase of infection persisted for at least 3 weeks and had fewer than 44% differentially expressed genes (DEGs) in common with other infectious or noninfectious syndromes. Early Lyme disease prior to antibiotic therapy was characterized by marked upregulation of Toll-like receptor signaling but lack of activation of the inflammatory T-cell apoptotic and B-cell developmental pathways seen in other acute infectious syndromes. Six months after completion of therapy, Lyme disease patients were found to have 31 to 60% of their pathways in common with three different immune-mediated chronic diseases. No differential gene expression signature was observed between Lyme disease patients with resolved illness to those with persistent symptoms at 6 months post-treatment. The identification of a sustained differential gene expression signature in Lyme disease suggests that a panel of selected human host-based biomarkers may address the need for sensitive clinical diagnostics during the “window period” of infection prior to the appearance of a detectable antibody response and may also inform the development of new therapeutic targets. PMID:26873097

  14. Gene Expression Profiling to Predict Outcome After Chemoradiation in Head and Neck Cancer

    SciTech Connect

    Pramana, Jimmy; Brekel, Michiel van den; Velthuysen, Marie-Louise F. van; Wessels, Lodewijk F.A.; Nuyten, Dimitry S.; Hofland, Ingrid; Atsma, Douwe; Pimentel, Nuno; Hoebers, Frank J.P.; Rasch, Coen; Begg, Adrian C.

    2007-12-01

    Purpose: The goal of the present study was to improve prediction of outcome after chemoradiation in advanced head and neck cancer using gene expression analysis. Materials and Methods: We collected 92 biopsies from untreated head and neck cancer patients subsequently given cisplatin-based chemoradiation (RADPLAT) for advanced squamous cell carcinomas (HNSCC). After RNA extraction and labeling, we performed dye swap experiments using 35k oligo-microarrays. Supervised analyses were performed to create classifiers to predict locoregional control and disease recurrence. Published gene sets with prognostic value in other studies were also tested. Results: Using supervised classification on the whole series, gene sets separating good and poor outcome could be found for all end points. However, when splitting tumors into training and validation groups, no robust classifiers could be found. Using Gene Set Enrichment analysis, several gene sets were found to be enriched in locoregional recurrences, although with high false-discovery rates. Previously published signatures for radiosensitivity, hypoxia, proliferation, 'wound,' stem cells, and chromosomal instability were not significantly correlated with outcome. However, a recently published signature for HNSCC defining a 'high-risk' group was shown to be predictive for locoregional control in our dataset. Conclusion: Gene sets can be found with predictive potential for locoregional control after combined radiation and chemotherapy in HNSCC. How treatment-specific these gene sets are needs further study.

  15. LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures

    PubMed Central

    Duan, Qiaonan; Flynn, Corey; Niepel, Mario; Hafner, Marc; Muhlich, Jeremy L.; Fernandez, Nicolas F.; Rouillard, Andrew D.; Tan, Christopher M.; Chen, Edward Y.; Golub, Todd R.; Sorger, Peter K.; Subramanian, Aravind; Ma'ayan, Avi

    2014-01-01

    For the Library of Integrated Network-based Cellular Signatures (LINCS) project many gene expression signatures using the L1000 technology have been produced. The L1000 technology is a cost-effective method to profile gene expression in large scale. LINCS Canvas Browser (LCB) is an interactive HTML5 web-based software application that facilitates querying, browsing and interrogating many of the currently available LINCS L1000 data. LCB implements two compacted layered canvases, one to visualize clustered L1000 expression data, and the other to display enrichment analysis results using 30 different gene set libraries. Clicking on an experimental condition highlights gene-sets enriched for the differentially expressed genes from the selected experiment. A search interface allows users to input gene lists and query them against over 100 000 conditions to find the top matching experiments. The tool integrates many resources for an unprecedented potential for new discoveries in systems biology and systems pharmacology. The LCB application is available at http://www.maayanlab.net/LINCS/LCB. Customized versions will be made part of the http://lincscloud.org and http://lincs.hms.harvard.edu websites. PMID:24906883

  16. LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures.

    PubMed

    Duan, Qiaonan; Flynn, Corey; Niepel, Mario; Hafner, Marc; Muhlich, Jeremy L; Fernandez, Nicolas F; Rouillard, Andrew D; Tan, Christopher M; Chen, Edward Y; Golub, Todd R; Sorger, Peter K; Subramanian, Aravind; Ma'ayan, Avi

    2014-07-01

    For the Library of Integrated Network-based Cellular Signatures (LINCS) project many gene expression signatures using the L1000 technology have been produced. The L1000 technology is a cost-effective method to profile gene expression in large scale. LINCS Canvas Browser (LCB) is an interactive HTML5 web-based software application that facilitates querying, browsing and interrogating many of the currently available LINCS L1000 data. LCB implements two compacted layered canvases, one to visualize clustered L1000 expression data, and the other to display enrichment analysis results using 30 different gene set libraries. Clicking on an experimental condition highlights gene-sets enriched for the differentially expressed genes from the selected experiment. A search interface allows users to input gene lists and query them against over 100 000 conditions to find the top matching experiments. The tool integrates many resources for an unprecedented potential for new discoveries in systems biology and systems pharmacology. The LCB application is available at http://www.maayanlab.net/LINCS/LCB. Customized versions will be made part of the http://lincscloud.org and http://lincs.hms.harvard.edu websites. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

  17. Gene Expression-Based Survival Prediction in Lung Adenocarcinoma: A Multi-Site, Blinded Validation Study

    PubMed Central

    Shedden, Kerby; Taylor, Jeremy M.G.; Enkemann, Steve A.; Tsao, Ming S.; Yeatman, Timothy J.; Gerald, William L.; Eschrich, Steve; Jurisica, Igor; Venkatraman, Seshan E.; Meyerson, Matthew; Kuick, Rork; Dobbin, Kevin K.; Lively, Tracy; Jacobson, James W.; Beer, David G.; Giordano, Thomas J.; Misek, David E.; Chang, Andrew C.; Zhu, Chang Qi; Strumpf, Dan; Hanash, Samir; Shepherd, Francis A.; Ding, Kuyue; Seymour, Lesley; Naoki, Katsuhiko; Pennell, Nathan; Weir, Barbara; Verhaak, Roel; Ladd-Acosta, Christine; Golub, Todd; Gruidl, Mike; Szoke, Janos; Zakowski, Maureen; Rusch, Valerie; Kris, Mark; Viale, Agnes; Motoi, Noriko; Travis, William; Sharma, Anupama

    2009-01-01

    Although prognostic gene expression signatures for survival in early stage lung cancer have been proposed, for clinical application it is critical to establish their performance across different subject populations and in different laboratories. Here we report a large, training-testing, multi-site blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measurements of gene expression either alone or combined with basic clinical covariates (stage, age, sex) can be used to predict overall survival in lung cancer subjects. Several models examined produced risk scores that substantially correlated with actual subject outcome. Most methods performed better with clinical data, supporting the combined use of clinical and molecular information when building prognostic models for early stage lung cancer. This study also provides the largest available set of microarray data with extensive pathological and clinical annotation for lung adenocarcinomas. PMID:18641660

  18. Weight loss predictability by plasma metabolic signatures in adults with obesity and morbid obesity of the DiOGenes study.

    PubMed

    Stroeve, Johanna H M; Saccenti, Edoardo; Bouwman, Jildau; Dane, Adrie; Strassburg, Katrin; Vervoort, Jacques; Hankemeier, Thomas; Astrup, Arne; Smilde, Age K; van Ommen, Ben; Saris, Wim H M

    2016-02-01

    Aim is to predict successful weight loss by metabolic signatures at baseline and to identify which differences in metabolic status may underlie variations in weight loss success. In DiOGenes, a randomized, controlled trial, weight loss was induced using a low-calorie diet (800 kcal) for 8 weeks. Men (N = 236) and women (N = 431) as well as groups with overweight/obesity and morbid obesity were studied separately. The relation between the metabolic status before weight loss and weight loss was assessed by stepwise regression on multiple data sets, including anthropometric parameters, NMR-based plasma metabolites, and LC-MS-based plasma lipid species. Maximally, 57% of the variation in weight loss success can be predicted by baseline parameters. The most powerful predictive models were obtained in subjects with morbid obesity. In these models, the metabolites most predictive for weight loss were acetoacetate, triacylglycerols, phosphatidylcholines, specific amino acids, and creatine and creatinine. This metabolic profile suggests that high energy metabolism activity results in higher amounts of weight loss. Possible predictive (pre-diet) markers were found for amount of weight loss for specific subgroups. © 2016 The Obesity Society.

  19. Dynamic classification using case-specific training cohorts outperforms static gene expression signatures in breast cancer

    PubMed Central

    Győrffy, Balázs; Karn, Thomas; Sztupinszki, Zsófia; Weltz, Boglárka; Müller, Volkmar; Pusztai, Lajos

    2015-01-01

    The molecular diversity of breast cancer makes it impossible to identify prognostic markers that are applicable to all breast cancers. To overcome limitations of previous multigene prognostic classifiers, we propose a new dynamic predictor: instead of using a single universal training cohort and an identical list of informative genes to predict the prognosis of new cases, a case-specific predictor is developed for each test case. Gene expression data from 3,534 breast cancers with clinical annotation including relapse-free survival is analyzed. For each test case, we select a case-specific training subset including only molecularly similar cases and a case-specific predictor is generated. This method yields different training sets and different predictors for each new patient. The model performance was assessed in leave-one-out validation and also in 325 independent cases. Prognostic discrimination was high for all cases (n = 3,534, HR = 3.68, p = 1.67 E−56). The dynamic predictor showed higher overall accuracy (0.68) than genomic surrogates for Oncotype DX (0.64), Genomic Grade Index (0.61) or MammaPrint (0.47). The dynamic predictor was also effective in triple-negative cancers (n = 427, HR = 3.08, p = 0.0093) where the above classifiers all failed. Validation in independent patients yielded similar classification power (HR = 3.57). The dynamic classifier is available online at http://www.recurrenceonline.com/?q=Re_training. In summary, we developed a new method to make personalized prognostic prediction using case-specific training cohorts. The dynamic predictors outperform static models developed from single historical training cohorts and they also predict well in triple-negative cancers. PMID:25274406

  20. Adipose Gene Expression Prior to Weight Loss Can Differentiate and Weakly Predict Dietary Responders

    PubMed Central

    Mutch, David M.; Temanni, M. Ramzi; Henegar, Corneliu; Combes, Florence; Pelloux, Véronique; Holst, Claus; Sørensen, Thorkild I. A.; Astrup, Arne; Martinez, J. Alfredo; Saris, Wim H. M.; Viguerie, Nathalie; Langin, Dominique; Zucker, Jean-Daniel; Clément, Karine

    2007-01-01

    Background The ability to identify obese individuals who will successfully lose weight in response to dietary intervention will revolutionize disease management. Therefore, we asked whether it is possible to identify subjects who will lose weight during dietary intervention using only a single gene expression snapshot. Methodology/Principal Findings The present study involved 54 female subjects from the Nutrient-Gene Interactions in Human Obesity-Implications for Dietary Guidelines (NUGENOB) trial to determine whether subcutaneous adipose tissue gene expression could be used to predict weight loss prior to the 10-week consumption of a low-fat hypocaloric diet. Using several statistical tests revealed that the gene expression profiles of responders (8–12 kgs weight loss) could always be differentiated from non-responders (<4 kgs weight loss). We also assessed whether this differentiation was sufficient for prediction. Using a bottom-up (i.e. black-box) approach, standard class prediction algorithms were able to predict dietary responders with up to 61.1%±8.1% accuracy. Using a top-down approach (i.e. using differentially expressed genes to build a classifier) improved prediction accuracy to 80.9%±2.2%. Conclusion Adipose gene expression profiling prior to the consumption of a low-fat diet is able to differentiate responders from non-responders as well as serve as a weak predictor of subjects destined to lose weight. While the degree of prediction accuracy currently achieved with a gene expression snapshot is perhaps insufficient for clinical use, this work reveals that the comprehensive molecular signature of adipose tissue paves the way for the future of personalized nutrition. PMID:18094752

  1. Telomerase expression predicts unfavorable outcome in osteosarcoma.

    PubMed

    Sanders, Robert P; Drissi, Rachid; Billups, Catherine A; Daw, Najat C; Valentine, Marcus B; Dome, Jeffrey S

    2004-09-15

    Osteosarcoma is distinct from most cancers in that the majority of osteosarcomas lack telomerase exp