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Sample records for cancer risk prediction

  1. Cancer Risk Prediction and Assessment

    Cancer.gov

    Cancer prediction models provide an important approach to assessing risk and prognosis by identifying individuals at high risk, facilitating the design and planning of clinical cancer trials, fostering the development of benefit-risk indices, and enabling estimates of the population burden and cost of cancer.

  2. Cervical Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  3. Prostate Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing prostate cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  4. Esophageal Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing esophageal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  5. Liver Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing liver cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  6. Testicular Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of testicular cervical cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  7. Breast Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing breast cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  8. Bladder Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing bladder cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  9. Lung Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing lung cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  10. Colorectal Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing colorectal cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  11. Pancreatic Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing pancreatic cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  12. Ovarian Cancer Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing ovarian cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  13. Predicting cancer risks from dental computed tomography.

    PubMed

    Wu, T-H; Lin, W-C; Chen, W-K; Chang, Y-C; Hwang, J-J

    2015-01-01

    Dental computed tomography (CT) has become a common tool when carrying out dental implants, yet there is little information available on its associated cancer risk. The objective of this study was to estimate the lifetime-attributable risk (LAR) of cancer incidence that is associated with the radiation dose from dental CT scans and to evaluate the effect of scan position, sex, and age on the cancer risk. This retrospective cohort study involved 505 participants who underwent CT scans. The mean effective doses for male and female patients in the maxilla group were 408 and 389 µSv (P = 0.055), respectively, whereas the mean effective doses for male and female patients in the mandible groups were 475 and 450 µSv (P < 0.001), respectively. The LAR for cancer incidence after mandible CT scanning varied from 1 in 16,196 for a 30-y-old woman to 1 in 114,680 for a 70-y-old man. The organ-specific cancer risks for thyroid cancer, other cancers, leukemia, and lung cancer account for 99% of the LAR. Among patients of all ages, the estimated LAR of a mandible scan was higher than that of a maxilla scan. Furthermore, the LAR for female thyroid cancer had a peak before age 45 y. The risk for a woman aged 30 y is roughly 8 times higher than that of a woman aged 50 y. After undergoing a dental CT scan, the possible cancer risks related to sex and age across various different anatomical regions are not similar. The greatest risk due to a dental CT scan is for a mandible scan when the woman is younger than 45 y. Given the limits of the sample size, machine parameters, and the retrospective nature of this study, the results need to be interpreted within the context of this patient population. Future studies will be of value to corroborate these findings. © International & American Associations for Dental Research 2014.

  14. Lung cancer risk prediction: a tool for early detection.

    PubMed

    Cassidy, Adrian; Duffy, Stephen W; Myles, Jonathan P; Liloglou, Triantafillos; Field, John K

    2007-01-01

    Although 45% of men and 39% of women will be diagnosed with cancer in their lifetime, it is difficult to predict which individuals will be affected. For some cancers, substantial progress in individual risk estimation has already been made. However, relatively few models have been developed to predict lung cancer risk beyond effects of age and smoking. This paper reviews published models for lung cancer risk prediction, discusses their potential contribution to clinical and research settings and suggests improvements to the risk modeling strategy for lung cancer. The sensitivity and specificity of existing cancer risk models is less than optimal. Improvement in individual risk prediction is important for selection of individuals for prevention or early detection interventions. In addition to smoking, factors related to occupational exposure, personal medical history and family history of cancer can add to the predictive power. A good risk prediction model is one that can identify a small fraction of the population in which a large proportion of the disease cases will occur. In the future, genetic and other biological markers are likely to be useful, although they will require rigorous evaluation. Validation is essential to establish the predictive effect and for ongoing monitoring of the model's continued relevance.

  15. Submission Form for Peer-Reviewed Cancer Risk Prediction Models

    Cancer.gov

    If you have information about a peer-reviewd cancer risk prediction model that you would like to be considered for inclusion on this list, submit as much information as possible through the form on this page.

  16. Individualized Risk Prediction Model for Lung Cancer in Korean Men

    PubMed Central

    Park, Sohee; Nam, Byung-Ho; Yang, Hye-Ryung; Lee, Ji An; Lim, Hyunsun; Han, Jun Tae; Park, Il Su; Shin, Hai-Rim; Lee, Jin Soo

    2013-01-01

    Purpose Lung cancer is the leading cause of cancer deaths in Korea. The objective of the present study was to develop an individualized risk prediction model for lung cancer in Korean men using population-based cohort data. Methods From a population-based cohort study of 1,324,804 Korean men free of cancer at baseline, the individualized absolute risk of developing lung cancer was estimated using the Cox proportional hazards model. We checked the validity of the model using C statistics and the Hosmer–Lemeshow chi-square test on an external validation dataset. Results The risk prediction model for lung cancer in Korean men included smoking exposure, age at smoking initiation, body mass index, physical activity, and fasting glucose levels. The model showed excellent performance (C statistic = 0.871, 95% CI = 0.867–0.876). Smoking was significantly associated with the risk of lung cancer in Korean men, with a four-fold increased risk in current smokers consuming more than one pack a day relative to non-smokers. Age at smoking initiation was also a significant predictor for developing lung cancer; a younger age at initiation was associated with a higher risk of developing lung cancer. Conclusion This is the first study to provide an individualized risk prediction model for lung cancer in an Asian population with very good model performance. In addition to current smoking status, earlier exposure to smoking was a very important factor for developing lung cancer. Since most of the risk factors are modifiable, this model can be used to identify those who are at a higher risk and who can subsequently modify their lifestyle choices to lower their risk of lung cancer. PMID:23408946

  17. Updating Risk Prediction Tools: A Case Study in Prostate Cancer

    PubMed Central

    Ankerst, Donna P.; Koniarski, Tim; Liang, Yuanyuan; Leach, Robin J.; Feng, Ziding; Sanda, Martin G.; Partin, Alan W.; Chan, Daniel W; Kagan, Jacob; Sokoll, Lori; Wei, John T; Thompson, Ian M.

    2013-01-01

    Online risk prediction tools for common cancers are now easily accessible and widely used by patients and doctors for informed decision-making concerning screening and diagnosis. A practical problem is as cancer research moves forward and new biomarkers and risk factors are discovered, there is a need to update the risk algorithms to include them. Typically the new markers and risk factors cannot be retrospectively measured on the same study participants used to develop the original prediction tool, necessitating the merging of a separate study of different participants, which may be much smaller in sample size and of a different design. Validation of the updated tool on a third independent data set is warranted before the updated tool can go online. This article reports on the application of Bayes rule for updating risk prediction tools to include a set of biomarkers measured in an external study to the original study used to develop the risk prediction tool. The procedure is illustrated in the context of updating the online Prostate Cancer Prevention Trial Risk Calculator to incorporate the new markers %freePSA and [−2]proPSA measured on an external case control study performed in Texas, U.S.. Recent state-of-the art methods in validation of risk prediction tools and evaluation of the improvement of updated to original tools are implemented using an external validation set provided by the U.S. Early Detection Research Network. PMID:22095849

  18. Cumulative Family Risk Predicts Sibling Adjustment to Childhood Cancer

    PubMed Central

    Long, Kristin A.; Marsland, Anna L.; Alderfer, Melissa A.

    2013-01-01

    Background Prolonged, intensive treatment regimens often disrupt families of children with cancer. Siblings are at increased risk for distress, but factors underlying this risk have received limited empirical attention. This study examined associations between the family context and sibling distress. Methods Siblings of children with cancer (ages 8–18, N=209) and parents (186 mothers, 70 fathers) completed measures of sibling distress, family functioning, parenting, and parent posttraumatic stress. Associations between sibling distress and each family risk factor were evaluated. Then, family risks were considered simultaneously by calculating cumulative family risk index scores. Results After controlling for socio-demographic covariates, greater sibling distress was associated with more sibling-reported problems with family functioning and parental psychological control, lower sibling-reported maternal acceptance, and lower paternal self-reported acceptance. When risk factors were considered together, results supported a quadratic model in which associations between family risk and sibling distress were stronger at higher levels of risk. Conclusions Findings support a contextual model of sibling adjustment to childhood cancer in which elevated distress is predicted by family risk factors, alone and in combination. PMID:23576115

  19. A model for individualized risk prediction of contralateral breast cancer.

    PubMed

    Chowdhury, Marzana; Euhus, David; Onega, Tracy; Biswas, Swati; Choudhary, Pankaj K

    2017-01-01

    Patients diagnosed with invasive breast cancer (BC) or ductal carcinoma in situ are increasingly choosing to undergo contralateral prophylactic mastectomy (CPM) to reduce their risk of contralateral BC (CBC). This is a particularly disturbing trend as a large proportion of these CPMs are believed to be medically unnecessary. Many BC patients tend to substantially overestimate their CBC risk. Thus, there is a pressing need to educate patients effectively on their CBC risk. We develop a CBC risk prediction model to aid physicians in this task. We used data from two sources: Breast Cancer Surveillance Consortium and Surveillance, Epidemiology, and End Results to build the model. The model building steps are similar to those used in developing the BC risk assessment tool (popularly known as Gail model) for counseling women on their BC risk. Our model, named CBCRisk, is exclusively designed for counseling women diagnosed with unilateral BC on the risk of developing CBC. We identified eight factors to be significantly associated with CBC-age at first BC diagnosis, anti-estrogen therapy, family history of BC, high-risk pre-neoplasia status, estrogen receptor status, breast density, type of first BC, and age at first birth. Combining the relative risk estimates with the relevant hazard rates, CBCRisk projects absolute risk of developing CBC over a given period. By providing individualized CBC risk estimates, CBCRisk may help in counseling of BC patients. In turn, this may potentially help alleviate the rate of medically unnecessary CPMs.

  20. Risk prediction tools for cancer in primary care

    PubMed Central

    Usher-Smith, Juliet; Emery, Jon; Hamilton, Willie; Griffin, Simon J; Walter, Fiona M

    2015-01-01

    Numerous risk tools are now available, which predict either current or future risk of a cancer diagnosis. In theory, these tools have the potential to improve patient outcomes through enhancing the consistency and quality of clinical decision-making, facilitating equitable and cost-effective distribution of finite resources such as screening tests or preventive interventions, and encouraging behaviour change. These potential uses have been recognised by the National Cancer Institute as an ‘area of extraordinary opportunity' and an increasing number of risk prediction models continue to be developed. The data on predictive utility (discrimination and calibration) of these models suggest that some have potential for clinical application; however, the focus on implementation and impact is much more recent and there remains considerable uncertainty about their clinical utility and how to implement them in order to maximise benefits and minimise harms such as over-medicalisation, anxiety and false reassurance. If the potential benefits of risk prediction models are to be realised in clinical practice, further validation of the underlying risk models and research to assess the acceptability, clinical impact and economic implications of incorporating them in practice are needed. PMID:26633558

  1. Predicting neutropenia risk in patients with cancer using electronic data.

    PubMed

    Pawloski, Pamala A; Thomas, Avis J; Kane, Sheryl; Vazquez-Benitez, Gabriela; Shapiro, Gary R; Lyman, Gary H

    2017-04-01

    Clinical guidelines recommending the use of myeloid growth factors are largely based on the prescribed chemotherapy regimen. The guidelines suggest that oncologists consider patient-specific characteristics when prescribing granulocyte-colony stimulating factor (G-CSF) prophylaxis; however, a mechanism to quantify individual patient risk is lacking. Readily available electronic health record (EHR) data can provide patient-specific information needed for individualized neutropenia risk estimation. An evidence-based, individualized neutropenia risk estimation algorithm has been developed. This study evaluated the automated extraction of EHR chemotherapy treatment data and externally validated the neutropenia risk prediction model. A retrospective cohort of adult patients with newly diagnosed breast, colorectal, lung, lymphoid, or ovarian cancer who received the first cycle of a cytotoxic chemotherapy regimen from 2008 to 2013 were recruited from a single cancer clinic. Electronically extracted EHR chemotherapy treatment data were validated by chart review. Neutropenia risk stratification was conducted and risk model performance was assessed using calibration and discrimination. Chemotherapy treatment data electronically extracted from the EHR were verified by chart review. The neutropenia risk prediction tool classified 126 patients (57%) as being low risk for febrile neutropenia, 44 (20%) as intermediate risk, and 51 (23%) as high risk. The model was well calibrated (Hosmer-Lemeshow goodness-of-fit test = 0.24). Discrimination was adequate and slightly less than in the original internal validation (c-statistic 0.75 vs 0.81). Chemotherapy treatment data were electronically extracted from the EHR successfully. The individualized neutropenia risk prediction model performed well in our retrospective external cohort.

  2. Landmark Risk Prediction of Residual Life for Breast Cancer Survival

    PubMed Central

    Parast, Layla; Cai, Tianxi

    2013-01-01

    The importance of developing personalized risk prediction estimates has become increasingly evident in recent years. In general, patient populations may be heterogenous and represent a mixture of different unknown subtypes of disease. When the source of this heterogeneity and resulting subtypes of disease are unknown, accurate prediction of survival may be difficult. However, in certain disease settings the onset time of an observable short term event may be highly associated with these unknown subtypes of disease and thus may be useful in predicting long term survival. One approach to incorporate short term event information along with baseline markers for the prediction of long term survival is through a landmark Cox model, which assumes a proportional hazards model for the residual life at a given landmark point. In this paper, we use this modeling framework to develop procedures to assess how a patient’s long term survival trajectory may change over time given good short term outcome indications along with prognosis based on baseline markers. We first propose time-varying accuracy measures to quantify the predictive performance of landmark prediction rules for residual life and provide resampling-based procedures to make inference about such accuracy measures. Simulation studies show that the proposed procedures perform well in finite samples. Throughout, we illustrate our proposed procedures using a breast cancer dataset with information on time to metastasis and time to death. In addition to baseline clinical markers available for each patient, a chromosome instability genetic score, denoted by CIN25, is also available for each patient and has been shown to be predictive of survival for various types of cancer. We provide procedures to evaluate the incremental value of CIN25 for the prediction of residual life and examine how the residual life profile changes over time. This allows us to identify an informative landmark point, t0, such that accurate risk

  3. Risk Prediction Models for Lung Cancer: A Systematic Review.

    PubMed

    Gray, Eoin P; Teare, M Dawn; Stevens, John; Archer, Rachel

    2016-03-01

    Many lung cancer risk prediction models have been published but there has been no systematic review or comprehensive assessment of these models to assess how they could be used in screening. We performed a systematic review of lung cancer prediction models and identified 31 articles that related to 25 distinct models, of which 11 considered epidemiological factors only and did not require a clinical input. Another 11 articles focused on models that required a clinical assessment such as a blood test or scan, and 8 articles considered the 2-stage clonal expansion model. More of the epidemiological models had been externally validated than the more recent clinical assessment models. There was varying discrimination, the ability of a model to distinguish between cases and controls, with an area under the curve between 0.57 and 0.879 and calibration, the model's ability to assign an accurate probability to an individual. In our review we found that further validation studies need to be considered; especially for the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial 2012 Model Version (PLCOM2012) and Hoggart models, which recorded the best overall performance. Future studies will need to focus on prediction rules, such as optimal risk thresholds, for models for selective screening trials. Only 3 validation studies considered prediction rules when validating the models and overall the models were validated using varied tests in distinct populations, which made direct comparisons difficult. To improve this, multiple models need to be tested on the same data set with considerations for sensitivity, specificity, model accuracy, and positive predictive values at the optimal risk thresholds. Copyright © 2016 Elsevier Inc. All rights reserved.

  4. Predicting the use of Individualized Risk Assessment for Breast Cancer

    PubMed Central

    Bartle-Haring, Suzanne; Toviessi, Paula; Katafiasz, Heather

    2008-01-01

    Purpose The purpose of this study was to investigate the decision to obtain individualized risk assessment after a breast cancer education session. Methods A sample of both African American and Caucasian women was used to determine if there were differences by race/ethnicity in uptake of the assessment and differences in the variables that were most predictive of uptake. The sample included 166 women between the ages of 18 and 80. Sixty-two percent of the sample were African American women. Key Findings The results suggested that African American women and Caucasian women used different factors and used other factors differently to decide whether or not to obtain an individualized risk assessment. Conclusions and Implications These results are discussed within the context of health disparities among ethnic minority and Caucasian women with implications for breast cancer control programs. The results of this study would suggest that knowledge alone does not lead to opting for a personalized risk assessment, and that African American and Caucasian women use different pieces of information, or information differently to make decision about getting more personalized information about risk. PMID:18319147

  5. Benign Breast Disease: Toward Molecular Prediction of Breast Cancer Risk

    DTIC Science & Technology

    2007-06-01

    cancer risk in women with radial scars in benign breast biopsies. Breast cancer Research and Treatment . Published online May 22, 2007... scars and involution. We explored the link between centrosome amplification, COX-2 expression and breast cancer outcomes and are currently exploring...5. Radial Scars The significance of radial scars to subsequent risk of breast cancer has been debated. Radial scars (RS) are benign breast

  6. Landmark risk prediction of residual life for breast cancer survival.

    PubMed

    Parast, Layla; Cai, Tianxi

    2013-09-10

    The importance of developing personalized risk prediction estimates has become increasingly evident in recent years. In general, patient populations may be heterogenous and represent a mixture of different unknown subtypes of disease. When the source of this heterogeneity and resulting subtypes of disease are unknown, accurate prediction of survival may be difficult. However, in certain disease settings, the onset time of an observable short-term event may be highly associated with these unknown subtypes of disease and thus may be useful in predicting long-term survival. One approach to incorporate short-term event information along with baseline markers for the prediction of long-term survival is through a landmark Cox model, which assumes a proportional hazards model for the residual life at a given landmark point. In this paper, we use this modeling framework to develop procedures to assess how a patient's long-term survival trajectory may change over time given good short-term outcome indications along with prognosis on the basis of baseline markers. We first propose time-varying accuracy measures to quantify the predictive performance of landmark prediction rules for residual life and provide resampling-based procedures to make inference about such accuracy measures. Simulation studies show that the proposed procedures perform well in finite samples. Throughout, we illustrate our proposed procedures by using a breast cancer dataset with information on time to metastasis and time to death. In addition to baseline clinical markers available for each patient, a chromosome instability genetic score, denoted by CIN25, is also available for each patient and has been shown to be predictive of survival for various types of cancer. We provide procedures to evaluate the incremental value of CIN25 for the prediction of residual life and examine how the residual life profile changes over time. This allows us to identify an informative landmark point, t(0) , such that

  7. Observed and Predicted Risk of Breast Cancer Death in Randomized Trials on Breast Cancer Screening

    PubMed Central

    Autier, Philippe; Sullivan, Richard; Boyle, Peter

    2016-01-01

    Background The role of breast screening in breast cancer mortality declines is debated. Screening impacts cancer mortality through decreasing the number of advanced cancers with poor diagnosis, while cancer treatment works through decreasing the case-fatality rate. Hence, reductions in cancer death rates thanks to screening should directly reflect reductions in advanced cancer rates. We verified whether in breast screening trials, the observed reductions in the risk of breast cancer death could be predicted from reductions of advanced breast cancer rates. Patients and Methods The Greater New York Health Insurance Plan trial (HIP) is the only breast screening trial that reported stage-specific cancer fatality for the screening and for the control group separately. The Swedish Two-County trial (TCT)) reported size-specific fatalities for cancer patients in both screening and control groups. We computed predicted numbers of breast cancer deaths, from which we calculated predicted relative risks (RR) and (95% confidence intervals). The Age trial in England performed its own calculations of predicted relative risk. Results The observed and predicted RR of breast cancer death were 0.72 (0.56–0.94) and 0.98 (0.77–1.24) in the HIP trial, and 0.79 (0.78–1.01) and 0.90 (0.80–1.01) in the Age trial. In the TCT, the observed RR was 0.73 (0.62–0.87), while the predicted RR was 0.89 (0.75–1.05) if overdiagnosis was assumed to be negligible and 0.83 (0.70–0.97) if extra cancers were excluded. Conclusions In breast screening trials, factors other than screening have contributed to reductions in the risk of breast cancer death most probably by reducing the fatality of advanced cancers in screening groups. These factors were the better management of breast cancer patients and the underreporting of breast cancer as the underlying cause of death. Breast screening trials should publish stage-specific fatalities observed in each group. PMID:27100174

  8. Risk Prediction Models for Other Cancers or Multiple Sites

    Cancer.gov

    Developing statistical models that estimate the probability of developing other multiple cancers over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  9. Predicting Low-Risk Prostate Cancer from Transperineal Saturation Biopsies

    PubMed Central

    van Leeuwen, Pim J.; Siriwardana, Amila; Roobol, Monique; Ting, Francis; Nieboer, Daan; Thompson, James; Delprado, Warick; Haynes, Anne-Marie; Brenner, Phillip; Stricker, Phillip

    2016-01-01

    Introduction. To assess the performance of five previously described clinicopathological definitions of low-risk prostate cancer (PC). Materials and Methods. Men who underwent radical prostatectomy (RP) for clinical stage ≤T2, PSA <10 ng/mL, Gleason score <8 PC, diagnosed by transperineal template-guided saturation biopsy were included. The performance of five previously described criteria (i.e., criteria 1–5, criterion 1 stringent (Gleason score 6 + ≤5 mm total max core length PC + ≤3 mm max per core length PC) up to criterion 5 less stringent (Gleason score 6-7 with ≤5% Gleason grade 4) was analysed to assess ability of each to predict insignificant disease in RP specimens (defined as Gleason score ≤6 and total tumour volume <2.5 mL, or Gleason score 7 with ≤5% grade 4 and total tumour volume <0.7 mL). Results. 994 men who underwent RP were included. Criterion 4 (Gleason score 6) performed best with area under the curve of receiver operating characteristics 0.792. At decision curve analysis, criterion 4 was deemed clinically the best performing transperineal saturation biopsy-based definition for low-risk PC. Conclusions. Gleason score 6 disease demonstrated a superior trade-off between sensitivity and specificity for clarifying low-risk PC that can guide treatment and be used as reference test in diagnostic studies. PMID:27148459

  10. Predicting reattendance at a high-risk breast cancer clinic.

    PubMed

    Ormseth, Sarah R; Wellisch, David K; Aréchiga, Adam E; Draper, Taylor L

    2015-10-01

    The research about follow-up patterns of women attending high-risk breast-cancer clinics is sparse. This study sought to profile daughters of breast-cancer patients who are likely to return versus those unlikely to return for follow-up care in a high-risk clinic. Our investigation included 131 patients attending the UCLA Revlon Breast Center High Risk Clinic. Predictor variables included age, computed breast-cancer risk, participants' perceived personal risk, clinically significant depressive symptomatology (CES-D score ≥ 16), current level of anxiety (State-Trait Anxiety Inventory), and survival status of participants' mothers (survived or passed away from breast cancer). A greater likelihood of reattendance was associated with older age (adjusted odds ratio [AOR] = 1.07, p = 0.004), computed breast-cancer risk (AOR = 1.10, p = 0.017), absence of depressive symptomatology (AOR = 0.25, p = 0.009), past psychiatric diagnosis (AOR = 3.14, p = 0.029), and maternal loss to breast cancer (AOR = 2.59, p = 0.034). Also, an interaction was found between mother's survival and perceived risk (p = 0.019), such that reattendance was associated with higher perceived risk among participants whose mothers survived (AOR = 1.04, p = 0.002), but not those whose mothers died (AOR = 0.99, p = 0.685). Furthermore, a nonlinear inverted "U" relationship was observed between state anxiety and reattendance (p = 0.037); participants with moderate anxiety were more likely to reattend than those with low or high anxiety levels. Demographic, medical, and psychosocial factors were found to be independently associated with reattendance to a high-risk breast-cancer clinic. Explication of the profiles of women who may or may not reattend may serve to inform the development and implementation of interventions to increase the likelihood of follow-up care.

  11. Absolute Risk Prediction of Second Primary Thyroid Cancer Among 5-Year Survivors of Childhood Cancer

    PubMed Central

    Kovalchik, Stephanie A.; Ronckers, Cécile M.; Veiga, Lene H.S.; Sigurdson, Alice J.; Inskip, Peter D.; de Vathaire, Florent; Sklar, Charles A.; Donaldson, Sarah S.; Anderson, Harald; Bhatti, Parveen; Hammond, Sue; Leisenring, Wendy M.; Mertens, Ann C.; Smith, Susan A.; Stovall, Marilyn; Tucker, Margaret A.; Weathers, Rita E.; Robison, Leslie L.; Pfeiffer, Ruth M.

    2013-01-01

    Purpose We developed three absolute risk models for second primary thyroid cancer to assist with long-term clinical monitoring of childhood cancer survivors. Patients and Methods We used data from the Childhood Cancer Survivor Study (CCSS) and two nested case-control studies (Nordic CCSS; Late Effects Study Group). Model M1 included self-reported risk factors, model M2 added basic radiation and chemotherapy treatment information abstracted from medical records, and model M3 refined M2 by incorporating reconstructed radiation absorbed dose to the thyroid. All models were validated in an independent cohort of French childhood cancer survivors. Results M1 included birth year, initial cancer type, age at diagnosis, sex, and past thyroid nodule diagnosis. M2 added radiation (yes/no), radiation to the neck (yes/no), and alkylating agent (yes/no). Past thyroid nodule was consistently the strongest risk factor (M1 relative risk [RR], 10.8; M2 RR, 6.8; M3 RR, 8.2). In the validation cohort, 20-year absolute risk predictions for second primary thyroid cancer ranged from 0.04% to 7.4% for M2. Expected events agreed well with observed events for each model, indicating good calibration. All models had good discriminatory ability (M1 area under the receiver operating characteristics curve [AUC], 0.71; 95% CI, 0.64 to 0.77; M2 AUC, 0.80; 95% CI, 0.73 to 0.86; M3 AUC, 0.75; 95% CI, 0.69 to 0.82). Conclusion We developed and validated three absolute risk models for second primary thyroid cancer. Model M2, with basic prior treatment information, could be useful for monitoring thyroid cancer risk in childhood cancer survivors. PMID:23169509

  12. Lung Cancer Risk Prediction: Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial Models and Validation

    PubMed Central

    Pinsky, Paul F.; Caporaso, Neil E.; Kvale, Paul A.; Hocking, William G.; Church, Timothy R.; Riley, Thomas L.; Commins, John; Oken, Martin M.; Berg, Christine D.; Prorok, Philip C.

    2011-01-01

    Introduction Identification of individuals at high risk for lung cancer should be of value to individuals, patients, clinicians, and researchers. Existing prediction models have only modest capabilities to classify persons at risk accurately. Methods Prospective data from 70 962 control subjects in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) were used in models for the general population (model 1) and for a subcohort of ever-smokers (N = 38 254) (model 2). Both models included age, socioeconomic status (education), body mass index, family history of lung cancer, chronic obstructive pulmonary disease, recent chest x-ray, smoking status (never, former, or current), pack-years smoked, and smoking duration. Model 2 also included smoking quit-time (time in years since ever-smokers permanently quit smoking). External validation was performed with 44 223 PLCO intervention arm participants who completed a supplemental questionnaire and were subsequently followed. Known available risk factors were included in logistic regression models. Bootstrap optimism-corrected estimates of predictive performance were calculated (internal validation). Nonlinear relationships for age, pack-years smoked, smoking duration, and quit-time were modeled using restricted cubic splines. All reported P values are two-sided. Results During follow-up (median 9.2 years) of the control arm subjects, 1040 lung cancers occurred. During follow-up of the external validation sample (median 3.0 years), 213 lung cancers occurred. For models 1 and 2, bootstrap optimism-corrected receiver operator characteristic area under the curves were 0.857 and 0.805, and calibration slopes (model-predicted probabilities vs observed probabilities) were 0.987 and 0.979, respectively. In the external validation sample, models 1 and 2 had area under the curves of 0.841 and 0.784, respectively. These models had high discrimination in women, men, whites, and nonwhites. Conclusion The PLCO

  13. Enhancement of Mammographic Density Measures in Breast Cancer Risk Prediction

    PubMed Central

    Cheddad, Abbas; Czene, Kamila; Shepherd, John A.; Li, Jingmei; Hall, Per; Humphreys, Keith

    2016-01-01

    Background Mammographic density is a strong risk factor for breast cancer. Methods We present a novel approach to enhance area density measures that takes advantage of the relative density of the pectoral muscle that appears in lateral mammographic views. We hypothesized that the grey scale of film mammograms is normalized to volume breast density but not pectoral density and thus pectoral density becomes an independent marker of volumetric density. Results From analysis of data from a Swedish case–control study (1,286 breast cancer cases and 1,391 control subjects, ages 50–75 years), we found that the mean intensity of the pectoral muscle (MIP) was highly associated with breast cancer risk [per SD: OR = 0.82; 95% confidence interval (CI), 0.75–0.88; P = 6 × 10−7] after adjusting for a validated computer-assisted measure of percent density (PD), Cumulus. The area under curve (AUC) changed from 0.600 to 0.618 due to using PD with the pectoral muscle as reference instead of a standard area-based PD measure. We showed that MIP is associated with a genetic variant known to be associated with mammographic density and breast cancer risk, rs10995190, in a subset of women with genetic data. We further replicated the association between MIP and rs10995190 in an additional cohort of 2,655 breast cancer cases (combined P = 0.0002). Conclusions MIP is a marker of volumetric density that can be used to complement area PD in mammographic density studies and breast cancer risk assessment. Impact Inclusion of MIP in risk models should be considered for studies using area PD from analog films. PMID:24722754

  14. Cross-sectional study to assess the association of population density with predicted breast cancer risk.

    PubMed

    Lee, Jeannette Y; Klimberg, Suzanne; Bondurant, Kristina L; Phillips, Martha M; Kadlubar, Susan A

    2014-01-01

    The Gail and CARE models estimate breast cancer risk for white and African-American (AA) women, respectively. The aims of this study were to compare metropolitan and nonmetropolitan women with respect to predicted breast cancer risks based on known risk factors, and to determine if population density was an independent risk factor for breast cancer risk. A cross-sectional survey was completed by 15,582 women between 35 and 85 years of age with no history of breast cancer. Metropolitan and nonmetropolitan women were compared with respect to risk factors, and breast cancer risk estimates, using general linear models adjusted for age. For both white and AA women, tisk factors used to estimate breast cancer risk included age at menarche, history of breast biopsies, and family history. For white women, age at first childbirth was an additional risk factor. In comparison to their nonmetropolitan counterparts, metropolitan white women were more likely to report having a breast biopsy, have family history of breast cancer, and delay childbirth. Among white metropolitan and nonmetropolitan women, mean estimated 5-year risks were 1.44% and 1.32% (p < 0.001), and lifetime risks of breast cancer were 10.81% and 10.01% (p < 0.001), respectively. AA metropolitan residents were more likely than those from nonmetropolitan areas to have had a breast biopsy. Among AA metropolitan and nonmetropolitan women, mean estimated 5-year risks were 1.16% and 1.12% (p = 0.039) and lifetime risks were 8.94%, and 8.85% (p = 0.344). Metropolitan residence was associated with higher predicted breast cancer risks for white women. Among AA women, metropolitan residence was associated with a higher predicted breast cancer risk at 5 years, but not over a lifetime. Population density was not an independent risk factor for breast cancer.

  15. Prediction of breast cancer risk based on common genetic variants in women of East Asian ancestry.

    PubMed

    Wen, Wanqing; Shu, Xiao-Ou; Guo, Xingyi; Cai, Qiuyin; Long, Jirong; Bolla, Manjeet K; Michailidou, Kyriaki; Dennis, Joe; Wang, Qin; Gao, Yu-Tang; Zheng, Ying; Dunning, Alison M; García-Closas, Montserrat; Brennan, Paul; Chen, Shou-Tung; Choi, Ji-Yeob; Hartman, Mikael; Ito, Hidemi; Lophatananon, Artitaya; Matsuo, Keitaro; Miao, Hui; Muir, Kenneth; Sangrajrang, Suleeporn; Shen, Chen-Yang; Teo, Soo H; Tseng, Chiu-Chen; Wu, Anna H; Yip, Cheng Har; Simard, Jacques; Pharoah, Paul D P; Hall, Per; Kang, Daehee; Xiang, Yongbing; Easton, Douglas F; Zheng, Wei

    2016-12-08

    Approximately 100 common breast cancer susceptibility alleles have been identified in genome-wide association studies (GWAS). The utility of these variants in breast cancer risk prediction models has not been evaluated adequately in women of Asian ancestry. We evaluated 88 breast cancer risk variants that were identified previously by GWAS in 11,760 cases and 11,612 controls of Asian ancestry. SNPs confirmed to be associated with breast cancer risk in Asian women were used to construct a polygenic risk score (PRS). The relative and absolute risks of breast cancer by the PRS percentiles were estimated based on the PRS distribution, and were used to stratify women into different levels of breast cancer risk. We confirmed significant associations with breast cancer risk for SNPs in 44 of the 78 previously reported loci at P < 0.05. Compared with women in the middle quintile of the PRS, women in the top 1% group had a 2.70-fold elevated risk of breast cancer (95% CI: 2.15-3.40). The risk prediction model with the PRS had an area under the receiver operating characteristic curve of 0.606. The lifetime risk of breast cancer for Shanghai Chinese women in the lowest and highest 1% of the PRS was 1.35% and 10.06%, respectively. Approximately one-half of GWAS-identified breast cancer risk variants can be directly replicated in East Asian women. Collectively, common genetic variants are important predictors for breast cancer risk. Using common genetic variants for breast cancer could help identify women at high risk of breast cancer.

  16. Recent developments in the ability to predict and modify breast cancer risk.

    PubMed

    Prado, Arturo; Andrades, Patricio; Parada, Francisco

    2010-10-01

    The identification of women at higher risk for breast cancer is a matter of public health and anyone who participates in any treatment modality of this condition (this includes the plastic surgeon) should be aware of the tools and predictive models of breast cancer. Screening for breast cancer in the community, and probably during the daily plastic surgery consultation, until recently, was limited to decisions about when to initiate a mammography study. New developments that predict and modify breast cancer risk must be clearly understood by our specialty through identification of women at higher risk for breast cancer and be familiar with the current issues related to screening and risk-reduction measures. In this review, we discuss current knowledge regarding the recent data of breast cancer risk, screening strategies for high-risk women and medical and surgical approaches to reduce breast cancer risk. Patients with breast cancer belong to one of three groups: a. Sporadic breast cancer (75%)--patients without family history or those who have a breast biopsy with proliferative changes. b. Genetic mutation breast cancer (5%)--women who have a genetic predisposition, and most of these are attributable to mutations in the breast cancer susceptibility gene 1 (BRCA1) and breast cancer susceptibility gene 2 (BRCA2). c. Cluster family breast cancer (20%)--seen in women with a relevant history of breast cancer in the family and breast biopsy with proliferative breast changes with no association with mutations.Those at high risk for breast cancer should investigate the family history with genetic testing consideration, clinical history, including prior breast biopsies and evaluation of mammographic density. Tools for breast cancer risk assessment include the Gail and Claus model, genetic screening,BRCAPRO and others that are evaluated in this review.

  17. Development and Validation of a Lung Cancer Risk Prediction Model for African-Americans

    PubMed Central

    Etzel, Carol J.; Kachroo, Sumesh; Liu, Mei; D'Amelio, Anthony; Dong, Qiong; Cote, Michele L.; Wenzlaff, Angela S.; Hong, Waun Ki; Greisinger, Anthony J.; Schwartz, Ann G.; Spitz, Margaret R.

    2009-01-01

    Because existing risk prediction models for lung cancer were developed in white populations, they may not be appropriate for predicting risk among African-Americans. Therefore, a need exists to construct and validate a risk prediction model for lung cancer that is specific to African-Americans. We analyzed data from 491 African-Americans with lung cancer and 497 matched African-American controls to identify specific risks and incorporate them into a multivariable risk model for lung cancer and estimate the 5-year absolute risk of lung cancer. We performed internal and external validations of the risk model using data on additional cases and controls from the same ongoing multiracial/ethnic lung cancer case-control study from which the model-building data were obtained as well as data from two different lung cancer studies in metropolitan Detroit, respectively. We also compared our African-American model with our previously developed risk prediction model for whites. The final risk model included smoking-related variables [smoking status, pack-years smoked, age at smoking cessation (former smokers), and number of years since smoking cessation (former smokers)], self- reported physician diagnoses of chronic obstructive pulmonary disease or hay fever, and exposures to asbestos or wood dusts. Our risk prediction model for African-Americans exhibited good discrimination [75% (95% confidence interval, 0.67−0.82)] for our internal data and moderate discrimination [63% (95% confidence interval, 0.57−0.69)] for the external data group, which is an improvement over the Spitz model for white subjects. Existing lung cancer prediction models may not be appropriate for predicting risk for African-Americans because (a) they were developed using white populations, (b) level of risk is different for risk factors that African-American share with whites, and (c) unique group-specific risk factors exist for African-Americans. This study developed and validated a risk prediction

  18. 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

  19. Prediction of near-term breast cancer risk using a Bayesian belief network

    NASA Astrophysics Data System (ADS)

    Zheng, Bin; Ramalingam, Pandiyarajan; Hariharan, Harishwaran; Leader, Joseph K.; Gur, David

    2013-03-01

    Accurately predicting near-term breast cancer risk is an important prerequisite for establishing an optimal personalized breast cancer screening paradigm. In previous studies, we investigated and tested the feasibility of developing a unique near-term breast cancer risk prediction model based on a new risk factor associated with bilateral mammographic density asymmetry between the left and right breasts of a woman using a single feature. In this study we developed a multi-feature based Bayesian belief network (BBN) that combines bilateral mammographic density asymmetry with three other popular risk factors, namely (1) age, (2) family history, and (3) average breast density, to further increase the discriminatory power of our cancer risk model. A dataset involving "prior" negative mammography examinations of 348 women was used in the study. Among these women, 174 had breast cancer detected and verified in the next sequential screening examinations, and 174 remained negative (cancer-free). A BBN was applied to predict the risk of each woman having cancer detected six to 18 months later following the negative screening mammography. The prediction results were compared with those using single features. The prediction accuracy was significantly increased when using the BBN. The area under the ROC curve increased from an AUC=0.70 to 0.84 (p<0.01), while the positive predictive value (PPV) and negative predictive value (NPV) also increased from a PPV=0.61 to 0.78 and an NPV=0.65 to 0.75, respectively. This study demonstrates that a multi-feature based BBN can more accurately predict the near-term breast cancer risk than with a single feature.

  20. One versus Two Breast Density Measures to Predict 5- and 10-Year Breast Cancer Risk.

    PubMed

    Kerlikowske, Karla; Gard, Charlotte C; Sprague, Brian L; Tice, Jeffrey A; Miglioretti, Diana L

    2015-06-01

    One measure of Breast Imaging Reporting and Data System (BI-RADS) breast density improves 5-year breast cancer risk prediction, but the value of sequential measures is unknown. We determined whether two BI-RADS density measures improve the predictive accuracy of the Breast Cancer Surveillance Consortium 5-year risk model compared with one measure. We included 722,654 women of ages 35 to 74 years with two mammograms with BI-RADS density measures on average 1.8 years apart; 13,715 developed invasive breast cancer. We used Cox regression to estimate the relative hazards of breast cancer for age, race/ethnicity, family history of breast cancer, history of breast biopsy, and one or two density measures. We developed a risk prediction model by combining these estimates with 2000-2010 Surveillance, Epidemiology, and End Results incidence and 2010 vital statistics for competing risk of death. The two-measure density model had marginally greater discriminatory accuracy than the one-measure model (AUC, 0.640 vs. 0.635). Of 18.6% of women (134,404 of 722,654) who decreased density categories, 15.4% (20,741 of 134,404) of women whose density decreased from heterogeneously or extremely dense to a lower density category with one other risk factor had a clinically meaningful increase in 5-year risk from <1.67% with the one-density model to ≥1.67% with the two-density model. The two-density model has similar overall discrimination to the one-density model for predicting 5-year breast cancer risk and improves risk classification for women with risk factors and a decrease in density. A two-density model should be considered for women whose density decreases when calculating breast cancer risk. ©2015 American Association for Cancer Research.

  1. Assessment of uncertainties in radiation-induced cancer risk predictions at clinically relevant doses

    SciTech Connect

    Nguyen, J.; Moteabbed, M.; Paganetti, H.

    2015-01-15

    Purpose: Theoretical dose–response models offer the possibility to assess second cancer induction risks after external beam therapy. The parameters used in these models are determined with limited data from epidemiological studies. Risk estimations are thus associated with considerable uncertainties. This study aims at illustrating uncertainties when predicting the risk for organ-specific second cancers in the primary radiation field illustrated by choosing selected treatment plans for brain cancer patients. Methods: A widely used risk model was considered in this study. The uncertainties of the model parameters were estimated with reported data of second cancer incidences for various organs. Standard error propagation was then subsequently applied to assess the uncertainty in the risk model. Next, second cancer risks of five pediatric patients treated for cancer in the head and neck regions were calculated. For each case, treatment plans for proton and photon therapy were designed to estimate the uncertainties (a) in the lifetime attributable risk (LAR) for a given treatment modality and (b) when comparing risks of two different treatment modalities. Results: Uncertainties in excess of 100% of the risk were found for almost all organs considered. When applied to treatment plans, the calculated LAR values have uncertainties of the same magnitude. A comparison between cancer risks of different treatment modalities, however, does allow statistically significant conclusions. In the studied cases, the patient averaged LAR ratio of proton and photon treatments was 0.35, 0.56, and 0.59 for brain carcinoma, brain sarcoma, and bone sarcoma, respectively. Their corresponding uncertainties were estimated to be potentially below 5%, depending on uncertainties in dosimetry. Conclusions: The uncertainty in the dose–response curve in cancer risk models makes it currently impractical to predict the risk for an individual external beam treatment. On the other hand, the ratio

  2. An epidemiologic risk prediction model for ovarian cancer in Europe: the EPIC study

    PubMed Central

    Li, K; Hüsing, A; Fortner, R T; Tjønneland, A; Hansen, L; Dossus, L; Chang-Claude, J; Bergmann, M; Steffen, A; Bamia, C; Trichopoulos, D; Trichopoulou, A; Palli, D; Mattiello, A; Agnoli, C; Tumino, R; Onland-Moret, N C; Peeters, P H; Bueno-de-Mesquita, H B(as); Gram, I T; Weiderpass, E; Sánchez-Cantalejo, E; Chirlaque, M-D; Duell, E J; Ardanaz, E; Idahl, A; Lundin, E; Khaw, K-T; Travis, R C; Merritt, M A; Gunter, M J; Riboli, E; Ferrari, P; Terry, K; Cramer, D; Kaaks, R

    2015-01-01

    Background: Ovarian cancer has a high case-fatality ratio, largely due to late diagnosis. Epidemiologic risk prediction models could help identify women at increased risk who may benefit from targeted prevention measures, such as screening or chemopreventive agents. Methods: We built an ovarian cancer risk prediction model with epidemiologic risk factors from 202 206 women in the European Prospective Investigation into Cancer and Nutrition study. Results: Older age at menopause, longer duration of hormone replacement therapy, and higher body mass index were included as increasing ovarian cancer risk, whereas unilateral ovariectomy, longer duration of oral contraceptive use, and higher number of full-term pregnancies were decreasing risk. The discriminatory power (overall concordance index) of this model, as examined with five-fold cross-validation, was 0.64 (95% confidence interval (CI): 0.57, 0.70). The ratio of the expected to observed number of ovarian cancer cases occurring in the first 5 years of follow-up was 0.90 (293 out of 324, 95% CI: 0.81–1.01), in general there was no evidence for miscalibration. Conclusion: Our ovarian cancer risk model containing only epidemiological data showed modest discriminatory power for a Western European population. Future studies should consider adding informative biomarkers to possibly improve the predictive ability of the model. PMID:25742479

  3. Identifying high risk individuals for targeted lung cancer screening: Independent validation of the PLCOm2012 risk prediction tool.

    PubMed

    Weber, Marianne; Yap, Sarsha; Goldsbury, David; Manners, David; Tammemagi, Martin; Marshall, Henry; Brims, Fraser; McWilliams, Annette; Fong, Kwun; Kang, Yoon Jung; Caruana, Michael; Banks, Emily; Canfell, Karen

    2017-07-15

    Lung cancer screening with computerised tomography holds promise, but optimising the balance of benefits and harms via selection of a high risk population is critical. PLCOm2012 is a logistic regression model based on U.S. data, incorporating sociodemographic and health factors, which predicts 6-year lung cancer risk among ever-smokers, and thus may better predict those who might benefit from screening than criteria based solely on age and smoking history. We aimed to validate the performance of PLCOm2012 in predicting lung cancer outcomes in a cohort of Australian smokers. Predicted risk of lung cancer was calculated using PLCOm2012 applied to baseline data from 95,882 ever-smokers aged ≥45 years in the 45 and Up Study (2006-2009). Predictions were compared to lung cancer outcomes captured to June 2014 via linkage to population-wide health databases; a total of 1,035 subsequent lung cancer diagnoses were identified. PLCOm2012 had good discrimination (area under the receiver-operating-characteristic-curve; AUC 0.80, 95%CI 0.78-0.81) and excellent calibration (mean and 90th percentiles of absolute risk difference between observed and predicted outcomes: 0.006 and 0.016, respectively). Sensitivity (69.4%, 95%CI, 65.6-73.0%) of the PLCOm2012 criteria in the 55-74 year age group for predicting lung cancers was greater than that using criteria based on ≥30 pack-years smoking and ≤15 years quit (57.3%, 53.3-61.3%; p < 0.0001), but specificity was lower (72.0%, 71.7-72.4% versus 75.2%, 74.8-75.6%, respectively; p < 0.0001). Targeting high risk people for lung cancer screening using PLCOm2012 might improve the balance of benefits versus harms, and cost-effectiveness of lung cancer screening. © 2017 UICC.

  4. One vs. Two Breast Density Measures to Predict 5- and 10- Year Breast Cancer Risk

    PubMed Central

    Kerlikowske, Karla; Gard, Charlotte C.; Sprague, Brian L.; Tice, Jeffrey A.; Miglioretti, Diana L.

    2015-01-01

    Background One measure of Breast Imaging Reporting and Data System (BI-RADS) breast density improves 5-year breast cancer risk prediction, but the value of sequential measures is unknown. We determined if two BI-RADS density measures improves the predictive accuracy of the Breast Cancer Surveillance Consortium 5-year risk model compared to one measure. Methods We included 722,654 women aged 35–74 years with two mammograms with BI-RADS density measures on average 1.8 years apart; 13,715 developed invasive breast cancer. We used Cox regression to estimate the relative hazards of breast cancer for age, race/ethnicity, family history of breast cancer, history of breast biopsy, and one or two density measures. We developed a risk prediction model by combining these estimates with 2000–2010 Surveillance, Epidemiology, and End Results incidence and 2010 vital statistics for competing risk of death. Results The two-measure density model had marginally greater discriminatory accuracy than the one-measure model (AUC=0.640 vs. 0.635). Of 18.6% of women (134,404/722,654) who decreased density categories, 15.4% (20,741/134,404) of women whose density decreased from heterogeneously or extremely dense to a lower density category with one other risk factor had a clinically meaningful increase in 5-year risk from <1.67% with the one-density model to ≥1.67% with the two-density model. Conclusion The two-density model has similar overall discrimination to the one-density model for predicting 5-year breast cancer risk and improves risk classification for women with risk factors and a decrease in density. Impact A two-density model should be considered for women whose density decreases when calculating breast cancer risk. PMID:25824444

  5. Risk factors and a predictive model for thyroid cancer in Korean women.

    PubMed

    Lee, Sun-Mi; Kwak, Keun-Hae

    2010-01-01

    Thyroid cancer incidence in Korean women has increased radically and is the highest in all cancer types. However, the rate of cancer screening among women is very low. The aim of the study was to determine the risk factors for thyroid cancer and to develop a predictive model based on these risk factors. The study design comprised a literature review and a case-control study. To construct a predictive model, the participants selected were 260 female outpatients diagnosed with malignant neoplasm of thyroid gland who had undergone thyroid removal surgery. A total of 259 people for the control group were selected by adopting a 5-year age-matching method. From the literature review, 6 categories of risk factors were identified. Nine variables, including occupation, live(d) in coastal region, family history of thyroid cancer, history of benign thyroid tumor, menopause status and weight gain, number of full-term deliveries, abortion, exercise intensity, and stress, remained as statistically significant risk factors in the stepwise regression model. Regarding the predictive power of the model, the area under the receiver operating characteristic curve was .79, accuracy was .77, sensitivity was .89, specificity was .65, positive predictive value was .72, and negative predictive value was .85. The predictive power of the model was relatively good, so it can be used to identify individuals at high risk for thyroid cancer. The predictive model can be used in promoting to participate in early cancer-screening tests. Thus, it will be possible to detect thyroid cancer in its earliest stage, diminish mortality, and improve quality of life.

  6. Biochemical Recurrence Prediction in High-Risk Prostate Cancer Patients, Following Robot-Assisted Radical Prostatectomy

    PubMed Central

    Yamaguchi, Noriya; Yumioka, Tetsuya; Iwamoto, Hideto; Masago, Toshihiko; Morizane, Shuichi; Honda, Masashi; Sejima, Takehiro; Takenaka, Atsushi

    2016-01-01

    Background High-risk prostate cancer treatment has been controversial. Some high-risk prostate cancer patients fail to respond to radical prostatectomy only. Thus, we aimed to investigate the predictive factors for biochemical recurrence (BCR) and identify patients who could achieve sufficient therapeutic effect by radical prostatectomy only. Methods Of 264 medical records reviewed, 141 low-intermediate-risk and 100 high-risk prostate cancer patients, excluding those who had received neoadjuvant hormone therapy, were analyzed. BCR was defined as the first increase in prostate-specific antigen levels (≥ 0.2 ng/mL), with levels not decreasing to undetectable limits, after radical prostatectomy. Log-rank test and Cox proportional hazards regression analyses were performed to determine the prognostic factors. We investigated the perioperative predictive factors for BCR and BCR-free survival rates, with the number of National Comprehensive Cancer Network (NCCN) high-risk factors for high-risk prostate cancer patients who underwent robot-assisted radical prostatectomy. Results Multivariate analyses showed that clinical T3 was significantly associated with BCR [hazard ratio (HR) = 4.052; 95% confidence interval (CI), 1.26–12.99; P = 0.019]. Of the 100 patients, 77 had 1 high-risk factor and 23 had ≥ 2 high-risk factors; the 1-year BCR-free survival rate of patients with 1 high-risk factor and those with ≥ 2 high-risk factors was 94.8% and 69.6%, respectively. Patients with ≥ 2 high-risk factors were significantly associated with BCR (P = 0.002). No difference in BCR rate between patients with 1 high-risk factor and those with low- and intermediate-risk was found. Conclusion High-risk prostate cancer patients with 1 NCCN high-risk factor can be considered for robot-assisted radical prostatectomy treatment only. PMID:28070166

  7. Prediction of individual genetic risk to prostate cancer using a polygenic score.

    PubMed

    Szulkin, Robert; Whitington, Thomas; Eklund, Martin; Aly, Markus; Eeles, Rosalind A; Easton, Douglas; Kote-Jarai, Z Sofia; Amin Al Olama, Ali; Benlloch, Sara; Muir, Kenneth; Giles, Graham G; Southey, Melissa C; Fitzgerald, Liesel M; Henderson, Brian E; Schumacher, Fredrick; Haiman, Christopher A; Schleutker, Johanna; Wahlfors, Tiina; Tammela, Teuvo L J; Nordestgaard, Børge G; Key, Tim J; Travis, Ruth C; Neal, David E; Donovan, Jenny L; Hamdy, Freddie C; Pharoah, Paul; Pashayan, Nora; Khaw, Kay-Tee; Stanford, Janet L; Thibodeau, Stephen N; McDonnell, Shannon K; Schaid, Daniel J; Maier, Christiane; Vogel, Walther; Luedeke, Manuel; Herkommer, Kathleen; Kibel, Adam S; Cybulski, Cezary; Lubiński, Jan; Kluźniak, Wojciech; Cannon-Albright, Lisa; Brenner, Hermann; Butterbach, Katja; Stegmaier, Christa; Park, Jong Y; Sellers, Thomas; Lin, Hui-Yi; Lim, Hui-Yi; Slavov, Chavdar; Kaneva, Radka; Mitev, Vanio; Batra, Jyotsna; Clements, Judith A; Spurdle, Amanda; Teixeira, Manuel R; Paulo, Paula; Maia, Sofia; Pandha, Hardev; Michael, Agnieszka; Kierzek, Andrzej; Gronberg, Henrik; Wiklund, Fredrik

    2015-09-01

    Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction. We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls. The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Adding an additional 68 novel variants significantly increased the AUC to 0.68 (P = 0.0012) and the net reclassification index with 0.21 (P = 8.5E-08). All novel variants were located in genomic regions established as associated with prostate cancer risk. Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction. © 2015 Wiley Periodicals, Inc.

  8. The utility and predictive value of combinations of low penetrance genes for screening and risk prediction of colorectal cancer.

    PubMed

    Hawken, Steven J; Greenwood, Celia M T; Hudson, Thomas J; Kustra, Rafal; McLaughlin, John; Yang, Quanhe; Zanke, Brent W; Little, Julian

    2010-07-01

    Despite the fact that colorectal cancer (CRC) is a highly treatable form of cancer if detected early, a very low proportion of the eligible population undergoes screening for this form of cancer. Integrating a genomic screening profile as a component of existing screening programs for CRC could potentially improve the effectiveness of population screening by allowing the assignment of individuals to different types and intensities of screening and also by potentially increasing the uptake of existing screening programs. We evaluated the utility and predictive value of genomic profiling as applied to CRC, and as a potential component of a population-based cancer screening program. We generated simulated data representing a typical North American population including a variety of genetic profiles, with a range of relative risks and prevalences for individual risk genes. We then used these data to estimate parameters characterizing the predictive value of a logistic regression model built on genetic markers for CRC. Meta-analyses of genetic associations with CRC were used in building science to inform the simulation work, and to select genetic variants to include in logistic regression model-building using data from the ARCTIC study in Ontario, which included 1,200 CRC cases and a similar number of cancer-free population-based controls. Our simulations demonstrate that for reasonable assumptions involving modest relative risks for individual genetic variants, that substantial predictive power can be achieved when risk variants are common (e.g., prevalence > 20%) and data for enough risk variants are available (e.g., approximately 140-160). Pilot work in population data shows modest, but statistically significant predictive utility for a small collection of risk variants, smaller in effect than age and gender alone in predicting an individual's CRC risk. Further genotyping and many more samples will be required, and indeed the discovery of many more risk loci

  9. Prediction of Potential Cancer-Risk Regions Based on Transcriptome Data: Towards a Comprehensive View

    PubMed Central

    Alisoltani, Arghavan; Fallahi, Hossein; Ebrahimi, Mahdi; Ebrahimi, Mansour; Ebrahimie, Esmaeil

    2014-01-01

    A novel integrative pipeline is presented for discovery of potential cancer-susceptibility regions (PCSRs) by calculating the number of altered genes at each chromosomal region, using expression microarray datasets of different human cancers (HCs). Our novel approach comprises primarily predicting PCSRs followed by identification of key genes in these regions to obtain potential regions harboring new cancer-associated variants. In addition to finding new cancer causal variants, another advantage in prediction of such risk regions is simultaneous study of different types of genomic variants in line with focusing on specific chromosomal regions. Using this pipeline we extracted numbers of regions with highly altered expression levels in cancer condition. Regulatory networks were also constructed for different types of cancers following the identification of altered mRNA and microRNAs. Interestingly, results showed that GAPDH, LIFR, ZEB2, mir-21, mir-30a, mir-141 and mir-200c, all located at PCSRs, are common altered factors in constructed networks. We found a number of clusters of altered mRNAs and miRNAs on predicted PCSRs (e.g.12p13.31) and their common regulators including KLF4 and SOX10. Large scale prediction of risk regions based on transcriptome data can open a window in comprehensive study of cancer risk factors and the other human diseases. PMID:24796549

  10. Predicting advanced neoplasia at colonoscopy in a diverse population with the National Cancer Institute colorectal cancer risk-assessment tool.

    PubMed

    Ladabaum, Uri; Patel, Ashley; Mannalithara, Ajitha; Sundaram, Vandana; Mitani, Aya; Desai, Manisha

    2016-09-01

    Tailoring screening to colorectal cancer (CRC) risk could improve screening effectiveness. Most CRCs arise from advanced neoplasia (AN) that dwells for years. To date, no available colorectal neoplasia risk score has been validated externally in a diverse population. The authors explored whether the National Cancer Institute (NCI) CRC risk-assessment tool, which was developed to predict future CRC risk, could predict current AN prevalence in a diverse population, thereby allowing its use in risk stratification for screening. This was a prospective examination of the relation between predicted 10-year CRC risk and the prevalence of AN, defined as advanced or multiple (≥3 adenomatous, ≥5 serrated) adenomatous or sessile serrated polyps, in individuals undergoing screening colonoscopy. Among 509 screenees (50% women; median age, 58 years; 61% white, 5% black, 10% Hispanic, and 24% Asian), 58 (11%) had AN. The prevalence of AN increased progressively from 6% in the lowest risk-score quintile to 17% in the highest risk-score quintile (P = .002). Risk-score distributions in individuals with versus without AN differed significantly (median, 1.38 [0.90-1.87] vs 1.02 [0.62-1.57], respectively; P = .003), with substantial overlap. The discriminatory accuracy of the tool was modest, with areas under the curve of 0.61 (95% confidence interval [CI], 0.54-0.69) overall, 0.59 (95% CI, 0.49-0.70) for women, and 0.63 (95% CI, 0.53-0.73) for men. The results did not change substantively when the analysis was restricted to adenomatous lesions or to screening procedures without any additional incidental indication. The NCI CRC risk-assessment tool displays modest discriminatory accuracy in predicting AN at screening colonoscopy in a diverse population. This tool may aid shared decision-making in clinical practice. Cancer 2016. © 2016 American Cancer Society. Cancer 2016;122:2663-2670. © 2016 American Cancer Society. © 2016 American Cancer Society.

  11. Determining the main risk factors and high-risk groups of breast cancer using a predictive model for breast cancer risk assessment in South Korea.

    PubMed

    Lee, Eun-Ok; Ahn, Sei-Hyun; You, Chunghee; Lee, Dong-Suk; Han, Wonshik; Choe, Kuk-Jin; Noh, Dong-Young

    2004-01-01

    This study was aimed at developing a predictive model for assessing the breast cancer risk of Korean women under the assumption of differences in the risk factors between Westerners and Koreans. The cohort comprised 384 breast cancer patients and 2 control groups: one comprising 166 hospitalized patients and the other comprising 104 nurses and teachers. Two initial models were produced by comparing cases and the 2 control groups, and the final equations were established by selecting highly significant variables of the initial models to test the accuracy of the models in terms of disease probability and predictability. Both the initial models and the final disease-probability models were confirmed to exhibit high degrees of accuracy and predictability. Major risk factors determined by comparing the patients with hospitalized controls were a family history, menstrual regularity, total menstrual duration, age at first full-term pregnancy, and duration of breastfeeding. Major risk factors determined by comparing patients with nurse/teacher controls were age, education level, menstrual regularity, drinking status, and smoking status. The predictive model developed here shows that risk factors for breast cancer differ between Korean and Western subjects in the aspect of breastfeeding behavior. However, identifying the relationship between genetic susceptibility and breast cancer will require further studies with larger samples. In a model with nurse/teacher controls, drinking and higher education were found to be protective variables, whereas smoking was a risk factor. Hence the predictive model in this group could not be generalized to the Korean population; instead, breast cancer incidence needs to be compared among nurses and teachers in a nurse-and-teacher cohort.

  12. Risk prediction model for colorectal cancer: National Health Insurance Corporation study, Korea.

    PubMed

    Shin, Aesun; Joo, Jungnam; Yang, Hye-Ryung; Bak, Jeongin; Park, Yunjin; Kim, Jeongseon; Oh, Jae Hwan; Nam, Byung-Ho

    2014-01-01

    Incidence and mortality rates of colorectal cancer have been rapidly increasing in Korea during last few decades. Development of risk prediction models for colorectal cancer in Korean men and women is urgently needed to enhance its prevention and early detection. Gender specific five-year risk prediction models were developed for overall colorectal cancer, proximal colon cancer, distal colon cancer, colon cancer and rectal cancer. The model was developed using data from a population of 846,559 men and 479,449 women who participated in health examinations by the National Health Insurance Corporation. Examinees were 30-80 years old and free of cancer in the baseline years of 1996 and 1997. An independent population of 547,874 men and 415,875 women who participated in 1998 and 1999 examinations was used to validate the model. Model validation was done by evaluating its performance in terms of discrimination and calibration ability using the C-statistic and Hosmer-Lemeshow-type chi-square statistics. Age, body mass index, serum cholesterol, family history of cancer, and alcohol consumption were included in all models for men, whereas age, height, and meat intake frequency were included in all models for women. Models showed moderately good discrimination ability with C-statistics between 0.69 and 0.78. The C-statistics were generally higher in the models for men, whereas the calibration abilities were generally better in the models for women. Colorectal cancer risk prediction models were developed from large-scale, population-based data. Those models can be used for identifying high risk groups and developing preventive intervention strategies for colorectal cancer.

  13. Prostate Health Index improves multivariable risk prediction of aggressive prostate cancer.

    PubMed

    Loeb, Stacy; Shin, Sanghyuk S; Broyles, Dennis L; Wei, John T; Sanda, Martin; Klee, George; Partin, Alan W; Sokoll, Lori; Chan, Daniel W; Bangma, Chris H; van Schaik, Ron H N; Slawin, Kevin M; Marks, Leonard S; Catalona, William J

    2017-07-01

    To examine the use of the Prostate Health Index (PHI) as a continuous variable in multivariable risk assessment for aggressive prostate cancer in a large multicentre US study. The study population included 728 men, with prostate-specific antigen (PSA) levels of 2-10 ng/mL and a negative digital rectal examination, enrolled in a prospective, multi-site early detection trial. The primary endpoint was aggressive prostate cancer, defined as biopsy Gleason score ≥7. First, we evaluated whether the addition of PHI improves the performance of currently available risk calculators (the Prostate Cancer Prevention Trial [PCPT] and European Randomised Study of Screening for Prostate Cancer [ERSPC] risk calculators). We also designed and internally validated a new PHI-based multivariable predictive model, and created a nomogram. Of 728 men undergoing biopsy, 118 (16.2%) had aggressive prostate cancer. The PHI predicted the risk of aggressive prostate cancer across the spectrum of values. Adding PHI significantly improved the predictive accuracy of the PCPT and ERSPC risk calculators for aggressive disease. A new model was created using age, previous biopsy, prostate volume, PSA and PHI, with an area under the curve of 0.746. The bootstrap-corrected model showed good calibration with observed risk for aggressive prostate cancer and had net benefit on decision-curve analysis. Using PHI as part of multivariable risk assessment leads to a significant improvement in the detection of aggressive prostate cancer, potentially reducing harms from unnecessary prostate biopsy and overdiagnosis. © 2016 The Authors BJU International © 2016 BJU International Published by John Wiley & Sons Ltd.

  14. A Validated Risk Score for Venous Thromboembolism Is Predictive of Cancer Progression and Mortality.

    PubMed

    Kuderer, Nicole M; Culakova, Eva; Lyman, Gary H; Francis, Charles; Falanga, Anna; Khorana, Alok A

    2016-07-01

    Retrospective studies have suggested an association between cancer-associated venous thromboembolism (VTE) and patient survival. We evaluated a previously validated VTE Clinical Risk Score in also predicting early mortality and cancer progression. A large, nationwide, prospective cohort study of adults with solid tumors or lymphoma initiating chemotherapy was conducted from 2002 to 2006 at 115 U.S. practice sites. Survival and cancer progression were estimated by the method of Kaplan and Meier. Multivariate analysis was based on Cox regression analysis adjusted for major prognostic factors including VTE itself. Of 4,405 patients, 134 (3.0%) died and 330 (7.5%) experienced disease progression during the first 4 months of therapy (median follow-up 75 days). Patients deemed high risk (n = 540, 12.3%) by the Clinical Risk Score had a 120-day mortality rate of 12.7% (adjusted hazard ratio [aHR] 3.00, 95% confidence interval [CI] 1.4-6.3), and intermediate-risk patients (n = 2,665, 60.5%) had a mortality rate of 5.9% (aHR 2.3, 95% CI 1.2-4.4) compared with only 1.4% for low-risk patients (n = 1,200, 27.2%). At 120 days of follow-up, cancer progression occurred in 27.2% of high-risk patients (aHR 2.2, 95% CI 1.4-3.5) and 16.4% of intermediate-risk patients (aHR 1.9, 95% CI 1.3-2.7) compared with only 8.5% of low-risk patients (p < .0001). The Clinical Risk Score, originally developed to predict the occurrence of VTE, is also predictive of early mortality and cancer progression during the first four cycles of outpatient chemotherapy, independent from other major prognostic factors including VTE itself. Ongoing and future studies will help determine the impact of VTE prophylaxis on survival. The risk of venous thromboembolism (VTE) is increased in patients receiving cancer chemotherapy. In this article, the authors demonstrate that a popular risk score for VTE in patients with cancer is also associated with the risk of early mortality in this setting. It is important that

  15. Relationship of Predicted Risk of Developing Invasive Breast Cancer, as Assessed with Three Models, and Breast Cancer Mortality among Breast Cancer Patients

    PubMed Central

    Pfeiffer, Ruth M.; Miglioretti, Diana L.; Kerlikowske, Karla; Tice, Jeffery; Vacek, Pamela M.; Gierach, Gretchen L.

    2016-01-01

    Purpose Breast cancer risk prediction models are used to plan clinical trials and counsel women; however, relationships of predicted risks of breast cancer incidence and prognosis after breast cancer diagnosis are unknown. Methods Using largely pre-diagnostic information from the Breast Cancer Surveillance Consortium (BCSC) for 37,939 invasive breast cancers (1996–2007), we estimated 5-year breast cancer risk (<1%; 1–1.66%; ≥1.67%) with three models: BCSC 1-year risk model (BCSC-1; adapted to 5-year predictions); Breast Cancer Risk Assessment Tool (BCRAT); and BCSC 5-year risk model (BCSC-5). Breast cancer-specific mortality post-diagnosis (range: 1–13 years; median: 5.4–5.6 years) was related to predicted risk of developing breast cancer using unadjusted Cox proportional hazards models, and in age-stratified (35–44; 45–54; 55–69; 70–89 years) models adjusted for continuous age, BCSC registry, calendar period, income, mode of presentation, stage and treatment. Mean age at diagnosis was 60 years. Results Of 6,021 deaths, 2,993 (49.7%) were ascribed to breast cancer. In unadjusted case-only analyses, predicted breast cancer risk ≥1.67% versus <1.0% was associated with lower risk of breast cancer death; BCSC-1: hazard ratio (HR) = 0.82 (95% CI = 0.75–0.90); BCRAT: HR = 0.72 (95% CI = 0.65–0.81) and BCSC-5: HR = 0.84 (95% CI = 0.75–0.94). Age-stratified, adjusted models showed similar, although mostly non-significant HRs. Among women ages 55–69 years, HRs approximated 1.0. Generally, higher predicted risk was inversely related to percentages of cancers with unfavorable prognostic characteristics, especially among women 35–44 years. Conclusions Among cases assessed with three models, higher predicted risk of developing breast cancer was not associated with greater risk of breast cancer death; thus, these models would have limited utility in planning studies to evaluate breast cancer mortality reduction strategies. Further, when offering

  16. Risk score predicts high-grade prostate cancer in DNA-methylation positive, histopathologically negative biopsies.

    PubMed

    Van Neste, Leander; Partin, Alan W; Stewart, Grant D; Epstein, Jonathan I; Harrison, David J; Van Criekinge, Wim

    2016-09-01

    Prostate cancer (PCa) diagnosis is challenging because efforts for effective, timely treatment of men with significant cancer typically result in over-diagnosis and repeat biopsies. The presence or absence of epigenetic aberrations, more specifically DNA-methylation of GSTP1, RASSF1, and APC in histopathologically negative prostate core biopsies has resulted in an increased negative predictive value (NPV) of ∼90% and thus could lead to a reduction of unnecessary repeat biopsies. Here, it is investigated whether, in methylation-positive men, DNA-methylation intensities could help to identify those men harboring high-grade (Gleason score ≥7) PCa, resulting in an improved positive predictive value. Two cohorts, consisting of men with histopathologically negative index biopsies, followed by a positive or negative repeat biopsy, were combined. EpiScore, a methylation intensity algorithm was developed in methylation-positive men, using area under the curve of the receiver operating characteristic as metric for performance. Next, a risk score was developed combining EpiScore with traditional clinical risk factors to further improve the identification of high-grade (Gleason Score ≥7) cancer. Compared to other risk factors, detection of DNA-methylation in histopathologically negative biopsies was the most significant and important predictor of high-grade cancer, resulting in a NPV of 96%. In methylation-positive men, EpiScore was significantly higher for those with high-grade cancer detected upon repeat biopsy, compared to those with either no or low-grade cancer. The risk score resulted in further improvement of patient risk stratification and was a significantly better predictor compared to currently used metrics as PSA and the prostate cancer prevention trial (PCPT) risk calculator (RC). A decision curve analysis indicated strong clinical utility for the risk score as decision-making tool for repeat biopsy. Low DNA-methylation levels in PCa-negative biopsies led

  17. Risk score predicts high‐grade prostate cancer in DNA‐methylation positive, histopathologically negative biopsies

    PubMed Central

    Van Neste, Leander; Partin, Alan W.; Stewart, Grant D.; Epstein, Jonathan I.; Harrison, David J.

    2016-01-01

    BACKGROUND Prostate cancer (PCa) diagnosis is challenging because efforts for effective, timely treatment of men with significant cancer typically result in over‐diagnosis and repeat biopsies. The presence or absence of epigenetic aberrations, more specifically DNA‐methylation of GSTP1, RASSF1, and APC in histopathologically negative prostate core biopsies has resulted in an increased negative predictive value (NPV) of ∼90% and thus could lead to a reduction of unnecessary repeat biopsies. Here, it is investigated whether, in methylation‐positive men, DNA‐methylation intensities could help to identify those men harboring high‐grade (Gleason score ≥7) PCa, resulting in an improved positive predictive value. METHODS Two cohorts, consisting of men with histopathologically negative index biopsies, followed by a positive or negative repeat biopsy, were combined. EpiScore, a methylation intensity algorithm was developed in methylation‐positive men, using area under the curve of the receiver operating characteristic as metric for performance. Next, a risk score was developed combining EpiScore with traditional clinical risk factors to further improve the identification of high‐grade (Gleason Score ≥7) cancer. RESULTS Compared to other risk factors, detection of DNA‐methylation in histopathologically negative biopsies was the most significant and important predictor of high‐grade cancer, resulting in a NPV of 96%. In methylation‐positive men, EpiScore was significantly higher for those with high‐grade cancer detected upon repeat biopsy, compared to those with either no or low‐grade cancer. The risk score resulted in further improvement of patient risk stratification and was a significantly better predictor compared to currently used metrics as PSA and the prostate cancer prevention trial (PCPT) risk calculator (RC). A decision curve analysis indicated strong clinical utility for the risk score as decision‐making tool for repeat biopsy

  18. Family history of cancer predicts endometrial cancer risk independently of Lynch Syndrome: Implications for genetic counselling.

    PubMed

    Johnatty, Sharon E; Tan, Yen Y; Buchanan, Daniel D; Bowman, Michael; Walters, Rhiannon J; Obermair, Andreas; Quinn, Michael A; Blomfield, Penelope B; Brand, Alison; Leung, Yee; Oehler, Martin K; Kirk, Judy A; O'Mara, Tracy A; Webb, Penelope M; Spurdle, Amanda B

    2017-08-16

    To determine endometrial cancer (EC) risk according to family cancer history, including assessment by degree of relatedness, type of and age at cancer diagnosis of relatives. Self-reported family cancer history was available for 1353 EC patients and 628 controls. Logistic regression was used to quantify the association between EC and cancer diagnosis in ≥1 first or second degree relative, and to assess whether level of risk differed by degree of relationship and/or relative's age at diagnosis. Risk was also evaluated for family history of up to three cancers from known familial syndromes (Lynch, Cowden, hereditary breast and ovarian cancer) overall, by histological subtype and, for a subset of 678 patients, by EC tumor mismatch repair (MMR) gene expression. Report of EC in ≥1 first- or second-degree relative was associated with significantly increased risk of EC (P=3.8×10(-7)), independent of lifestyle risk factors. There was a trend in increasing EC risk with closer relatedness and younger age at EC diagnosis in relatives (PTrend=4.43×10(-6)), and with increasing numbers of Lynch cancers in relatives (PTrend≤0.0001). EC risk associated with family history did not differ by proband tumor MMR status, or histological subtype. Reported EC in first- or second-degree relatives remained associated with EC risk after conservative correction for potential misreported family history (OR 2.0; 95% CI, 1.24-3.37, P=0.004). The strongest predictor of EC risk was closer relatedness and younger EC diagnosis age in ≥1 relative. Associations remained significant irrespective of proband MMR status, and after excluding MMR pathogenic variant carriers, indicating that Lynch syndrome genes do not fully explain familial EC risk. Copyright © 2017 Elsevier Inc. All rights reserved.

  19. A Utility/Cost Analysis of Breast Cancer Risk Prediction Algorithms.

    PubMed

    Abbey, Craig K; Wu, Yirong; Burnside, Elizabeth S; Wunderlich, Adam; Samuelson, Frank W; Boone, John M

    2016-02-27

    Breast cancer risk prediction algorithms are used to identify subpopulations that are at increased risk for developing breast cancer. They can be based on many different sources of data such as demographics, relatives with cancer, gene expression, and various phenotypic features such as breast density. Women who are identified as high risk may undergo a more extensive (and expensive) screening process that includes MRI or ultrasound imaging in addition to the standard full-field digital mammography (FFDM) exam. Given that there are many ways that risk prediction may be accomplished, it is of interest to evaluate them in terms of expected cost, which includes the costs of diagnostic outcomes. In this work we perform an expected-cost analysis of risk prediction algorithms that is based on a published model that includes the costs associated with diagnostic outcomes (true-positive, false-positive, etc.). We assume the existence of a standard screening method and an enhanced screening method with higher scan cost, higher sensitivity, and lower specificity. We then assess expected cost of using a risk prediction algorithm to determine who gets the enhanced screening method under the strong assumption that risk and diagnostic performance are independent. We find that if risk prediction leads to a high enough positive predictive value, it will be cost-effective regardless of the size of the subpopulation. Furthermore, in terms of the hit-rate and false-alarm rate of the of the risk-prediction algorithm, iso-cost contours are lines with slope determined by properties of the available diagnostic systems for screening.

  20. A utility/cost analysis of breast cancer risk prediction algorithms

    NASA Astrophysics Data System (ADS)

    Abbey, Craig K.; Wu, Yirong; Burnside, Elizabeth S.; Wunderlich, Adam; Samuelson, Frank W.; Boone, John M.

    2016-03-01

    Breast cancer risk prediction algorithms are used to identify subpopulations that are at increased risk for developing breast cancer. They can be based on many different sources of data such as demographics, relatives with cancer, gene expression, and various phenotypic features such as breast density. Women who are identified as high risk may undergo a more extensive (and expensive) screening process that includes MRI or ultrasound imaging in addition to the standard full-field digital mammography (FFDM) exam. Given that there are many ways that risk prediction may be accomplished, it is of interest to evaluate them in terms of expected cost, which includes the costs of diagnostic outcomes. In this work we perform an expected-cost analysis of risk prediction algorithms that is based on a published model that includes the costs associated with diagnostic outcomes (true-positive, false-positive, etc.). We assume the existence of a standard screening method and an enhanced screening method with higher scan cost, higher sensitivity, and lower specificity. We then assess expected cost of using a risk prediction algorithm to determine who gets the enhanced screening method under the strong assumption that risk and diagnostic performance are independent. We find that if risk prediction leads to a high enough positive predictive value, it will be cost-effective regardless of the size of the subpopulation. Furthermore, in terms of the hit-rate and false-alarm rate of the of the risk prediction algorithm, iso-cost contours are lines with slope determined by properties of the available diagnostic systems for screening.

  1. A Utility/Cost Analysis of Breast Cancer Risk Prediction Algorithms

    PubMed Central

    Abbey, Craig K.; Wu, Yirong; Burnside, Elizabeth S.; Wunderlich, Adam; Samuelson, Frank W.; Boone, John M.

    2016-01-01

    Breast cancer risk prediction algorithms are used to identify subpopulations that are at increased risk for developing breast cancer. They can be based on many different sources of data such as demographics, relatives with cancer, gene expression, and various phenotypic features such as breast density. Women who are identified as high risk may undergo a more extensive (and expensive) screening process that includes MRI or ultrasound imaging in addition to the standard full-field digital mammography (FFDM) exam. Given that there are many ways that risk prediction may be accomplished, it is of interest to evaluate them in terms of expected cost, which includes the costs of diagnostic outcomes. In this work we perform an expected-cost analysis of risk prediction algorithms that is based on a published model that includes the costs associated with diagnostic outcomes (true-positive, false-positive, etc.). We assume the existence of a standard screening method and an enhanced screening method with higher scan cost, higher sensitivity, and lower specificity. We then assess expected cost of using a risk prediction algorithm to determine who gets the enhanced screening method under the strong assumption that risk and diagnostic performance are independent. We find that if risk prediction leads to a high enough positive predictive value, it will be cost-effective regardless of the size of the subpopulation. Furthermore, in terms of the hit-rate and false-alarm rate of the of the risk-prediction algorithm, iso-cost contours are lines with slope determined by properties of the available diagnostic systems for screening. PMID:27335532

  2. Prediction of Breast and Prostate Cancer Risks in Male BRCA1 and BRCA2 Mutation Carriers Using Polygenic Risk Scores.

    PubMed

    Lecarpentier, Julie; Silvestri, Valentina; Kuchenbaecker, Karoline B; Barrowdale, Daniel; Dennis, Joe; McGuffog, Lesley; Soucy, Penny; Leslie, Goska; Rizzolo, Piera; Navazio, Anna Sara; Valentini, Virginia; Zelli, Veronica; Lee, Andrew; Amin Al Olama, Ali; Tyrer, Jonathan P; Southey, Melissa; John, Esther M; Conner, Thomas A; Goldgar, David E; Buys, Saundra S; Janavicius, Ramunas; Steele, Linda; Ding, Yuan Chun; Neuhausen, Susan L; Hansen, Thomas V O; Osorio, Ana; Weitzel, Jeffrey N; Toss, Angela; Medici, Veronica; Cortesi, Laura; Zanna, Ines; Palli, Domenico; Radice, Paolo; Manoukian, Siranoush; Peissel, Bernard; Azzollini, Jacopo; Viel, Alessandra; Cini, Giulia; Damante, Giuseppe; Tommasi, Stefania; Peterlongo, Paolo; Fostira, Florentia; Hamann, Ute; Evans, D Gareth; Henderson, Alex; Brewer, Carole; Eccles, Diana; Cook, Jackie; Ong, Kai-Ren; Walker, Lisa; Side, Lucy E; Porteous, Mary E; Davidson, Rosemarie; Hodgson, Shirley; Frost, Debra; Adlard, Julian; Izatt, Louise; Eeles, Ros; Ellis, Steve; Tischkowitz, Marc; Godwin, Andrew K; Meindl, Alfons; Gehrig, Andrea; Dworniczak, Bernd; Sutter, Christian; Engel, Christoph; Niederacher, Dieter; Steinemann, Doris; Hahnen, Eric; Hauke, Jan; Rhiem, Kerstin; Kast, Karin; Arnold, Norbert; Ditsch, Nina; Wang-Gohrke, Shan; Wappenschmidt, Barbara; Wand, Dorothea; Lasset, Christine; Stoppa-Lyonnet, Dominique; Belotti, Muriel; Damiola, Francesca; Barjhoux, Laure; Mazoyer, Sylvie; Van Heetvelde, Mattias; Poppe, Bruce; De Leeneer, Kim; Claes, Kathleen B M; de la Hoya, Miguel; Garcia-Barberan, Vanesa; Caldes, Trinidad; Perez Segura, Pedro; Kiiski, Johanna I; Aittomäki, Kristiina; Khan, Sofia; Nevanlinna, Heli; van Asperen, Christi J; Vaszko, Tibor; Kasler, Miklos; Olah, Edith; Balmaña, Judith; Gutiérrez-Enríquez, Sara; Diez, Orland; Teulé, Alex; Izquierdo, Angel; Darder, Esther; Brunet, Joan; Del Valle, Jesús; Feliubadalo, Lidia; Pujana, Miquel Angel; Lazaro, Conxi; Arason, Adalgeir; Agnarsson, Bjarni A; Johannsson, Oskar Th; Barkardottir, Rosa B; Alducci, Elisa; Tognazzo, Silvia; Montagna, Marco; Teixeira, Manuel R; Pinto, Pedro; Spurdle, Amanda B; Holland, Helene; Lee, Jong Won; Lee, Min Hyuk; Lee, Jihyoun; Kim, Sung-Won; Kang, Eunyoung; Kim, Zisun; Sharma, Priyanka; Rebbeck, Timothy R; Vijai, Joseph; Robson, Mark; Lincoln, Anne; Musinsky, Jacob; Gaddam, Pragna; Tan, Yen Y; Berger, Andreas; Singer, Christian F; Loud, Jennifer T; Greene, Mark H; Mulligan, Anna Marie; Glendon, Gord; Andrulis, Irene L; Toland, Amanda Ewart; Senter, Leigha; Bojesen, Anders; Nielsen, Henriette Roed; Skytte, Anne-Bine; Sunde, Lone; Jensen, Uffe Birk; Pedersen, Inge Sokilde; Krogh, Lotte; Kruse, Torben A; Caligo, Maria A; Yoon, Sook-Yee; Teo, Soo-Hwang; von Wachenfeldt, Anna; Huo, Dezheng; Nielsen, Sarah M; Olopade, Olufunmilayo I; Nathanson, Katherine L; Domchek, Susan M; Lorenchick, Christa; Jankowitz, Rachel C; Campbell, Ian; James, Paul; Mitchell, Gillian; Orr, Nick; Park, Sue Kyung; Thomassen, Mads; Offit, Kenneth; Couch, Fergus J; Simard, Jacques; Easton, Douglas F; Chenevix-Trench, Georgia; Schmutzler, Rita K; Antoniou, Antonis C; Ottini, Laura

    2017-07-10

    Purpose BRCA1/2 mutations increase the risk of breast and prostate cancer in men. Common genetic variants modify cancer risks for female carriers of BRCA1/2 mutations. We investigated-for the first time to our knowledge-associations of common genetic variants with breast and prostate cancer risks for male carriers of BRCA1/ 2 mutations and implications for cancer risk prediction. Materials and Methods We genotyped 1,802 male carriers of BRCA1/2 mutations from the Consortium of Investigators of Modifiers of BRCA1/2 by using the custom Illumina OncoArray. We investigated the combined effects of established breast and prostate cancer susceptibility variants on cancer risks for male carriers of BRCA1/2 mutations by constructing weighted polygenic risk scores (PRSs) using published effect estimates as weights. Results In male carriers of BRCA1/2 mutations, PRS that was based on 88 female breast cancer susceptibility variants was associated with breast cancer risk (odds ratio per standard deviation of PRS, 1.36; 95% CI, 1.19 to 1.56; P = 8.6 × 10(-6)). Similarly, PRS that was based on 103 prostate cancer susceptibility variants was associated with prostate cancer risk (odds ratio per SD of PRS, 1.56; 95% CI, 1.35 to 1.81; P = 3.2 × 10(-9)). Large differences in absolute cancer risks were observed at the extremes of the PRS distribution. For example, prostate cancer risk by age 80 years at the 5th and 95th percentiles of the PRS varies from 7% to 26% for carriers of BRCA1 mutations and from 19% to 61% for carriers of BRCA2 mutations, respectively. Conclusion PRSs may provide informative cancer risk stratification for male carriers of BRCA1/2 mutations that might enable these men and their physicians to make informed decisions on the type and timing of breast and prostate cancer risk management.

  3. Validation of a model of lung cancer risk prediction among smokers.

    PubMed

    Cronin, Kathleen A; Gail, Mitchell H; Zou, Zhaohui; Bach, Peter B; Virtamo, Jarmo; Albanes, Demetrius

    2006-05-03

    The Bach model was developed to predict the absolute 10-year risk of developing lung cancer among smokers by use of participants in the Carotene and Retinol Efficacy Trial of lung cancer prevention. We assessed the validity of the Bach model among 6239 smokers from the placebo arm of the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. The expected numbers of lung cancer cases and deaths without lung cancer were calculated from the Bach model and compared with the observed numbers of corresponding events over 10 years. We found that the risk model slightly underestimated the observed lung cancer risk (number of lung cancers expected/number observed = 0.89, 95% confidence interval [CI] = 0.80 to 0.99) over 10 years. The competing risk portion of the model substantially underestimated risk of non-lung cancer mortality (number of non-lung cancer deaths expected/number observed = 0.61, 95% CI = 0.57 to 0.64) over 10 years. The age-specific concordance indices for 10-year predictions were 0.77 (95% CI = 0.70 to 0.84), 0.59 (95% CI = 0.53 to 0.65), 0.62 (95% CI = 0.57 to 0.67), and 0.57 (95% CI = 0.49 to 0.67) for the age groups 50-54, 55-59, 60-64, and 65-69 years, respectively. Periodic radiographic screening in the ATBC Study may explain why slightly more cancers were observed than expected from the Bach model.

  4. Predicting Breast Cancer Risk by Assaying Peripheral Blood Methylation. Addendum

    DTIC Science & Technology

    2007-10-01

    Gardiner-Garden,M. and Frommer ,M. (1987) CpG islands in vertebrate genomes. J. Mol. Biol., 196, 261–282. 2. Jones,P.A. and Baylin,S.B. (2002) The...gastric cancer. Clin. Cancer Res., 11, 1021–1027. 18. Frommer ,M., Mcdonald,L.E., Millar,D.S., Collis,C.M., Watt,F., Grigg,G.W., Molloy,P.L. and Paul,C.L...U.S.A., 89, 1827–1831. 19. Clark,S.J., Harrison,J., Paul,C.L. and Frommer ,M. (1994) High sensitivity mapping of methylated cytosines. Nucleic Acids Res

  5. Risk Factors Predicting Colorectal Cancer Recurrence Following Initial Treatment: A 5-year Cohort Study

    PubMed

    Zare-Bandamiri, Mohammad; Fararouei, Mohammad; Zohourinia, Shadi; Daneshi, Nima; Dianatinasab, Mostafa

    2017-09-27

    Purpose: Recurrence is one of the most important factors influencing survival of colorectal cancer patients. Subjects and Methods: In this cohort study, clinical and demographic characteristics of 561 patients with colorectal cancer were collected from 2010 to 2015. Medical records and telephone interviews were used to define the patient’s clinical status including the date of any recurrence during the study period. The multivariate Cox model was used as the main strategy for analyzing data. Results: Some 239 (42.6%) patients experienced cancer recurrence during the 5-year follow-up period. Those with an older age at diagnosis had a higher risk of cancer recurrence than their younger counterparts [Hazard Ratio (HR) >70 y /<50 y= 1.65, P=0.01]. Rectal cancer had a greater risk of disease recurrence compared with other tumor sites [HR colon/ rectum=1.53, P=0.02]. Stage 3 cancer had a higher risk than stage 1 cancer [HR stage 3/ stage 1=4.30, P<0.001], and positive lympho-vascular invasion was also a risk factor [HR yes/ no=2.03, P<0.001]. Finally, tumor size , number of dissected lymph nodes, proportion of positive lymph nodes, perineural invasion and type of treatment did not significantly predict recurrence. Conclusion: Access to enhanced medical services including cancer diagnosis at an early stage and optimal treatment is needed to improve the survival and quality of life of CRC patients. Creative Commons Attribution License

  6. Proteomic Prediction of Breast Cancer Risk: A Cohort Study

    DTIC Science & Technology

    2007-03-01

    ABC1), member 13 203. B3GALT2, encompassing the hereditary prostate cancer (HPC1) locus 204. lung adenoma susceptibility 1-like protein 205. DEK... Pituitary tumor-transforming 2 215. calbindin 1 216. kinesin family member 18A 217. Heommbor asnaep-ieansss]ociated guanylate kinase-related 3 (MAGI-3

  7. A comprehensive genetic approach for improving prediction of skin cancer risk in humans.

    PubMed

    Vazquez, Ana I; de los Campos, Gustavo; Klimentidis, Yann C; Rosa, Guilherme J M; Gianola, Daniel; Yi, Nengjun; Allison, David B

    2012-12-01

    Prediction of genetic risk for disease is needed for preventive and personalized medicine. Genome-wide association studies have found unprecedented numbers of variants associated with complex human traits and diseases. However, these variants explain only a small proportion of genetic risk. Mounting evidence suggests that many traits, relevant to public health, are affected by large numbers of small-effect genes and that prediction of genetic risk to those traits and diseases could be improved by incorporating large numbers of markers into whole-genome prediction (WGP) models. We developed a WGP model incorporating thousands of markers for prediction of skin cancer risk in humans. We also considered other ways of incorporating genetic information into prediction models, such as family history or ancestry (using principal components, PCs, of informative markers). Prediction accuracy was evaluated using the area under the receiver operating characteristic curve (AUC) estimated in a cross-validation. Incorporation of genetic information (i.e., familial relationships, PCs, or WGP) yielded a significant increase in prediction accuracy: from an AUC of 0.53 for a baseline model that accounted for nongenetic covariates to AUCs of 0.58 (pedigree), 0.62 (PCs), and 0.64 (WGP). In summary, prediction of skin cancer risk could be improved by considering genetic information and using a large number of single-nucleotide polymorphisms (SNPs) in a WGP model, which allows for the detection of patterns of genetic risk that are above and beyond those that can be captured using family history. We discuss avenues for improving prediction accuracy and speculate on the possible use of WGP to prospectively identify individuals at high risk.

  8. Exploring a new bilateral focal density asymmetry based image marker to predict breast cancer risk

    NASA Astrophysics Data System (ADS)

    Aghaei, Faranak; Mirniaharikandehei, Seyedehnafiseh; Hollingsworth, Alan B.; Wang, Yunzhi; Qiu, Yuchen; Liu, Hong; Zheng, Bin

    2017-03-01

    Although breast density has been widely considered an important breast cancer risk factor, it is not very effective to predict risk of developing breast cancer in a short-term or harboring cancer in mammograms. Based on our recent studies to build short-term breast cancer risk stratification models based on bilateral mammographic density asymmetry, we in this study explored a new quantitative image marker based on bilateral focal density asymmetry to predict the risk of harboring cancers in mammograms. For this purpose, we assembled a testing dataset involving 100 positive and 100 negative cases. In each of positive case, no any solid masses are visible on mammograms. We developed a computer-aided detection (CAD) scheme to automatically detect focal dense regions depicting on two bilateral mammograms of left and right breasts. CAD selects one focal dense region with the maximum size on each image and computes its asymmetrical ratio. We used this focal density asymmetry as a new imaging marker to divide testing cases into two groups of higher and lower focal density asymmetry. The first group included 70 cases in which 62.9% are positive, while the second group included 130 cases in which 43.1% are positive. The odds ratio is 2.24. As a result, this preliminary study supported the feasibility of applying a new focal density asymmetry based imaging marker to predict the risk of having mammography-occult cancers. The goal is to assist radiologists more effectively and accurately detect early subtle cancers using mammography and/or other adjunctive imaging modalities in the future.

  9. The metabolic syndrome and cancer: Is the metabolic syndrome useful for predicting cancer risk above and beyond its individual components?

    PubMed

    Harding, J; Sooriyakumaran, M; Anstey, K J; Adams, R; Balkau, B; Briffa, T; Davis, T M E; Davis, W A; Dobson, A; Giles, G G; Grant, J; Knuiman, M; Luszcz, M; Mitchell, P; Pasco, J A; Reid, C; Simmons, D; Simons, L; Tonkin, A; Woodward, M; Shaw, J E; Magliano, D J

    2015-12-01

    The metabolic syndrome (MetS) is a risk factor for cancer. However, it is not known if the MetS confers a greater cancer risk than the sum of its individual components, which components drive the association, or if the MetS predicts future cancer risk. We linked 20,648 participants from the Australian and New Zealand Diabetes and Cancer Collaboration with complete data on the MetS to national cancer registries and used Cox proportional hazards models to estimate associations of the MetS, the number of positive MetS components, and each of the five MetS components separately with the risk for overall, colorectal, prostate and breast cancer. Hazard ratios (HR) and 95% confidence intervals (95%CI) are reported. We assessed predictive ability of the MetS using Harrell's c-statistic. The MetS was inversely associated with prostate cancer (HR 0.85; 95% CI 0.72-0.99). We found no evidence of an association between the MetS overall, colorectal and breast cancers. For those with five positive MetS components the HR was 1.12 (1.02-1.48) and 2.07 (1.26-3.39) for overall, and colorectal cancer, respectively, compared with those with zero positive MetS components. Greater waist circumference (WC) (1.38; 1.13-1.70) and elevated blood pressure (1.29; 1.01-1.64) were associated with colorectal cancer. Elevated WC and triglycerides were (inversely) associated with prostate cancer. MetS models were only poor to moderate discriminators for all cancer outcomes. We show that the MetS is (inversely) associated with prostate cancer, but is not associated with overall, colorectal or breast cancer. Although, persons with five positive components of the MetS are at a 1.2 and 2.1 increased risk for overall and colorectal cancer, respectively, and these associations appear to be driven, largely, by elevated WC and BP. We also demonstrate that the MetS is only a moderate discriminator of cancer risk. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

  10. An epidemiological model for prediction of endometrial cancer risk in Europe.

    PubMed

    Hüsing, Anika; Dossus, Laure; Ferrari, Pietro; Tjønneland, Anne; Hansen, Louise; Fagherazzi, Guy; Baglietto, Laura; Schock, Helena; Chang-Claude, Jenny; Boeing, Heiner; Steffen, Annika; Trichopoulou, Antonia; Bamia, Christina; Katsoulis, Michalis; Krogh, Vittorio; Palli, Domenico; Panico, Salvatore; Onland-Moret, N Charlotte; Peeters, Petra H; Bueno-de-Mesquita, H Bas; Weiderpass, Elisabete; Gram, Inger T; Ardanaz, Eva; Obón-Santacana, Mireia; Navarro, Carmen; Sánchez-Cantalejo, Emilio; Etxezarreta, Nerea; Allen, Naomi E; Khaw, Kay Tee; Wareham, Nick; Rinaldi, Sabina; Romieu, Isabelle; Merritt, Melissa A; Gunter, Marc; Riboli, Elio; Kaaks, Rudolf

    2016-01-01

    Endometrial cancer (EC) is the fourth most frequent cancer in women in Europe, and as its incidence is increasing, prevention strategies gain further pertinence. Risk prediction models can be a useful tool for identifying women likely to benefit from targeted prevention measures. On the basis of data from 201,811 women (mostly aged 30-65 years) including 855 incident EC cases from eight countries in the European Prospective Investigation into Cancer and Nutrition cohort, a model to predict EC was developed. A step-wise model selection process was used to select confirmed predictive epidemiologic risk factors. Piece-wise constant hazard rates in 5-year age-intervals were estimated in a cause-specific competing risks model, five-fold-cross-validation was applied for internal validation. Risk factors included in the risk prediction model were body-mass index (BMI), menopausal status, age at menarche and at menopause, oral contraceptive use, overall and by different BMI categories and overall duration of use, parity, age at first full-term pregnancy, duration of menopausal hormone therapy and smoking status (specific for pre, peri- and post-menopausal women). These variables improved the discriminating capacity to predict risk over 5 years from 71% for a model based on age alone to 77% (overall C statistic), and the model was well-calibrated (ratio of expected to observed cases = 0.99). Our model could be used for the identification of women at increased risk of EC in Western Europe. To achieve an EC-risk model with general validity, a large-scale cohort-consortium approach would be needed to assess and adjust for population variation.

  11. Predictive Accuracy of the PanCan Lung Cancer Risk Prediction Model -External Validation based on CT from the Danish Lung Cancer Screening Trial.

    PubMed

    Winkler Wille, Mathilde M; van Riel, Sarah J; Saghir, Zaigham; Dirksen, Asger; Pedersen, Jesper Holst; Jacobs, Colin; Thomsen, Laura Hohwü; Scholten, Ernst Th; Skovgaard, Lene T; van Ginneken, Bram

    2015-10-01

    Lung cancer risk models should be externally validated to test generalizability and clinical usefulness. The Danish Lung Cancer Screening Trial (DLCST) is a population-based prospective cohort study, used to assess the discriminative performances of the PanCan models. From the DLCST database, 1,152 nodules from 718 participants were included. Parsimonious and full PanCan risk prediction models were applied to DLCST data, and also coefficients of the model were recalculated using DLCST data. Receiver operating characteristics (ROC) curves and area under the curve (AUC) were used to evaluate risk discrimination. AUCs of 0.826-0.870 were found for DLCST data based on PanCan risk prediction models. In the DLCST, age and family history were significant predictors (p = 0.001 and p = 0.013). Female sex was not confirmed to be associated with higher risk of lung cancer; in fact opposing effects of sex were observed in the two cohorts. Thus, female sex appeared to lower the risk (p = 0.047 and p = 0.040) in the DLCST. High risk discrimination was validated in the DLCST cohort, mainly determined by nodule size. Age and family history of lung cancer were significant predictors and could be included in the parsimonious model. Sex appears to be a less useful predictor. • High accuracy in logistic modelling for lung cancer risk stratification of nodules. • Lung cancer risk prediction is primarily based on size of pulmonary nodules. • Nodule spiculation, age and family history of lung cancer are significant predictors. • Sex does not appear to be a useful risk predictor.

  12. Benign Breast Disease: Toward Molecular Prediction of Breast Cancer Risk

    DTIC Science & Technology

    2009-06-01

    also showed recently that lobular neoplasia expressed COX-2 at high levels. LobulaJ’ neoplasia is dlaJ’aeterized by loss of adhesion molecules such...cadherin gene CDHJ . OW’ data in atypia suggest that COX-2 may be responsible, at least i11 pan, for the prommongenk loss of adhesion molecules that...COX -2 inhibitors and no nse lective inhibitors, u1cluding regular aspirin , ibuprofen. or naproxen. had a reduced risk of subsequently devel- o

  13. Developing a utility decision framework to evaluate predictive models in breast cancer risk estimation

    PubMed Central

    Wu, Yirong; Abbey, Craig K.; Chen, Xianqiao; Liu, Jie; Page, David C.; Alagoz, Oguzhan; Peissig, Peggy; Onitilo, Adedayo A.; Burnside, Elizabeth S.

    2015-01-01

    Abstract. Combining imaging and genetic information to predict disease presence and progression is being codified into an emerging discipline called “radiogenomics.” Optimal evaluation methodologies for radiogenomics have not been well established. We aim to develop a decision framework based on utility analysis to assess predictive models for breast cancer diagnosis. We garnered Gail risk factors, single nucleotide polymorphisms (SNPs), and mammographic features from a retrospective case-control study. We constructed three logistic regression models built on different sets of predictive features: (1) Gail, (2) Gail + Mammo, and (3) Gail + Mammo + SNP. Then we generated receiver operating characteristic (ROC) curves for three models. After we assigned utility values for each category of outcomes (true negatives, false positives, false negatives, and true positives), we pursued optimal operating points on ROC curves to achieve maximum expected utility of breast cancer diagnosis. We performed McNemar’s test based on threshold levels at optimal operating points, and found that SNPs and mammographic features played a significant role in breast cancer risk estimation. Our study comprising utility analysis and McNemar’s test provides a decision framework to evaluate predictive models in breast cancer risk estimation. PMID:26835489

  14. Computerized prediction of breast cancer risk: comparison between the global and local bilateral mammographic tissue asymmetry

    NASA Astrophysics Data System (ADS)

    Wang, Xingwei; Lederman, Dror; Tan, Jun; Wang, Xiao Hui; Zheng, Bin

    2011-03-01

    We have developed and preliminarily tested a new breast cancer risk prediction model based on computerized bilateral mammographic tissue asymmetry. In this study, we investigated and compared the performance difference of our risk prediction model when the bilateral mammographic tissue asymmetrical features were extracted in two different methods namely (1) the entire breast area and (2) the mirror-matched local strips between the left and right breast. A testing dataset including bilateral craniocaudal (CC) view images of 100 negative and 100 positive cases for developing breast abnormalities or cancer was selected from a large and diverse full-field digital mammography (FFDM) image database. To detect bilateral mammographic tissue asymmetry, a set of 20 initial "global" features were extracted from the entire breast areas of two bilateral mammograms in CC view and their differences were computed. Meanwhile, a pool of 16 local histogram-based statistic features was computed from eight mirror-matched strips between the left and right breast. Using a genetic algorithm (GA) to select optimal features, two artificial neural networks (ANN) were built to predict the risk of a test case developing cancer. Using the leave-one-case-out training and testing method, two GAoptimized ANNs yielded the areas under receiver operating characteristic (ROC) curves of 0.754+/-0.024 (using feature differences extracted from the entire breast area) and 0.726+/-0.026 (using the feature differences extracted from 8 pairs of local strips), respectively. The risk prediction model using either ANN is able to detect 58.3% (35/60) of cancer cases 6 to 18 months earlier at 80% specificity level. This study compared two methods to compute bilateral mammographic tissue asymmetry and demonstrated that bilateral mammographic tissue asymmetry was a useful breast cancer risk indicator with high discriminatory power.

  15. Inclusion of endogenous hormone levels in risk prediction models of postmenopausal breast cancer.

    PubMed

    Tworoger, Shelley S; Zhang, Xuehong; Eliassen, A Heather; Qian, Jing; Colditz, Graham A; Willett, Walter C; Rosner, Bernard A; Kraft, Peter; Hankinson, Susan E

    2014-10-01

    Endogenous hormones are risk factors for postmenopausal breast cancer, and their measurement may improve our ability to identify high-risk women. Therefore, we evaluated whether inclusion of plasma estradiol, estrone, estrone sulfate, testosterone, dehydroepiandrosterone sulfate, prolactin, and sex hormone-binding globulin (SHBG) improved risk prediction for postmenopausal invasive breast cancer (n = 437 patient cases and n = 775 controls not using postmenopausal hormones) in the Nurses' Health Study. We evaluated improvement in the area under the curve (AUC) for 5-year risk of invasive breast cancer by adding each hormone to the Gail and Rosner-Colditz risk scores. We used stepwise regression to identify the subset of hormones most associated with risk and assessed AUC improvement; we used 10-fold cross validation to assess model overfitting. Each hormone was associated with breast cancer risk (odds ratio doubling, 0.82 [SHBG] to 1.37 [estrone sulfate]). Individual hormones improved the AUC by 1.3 to 5.2 units relative to the Gail score and 0.3 to 2.9 for the Rosner-Colditz score. Estrone sulfate, testosterone, and prolactin were selected by stepwise regression and increased the AUC by 5.9 units (P = .003) for the Gail score and 3.4 (P = .04) for the Rosner-Colditz score. In cross validation, the average AUC change across the validation data sets was 6.0 (P = .002) and 3.0 units (P = .03), respectively. Similar results were observed for estrogen receptor-positive disease (selected hormones: estrone sulfate, testosterone, prolactin, and SHBG; change in AUC, 8.8 [P < .001] for Gail score and 5.8 [P = .004] for Rosner-Colditz score). Our results support that endogenous hormones improve risk prediction for invasive breast cancer and could help identify women who may benefit from chemoprevention or more screening. © 2014 by American Society of Clinical Oncology.

  16. Inclusion of Endogenous Hormone Levels in Risk Prediction Models of Postmenopausal Breast Cancer

    PubMed Central

    Tworoger, Shelley S.; Zhang, Xuehong; Eliassen, A. Heather; Qian, Jing; Colditz, Graham A.; Willett, Walter C.; Rosner, Bernard A.; Kraft, Peter; Hankinson, Susan E.

    2014-01-01

    Purpose Endogenous hormones are risk factors for postmenopausal breast cancer, and their measurement may improve our ability to identify high-risk women. Therefore, we evaluated whether inclusion of plasma estradiol, estrone, estrone sulfate, testosterone, dehydroepiandrosterone sulfate, prolactin, and sex hormone–binding globulin (SHBG) improved risk prediction for postmenopausal invasive breast cancer (n = 437 patient cases and n = 775 controls not using postmenopausal hormones) in the Nurses' Health Study. Methods We evaluated improvement in the area under the curve (AUC) for 5-year risk of invasive breast cancer by adding each hormone to the Gail and Rosner-Colditz risk scores. We used stepwise regression to identify the subset of hormones most associated with risk and assessed AUC improvement; we used 10-fold cross validation to assess model overfitting. Results Each hormone was associated with breast cancer risk (odds ratio doubling, 0.82 [SHBG] to 1.37 [estrone sulfate]). Individual hormones improved the AUC by 1.3 to 5.2 units relative to the Gail score and 0.3 to 2.9 for the Rosner-Colditz score. Estrone sulfate, testosterone, and prolactin were selected by stepwise regression and increased the AUC by 5.9 units (P = .003) for the Gail score and 3.4 (P = .04) for the Rosner-Colditz score. In cross validation, the average AUC change across the validation data sets was 6.0 (P = .002) and 3.0 units (P = .03), respectively. Similar results were observed for estrogen receptor–positive disease (selected hormones: estrone sulfate, testosterone, prolactin, and SHBG; change in AUC, 8.8 [P < .001] for Gail score and 5.8 [P = .004] for Rosner-Colditz score). Conclusion Our results support that endogenous hormones improve risk prediction for invasive breast cancer and could help identify women who may benefit from chemoprevention or more screening. PMID:25135988

  17. The utility and predictive value of combinations of low penetrance genes for screening and risk prediction of colorectal cancer

    PubMed Central

    Hawken, Steven J.; Greenwood, Celia M. T.; Hudson, Thomas J.; Kustra, Rafal; McLaughlin, John; Yang, Quanhe; Zanke, Brent W.

    2010-01-01

    Despite the fact that colorectal cancer (CRC) is a highly treatable form of cancer if detected early, a very low proportion of the eligible population undergoes screening for this form of cancer. Integrating a genomic screening profile as a component of existing screening programs for CRC could potentially improve the effectiveness of population screening by allowing the assignment of individuals to different types and intensities of screening and also by potentially increasing the uptake of existing screening programs. We evaluated the utility and predictive value of genomic profiling as applied to CRC, and as a potential component of a population-based cancer screening program. We generated simulated data representing a typical North American population including a variety of genetic profiles, with a range of relative risks and prevalences for individual risk genes. We then used these data to estimate parameters characterizing the predictive value of a logistic regression model built on genetic markers for CRC. Meta-analyses of genetic associations with CRC were used in building science to inform the simulation work, and to select genetic variants to include in logistic regression model-building using data from the ARCTIC study in Ontario, which included 1,200 CRC cases and a similar number of cancer-free population-based controls. Our simulations demonstrate that for reasonable assumptions involving modest relative risks for individual genetic variants, that substantial predictive power can be achieved when risk variants are common (e.g., prevalence > 20%) and data for enough risk variants are available (e.g., ~140–160). Pilot work in population data shows modest, but statistically significant predictive utility for a small collection of risk variants, smaller in effect than age and gender alone in predicting an individual’s CRC risk. Further genotyping and many more samples will be required, and indeed the discovery of many more risk loci associated with

  18. Clinical utility of genetic variants of glutamate carboxypeptidase II in predicting breast cancer and prostate cancer risk.

    PubMed

    Naushad, Shaik Mohammad; Shree Divyya, Parvathaneni; Janaki Ramaiah, M; Alex Stanley, Balraj; Prasanna Lakshmi, S; Vishnupriya, J; Kutala, Vijay Kumar

    2015-11-01

    In view of documented evidence showing glutamate carboxypeptidase II (GCPII) inhibitors as promising anti-cancer agents, certain variants of GCPII modulate breast and prostate cancer risk, and we developed an artificial neural network (ANN) model of GCPII variants and ascertained the risk associated with eight genetic variants of GCPII. In parallel, an in silico model was developed to substantiate the ANN simulations. The ANN model with modified sigmoid function was used for disease prediction, whereas the hyperbolic tangent function was used to predict folate hydrolase 1 (FOLH1) and prostate specific membrane antigen (PSMA) expression. PyMOL models of GCPII variants were developed, and their affinity toward the folylpolyglutamate (FPG) ligand was tested using glide score analysis. Of the eight genetic variants of GCPII, p.P160S alone conferred protection against both of the cancers. This variant exhibited higher affinity toward FPG compared with wild GCPII (-2.06 vs. -1.69); and positive correlation was observed between the P160S variant and circulating folate (r = 0.60). The ANN model for disease prediction showed significant predictability associated with GCPII variants toward breast cancer (area under the curve (AUC): 1.00) and prostate cancer (AUC: 0.97), with clear distinguishing ability of healthy controls (AUC: 0.96). The ANN models for the expression of FOLH1 and PSMA explained 60.5% and 86.7% of the variability, respectively. Thus, GCPII variants are potential contributors of risk toward breast cancer and prostate cancer. Risk modulation appeared to be mediated through changes in the expression of FOLH1 and PSMA.

  19. Risk factors assessment and risk prediction models in lung cancer screening candidates

    PubMed Central

    Wachuła, Ewa; Szabłowska-Siwik, Sylwia; Boratyn-Nowicka, Agnieszka; Czyżewski, Damian

    2016-01-01

    From February 2015, low-dose computed tomography (LDCT) screening entered the armamentarium of diagnostic tools broadly available to individuals at high-risk of developing lung cancer. While a huge number of pulmonary nodules are identified, only a small fraction turns out to be early lung cancers. The majority of them constitute a variety of benign lesions. Although it entails a burden of the diagnostic work-up, the undisputable benefit emerges from: (I) lung cancer diagnosis at earlier stages (stage shift); (II) additional findings enabling the implementation of a preventive action beyond the realm of thoracic oncology. This review presents how to utilize the risk factors from distinct categories such as epidemiology, radiology and biomarkers to target the fraction of population, which may benefit most from the introduced screening modality. PMID:27195269

  20. Prediction of Germline Mutations and Cancer Risk in the Lynch Syndrome

    PubMed Central

    Chen, Sining; Wang, Wenyi; Lee, Shing; Nafa, Khedoudja; Lee, Johanna; Romans, Kathy; Watson, Patrice; Gruber, Stephen B.; Euhus, David; Kinzler, Kenneth W.; Jass, Jeremy; Gallinger, Steven; Lindor, Noralane M.; Casey, Graham; Ellis, Nathan; Giardiello, Francis M.; Offit, Kenneth; Parmigiani, Giovanni

    2008-01-01

    Context Identifying families at high risk for the Lynch syndrome (ie, hereditary non-polyposis colorectal cancer) is critical for both genetic counseling and cancer prevention. Current clinical guidelines are effective but limited by applicability and cost. Objective To develop and validate a genetic counseling and risk prediction tool that estimates the probability of carrying a deleterious mutation in mismatch repair genes MLH1, MSH2, or MSH6 and the probability of developing colorectal or endometrial cancer. Design, Setting, and Patients External validation of the MMRpro model was conducted on 279 individuals from 226 clinic-based families in the United States, Canada, and Australia (referred between 1993–2005) by comparing model predictions with results of highly sensitive germline mutation detection techniques. MMRpro models the autosomal dominant inheritance of mismatch repair mutations, with parameters based on meta-analyses of the penetrance and prevalence of mutations and of the predictive values of tumor characteristics. The model’s prediction is tailored to each individual’s detailed family history information on colorectal and endometrial cancer and to tumor characteristics including microsatellite instability. Main Outcome Measure Ability of MMRpro to correctly predict mutation carrier status, as measured by operating characteristics, calibration, and overall accuracy. Results In the independent validation, MMRpro provided a concordance index of 0.83 (95% confidence interval, 0.78–0.88) and a ratio of observed to predicted cases of 0.94 (95% confidence interval, 0.84–1.05). This results in higher accuracy than existing alternatives and current clinical guidelines. Conclusions MMRpro is a broadly applicable, accurate prediction model that can contribute to current screening and genetic counseling practices in a high-risk population. It is more sensitive and more specific than existing clinical guidelines for identifying individuals who may

  1. Population-based genetic risk prediction and stratification for ovarian cancer: views from women at high risk.

    PubMed

    Rahman, Belinda; Meisel, Susanne F; Fraser, Lindsay; Side, Lucy; Gessler, Sue; Wardle, Jane; Lanceley, Anne

    2015-03-01

    There is an opportunity to improve outcomes for ovarian cancer (OC) through advances in risk stratification, early detection and diagnosis. A population-based OC genetic risk prediction and stratification program is being developed. A previous focus group study with individuals from the general population showed support for the proposed program. This qualitative interview study explores the attitudes of women at high risk of OC. Eight women participated in one-on-one, in-depth, semi-structured interviews to explore: experiences of learning of OC risk, risk perceptions, OC knowledge and awareness, and opinions on risk stratification approach. There was evidence of strong support for the proposed program. Benefits were seen as providing reassurance to women at low risk, and reducing worry in women at high risk through appropriate clinical management. Stratification into 'low' and 'high' risk groups was well-received. Participants were more hesitant about stratification to the 'intermediate' risk group. The data suggest formats to effectively communicate OC risk estimates will require careful thought. Interactions with GPs were highlighted as a barrier to OC risk assessment and diagnosis. These results are encouraging for the possible introduction and uptake of a risk prediction and stratification program for OC in the general population.

  2. Endometrial cancer risk prediction including serum-based biomarkers: results from the EPIC cohort.

    PubMed

    Fortner, Renée T; Hüsing, Anika; Kühn, Tilman; Konar, Meric; Overvad, Kim; Tjønneland, Anne; Hansen, Louise; Boutron-Ruault, Marie-Christine; Severi, Gianluca; Fournier, Agnès; Boeing, Heiner; Trichopoulou, Antonia; Benetou, Vasiliki; Orfanos, Philippos; Masala, Giovanna; Agnoli, Claudia; Mattiello, Amalia; Tumino, Rosario; Sacerdote, Carlotta; Bueno-de-Mesquita, H B As; Peeters, Petra H M; Weiderpass, Elisabete; Gram, Inger T; Gavrilyuk, Oxana; Quirós, J Ramón; Maria Huerta, José; Ardanaz, Eva; Larrañaga, Nerea; Lujan-Barroso, Leila; Sánchez-Cantalejo, Emilio; Butt, Salma Tunå; Borgquist, Signe; Idahl, Annika; Lundin, Eva; Khaw, Kay-Tee; Allen, Naomi E; Rinaldi, Sabina; Dossus, Laure; Gunter, Marc; Merritt, Melissa A; Tzoulaki, Ioanna; Riboli, Elio; Kaaks, Rudolf

    2017-03-15

    Endometrial cancer risk prediction models including lifestyle, anthropometric and reproductive factors have limited discrimination. Adding biomarker data to these models may improve predictive capacity; to our knowledge, this has not been investigated for endometrial cancer. Using a nested case-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we investigated the improvement in discrimination gained by adding serum biomarker concentrations to risk estimates derived from an existing risk prediction model based on epidemiologic factors. Serum concentrations of sex steroid hormones, metabolic markers, growth factors, adipokines and cytokines were evaluated in a step-wise backward selection process; biomarkers were retained at p < 0.157 indicating improvement in the Akaike information criterion (AIC). Improvement in discrimination was assessed using the C-statistic for all biomarkers alone, and change in C-statistic from addition of biomarkers to preexisting absolute risk estimates. We used internal validation with bootstrapping (1000-fold) to adjust for over-fitting. Adiponectin, estrone, interleukin-1 receptor antagonist, tumor necrosis factor-alpha and triglycerides were selected into the model. After accounting for over-fitting, discrimination was improved by 2.0 percentage points when all evaluated biomarkers were included and 1.7 percentage points in the model including the selected biomarkers. Models including etiologic markers on independent pathways and genetic markers may further improve discrimination. © 2016 UICC.

  3. Evaluation of Polygenic Risk Scores for Breast and Ovarian Cancer Risk Prediction in BRCA1 and BRCA2 Mutation Carriers

    PubMed Central

    Kuchenbaecker, Karoline B.; McGuffog, Lesley; Barrowdale, Daniel; Lee, Andrew; Soucy, Penny; Healey, Sue; Dennis, Joe; Lush, Michael; Robson, Mark; Spurdle, Amanda B.; Ramus, Susan J.; Mavaddat, Nasim; Terry, Mary Beth; Neuhausen, Susan L.; Hamann, Ute; Southey, Melissa; John, Esther M.; Chung, Wendy K.; Daly, Mary B.; Buys, Saundra S.; Goldgar, David E.; Dorfling, Cecilia M.; van Rensburg, Elizabeth J.; Ding, Yuan Chun; Ejlertsen, Bent; Gerdes, Anne-Marie; Hansen, Thomas V. O.; Slager, Susan; Hallberg, Emily; Benitez, Javier; Osorio, Ana; Cohen, Nancy; Lawler, William; Weitzel, Jeffrey N.; Peterlongo, Paolo; Pensotti, Valeria; Dolcetti, Riccardo; Barile, Monica; Bonanni, Bernardo; Azzollini, Jacopo; Manoukian, Siranoush; Peissel, Bernard; Radice, Paolo; Savarese, Antonella; Papi, Laura; Giannini, Giuseppe; Fostira, Florentia; Konstantopoulou, Irene; Adlard, Julian; Brewer, Carole; Cook, Jackie; Davidson, Rosemarie; Eccles, Diana; Eeles, Ros; Ellis, Steve; Frost, Debra; Hodgson, Shirley; Izatt, Louise; Lalloo, Fiona; Ong, Kai-ren; Godwin, Andrew K.; Arnold, Norbert; Dworniczak, Bernd; Engel, Christoph; Gehrig, Andrea; Hahnen, Eric; Hauke, Jan; Kast, Karin; Meindl, Alfons; Niederacher, Dieter; Schmutzler, Rita Katharina; Varon-Mateeva, Raymonda; Wang-Gohrke, Shan; Wappenschmidt, Barbara; Barjhoux, Laure; Collonge-Rame, Marie-Agnès; Elan, Camille; Golmard, Lisa; Barouk-Simonet, Emmanuelle; Lesueur, Fabienne; Mazoyer, Sylvie; Sokolowska, Joanna; Stoppa-Lyonnet, Dominique; Isaacs, Claudine; Claes, Kathleen B. M.; Poppe, Bruce; de la Hoya, Miguel; Garcia-Barberan, Vanesa; Aittomäki, Kristiina; Nevanlinna, Heli; Ausems, Margreet G. E. M.; de Lange, J. L.; Gómez Garcia, Encarna B.; Hogervorst, Frans B. L.; Kets, Carolien M.; Meijers-Heijboer, Hanne E. J.; Oosterwijk, Jan C.; Rookus, Matti A.; van Asperen, Christi J.; van den Ouweland, Ans M. W.; van Doorn, Helena C.; van Os, Theo A. M.; Kwong, Ava; Olah, Edith; Diez, Orland; Brunet, Joan; Lazaro, Conxi; Teulé, Alex; Gronwald, Jacek; Jakubowska, Anna; Kaczmarek, Katarzyna; Lubinski, Jan; Sukiennicki, Grzegorz; Barkardottir, Rosa B.; Chiquette, Jocelyne; Agata, Simona; Montagna, Marco; Teixeira, Manuel R.; Park, Sue Kyung; Olswold, Curtis; Tischkowitz, Marc; Foretova, Lenka; Gaddam, Pragna; Vijai, Joseph; Pfeiler, Georg; Rappaport-Fuerhauser, Christine; Singer, Christian F.; Tea, Muy-Kheng M.; Greene, Mark H.; Loud, Jennifer T.; Rennert, Gad; Imyanitov, Evgeny N.; Hulick, Peter J.; Hays, John L.; Piedmonte, Marion; Rodriguez, Gustavo C.; Martyn, Julie; Glendon, Gord; Mulligan, Anna Marie; Andrulis, Irene L.; Toland, Amanda Ewart; Jensen, Uffe Birk; Kruse, Torben A.; Pedersen, Inge Sokilde; Thomassen, Mads; Caligo, Maria A.; Teo, Soo-Hwang; Berger, Raanan; Friedman, Eitan; Laitman, Yael; Arver, Brita; Borg, Ake; Ehrencrona, Hans; Rantala, Johanna; Olopade, Olufunmilayo I.; Ganz, Patricia A.; Nussbaum, Robert L.; Bradbury, Angela R.; Domchek, Susan M.; Nathanson, Katherine L.; Arun, Banu K.; James, Paul; Karlan, Beth Y.; Lester, Jenny; Simard, Jacques; Pharoah, Paul D. P.; Offit, Kenneth; Couch, Fergus J.; Chenevix-Trench, Georgia; Easton, Douglas F.

    2017-01-01

    Background: Genome-wide association studies (GWAS) have identified 94 common single-nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk and 18 associated with ovarian cancer (OC) risk. Several of these are also associated with risk of BC or OC for women who carry a pathogenic mutation in the high-risk BC and OC genes BRCA1 or BRCA2. The combined effects of these variants on BC or OC risk for BRCA1 and BRCA2 mutation carriers have not yet been assessed while their clinical management could benefit from improved personalized risk estimates. Methods: We constructed polygenic risk scores (PRS) using BC and OC susceptibility SNPs identified through population-based GWAS: for BC (overall, estrogen receptor [ER]–positive, and ER-negative) and for OC. Using data from 15 252 female BRCA1 and 8211 BRCA2 carriers, the association of each PRS with BC or OC risk was evaluated using a weighted cohort approach, with time to diagnosis as the outcome and estimation of the hazard ratios (HRs) per standard deviation increase in the PRS. Results: The PRS for ER-negative BC displayed the strongest association with BC risk in BRCA1 carriers (HR = 1.27, 95% confidence interval [CI] = 1.23 to 1.31, P = 8.2×10−53). In BRCA2 carriers, the strongest association with BC risk was seen for the overall BC PRS (HR = 1.22, 95% CI = 1.17 to 1.28, P = 7.2×10−20). The OC PRS was strongly associated with OC risk for both BRCA1 and BRCA2 carriers. These translate to differences in absolute risks (more than 10% in each case) between the top and bottom deciles of the PRS distribution; for example, the OC risk was 6% by age 80 years for BRCA2 carriers at the 10th percentile of the OC PRS compared with 19% risk for those at the 90th percentile of PRS. Conclusions: BC and OC PRS are predictive of cancer risk in BRCA1 and BRCA2 carriers. Incorporation of the PRS into risk prediction models has promise to better inform decisions on cancer risk management. PMID

  4. Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures.

    PubMed

    Stone, Jennifer; Thompson, Deborah J; Dos Santos Silva, Isabel; Scott, Christopher; Tamimi, Rulla M; Lindstrom, Sara; Kraft, Peter; Hazra, Aditi; Li, Jingmei; Eriksson, Louise; Czene, Kamila; Hall, Per; Jensen, Matt; Cunningham, Julie; Olson, Janet E; Purrington, Kristen; Couch, Fergus J; Brown, Judith; Leyland, Jean; Warren, Ruth M L; Luben, Robert N; Khaw, Kay-Tee; Smith, Paula; Wareham, Nicholas J; Jud, Sebastian M; Heusinger, Katharina; Beckmann, Matthias W; Douglas, Julie A; Shah, Kaanan P; Chan, Heang-Ping; Helvie, Mark A; Le Marchand, Loic; Kolonel, Laurence N; Woolcott, Christy; Maskarinec, Gertraud; Haiman, Christopher; Giles, Graham G; Baglietto, Laura; Krishnan, Kavitha; Southey, Melissa C; Apicella, Carmel; Andrulis, Irene L; Knight, Julia A; Ursin, Giske; Alnaes, Grethe I Grenaker; Kristensen, Vessela N; Borresen-Dale, Anne-Lise; Gram, Inger Torhild; Bolla, Manjeet K; Wang, Qin; Michailidou, Kyriaki; Dennis, Joe; Simard, Jacques; Pharoah, Paul; Dunning, Alison M; Easton, Douglas F; Fasching, Peter A; Pankratz, V Shane; Hopper, John L; Vachon, Celine M

    2015-06-15

    Mammographic density measures adjusted for age and body mass index (BMI) are heritable predictors of breast cancer risk, but few mammographic density-associated genetic variants have been identified. Using data for 10,727 women from two international consortia, we estimated associations between 77 common breast cancer susceptibility variants and absolute dense area, percent dense area and absolute nondense area adjusted for study, age, and BMI using mixed linear modeling. We found strong support for established associations between rs10995190 (in the region of ZNF365), rs2046210 (ESR1), and rs3817198 (LSP1) and adjusted absolute and percent dense areas (all P < 10(-5)). Of 41 recently discovered breast cancer susceptibility variants, associations were found between rs1432679 (EBF1), rs17817449 (MIR1972-2: FTO), rs12710696 (2p24.1), and rs3757318 (ESR1) and adjusted absolute and percent dense areas, respectively. There were associations between rs6001930 (MKL1) and both adjusted absolute dense and nondense areas, and between rs17356907 (NTN4) and adjusted absolute nondense area. Trends in all but two associations were consistent with those for breast cancer risk. Results suggested that 18% of breast cancer susceptibility variants were associated with at least one mammographic density measure. Genetic variants at multiple loci were associated with both breast cancer risk and the mammographic density measures. Further understanding of the underlying mechanisms at these loci could help identify etiologic pathways implicated in how mammographic density predicts breast cancer risk. ©2015 American Association for Cancer Research.

  5. Clinical Prediction Model and Tool for Assessing Risk of Persistent Pain After Breast Cancer Surgery.

    PubMed

    Meretoja, Tuomo J; Andersen, Kenneth Geving; Bruce, Julie; Haasio, Lassi; Sipilä, Reetta; Scott, Neil W; Ripatti, Samuli; Kehlet, Henrik; Kalso, Eija

    2017-05-20

    Purpose Persistent pain after breast cancer surgery is a well-recognized problem, with moderate to severe pain affecting 15% to 20% of women at 1 year from surgery. Several risk factors for persistent pain have been recognized, but tools to identify high-risk patients and preventive interventions are missing. The aim was to develop a clinically applicable risk prediction tool. Methods The prediction models were developed and tested using three prospective data sets from Finland (n = 860), Denmark (n = 453), and Scotland (n = 231). Prediction models for persistent pain of moderate to severe intensity at 1 year postoperatively were developed by logistic regression analyses in the Finnish patient cohort. The models were tested in two independent cohorts from Denmark and Scotland by assessing the areas under the receiver operating characteristics curves (ROC-AUCs). The outcome variable was moderate to severe persistent pain at 1 year from surgery in the Finnish and Danish cohorts and at 9 months in the Scottish cohort. Results Moderate to severe persistent pain occurred in 13.5%, 13.9%, and 20.3% of the patients in the three studies, respectively. Preoperative pain in the operative area ( P < .001), high body mass index ( P = .039), axillary lymph node dissection ( P = .008), and more severe acute postoperative pain intensity at the seventh postoperative day ( P = .003) predicted persistent pain in the final prediction model, which performed well in the Danish (ROC-AUC, 0.739) and Scottish (ROC-AUC, 0.740) cohorts. At the 20% risk level, the model had 32.8% and 47.4% sensitivity and 94.4% and 82.4% specificity in the Danish and Scottish cohorts, respectively. Conclusion Our validated prediction models and an online risk calculator provide clinicians and researchers with a simple tool to screen for patients at high risk of developing persistent pain after breast cancer surgery.

  6. Predicting the risk of endometrial cancer in postmenopausal women presenting with vaginal bleeding: the Norwich DEFAB risk assessment tool

    PubMed Central

    Burbos, N; Musonda, P; Giarenis, I; Shiner, A M; Giamougiannis, P; Morris, E P; Nieto, J J

    2010-01-01

    Background: This study aimed to show the longitudinal use of routinely collected clinical data from history and ultrasound evaluation of the endometrium in developing an algorithm to predict the risk of endometrial carcinoma for postmenopausal women presenting with vaginal bleeding. Methods: This prospective study collected data from 3047 women presenting with postmenopausal bleeding. Data regarding the presence of risk factors for endometrial cancer was collected and univariate and multivariate analyses were performed. Results: Age distribution ranged from 35 to 97 years with a median of 59 years. A total of 149 women (5% of total) were diagnosed with endometrial carcinoma. Women in the endometrial cancer group were significantly more likely to be older, have higher BMI, recurrent episodes of bleeding, diabetes, hypertension, or a previous history of breast cancer. An investigator best model selection approach was used to select the best predictors of cancer, and using logistic regression analysis we created a model, ‘Norwich DEFAB', which is a clinical prediction rule for endometrial cancer. The calculated Norwich DEFAB score can vary from a value of 0 to 9. A Norwich DEFAB value equal to or greater than 3 has a positive predictive value (PPV) of 7.78% and negative predictive value (NPV) of 98.2%, whereas a score equal to or greater than 5 has a PPV of 11.9% and NPV of 97.8%. Conclusion: The combination of clinical information with our investigation tool for women with postmenopausal vaginal bleeding allows the clinician to calculate a predicted risk of endometrial malignancy and prioritise subsequent clinical investigations. PMID:20354525

  7. Gene expression profiling to predict the risk of locoregional recurrence in breast cancer: a pooled analysis.

    PubMed

    Drukker, C A; Elias, S G; Nijenhuis, M V; Wesseling, J; Bartelink, H; Elkhuizen, P; Fowble, B; Whitworth, P W; Patel, R R; de Snoo, F A; van 't Veer, L J; Beitsch, P D; Rutgers, E J Th

    2014-12-01

    The 70-gene signature (MammaPrint) has been developed to predict the risk of distant metastases in breast cancer and select those patients who may benefit from adjuvant treatment. Given the strong association between locoregional and distant recurrence, we hypothesize that the 70-gene signature will also be able to predict the risk of locoregional recurrence (LRR). 1,053 breast cancer patients primarily treated with breast-conserving treatment or mastectomy at the Netherlands Cancer Institute between 1984 and 2006 were included. Adjuvant treatment consisted of radiotherapy, chemotherapy, and/or endocrine therapy as indicated by guidelines used at the time. All patients were included in various 70-gene signature validation studies. After a median follow-up of 8.96 years with 87 LRRs, patients with a high-risk 70-gene signature (n = 492) had an LRR risk of 12.6% (95% CI 9.7-15.8) at 10 years, compared to 6.1% (95% CI 4.1-8.5) for low-risk patients (n = 561; P < 0.001). Adjusting the 70-gene signature in a competing risk model for the clinicopathological factors such as age, tumour size, grade, hormone receptor status, LVI, axillary lymph node involvement, surgical treatment, endocrine treatment, and chemotherapy resulted in a multivariable HR of 1.73 (95% CI 1.02-2.93; P = 0.042). Adding the signature to the model based on clinicopathological factors improved the discrimination, albeit non-significantly [C-index through 10 years changed from 0.731 (95% CI 0.682-0.782) to 0.741 (95% CI 0.693-0.790)]. Calibration of the prognostic models was excellent. The 70-gene signature is an independent prognostic factor for LRR. A significantly lower local recurrence risk was seen in patients with a low-risk 70-gene signature compared to those with high-risk 70-gene signature.

  8. Assessment of two mammographic density related features in predicting near-term breast cancer risk

    NASA Astrophysics Data System (ADS)

    Zheng, Bin; Sumkin, Jules H.; Zuley, Margarita L.; Wang, Xingwei; Klym, Amy H.; Gur, David

    2012-02-01

    In order to establish a personalized breast cancer screening program, it is important to develop risk models that have high discriminatory power in predicting the likelihood of a woman developing an imaging detectable breast cancer in near-term (e.g., <3 years after a negative examination in question). In epidemiology-based breast cancer risk models, mammographic density is considered the second highest breast cancer risk factor (second to woman's age). In this study we explored a new feature, namely bilateral mammographic density asymmetry, and investigated the feasibility of predicting near-term screening outcome. The database consisted of 343 negative examinations, of which 187 depicted cancers that were detected during the subsequent screening examination and 155 that remained negative. We computed the average pixel value of the segmented breast areas depicted on each cranio-caudal view of the initial negative examinations. We then computed the mean and difference mammographic density for paired bilateral images. Using woman's age, subjectively rated density (BIRADS), and computed mammographic density related features we compared classification performance in estimating the likelihood of detecting cancer during the subsequent examination using areas under the ROC curves (AUC). The AUCs were 0.63+/-0.03, 0.54+/-0.04, 0.57+/-0.03, 0.68+/-0.03 when using woman's age, BIRADS rating, computed mean density and difference in computed bilateral mammographic density, respectively. Performance increased to 0.62+/-0.03 and 0.72+/-0.03 when we fused mean and difference in density with woman's age. The results suggest that, in this study, bilateral mammographic tissue density is a significantly stronger (p<0.01) risk indicator than both woman's age and mean breast density.

  9. Prediction of near-term risk of developing breast cancer using computerized features from bilateral mammograms.

    PubMed

    Sun, Wenqing; Zheng, Bin; Lure, Fleming; Wu, Teresa; Zhang, Jianying; Wang, Benjamin Y; Saltzstein, Edward C; Qian, Wei

    2014-07-01

    Asymmetry of bilateral mammographic tissue density and patterns is a potentially strong indicator of having or developing breast abnormalities or early cancers. The purpose of this study is to design and test the global asymmetry features from bilateral mammograms to predict the near-term risk of women developing detectable high risk breast lesions or cancer in the next sequential screening mammography examination. The image dataset includes mammograms acquired from 90 women who underwent routine screening examinations, all interpreted as negative and not recalled by the radiologists during the original screening procedures. A computerized breast cancer risk analysis scheme using four image processing modules, including image preprocessing, suspicious region segmentation, image feature extraction, and classification was designed to detect and compute image feature asymmetry between the left and right breasts imaged on the mammograms. The highest computed area under curve (AUC) is 0.754±0.024 when applying the new computerized aided diagnosis (CAD) scheme to our testing dataset. The positive predictive value and the negative predictive value were 0.58 and 0.80, respectively. Copyright © 2014 Elsevier Ltd. All rights reserved.

  10. SNPs and breast cancer risk prediction for African American and Hispanic women.

    PubMed

    Allman, Richard; Dite, Gillian S; Hopper, John L; Gordon, Ora; Starlard-Davenport, Athena; Chlebowski, Rowan; Kooperberg, Charles

    2015-12-01

    For African American or Hispanic women, the extent to which clinical breast cancer risk prediction models are improved by including information on susceptibility single nucleotide polymorphisms (SNPs) is unknown, even though these women comprise increasing proportions of the US population and represent a large proportion of the world's population. We studied 7539 African American and 3363 Hispanic women from the Women's Health Initiative. The age-adjusted 5-year risks from the BCRAT and IBIS risk prediction models were measured and combined with a risk score based on >70 independent susceptibility SNPs. Logistic regression, adjusting for age group, was used to estimate risk associations with log-transformed age-adjusted 5-year risks. Discrimination was measured by the odds ratio (OR) per standard deviation (SD) and the area under the receiver operator curve (AUC). When considered alone, the ORs for African American women were 1.28 for BCRAT, and 1.04 for IBIS. When combined with the SNP risk score (OR 1.23), the corresponding ORs were 1.39 and 1.22. For Hispanic women the corresponding ORs were 1.25 for BCRAT, and 1.15 for IBIS. When combined with the SNP risk score (OR 1.39), the corresponding ORs were 1.48 and 1.42. There was no evidence that any of the combined models were not well calibrated. Including information on known breast cancer susceptibility loci provides approximately 10 and 19% improvement in risk prediction using BCRAT for African Americans and Hispanics, respectively. The corresponding figures for IBIS are approximately 18 and 26%, respectively.

  11. Nottingham prognostic index plus (NPI+) predicts risk of distant metastases in primary breast cancer.

    PubMed

    Green, Andrew R; Soria, D; Powe, D G; Nolan, C C; Aleskandarany, M; Szász, M A; Tőkés, A M; Ball, G R; Garibaldi, J M; Rakha, E A; Kulka, J; Ellis, I O

    2016-05-01

    The Nottingham prognostic index plus (NPI+) is based on the assessment of biological class combined with established clinicopathologic prognostic variables providing improved patient outcome stratification for breast cancer superior to the traditional NPI. This study aimed to determine prognostic capability of the NPI+ in predicting risk of development of distant disease. A well-characterised series of 1073 primary early-stage BC cases treated in Nottingham and 251 cases from Budapest were immunohistochemically assessed for cytokeratin (Ck)5/6, Ck18, EGFR, oestrogen receptor (ER), progesterone receptor, HER2, HER3, HER4, Mucin 1 and p53 expression. NPI+ biological class and prognostic scores were assigned using individual algorithms for each biological class incorporating clinicopathologic parameters and investigated in terms of prediction of distant metastases-free survival (MFS). The NPI+ identified distinct prognostic groups (PG) within each molecular class which were predictive of MFS providing improved patient outcome stratification superior to the traditional NPI. NPI+ PGs, between series, were comparable in predicting patient outcome between series in luminal A, basal p53 altered and HER2+/ER+ (p > 0.01) tumours. The low-risk groups were similarly validated in luminal B, luminal N, basal p53 normal tumours (p > 0.01). Due to small patient numbers the remaining PGs could not be validated. NPI+ was additionally able to predict a higher risk of metastases at certain distant sites. This study may indicate the NPI+ as a useful tool in predicting the risk of metastases. The NPI+ provides accurate risk stratification allowing improved individualised clinical decision making for breast cancer.

  12. Non-Targeted Effects Models Predict Significantly Higher Mars Mission Cancer Risk than Targeted Effects Models.

    PubMed

    Cucinotta, Francis A; Cacao, Eliedonna

    2017-05-12

    Cancer risk is an important concern for galactic cosmic ray (GCR) exposures, which consist of a wide-energy range of protons, heavy ions and secondary radiation produced in shielding and tissues. Relative biological effectiveness (RBE) factors for surrogate cancer endpoints in cell culture models and tumor induction in mice vary considerable, including significant variations for different tissues and mouse strains. Many studies suggest non-targeted effects (NTE) occur for low doses of high linear energy transfer (LET) radiation, leading to deviation from the linear dose response model used in radiation protection. Using the mouse Harderian gland tumor experiment, the only extensive data-set for dose response modelling with a variety of particle types (>4), for the first-time a particle track structure model of tumor prevalence is used to investigate the effects of NTEs in predictions of chronic GCR exposure risk. The NTE model led to a predicted risk 2-fold higher compared to a targeted effects model. The scarcity of data with animal models for tissues that dominate human radiation cancer risk, including lung, colon, breast, liver, and stomach, suggest that studies of NTEs in other tissues are urgently needed prior to long-term space missions outside the protection of the Earth's geomagnetic sphere.

  13. Non-Targeted Effects Models Predict Significantly Higher Mars Mission Cancer Risk than Targeted Effects Models

    DOE PAGES

    Cucinotta, Francis A.; Cacao, Eliedonna

    2017-05-12

    Cancer risk is an important concern for galactic cosmic ray (GCR) exposures, which consist of a wide-energy range of protons, heavy ions and secondary radiation produced in shielding and tissues. Relative biological effectiveness (RBE) factors for surrogate cancer endpoints in cell culture models and tumor induction in mice vary considerable, including significant variations for different tissues and mouse strains. Many studies suggest non-targeted effects (NTE) occur for low doses of high linear energy transfer (LET) radiation, leading to deviation from the linear dose response model used in radiation protection. Using the mouse Harderian gland tumor experiment, the only extensive data-setmore » for dose response modelling with a variety of particle types (>4), for the first-time a particle track structure model of tumor prevalence is used to investigate the effects of NTEs in predictions of chronic GCR exposure risk. The NTE model led to a predicted risk 2-fold higher compared to a targeted effects model. The scarcity of data with animal models for tissues that dominate human radiation cancer risk, including lung, colon, breast, liver, and stomach, suggest that studies of NTEs in other tissues are urgently needed prior to long-term space missions outside the protection of the Earth’s geomagnetic sphere.« less

  14. Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures

    PubMed Central

    Stone, Jennifer; Thompson, Deborah J.; dos-Santos-Silva, Isabel; Scott, Christopher; Tamimi, Rulla M.; Lindstrom, Sara; Kraft, Peter; Hazra, Aditi; Li, Jingmei; Eriksson, Louise; Czene, Kamila; Hall, Per; Jensen, Matt; Cunningham, Julie; Olson, Janet E.; Purrington, Kristen; Couch, Fergus J.; Brown, Judith; Leyland, Jean; Warren, Ruth M. L.; Luben, Robert N.; Khaw, Kay-Tee; Smith, Paula; Wareham, Nicholas J.; Jud, Sebastian M.; Heusinger, Katharina; Beckmann, Matthias W.; Douglas, Julie A.; Shah, Kaanan P.; Chan, Heang-Ping; Helvie, Mark A.; Le Marchand, Loic; Kolonel, Laurence N.; Woolcott, Christy; Maskarinec, Gertraud; Haiman, Christopher; Giles, Graham G.; Baglietto, Laura; Krishnan, Kavitha; Southey, Melissa C.; Apicella, Carmel; Andrulis, Irene L.; Knight, Julia A.; Ursin, Giske; Grenaker Alnaes, Grethe I.; Kristensen, Vessela N.; Borresen-Dale, Anne-Lise; Gram, Inger Torhild; Bolla, Manjeet K.; Wang, Qin; Michailidou, Kyriaki; Dennis, Joe; Simard, Jacques; Paroah, Paul; Dunning, Alison M.; Easton, Douglas F.; Fasching, Peter A.; Pankratz, V. Shane; Hopper, John; Vachon, Celine M.

    2015-01-01

    Mammographic density measures adjusted for age and body mass index (BMI) are heritable predictors of breast cancer risk but few mammographic density-associated genetic variants have been identified. Using data for 10,727 women from two international consortia, we estimated associations between 77 common breast cancer susceptibility variants and absolute dense area, percent dense area and absolute non-dense area adjusted for study, age and BMI using mixed linear modeling. We found strong support for established associations between rs10995190 (in the region of ZNF365), rs2046210 (ESR1) and rs3817198 (LSP1) and adjusted absolute and percent dense areas (all p <10−5). Of 41 recently discovered breast cancer susceptibility variants, associations were found between rs1432679 (EBF1), rs17817449 (MIR1972-2: FTO), rs12710696 (2p24.1), and rs3757318 (ESR1) and adjusted absolute and percent dense areas, respectively. There were associations between rs6001930 (MKL1) and both adjusted absolute dense and non-dense areas, and between rs17356907 (NTN4) and adjusted absolute non-dense area. Trends in all but two associations were consistent with those for breast cancer risk. Results suggested that 18% of breast cancer susceptibility variants were associated with at least one mammographic density measure. Genetic variants at multiple loci were associated with both breast cancer risk and the mammographic density measures. Further understanding of the underlying mechanisms at these loci could help identify etiological pathways implicated in how mammographic density predicts breast cancer risk. PMID:25862352

  15. Prediction of near-term breast cancer risk based on bilateral mammographic feature asymmetry.

    PubMed

    Tan, Maxine; Zheng, Bin; Ramalingam, Pandiyarajan; Gur, David

    2013-12-01

    The objective of this study is to investigate the feasibility of predicting near-term risk of breast cancer development in women after a negative mammography screening examination. It is based on a statistical learning model that combines computerized image features related to bilateral mammographic tissue asymmetry and other clinical factors. A database of negative digital mammograms acquired from 994 women was retrospectively collected. In the next sequential screening examination (12 to 36 months later), 283 women were diagnosed positive for cancer, 349 were recalled for additional diagnostic workups and later proved to be benign, and 362 remain negative (not recalled). From an initial pool of 183 features, we applied a Sequential Forward Floating Selection feature selection method to search for effective features. Using 10 selected features, we developed and trained a support vector machine classification model to compute a cancer risk or probability score for each case. The area under the receiver operating characteristic curve and odds ratios (ORs) were used as the two performance assessment indices. The area under the receiver operating characteristic curve = 0.725 ± 0.018 was obtained for positive and negative/benign case classification. The ORs showed an increasing risk trend with increasing model-generated risk scores (from 1.00 to 12.34, between positive and negative/benign case groups). Regression analysis of ORs also indicated a significant increase trend in slope (P = .006). This study demonstrates that the risk scores computed by a new support vector machine model involving bilateral mammographic feature asymmetry have potential to assist the prediction of near-term risk of women for developing breast cancer. Copyright © 2013 AUR. Published by Elsevier Inc. All rights reserved.

  16. Estrogen Metabolism and Exposure in a Genotypic-Phenotypic Model for Breast Cancer Risk Prediction

    PubMed Central

    Crooke, Philip S.; Justenhoven, Christina; Brauch, Hiltrud; Dawling, Sheila; Roodi, Nady; Higginbotham, Kathryn S. P.; Plummer, W. Dale; Schuyler, Peggy A.; Sanders, Melinda E; Page, David L.; Smith, Jeffrey R.; Dupont, William D.; Parl, Fritz F.

    2012-01-01

    Background Current models of breast cancer risk prediction do not directly reflect mammary estrogen metabolism or genetic variability in exposure to carcinogenic estrogen metabolites. Methods We developed a model that simulates the kinetic effect of genetic variants of the enzymes CYP1A1, CYP1B1, and COMT on the production of the main carcinogenic estrogen metabolite, 4-hydroxyestradiol (4-OHE2), expressed as area under the curve metric (4-OHE2-AUC). The model also incorporates phenotypic factors (age, body mass index, hormone replacement therapy, oral contraceptives, family history), which plausibly influence estrogen metabolism and the production of 4-OHE2. We applied the model to two independent, population-based breast cancer case-control groups, the German GENICA study (967 cases, 971 controls) and the Nashville Breast Cohort (NBC; 465 cases, 885 controls). Results In the GENICA study, premenopausal women at the 90th percentile of 4-OHE2-AUC among control subjects had a risk of breast cancer that was 2.30 times that of women at the 10th control 4-OHE2-AUC percentile (95% CI 1.7 – 3.2, P = 2.9 × 10−7). This relative risk was 1.89 (95% CI 1.5 – 2.4, P = 2.2 × 10−8) in postmenopausal women. In the NBC, this relative risk in postmenopausal women was 1.81 (95% CI 1.3 – 2.6, P = 7.6 × 10−4), which increased to 1.83 (95% CI 1.4 – 2.3, P = 9.5 × 10−7) when a history of proliferative breast disease was included in the model. Conclusions The model combines genotypic and phenotypic factors involved in carcinogenic estrogen metabolite production and cumulative estrogen exposure to predict breast cancer risk. Impact The estrogen carcinogenesis-based model has the potential to provide personalized risk estimates. PMID:21610218

  17. MicroRNA biomarkers predicting risk, initiation and progression of colorectal cancer

    PubMed Central

    Lee, Kyungjin; Ferguson, Lynnette R

    2016-01-01

    Colorectal cancer is a major global cause of morbidity and mortality. Current strategies employed to increase detection of early, curable stages of this disease are contributing to a reduction of the negative health impact from it. While there is a genetic component to the risk of disease, diet and environment are known to have major effects on the risk of an individual for developing the disease. However, there is the potential to reduce the impact of this disease further by preventing disease development. Biomarkers which can either predict the risk for or early stages of colorectal cancer could allow intervention at a time when prospects could be modified by environmental factors, including lifestyle and diet choices. Thus, such biomarkers could be used to identify high risk individuals who would benefit from lifestyle and dietary interventions to prevent this disease. This review will give an overview on one type of biomarker in the form of microRNAs, which have the potential to predict an individual’s risk for colorectal cancer, as well as providing a highly sensitive and non-invasive warning of disease presence and/or progression. MicroRNA biomarkers which have been studied and whose levels look promising for this purpose include MiR-18a, MiR-21, MiR-92a, MiR-135b, MiR-760, MiR-601. Not only have several individual microRNAs appeared promising as biomarkers, but panels of these may be even more useful. Furthermore, understanding dietary sources and ways of dietary modulation of these microRNAs might be fruitful in reducing the incidence and slowing the progression of colorectal cancer. PMID:27672263

  18. Colorectal Cancer Risk Prediction Tool for White Men and Women Without Known Susceptibility

    PubMed Central

    Freedman, Andrew N.; Slattery, Martha L.; Ballard-Barbash, Rachel; Willis, Gordon; Cann, Bette J.; Pee, David; Gail, Mitchell H.; Pfeiffer, Ruth M.

    2009-01-01

    Purpose Given the high incidence of colorectal cancer (CRC), and the availability of procedures that can detect disease and remove precancerous lesions, there is a need for a model that estimates the probability of developing CRC across various age intervals and risk factor profiles. Methods The development of separate CRC absolute risk models for men and women included estimating relative risks and attributable risk parameters from population-based case-control data separately for proximal, distal, and rectal cancer and combining these estimates with baseline age-specific cancer hazard rates based on Surveillance, Epidemiology, and End Results (SEER) incidence rates and competing mortality risks. Results For men, the model included a cancer-negative sigmoidoscopy/colonoscopy in the last 10 years, polyp history in the last 10 years, history of CRC in first-degree relatives, aspirin and nonsteroidal anti-inflammatory drug (NSAID) use, cigarette smoking, body mass index (BMI), current leisure-time vigorous activity, and vegetable consumption. For women, the model included sigmoidoscopy/colonoscopy, polyp history, history of CRC in first-degree relatives, aspirin and NSAID use, BMI, leisure-time vigorous activity, vegetable consumption, hormone-replacement therapy (HRT), and estrogen exposure on the basis of menopausal status. For men and women, relative risks differed slightly by tumor site. A validation study in independent data indicates that the models for men and women are well calibrated. Conclusion We developed absolute risk prediction models for CRC from population-based data, and a simple questionnaire suitable for self-administration. This model is potentially useful for counseling, for designing research intervention studies, and for other applications. PMID:19114701

  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. Risk factors predictive of occult cancer detection in patients with unprovoked venous thromboembolism

    PubMed Central

    Ihaddadene, Ryma; Corsi, Daniel J.; Lazo-Langner, Alejandro; Shivakumar, Sudeep; Zarychanski, Ryan; Tagalakis, Vicky; Solymoss, Susan; Routhier, Nathalie; Douketis, James; Le Gal, Gregoire

    2016-01-01

    Risk factors predictive of occult cancer detection in patients with a first unprovoked symptomatic venous thromboembolism (VTE) are unknown. Cox proportional hazard models and multivariate analyses were performed to assess the effect of specific risk factors on occult cancer detection within 1 year of a diagnosis of unprovoked VTE in patients randomized in the Screening for Occult Malignancy in Patients with Idiopathic Venous Thromboembolism (SOME) trial. A total of 33 (3.9%; 95% CI, 2.8%-5.4%) out of the 854 included patients received a new diagnosis of cancer at 1-year follow-up. Age ≥ 60 years (hazard ratio [HR], 3.11; 95% CI, 1.41-6.89; P = .005), previous provoked VTE (HR, 3.20; 95% CI, 1.19-8.62; P = .022), and current smoker status (HR, 2.80; 95% CI, 1.24-6.33; P = .014) were associated with occult cancer detection. Age, prior provoked VTE, and smoking status may be important predictors of occult cancer detection in patients with first unprovoked VTE. This trial was registered at www.clinicaltrials.gov as #NCT00773448. PMID:26817957

  1. In silico approaches to predicting cancer potency for risk assessment of genotoxic impurities in drug substances.

    PubMed

    Bercu, Joel P; Morton, Stuart M; Deahl, J Thom; Gombar, Vijay K; Callis, Courtney M; van Lier, Robert B L

    2010-01-01

    The current risk assessment approach for addressing the safety of very small concentrations of genotoxic impurities (GTIs) in drug substances is the threshold of toxicological concern (TTC). The TTC is based on several conservative assumptions because of the uncertainty associated with deriving an excess cancer risk when no carcinogenicity data are available for the impurity. It is a default approach derived from a distribution of carcinogens and does not take into account the properties of a specific chemical. The purpose of the study was to use in silico tools to predict the cancer potency (TD(50)) of a compound based on its structure. Structure activity relationship (SAR) models (classification/regression) were developed from the carcinogenicity potency database using MultiCASE and VISDOM. The MultiCASE classification models allowed the prediction of carcinogenic potency class, while the VISDOM regression models predicted a numerical TD(50). A step-wise approach is proposed to calculate predicted numerical TD(50) values for compounds categorized as not potent. This approach for non-potent compounds can be used to establish safe levels greater than the TTC for GTIs in a drug substance.

  2. Validation of a Colorectal Cancer Risk Prediction Model Among White Patients Age 50 Years and Older

    PubMed Central

    Park, Yikyung; Freedman, Andrew Nathan; Gail, Mitchell H.; Pee, David; Hollenbeck, Albert; Schatzkin, Arthur; Pfeiffer, Ruth M.

    2009-01-01

    Purpose Validation of an absolute risk prediction model for colorectal cancer (CRC) by using a large, population-based cohort. Patients and Methods The National Institutes of Health (NIH) –American Association of Retired Persons (AARP) diet and health study, a prospective cohort study, was used to validate the model. Men and women age 50 to 71 years at baseline answered self-administered questionnaires that asked about demographic characteristics, diet, lifestyle, and medical histories. We compared expected numbers of CRC patient cases predicted by the model to the observed numbers of CRC patient cases identified in the NIH-AARP study overall and in subgroups defined by risk factor combinations. The discriminatory power was measured by the area under the receiver-operating characteristic curve (AUC). Results During an average of 6.9 years of follow-up, we identified 2,092 and 832 incident CRC patient cases in men and women, respectively. The overall expected/observed ratio was 0.99 (95% CI, 0.95 to 1.04) in men and 1.05 (95% CI, 0.98 to 1.11) in women. Agreement between the expected and the observed number of cases was good in most risk factor categories, except for in subgroups defined by CRC screening and polyp history. This discrepancy may be caused by differences in the question on screening and polyp history between two studies. The AUC was 0.61 (95% CI, 0.60 to 0.62) for men and 0.61 (95% CI, 0.59 to 0.62) for women, which was similar to other risk prediction models. Conclusion The absolute risk model for CRC was well calibrated in a large prospective cohort study. This prediction model, which estimates an individual's risk of CRC given age and risk factors, may be a useful tool for physicians, researchers, and policy makers. PMID:19114700

  3. Risk of colon cancer in hereditary non-polyposis colorectal cancer patients as predicted by fuzzy modeling: Influence of smoking

    PubMed Central

    Brand, Rhonda M; Jones, David D; Lynch, Henry T; Brand, Randall E; Watson, Patrice; Ashwathnayaran, Ramesh; Roy, Hemant K

    2006-01-01

    AIM: To investigate whether a fuzzy logic model could predict colorectal cancer (CRC) risk engendered by smoking in hereditary non-polyposis colorectal cancer (HNPCC) patients. METHODS: Three hundred and forty HNPCC mismatch repair (MMR) mutation carriers from the Creighton University Hereditary Cancer Institute Registry were selected for modeling. Age-dependent curves were generated to elucidate the joint effects between gene mutation (hMLH1 or hMSH2), gender, and smoking status on the probability of developing CRC. RESULTS: Smoking significantly increased CRC risk in male hMSH2 mutation carriers (P < 0.05). hMLH1 mutations augmented CRC risk relative to hMSH2 mutation carriers for males (P < 0.05). Males had a significantly higher risk of CRC than females for hMLH1 non smokers (P < 0.05), hMLH1 smokers (P < 0.1) and hMSH2 smokers (P < 0.1). Smoking promoted CRC in a dose-dependent manner in hMSH2 in males (P < 0.05). Females with hMSH2 mutations and both sexes with the hMLH1 groups only demonstrated a smoking effect after an extensive smoking history (P < 0.05). CONCLUSION: CRC promotion by smoking in HNPCC patients is dependent on gene mutation, gender and age. These data demonstrate that fuzzy modeling may enable formulation of clinical risk scores, thereby allowing individualization of CRC prevention strategies. PMID:16874859

  4. Concepts and challenges in cancer risk prediction for the space radiation environment

    NASA Astrophysics Data System (ADS)

    Barcellos-Hoff, Mary Helen; Blakely, Eleanor A.; Burma, Sandeep; Fornace, Albert J.; Gerson, Stanton; Hlatky, Lynn; Kirsch, David G.; Luderer, Ulrike; Shay, Jerry; Wang, Ya; Weil, Michael M.

    2015-07-01

    Cancer is an important long-term risk for astronauts exposed to protons and high-energy charged particles during travel and residence on asteroids, the moon, and other planets. NASA's Biomedical Critical Path Roadmap defines the carcinogenic risks of radiation exposure as one of four type I risks. A type I risk represents a demonstrated, serious problem with no countermeasure concepts, and may be a potential "show-stopper" for long duration spaceflight. Estimating the carcinogenic risks for humans who will be exposed to heavy ions during deep space exploration has very large uncertainties at present. There are no human data that address risk from extended exposure to complex radiation fields. The overarching goal in this area to improve risk modeling is to provide biological insight and mechanistic analysis of radiation quality effects on carcinogenesis. Understanding mechanisms will provide routes to modeling and predicting risk and designing countermeasures. This white paper reviews broad issues related to experimental models and concepts in space radiation carcinogenesis as well as the current state of the field to place into context recent findings and concepts derived from the NASA Space Radiation Program.

  5. Concepts and challenges in cancer risk prediction for the space radiation environment.

    PubMed

    Barcellos-Hoff, Mary Helen; Blakely, Eleanor A; Burma, Sandeep; Fornace, Albert J; Gerson, Stanton; Hlatky, Lynn; Kirsch, David G; Luderer, Ulrike; Shay, Jerry; Wang, Ya; Weil, Michael M

    2015-07-01

    Cancer is an important long-term risk for astronauts exposed to protons and high-energy charged particles during travel and residence on asteroids, the moon, and other planets. NASA's Biomedical Critical Path Roadmap defines the carcinogenic risks of radiation exposure as one of four type I risks. A type I risk represents a demonstrated, serious problem with no countermeasure concepts, and may be a potential "show-stopper" for long duration spaceflight. Estimating the carcinogenic risks for humans who will be exposed to heavy ions during deep space exploration has very large uncertainties at present. There are no human data that address risk from extended exposure to complex radiation fields. The overarching goal in this area to improve risk modeling is to provide biological insight and mechanistic analysis of radiation quality effects on carcinogenesis. Understanding mechanisms will provide routes to modeling and predicting risk and designing countermeasures. This white paper reviews broad issues related to experimental models and concepts in space radiation carcinogenesis as well as the current state of the field to place into context recent findings and concepts derived from the NASA Space Radiation Program. Copyright © 2015 The Committee on Space Research (COSPAR). Published by Elsevier Ltd. All rights reserved.

  6. Common breast cancer susceptibility alleles and the risk of breast cancer for BRCA1 and BRCA2 mutation carriers: implications for risk prediction.

    PubMed

    Antoniou, Antonis C; Beesley, Jonathan; McGuffog, Lesley; Sinilnikova, Olga M; Healey, Sue; Neuhausen, Susan L; Ding, Yuan Chun; Rebbeck, Timothy R; Weitzel, Jeffrey N; Lynch, Henry T; Isaacs, Claudine; Ganz, Patricia A; Tomlinson, Gail; Olopade, Olufunmilayo I; Couch, Fergus J; Wang, Xianshu; Lindor, Noralane M; Pankratz, Vernon S; Radice, Paolo; Manoukian, Siranoush; Peissel, Bernard; Zaffaroni, Daniela; Barile, Monica; Viel, Alessandra; Allavena, Anna; Dall'Olio, Valentina; Peterlongo, Paolo; Szabo, Csilla I; Zikan, Michal; Claes, Kathleen; Poppe, Bruce; Foretova, Lenka; Mai, Phuong L; Greene, Mark H; Rennert, Gad; Lejbkowicz, Flavio; Glendon, Gord; Ozcelik, Hilmi; Andrulis, Irene L; Thomassen, Mads; Gerdes, Anne-Marie; Sunde, Lone; Cruger, Dorthe; Birk Jensen, Uffe; Caligo, Maria; Friedman, Eitan; Kaufman, Bella; Laitman, Yael; Milgrom, Roni; Dubrovsky, Maya; Cohen, Shimrit; Borg, Ake; Jernström, Helena; Lindblom, Annika; Rantala, Johanna; Stenmark-Askmalm, Marie; Melin, Beatrice; Nathanson, Kate; Domchek, Susan; Jakubowska, Ania; Lubinski, Jan; Huzarski, Tomasz; Osorio, Ana; Lasa, Adriana; Durán, Mercedes; Tejada, Maria-Isabel; Godino, Javier; Benitez, Javier; Hamann, Ute; Kriege, Mieke; Hoogerbrugge, Nicoline; van der Luijt, Rob B; van Asperen, Christi J; Devilee, Peter; Meijers-Heijboer, E J; Blok, Marinus J; Aalfs, Cora M; Hogervorst, Frans; Rookus, Matti; Cook, Margaret; Oliver, Clare; Frost, Debra; Conroy, Don; Evans, D Gareth; Lalloo, Fiona; Pichert, Gabriella; Davidson, Rosemarie; Cole, Trevor; Cook, Jackie; Paterson, Joan; Hodgson, Shirley; Morrison, Patrick J; Porteous, Mary E; Walker, Lisa; Kennedy, M John; Dorkins, Huw; Peock, Susan; Godwin, Andrew K; Stoppa-Lyonnet, Dominique; de Pauw, Antoine; Mazoyer, Sylvie; Bonadona, Valérie; Lasset, Christine; Dreyfus, Hélène; Leroux, Dominique; Hardouin, Agnès; Berthet, Pascaline; Faivre, Laurence; Loustalot, Catherine; Noguchi, Tetsuro; Sobol, Hagay; Rouleau, Etienne; Nogues, Catherine; Frénay, Marc; Vénat-Bouvet, Laurence; Hopper, John L; Daly, Mary B; Terry, Mary B; John, Esther M; Buys, Saundra S; Yassin, Yosuf; Miron, Alexander; Goldgar, David; Singer, Christian F; Dressler, Anne Catharina; Gschwantler-Kaulich, Daphne; Pfeiler, Georg; Hansen, Thomas V O; Jønson, Lars; Agnarsson, Bjarni A; Kirchhoff, Tomas; Offit, Kenneth; Devlin, Vincent; Dutra-Clarke, Ana; Piedmonte, Marion; Rodriguez, Gustavo C; Wakeley, Katie; Boggess, John F; Basil, Jack; Schwartz, Peter E; Blank, Stephanie V; Toland, Amanda Ewart; Montagna, Marco; Casella, Cinzia; Imyanitov, Evgeny; Tihomirova, Laima; Blanco, Ignacio; Lazaro, Conxi; Ramus, Susan J; Sucheston, Lara; Karlan, Beth Y; Gross, Jenny; Schmutzler, Rita; Wappenschmidt, Barbara; Engel, Christoph; Meindl, Alfons; Lochmann, Magdalena; Arnold, Norbert; Heidemann, Simone; Varon-Mateeva, Raymonda; Niederacher, Dieter; Sutter, Christian; Deissler, Helmut; Gadzicki, Dorothea; Preisler-Adams, Sabine; Kast, Karin; Schönbuchner, Ines; Caldes, Trinidad; de la Hoya, Miguel; Aittomäki, Kristiina; Nevanlinna, Heli; Simard, Jacques; Spurdle, Amanda B; Holland, Helene; Chen, Xiaoqing; Platte, Radka; Chenevix-Trench, Georgia; Easton, Douglas F

    2010-12-01

    The known breast cancer susceptibility polymorphisms in FGFR2, TNRC9/TOX3, MAP3K1, LSP1, and 2q35 confer increased risks of breast cancer for BRCA1 or BRCA2 mutation carriers. We evaluated the associations of 3 additional single nucleotide polymorphisms (SNPs), rs4973768 in SLC4A7/NEK10, rs6504950 in STXBP4/COX11, and rs10941679 at 5p12, and reanalyzed the previous associations using additional carriers in a sample of 12,525 BRCA1 and 7,409 BRCA2 carriers. Additionally, we investigated potential interactions between SNPs and assessed the implications for risk prediction. The minor alleles of rs4973768 and rs10941679 were associated with increased breast cancer risk for BRCA2 carriers (per-allele HR = 1.10, 95% CI: 1.03-1.18, P = 0.006 and HR = 1.09, 95% CI: 1.01-1.19, P = 0.03, respectively). Neither SNP was associated with breast cancer risk for BRCA1 carriers, and rs6504950 was not associated with breast cancer for either BRCA1 or BRCA2 carriers. Of the 9 polymorphisms investigated, 7 were associated with breast cancer for BRCA2 carriers (FGFR2, TOX3, MAP3K1, LSP1, 2q35, SLC4A7, 5p12, P = 7 × 10(-11) - 0.03), but only TOX3 and 2q35 were associated with the risk for BRCA1 carriers (P = 0.0049, 0.03, respectively). All risk-associated polymorphisms appear to interact multiplicatively on breast cancer risk for mutation carriers. Based on the joint genotype distribution of the 7 risk-associated SNPs in BRCA2 mutation carriers, the 5% of BRCA2 carriers at highest risk (i.e., between 95th and 100th percentiles) were predicted to have a probability between 80% and 96% of developing breast cancer by age 80, compared with 42% to 50% for the 5% of carriers at lowest risk. Our findings indicated that these risk differences might be sufficient to influence the clinical management of mutation carriers.

  7. DNA Methylation-Guided Prediction of Clinical Failure in High-Risk Prostate Cancer

    PubMed Central

    Joniau, Steven; Lerut, Evelyne; Laenen, Annouschka; Gevaert, Thomas; Gevaert, Olivier; Spahn, Martin; Kneitz, Burkhard; Gramme, Pierre; Helleputte, Thibault; Isebaert, Sofie; Haustermans, Karin; Bollen, Mathieu

    2015-01-01

    Background Prostate cancer (PCa) is a very heterogeneous disease with respect to clinical outcome. This study explored differential DNA methylation in a priori selected genes to diagnose PCa and predict clinical failure (CF) in high-risk patients. Methods A quantitative multiplex, methylation-specific PCR assay was developed to assess promoter methylation of the APC, CCND2, GSTP1, PTGS2 and RARB genes in formalin-fixed, paraffin-embedded tissue samples from 42 patients with benign prostatic hyperplasia and radical prostatectomy specimens of patients with high-risk PCa, encompassing training and validation cohorts of 147 and 71 patients, respectively. Log-rank tests, univariate and multivariate Cox models were used to investigate the prognostic value of the DNA methylation. Results Hypermethylation of APC, CCND2, GSTP1, PTGS2 and RARB was highly cancer-specific. However, only GSTP1 methylation was significantly associated with CF in both independent high-risk PCa cohorts. Importantly, trichotomization into low, moderate and high GSTP1 methylation level subgroups was highly predictive for CF. Patients with either a low or high GSTP1 methylation level, as compared to the moderate methylation groups, were at a higher risk for CF in both the training (Hazard ratio [HR], 3.65; 95% CI, 1.65 to 8.07) and validation sets (HR, 4.27; 95% CI, 1.03 to 17.72) as well as in the combined cohort (HR, 2.74; 95% CI, 1.42 to 5.27) in multivariate analysis. Conclusions Classification of primary high-risk tumors into three subtypes based on DNA methylation can be combined with clinico-pathological parameters for a more informative risk-stratification of these PCa patients. PMID:26086362

  8. DNA Methylation-Guided Prediction of Clinical Failure in High-Risk Prostate Cancer.

    PubMed

    Litovkin, Kirill; Van Eynde, Aleyde; Joniau, Steven; Lerut, Evelyne; Laenen, Annouschka; Gevaert, Thomas; Gevaert, Olivier; Spahn, Martin; Kneitz, Burkhard; Gramme, Pierre; Helleputte, Thibault; Isebaert, Sofie; Haustermans, Karin; Bollen, Mathieu

    2015-01-01

    Prostate cancer (PCa) is a very heterogeneous disease with respect to clinical outcome. This study explored differential DNA methylation in a priori selected genes to diagnose PCa and predict clinical failure (CF) in high-risk patients. A quantitative multiplex, methylation-specific PCR assay was developed to assess promoter methylation of the APC, CCND2, GSTP1, PTGS2 and RARB genes in formalin-fixed, paraffin-embedded tissue samples from 42 patients with benign prostatic hyperplasia and radical prostatectomy specimens of patients with high-risk PCa, encompassing training and validation cohorts of 147 and 71 patients, respectively. Log-rank tests, univariate and multivariate Cox models were used to investigate the prognostic value of the DNA methylation. Hypermethylation of APC, CCND2, GSTP1, PTGS2 and RARB was highly cancer-specific. However, only GSTP1 methylation was significantly associated with CF in both independent high-risk PCa cohorts. Importantly, trichotomization into low, moderate and high GSTP1 methylation level subgroups was highly predictive for CF. Patients with either a low or high GSTP1 methylation level, as compared to the moderate methylation groups, were at a higher risk for CF in both the training (Hazard ratio [HR], 3.65; 95% CI, 1.65 to 8.07) and validation sets (HR, 4.27; 95% CI, 1.03 to 17.72) as well as in the combined cohort (HR, 2.74; 95% CI, 1.42 to 5.27) in multivariate analysis. Classification of primary high-risk tumors into three subtypes based on DNA methylation can be combined with clinico-pathological parameters for a more informative risk-stratification of these PCa patients.

  9. Predicting the Risk of Pelvic Node Involvement Among Men With Prostate Cancer in the Contemporary Era

    SciTech Connect

    Nguyen, Paul L. Chen, M.-H.; Hoffman, Karen E.; Katz, Matthew S.; D'Amico, Anthony V.

    2009-05-01

    Purpose: The 'Roach formula' for the risk of pelvic lymph node metastases [(2/3) * PSA + (Gleason score - 6) * 10] was developed in the early prostate-specific antigen (PSA) era. We examined the accuracy of this formula in contemporary patients. Methods: We included men in the Surveillance, Epidemiology, and End Results Registry with a diagnosis of clinical T1c-T4 prostate cancer in 2004 who had a surgical lymph node evaluation, Gleason score (typically from prostatectomy), and baseline PSA level (n = 9,387). Expected and observed rates of node positivity were compared. Results: Ninety-eight percent were clinical T1c/T2, and 97% underwent prostatectomy. Overall, 309 patients (3.29%) had positive lymph nodes. Roach scores overestimated the actual rate of positive lymph nodes in the derivation set by 16-fold for patients with Roach score less than or equal to 10%, by 7-fold for scores greater than 10-20%, and by approximately 2.5-fold for scores greater than 20%. Applying these adjustment factors to Roach scores in the validation data set yielded accurate predictions of risk. For those with Roach score less than or equal to 10%, adjusted expected risk was 0.2% and observed risk was 0.2%. For Roach score greater than 10-20%, adjusted expected risk was 2.0% and observed risk was 2.1%. For Roach score greater than 20-30%, adjusted expected risk was 9.7% and observed risk was 6.5%. For Roach score greater than 30-40%, adjusted expected risk was 13.9% and observed risk was 13.9%. Conclusion: Applied to contemporary patients with mainly T1c/T2 disease, the Roach formula appears to overestimate pelvic lymph node risk. The adjustment factors presented here should be validated by using biopsy Gleason scores and extended lymphadenectomies.

  10. Clinical factors predicting bacteremia in low-risk febrile neutropenia after anti-cancer chemotherapy.

    PubMed

    Ha, Young Eun; Song, Jae-Hoon; Kang, Won Ki; Peck, Kyong Ran; Chung, Doo Ryeon; Kang, Cheol-In; Joung, Mi-Kyong; Joo, Eun-Jeong; Shon, Kyung Mok

    2011-11-01

    Bacteremia is an important clinical condition in febrile neutropenia that can cause clinical failure of antimicrobial therapy. The purpose of this study was to investigate the clinical factors predictive of bacteremia in low-risk febrile neutropenia at initial patient evaluation. We performed a retrospective cohort study in a university hospital in Seoul, Korea, between May 1995 and May 2007. Patients who met the criteria of low-risk febrile neutropenia at the time of visit to emergency department after anti-cancer chemotherapy were included in the analysis. During the study period, 102 episodes of bacteremia were documented among the 993 episodes of low-risk febrile neutropenia. Single gram-negative bacteremia was most frequent. In multivariate regression analysis, initial body temperature ≥39°C, initial hypotension, presence of clinical sites of infection, presence of central venous catheter, initial absolute neutrophil count <50/mm(3), and the CRP ≥10 mg/dL were statistically significant predictors for bacteremia. A scoring system using these variables was derived and the likelihood of bacteremia was well correlated with the score points with AUC under ROC curve of 0.785. Patients with low score points had low rate of bacteremia, thus, would be candidates for outpatient-based or oral antibiotic therapy. We identified major clinical factors that can predict bacteremia in low-risk febrile neutropenia.

  11. Meta-Prediction of MTHFR Gene Polymorphism Mutations and Associated Risk for Colorectal Cancer

    PubMed Central

    Yu, C. H.

    2016-01-01

    The methylenetetrahydrofolate reductase (MTHFR) gene is one of the most investigated of the genes associated with chronic human diseases because of its associations with hyperhomocysteinemia and toxicity. It has been proposed as a prototype gene for the prevention of colorectal cancer (CRC). The major objectives of this meta-analysis were to examine the polymorphism-mutation patterns of MTHFR and their associations with risk for CRC as well as potential contributing factors for mutations and disease risks. This analysis included 33,626 CRC cases and 48,688 controls across 92 studies for MTHFR 677 and 16,367 cases and 24,874 controls across 54 studies for MTHFR 1298, comprising data for various racial and ethnic groups, both genders, and multiple cancer sites. MTHFR 677 homozygous TT genotype was protective (p < .05) for CRC for all included populations; however, with heterogeneity across various racial–ethnic groups and opposing findings, it was a risk genotype for the subgroup of Hispanics (p < .01). Additional countries for which subgroup analyses resulted in 677 TT as a risk genotype included Turkey, Romania, Croatia, Hungary, Portugal, Mexico, Brazil, U.S. Hawai’i, Taiwan, India, and Egypt. Countries with the highest mutation rates and risks for both MTHFR 677 and 1298 genotypes are presented using global maps to visualize the grouping patterns. Meta-predictive analyses revealed that air pollution levels were associated with gene polymorphisms for both genotypes. Future nursing research should be conducted to develop proactive measures to protect populations in cities where air pollution causes more deaths. PMID:26858257

  12. A steroid metabolizing gene variant in a polyfactorial model improves risk prediction in a high incidence breast cancer population

    PubMed Central

    Jupe, Eldon R.; Dalessandri, Kathie M.; Mulvihill, John J.; Miike, Rei; Knowlton, Nicholas S.; Pugh, Thomas W.; Zhao, Lue Ping; DeFreese, Daniele C.; Manjeshwar, Sharmila; Gramling, Bobby A.; Wiencke, John K.; Benz, Christopher C.

    2014-01-01

    Background We have combined functional gene polymorphisms with clinical factors to improve prediction and understanding of sporadic breast cancer risk, particularly within a high incidence Caucasian population. Methods A polyfactorial risk model (PFRM) was built from both clinical data and functional single nucleotide polymorphism (SNP) gene candidates using multivariate logistic regression analysis on data from 5022 US Caucasian females (1671 breast cancer cases, 3351 controls), validated in an independent set of 1193 women (400 cases, 793 controls), and reassessed in a unique high incidence breast cancer population (165 cases, 173 controls) from Marin County, CA. Results The optimized PFRM consisted of 22 SNPs (19 genes, 6 regulating steroid metabolism) and 5 clinical risk factors, and its 5-year and lifetime risk prediction performance proved significantly superior (~ 2-fold) over the Gail model (Breast Cancer Risk Assessment Tool, BCRAT), whether assessed by odds (OR) or positive likelihood (PLR) ratios over increasing model risk levels. Improved performance of the PFRM in high risk Marin women was due in part to genotype enrichment by a CYP11B2 (-344T/C) variant. Conclusions and general significance Since the optimized PFRM consistently outperformed BCRAT in all Caucasian study populations, it represents an improved personalized risk assessment tool. The finding of higher Marin County risk linked to a CYP11B2 aldosterone synthase SNP associated with essential hypertension offers a new genetic clue to sporadic breast cancer predisposition. PMID:26673457

  13. Prediction of Breast Cancer Risk Based on Profiling With Common Genetic Variants

    PubMed Central

    Pharoah, Paul D. P.; Michailidou, Kyriaki; Tyrer, Jonathan; Brook, Mark N.; Bolla, Manjeet K.; Wang, Qin; Dennis, Joe; Dunning, Alison M.; Shah, Mitul; Luben, Robert; Brown, Judith; Bojesen, Stig E.; Nordestgaard, Børge G.; Nielsen, Sune F.; Flyger, Henrik; Czene, Kamila; Darabi, Hatef; Eriksson, Mikael; Peto, Julian; dos-Santos-Silva, Isabel; Dudbridge, Frank; Johnson, Nichola; Schmidt, Marjanka K.; Broeks, Annegien; Verhoef, Senno; Rutgers, Emiel J.; Swerdlow, Anthony; Ashworth, Alan; Orr, Nick; Schoemaker, Minouk J.; Figueroa, Jonine; Chanock, Stephen J.; Brinton, Louise; Lissowska, Jolanta; Couch, Fergus J.; Olson, Janet E.; Vachon, Celine; Pankratz, Vernon S.; Lambrechts, Diether; Wildiers, Hans; Van Ongeval, Chantal; van Limbergen, Erik; Kristensen, Vessela; Grenaker Alnæs, Grethe; Nord, Silje; Borresen-Dale, Anne-Lise; Nevanlinna, Heli; Muranen, Taru A.; Aittomäki, Kristiina; Blomqvist, Carl; Chang-Claude, Jenny; Rudolph, Anja; Seibold, Petra; Flesch-Janys, Dieter; Fasching, Peter A.; Haeberle, Lothar; Ekici, Arif B.; Beckmann, Matthias W.; Burwinkel, Barbara; Marme, Frederik; Schneeweiss, Andreas; Sohn, Christof; Trentham-Dietz, Amy; Newcomb, Polly; Titus, Linda; Egan, Kathleen M.; Hunter, David J.; Lindstrom, Sara; Tamimi, Rulla M.; Kraft, Peter; Rahman, Nazneen; Turnbull, Clare; Renwick, Anthony; Seal, Sheila; Li, Jingmei; Liu, Jianjun; Humphreys, Keith; Benitez, Javier; Pilar Zamora, M.; Arias Perez, Jose Ignacio; Menéndez, Primitiva; Jakubowska, Anna; Lubinski, Jan; Jaworska-Bieniek, Katarzyna; Durda, Katarzyna; Bogdanova, Natalia V.; Antonenkova, Natalia N.; Dörk, Thilo; Anton-Culver, Hoda; Neuhausen, Susan L.; Ziogas, Argyrios; Bernstein, Leslie; Devilee, Peter; Tollenaar, Robert A. E. M.; Seynaeve, Caroline; van Asperen, Christi J.; Cox, Angela; Cross, Simon S.; Reed, Malcolm W. R.; Khusnutdinova, Elza; Bermisheva, Marina; Prokofyeva, Darya; Takhirova, Zalina; Meindl, Alfons; Schmutzler, Rita K.; Sutter, Christian; Yang, Rongxi; Schürmann, Peter; Bremer, Michael; Christiansen, Hans; Park-Simon, Tjoung-Won; Hillemanns, Peter; Guénel, Pascal; Truong, Thérèse; Menegaux, Florence; Sanchez, Marie; Radice, Paolo; Peterlongo, Paolo; Manoukian, Siranoush; Pensotti, Valeria; Hopper, John L.; Tsimiklis, Helen; Apicella, Carmel; Southey, Melissa C.; Brauch, Hiltrud; Brüning, Thomas; Ko, Yon-Dschun; Sigurdson, Alice J.; Doody, Michele M.; Hamann, Ute; Torres, Diana; Ulmer, Hans-Ulrich; Försti, Asta; Sawyer, Elinor J.; Tomlinson, Ian; Kerin, Michael J.; Miller, Nicola; Andrulis, Irene L.; Knight, Julia A.; Glendon, Gord; Marie Mulligan, Anna; Chenevix-Trench, Georgia; Balleine, Rosemary; Giles, Graham G.; Milne, Roger L.; McLean, Catriona; Lindblom, Annika; Margolin, Sara; Haiman, Christopher A.; Henderson, Brian E.; Schumacher, Fredrick; Le Marchand, Loic; Eilber, Ursula; Wang-Gohrke, Shan; Hooning, Maartje J.; Hollestelle, Antoinette; van den Ouweland, Ans M. W.; Koppert, Linetta B.; Carpenter, Jane; Clarke, Christine; Scott, Rodney; Mannermaa, Arto; Kataja, Vesa; Kosma, Veli-Matti; Hartikainen, Jaana M.; Brenner, Hermann; Arndt, Volker; Stegmaier, Christa; Karina Dieffenbach, Aida; Winqvist, Robert; Pylkäs, Katri; Jukkola-Vuorinen, Arja; Grip, Mervi; Offit, Kenneth; Vijai, Joseph; Robson, Mark; Rau-Murthy, Rohini; Dwek, Miriam; Swann, Ruth; Annie Perkins, Katherine; Goldberg, Mark S.; Labrèche, France; Dumont, Martine; Eccles, Diana M.; Tapper, William J.; Rafiq, Sajjad; John, Esther M.; Whittemore, Alice S.; Slager, Susan; Yannoukakos, Drakoulis; Toland, Amanda E.; Yao, Song; Zheng, Wei; Halverson, Sandra L.; González-Neira, Anna; Pita, Guillermo; Rosario Alonso, M.; Álvarez, Nuria; Herrero, Daniel; Tessier, Daniel C.; Vincent, Daniel; Bacot, Francois; Luccarini, Craig; Baynes, Caroline; Ahmed, Shahana; Maranian, Mel; Healey, Catherine S.; Simard, Jacques; Hall, Per; Easton, Douglas F.; Garcia-Closas, Montserrat

    2015-01-01

    Background: Data for multiple common susceptibility alleles for breast cancer may be combined to identify women at different levels of breast cancer risk. Such stratification could guide preventive and screening strategies. However, empirical evidence for genetic risk stratification is lacking. Methods: We investigated the value of using 77 breast cancer-associated single nucleotide polymorphisms (SNPs) for risk stratification, in a study of 33 673 breast cancer cases and 33 381 control women of European origin. We tested all possible pair-wise multiplicative interactions and constructed a 77-SNP polygenic risk score (PRS) for breast cancer overall and by estrogen receptor (ER) status. Absolute risks of breast cancer by PRS were derived from relative risk estimates and UK incidence and mortality rates. Results: There was no strong evidence for departure from a multiplicative model for any SNP pair. Women in the highest 1% of the PRS had a three-fold increased risk of developing breast cancer compared with women in the middle quintile (odds ratio [OR] = 3.36, 95% confidence interval [CI] = 2.95 to 3.83). The ORs for ER-positive and ER-negative disease were 3.73 (95% CI = 3.24 to 4.30) and 2.80 (95% CI = 2.26 to 3.46), respectively. Lifetime risk of breast cancer for women in the lowest and highest quintiles of the PRS were 5.2% and 16.6% for a woman without family history, and 8.6% and 24.4% for a woman with a first-degree family history of breast cancer. Conclusions: The PRS stratifies breast cancer risk in women both with and without a family history of breast cancer. The observed level of risk discrimination could inform targeted screening and prevention strategies. Further discrimination may be achievable through combining the PRS with lifestyle/environmental factors, although these were not considered in this report. PMID:25855707

  14. Prediction of breast cancer risk based on profiling with common genetic variants.

    PubMed

    Mavaddat, Nasim; Pharoah, Paul D P; Michailidou, Kyriaki; Tyrer, Jonathan; Brook, Mark N; Bolla, Manjeet K; Wang, Qin; Dennis, Joe; Dunning, Alison M; Shah, Mitul; Luben, Robert; Brown, Judith; Bojesen, Stig E; Nordestgaard, Børge G; Nielsen, Sune F; Flyger, Henrik; Czene, Kamila; Darabi, Hatef; Eriksson, Mikael; Peto, Julian; Dos-Santos-Silva, Isabel; Dudbridge, Frank; Johnson, Nichola; Schmidt, Marjanka K; Broeks, Annegien; Verhoef, Senno; Rutgers, Emiel J; Swerdlow, Anthony; Ashworth, Alan; Orr, Nick; Schoemaker, Minouk J; Figueroa, Jonine; Chanock, Stephen J; Brinton, Louise; Lissowska, Jolanta; Couch, Fergus J; Olson, Janet E; Vachon, Celine; Pankratz, Vernon S; Lambrechts, Diether; Wildiers, Hans; Van Ongeval, Chantal; van Limbergen, Erik; Kristensen, Vessela; Grenaker Alnæs, Grethe; Nord, Silje; Borresen-Dale, Anne-Lise; Nevanlinna, Heli; Muranen, Taru A; Aittomäki, Kristiina; Blomqvist, Carl; Chang-Claude, Jenny; Rudolph, Anja; Seibold, Petra; Flesch-Janys, Dieter; Fasching, Peter A; Haeberle, Lothar; Ekici, Arif B; Beckmann, Matthias W; Burwinkel, Barbara; Marme, Frederik; Schneeweiss, Andreas; Sohn, Christof; Trentham-Dietz, Amy; Newcomb, Polly; Titus, Linda; Egan, Kathleen M; Hunter, David J; Lindstrom, Sara; Tamimi, Rulla M; Kraft, Peter; Rahman, Nazneen; Turnbull, Clare; Renwick, Anthony; Seal, Sheila; Li, Jingmei; Liu, Jianjun; Humphreys, Keith; Benitez, Javier; Pilar Zamora, M; Arias Perez, Jose Ignacio; Menéndez, Primitiva; Jakubowska, Anna; Lubinski, Jan; Jaworska-Bieniek, Katarzyna; Durda, Katarzyna; Bogdanova, Natalia V; Antonenkova, Natalia N; Dörk, Thilo; Anton-Culver, Hoda; Neuhausen, Susan L; Ziogas, Argyrios; Bernstein, Leslie; Devilee, Peter; Tollenaar, Robert A E M; Seynaeve, Caroline; van Asperen, Christi J; Cox, Angela; Cross, Simon S; Reed, Malcolm W R; Khusnutdinova, Elza; Bermisheva, Marina; Prokofyeva, Darya; Takhirova, Zalina; Meindl, Alfons; Schmutzler, Rita K; Sutter, Christian; Yang, Rongxi; Schürmann, Peter; Bremer, Michael; Christiansen, Hans; Park-Simon, Tjoung-Won; Hillemanns, Peter; Guénel, Pascal; Truong, Thérèse; Menegaux, Florence; Sanchez, Marie; Radice, Paolo; Peterlongo, Paolo; Manoukian, Siranoush; Pensotti, Valeria; Hopper, John L; Tsimiklis, Helen; Apicella, Carmel; Southey, Melissa C; Brauch, Hiltrud; Brüning, Thomas; Ko, Yon-Dschun; Sigurdson, Alice J; Doody, Michele M; Hamann, Ute; Torres, Diana; Ulmer, Hans-Ulrich; Försti, Asta; Sawyer, Elinor J; Tomlinson, Ian; Kerin, Michael J; Miller, Nicola; Andrulis, Irene L; Knight, Julia A; Glendon, Gord; Marie Mulligan, Anna; Chenevix-Trench, Georgia; Balleine, Rosemary; Giles, Graham G; Milne, Roger L; McLean, Catriona; Lindblom, Annika; Margolin, Sara; Haiman, Christopher A; Henderson, Brian E; Schumacher, Fredrick; Le Marchand, Loic; Eilber, Ursula; Wang-Gohrke, Shan; Hooning, Maartje J; Hollestelle, Antoinette; van den Ouweland, Ans M W; Koppert, Linetta B; Carpenter, Jane; Clarke, Christine; Scott, Rodney; Mannermaa, Arto; Kataja, Vesa; Kosma, Veli-Matti; Hartikainen, Jaana M; Brenner, Hermann; Arndt, Volker; Stegmaier, Christa; Karina Dieffenbach, Aida; Winqvist, Robert; Pylkäs, Katri; Jukkola-Vuorinen, Arja; Grip, Mervi; Offit, Kenneth; Vijai, Joseph; Robson, Mark; Rau-Murthy, Rohini; Dwek, Miriam; Swann, Ruth; Annie Perkins, Katherine; Goldberg, Mark S; Labrèche, France; Dumont, Martine; Eccles, Diana M; Tapper, William J; Rafiq, Sajjad; John, Esther M; Whittemore, Alice S; Slager, Susan; Yannoukakos, Drakoulis; Toland, Amanda E; Yao, Song; Zheng, Wei; Halverson, Sandra L; González-Neira, Anna; Pita, Guillermo; Rosario Alonso, M; Álvarez, Nuria; Herrero, Daniel; Tessier, Daniel C; Vincent, Daniel; Bacot, Francois; Luccarini, Craig; Baynes, Caroline; Ahmed, Shahana; Maranian, Mel; Healey, Catherine S; Simard, Jacques; Hall, Per; Easton, Douglas F; Garcia-Closas, Montserrat

    2015-05-01

    Data for multiple common susceptibility alleles for breast cancer may be combined to identify women at different levels of breast cancer risk. Such stratification could guide preventive and screening strategies. However, empirical evidence for genetic risk stratification is lacking. We investigated the value of using 77 breast cancer-associated single nucleotide polymorphisms (SNPs) for risk stratification, in a study of 33 673 breast cancer cases and 33 381 control women of European origin. We tested all possible pair-wise multiplicative interactions and constructed a 77-SNP polygenic risk score (PRS) for breast cancer overall and by estrogen receptor (ER) status. Absolute risks of breast cancer by PRS were derived from relative risk estimates and UK incidence and mortality rates. There was no strong evidence for departure from a multiplicative model for any SNP pair. Women in the highest 1% of the PRS had a three-fold increased risk of developing breast cancer compared with women in the middle quintile (odds ratio [OR] = 3.36, 95% confidence interval [CI] = 2.95 to 3.83). The ORs for ER-positive and ER-negative disease were 3.73 (95% CI = 3.24 to 4.30) and 2.80 (95% CI = 2.26 to 3.46), respectively. Lifetime risk of breast cancer for women in the lowest and highest quintiles of the PRS were 5.2% and 16.6% for a woman without family history, and 8.6% and 24.4% for a woman with a first-degree family history of breast cancer. The PRS stratifies breast cancer risk in women both with and without a family history of breast cancer. The observed level of risk discrimination could inform targeted screening and prevention strategies. Further discrimination may be achievable through combining the PRS with lifestyle/environmental factors, although these were not considered in this report. © The Author 2015. Published by Oxford University Press.

  15. Childhood body mass index and adult mammographic density measures that predict breast cancer risk.

    PubMed

    Hopper, John L; Nguyen, Tuong L; Stone, Jennifer; Aujard, Kelly; Matheson, Melanie C; Abramson, Michael J; Burgess, John A; Walters, E Haydn; Dite, Gillian S; Bui, Minh; Evans, Christopher; Makalic, Enes; Schmidt, Daniel F; Ward, Gail; Jenkins, Mark A; Giles, Graham G; Dharmage, Shyamali C; Apicella, Carmel

    2016-02-01

    The aim of the present study is to determine if body mass index (BMI) during childhood is associated with the breast cancer risk factor 'adult mammographic density adjusted for age and BMI'. In 1968, the Tasmanian Longitudinal Health Study studied every Tasmanian school child born in 1961. We obtained measured heights and weights from annual school medical records across ages 7-15 years and imputed missing values. Between 2009 and 2012, we administered to 490 women a questionnaire that asked current height and weight and digitised at least one mammogram per woman. Absolute and percent mammographic densities were measured using the computer-assisted method CUMULUS. We used linear regression and adjusted for age at interview and log current BMI. The mammographic density measures were negatively associated: with log BMI at each age from 7 to 15 years (all p < 0.05); with the average of standardised log BMIs across ages 7-15 years (p < 0.0005); and more strongly with standardised log BMI measures closer to age 15 years (p < 0.03). Childhood BMI measures explained 7 and 10 % of the variance in absolute and percent mammographic densities, respectively, and 25 and 20 % of the association between current BMI and absolute and percent mammographic densities, respectively. Associations were not altered by adjustment for age at menarche. There is a negative association between BMI in late childhood and the adult mammographic density measures that predict breast cancer risk. This could explain, at least in part, why BMI in adolescence is negatively associated with breast cancer risk.

  16. Rs488087 single nucleotide polymorphism as predictive risk factor for pancreatic cancers

    PubMed Central

    Martinez, Emmanuelle; Silvy, Françoise; Fina, Fréderic; Bartoli, Marc; Krahn, Martin; Barlesi, Fabrice; Figarella-Branger, Dominique; Iovanna, Juan; Laugier, René; Ouaissi, Mehdi; Lombardo, Dominique; Mas, Eric

    2015-01-01

    Pancreatic cancer (PC) is a devastating disease progressing asymptomatically until death within months after diagnosis. Defining at-risk populations should promote its earlier diagnosis and hence also avoid its development. Considering the known involvement in pancreatic disease of exon 11 of the bile salt-dependent lipase (BSDL) gene that encodes variable number of tandem repeat (VNTR) sequences, we hypothesized upon the existence of a genetic link between predisposition to PC and mutations in VNTR loci. To test this, BSDL VNTR were amplified by touchdown-PCR performed on genomic DNA extracted from cancer tissue or blood samples from a French patient cohort and amplicons were Sanger sequenced. A robust method using probes for droplet digital (dd)-PCR was designed to discriminate the C/C major from C/T or T/T minor genotypes. We report that the c.1719C > T transition (SNP rs488087) present in BSDL VNTR may be a useful marker for defining a population at risk of developing PC (occurrence: 63.90% in the PC versus 27.30% in the control group). The odds ratio of 4.7 for the T allele was larger than those already determined for other SNPs suspected to be predictive of PC. Further studies on tumor pancreatic tissue suggested that a germline T allele may favor Kras G12R/G12D somatic mutations which represent negative prognostic factors associated with reduced survival. We propose that the detection of the T allele in rs488087 SNP should lead to an in-depth follow-up of patients in whom an association with other potential risk factors of pancreatic cancer may be present. PMID:26498142

  17. Prospective Approach to Breast Cancer Risk Prediction in African American Women: The Black Women's Health Study Model

    PubMed Central

    Boggs, Deborah A.; Rosenberg, Lynn; Adams-Campbell, Lucile L.; Palmer, Julie R.

    2015-01-01

    Purpose Breast cancer risk prediction models have underestimated risk for African American women, contributing to lower recruitment rates in prevention trials. A model previously developed for African American women was found to underestimate risk in the Black Women's Health Study (BWHS). Methods We developed a breast cancer risk model for African American women using relative risks derived from 10 years of follow-up of BWHS participants age 30 to 69 years at baseline. Using the subsequent 5 years of follow-up data, we evaluated calibration as the ratio of expected to observed number of breast cancers and assessed discriminatory accuracy using the concordance statistic. Results The BWHS model included family history, previous biopsy, body mass index at age 18 years, age at menarche, age at first birth, oral contraceptive use, bilateral oophorectomy, estrogen plus progestin use, and height. There was good agreement between predicted and observed number of breast cancers overall (expected-to-observed ratio, 0.96; 95% CI, 0.88 to 1.05) and in most risk factor categories. Discriminatory accuracy was higher for women younger than age 50 years (area under the curve [AUC], 0.62; 95% CI, 0.58 to 0.65) than for women age ≥ 50 years (AUC, 0.56; 95% CI, 0.53 to 0.59). Using a 5-year predicted risk of 1.66% or greater as a cut point, 2.8% of women younger than 50 years old and 32.2% of women ≥ 50 years old were classified as being at elevated risk of invasive breast cancer. Conclusion The BWHS model was well calibrated overall, and the predictive ability was best for younger women. The proportion of women predicted to meet the 1.66% cut point commonly used to determine eligibility for breast cancer prevention trials was greatly increased relative to previous models. PMID:25624428

  18. A New Approach to Reduce Uncertainties in Space Radiation Cancer Risk Predictions

    PubMed Central

    Cucinotta, Francis A.

    2015-01-01

    The prediction of space radiation induced cancer risk carries large uncertainties with two of the largest uncertainties being radiation quality and dose-rate effects. In risk models the ratio of the quality factor (QF) to the dose and dose-rate reduction effectiveness factor (DDREF) parameter is used to scale organ doses for cosmic ray proton and high charge and energy (HZE) particles to a hazard rate for γ-rays derived from human epidemiology data. In previous work, particle track structure concepts were used to formulate a space radiation QF function that is dependent on particle charge number Z, and kinetic energy per atomic mass unit, E. QF uncertainties where represented by subjective probability distribution functions (PDF) for the three QF parameters that described its maximum value and shape parameters for Z and E dependences. Here I report on an analysis of a maximum QF parameter and its uncertainty using mouse tumor induction data. Because experimental data for risks at low doses of γ-rays are highly uncertain which impacts estimates of maximum values of relative biological effectiveness (RBEmax), I developed an alternate QF model, denoted QFγAcute where QFs are defined relative to higher acute γ-ray doses (0.5 to 3 Gy). The alternate model reduces the dependence of risk projections on the DDREF, however a DDREF is still needed for risk estimates for high-energy protons and other primary or secondary sparsely ionizing space radiation components. Risk projections (upper confidence levels (CL)) for space missions show a reduction of about 40% (CL∼50%) using the QFγAcute model compared the QFs based on RBEmax and about 25% (CL∼35%) compared to previous estimates. In addition, I discuss how a possible qualitative difference leading to increased tumor lethality for HZE particles compared to low LET radiation and background tumors remains a large uncertainty in risk estimates. PMID:25789764

  19. A new approach to reduce uncertainties in space radiation cancer risk predictions.

    PubMed

    Cucinotta, Francis A

    2015-01-01

    The prediction of space radiation induced cancer risk carries large uncertainties with two of the largest uncertainties being radiation quality and dose-rate effects. In risk models the ratio of the quality factor (QF) to the dose and dose-rate reduction effectiveness factor (DDREF) parameter is used to scale organ doses for cosmic ray proton and high charge and energy (HZE) particles to a hazard rate for γ-rays derived from human epidemiology data. In previous work, particle track structure concepts were used to formulate a space radiation QF function that is dependent on particle charge number Z, and kinetic energy per atomic mass unit, E. QF uncertainties where represented by subjective probability distribution functions (PDF) for the three QF parameters that described its maximum value and shape parameters for Z and E dependences. Here I report on an analysis of a maximum QF parameter and its uncertainty using mouse tumor induction data. Because experimental data for risks at low doses of γ-rays are highly uncertain which impacts estimates of maximum values of relative biological effectiveness (RBEmax), I developed an alternate QF model, denoted QFγAcute where QFs are defined relative to higher acute γ-ray doses (0.5 to 3 Gy). The alternate model reduces the dependence of risk projections on the DDREF, however a DDREF is still needed for risk estimates for high-energy protons and other primary or secondary sparsely ionizing space radiation components. Risk projections (upper confidence levels (CL)) for space missions show a reduction of about 40% (CL∼50%) using the QFγAcute model compared the QFs based on RBEmax and about 25% (CL∼35%) compared to previous estimates. In addition, I discuss how a possible qualitative difference leading to increased tumor lethality for HZE particles compared to low LET radiation and background tumors remains a large uncertainty in risk estimates.

  20. A simple risk scoring system for predicting acute exacerbation of interstitial pneumonia after pulmonary resection in lung cancer patients.

    PubMed

    Sato, Toshihiko; Kondo, Haruhiko; Watanabe, Atsushi; Nakajima, Jun; Niwa, Hiroshi; Horio, Hirotoshi; Okami, Jiro; Okumura, Norihito; Sugio, Kenji; Teramukai, Satoshi; Kishi, Kazuma; Ebina, Masahito; Sugiyama, Yukihiko; Kondo, Takashi; Date, Hiroshi

    2015-03-01

    Lung cancer patients with interstitial lung diseases (ILDs) who have undergone pulmonary resection often develop acute exacerbation of interstitial pneumonia (AE) in the post-operative period. To predict who is at high risk of AE, we propose a scoring system that evaluates the risk of AE in lung cancer patients with ILDs. We derived a score for 30-day risk of AE onset after pulmonary resection in lung cancer patients with ILDs (n = 1,022; outcome: risk of AE) based on seven risk factors for AE that were identified in a previous retrospective multi-institutional cohort study. A logistic regression model was employed to develop a risk prediction model for AE. A risk score (RS) was derived: 5 × (history of AE) + 4 × (surgical procedures) + 4 × (UIP appearance in CT scan) + 3 × (male sex) + 3 × (preoperative steroid use) + 2 × (elevated serum sialylated carbohydrate antigen, KL-6 level) + 1 × (low vital capacity). The RS was shown to be moderately discriminatory with a c-index of 0.709 and accurate with the Hosmer-Lemeshow goodness-of-fit test (p = 0.907). The patients were classified into three groups: low risk (RS: 0-10; predicted probability <0.1; n = 439), intermediate risk (RS: 11-14; predicted probability 0.1-0.25; n = 559), and high risk (RS: 15-22; predicted probability >0.25; n = 24). Although further validation and refinement are needed, the risk score can be used in routine clinical practice to identify high risk individuals and to select proper treatment strategies.

  1. Multi-institutional comparison of non-sentinel lymph node predictive tools in breast cancer patients with high predicted risk of further axillary metastasis.

    PubMed

    Cserni, Gábor; Bori, Rita; Maráz, Róbert; Leidenius, Marjut H K; Meretoja, Tuomo J; Heikkila, Paivi S; Regitnig, Peter; Luschin-Ebengreuth, Gero; Zgajnar, Janez; Perhavec, Andraz; Gazic, Barbara; Lázár, György; Takács, Tibor; Vörös, András; Audisio, Riccardo A

    2013-01-01

    Although axillary lymph node dissection (ALND) has been the standard intervention in breast cancer patients with sentinel lymph node (SLN) metastasis, only a small proportion of patients benefit from this operation, because most do not harbor additional metastases in the axilla. Several predictive tools have been constructed to identify patients with low risk of non-SLN metastasis who could be candidates for the omission of ALND. In the present work, predictive nomograms were used to predict a high (>50 %) risk of non-SLN metastasis in order to identify patients who would most probably benefit from further axillary treatment. Data of 1000 breast cancer patients with SLN metastasis and completion ALND from 5 institutions were tested in 4 nomograms. A subset of 313 patients with micrometastatic SLNs were also tested in 3 different nomograms devised for the micrometastatic population (the high risk cut-off being 20 %). Patients with a high predicted risk of non-SLN metastasis had higher rates of metastasis in the non-SLNs than patients with low predicted risk. The positive predictive values of the nomograms ranged from 44 % to 64 % with relevant inter-institutional variability. The nomograms for micrometastatic SLNs performed much better in identifying patients with low risk of non-SLN involvement than in high-risk-patients; for the latter, the positive predictive values ranged from 13 % to 20 %. The nomograms show inter-institutional differences in their predictive values and behave differently in different settings. They are worse in identifying high risk patients than low-risk ones, creating a need for new predictive models to identify high-risk patients.

  2. Lung Cancer Risk Prediction Model Incorporating Lung Function: Development and Validation in the UK Biobank Prospective Cohort Study.

    PubMed

    Muller, David C; Johansson, Mattias; Brennan, Paul

    2017-03-10

    Purpose Several lung cancer risk prediction models have been developed, but none to date have assessed the predictive ability of lung function in a population-based cohort. We sought to develop and internally validate a model incorporating lung function using data from the UK Biobank prospective cohort study. Methods This analysis included 502,321 participants without a previous diagnosis of lung cancer, predominantly between 40 and 70 years of age. We used flexible parametric survival models to estimate the 2-year probability of lung cancer, accounting for the competing risk of death. Models included predictors previously shown to be associated with lung cancer risk, including sex, variables related to smoking history and nicotine addiction, medical history, family history of lung cancer, and lung function (forced expiratory volume in 1 second [FEV1]). Results During accumulated follow-up of 1,469,518 person-years, there were 738 lung cancer diagnoses. A model incorporating all predictors had excellent discrimination (concordance (c)-statistic [95% CI] = 0.85 [0.82 to 0.87]). Internal validation suggested that the model will discriminate well when applied to new data (optimism-corrected c-statistic = 0.84). The full model, including FEV1, also had modestly superior discriminatory power than one that was designed solely on the basis of questionnaire variables (c-statistic = 0.84 [0.82 to 0.86]; optimism-corrected c-statistic = 0.83; pFEV1 = 3.4 × 10(-13)). The full model had better discrimination than standard lung cancer screening eligibility criteria (c-statistic = 0.66 [0.64 to 0.69]). Conclusion A risk prediction model that includes lung function has strong predictive ability, which could improve eligibility criteria for lung cancer screening programs.

  3. Negative HPV screening test predicts low cervical cancer risk better than negative Pap test

    Cancer.gov

    Based on a study that included more than 1 million women, investigators at NCI have determined that a negative test for HPV infection compared to a negative Pap test provides greater safety, or assurance, against future risk of cervical cancer.

  4. Oncotype DX(®) colon cancer assay for prediction of recurrence risk in patients with stage II and III colon cancer: A review of the evidence.

    PubMed

    You, Y Nancy; Rustin, Rudolph B; Sullivan, James D

    2015-06-01

    Advances in molecular biology have enabled identification of tumor biomarkers that allow for individualized risk assessment for patients with cancer. Molecular predictors of clinical outcome can help inform discussion regarding the role of adjuvant chemotherapy in patients with resected colon cancer, such as those with stage II colon cancer in which the benefit of adjuvant therapy is controversial or those with stage III colon cancer who may have a lower risk of recurrence and less absolute benefit from oxaliplatin therapy. This article summarizes the data surrounding the development, validation, and clinical and economic utility of the Oncotype DX(®) colon cancer assay, a multigene expression assay validated to independently predict recurrence risk in patients with stage II and III colon cancer beyond traditional factors.

  5. Impact of margin size on the predicted risk of radiogenic second cancers following proton arc therapy and volumetric modulated arc therapy for prostate cancer

    NASA Astrophysics Data System (ADS)

    Rechner, Laura A.; Howell, Rebecca M.; Zhang, Rui; Newhauser, Wayne D.

    2012-12-01

    We previously determined that the predicted risk of radiogenic second cancer in the bladder and rectum after proton arc therapy (PAT) was less than or equal to that after volumetric modulated arc therapy (VMAT) with photons, but we did not consider the impact of margin size on that risk. The current study was thus conducted to evaluate margin size's effect on the predicted risks of second cancer for the two modalities and the relative risk between them. Seven treatment plans with margins ranging from 0 mm in all directions to 6 mm posteriorly and 8 mm in all other directions were considered for both modalities. We performed risk analyses using three risk models with varying amounts of cell sterilization and calculated ratios of risk for the corresponding PAT and VMAT plans. We found that the change in risk with margin size depended on the risk model but that the relative risk remained nearly constant with margin size, regardless of the amount of cell sterilization modeled. We conclude that while margin size influences the predicted risk of a second cancer for a given modality, it appears to affect both modalities in roughly equal proportions so that the relative risk between PAT and VMAT is approximately equivalent.

  6. Common genetic variants in prostate cancer risk prediction – Results from the NCI Breast and Prostate Cancer Cohort Consortium (BPC3)

    PubMed Central

    Lindström, Sara; Schumacher, Fredrick R.; Cox, David; Travis, Ruth C.; Albanes, Demetrius; Allen, Naomi E.; Andriole, Gerald; Berndt, Sonja I.; Boeing, Heiner; Bueno-de-Mesquita, H. Bas; Crawford, E. David; Diver, W. Ryan; Ganziano, J. Michael; Giles, Graham G.; Giovannucci, Edward; Gonzalez, Carlos A.; Henderson, Brian; Hunter, David J.; Johansson, Mattias; Kolonel, Laurence N.; Ma, Jing; Le Marchand, Loic; Pala, Valeria; Stampfer, Meir; Stram, Daniel O.; Thun, Michael J.; Tjonneland, Anne; Trichopoulos, Dimitrios; Virtamo, Jarmo; Weinstein, Stephanie J.; Willett, Walter C.; Yeager, Meredith; Hayes, Richard B.; Severi, Gianluca; Haiman, Christopher A.; Chanock, Stephen J.; Kraft, Peter

    2012-01-01

    Background One of the goals of personalized medicine is to generate individual risk profiles that could identify individuals in the population that exhibit high risk. The discovery of more than two-dozen independent SNP markers in prostate cancer has raised the possibility for such risk stratification. In this study, we evaluated the discriminative and predictive ability for prostate cancer risk models incorporating 25 common prostate cancer genetic markers, family history of prostate cancer and age. Methods We fit a series of risk models and estimated their performance in 7,509 prostate cancer cases and 7,652 controls within the NCI Breast and Prostate Cancer Cohort Consortium (BPC3). We also calculated absolute risks based on SEER incidence data. Results The best risk model (C-statistic=0.642) included individual genetic markers and family history of prostate cancer. We observed a decreasing trend in discriminative ability with advancing age (P=0.009), with highest accuracy in men younger than 60 years (C-statistic=0.679). The absolute ten-year risk for 50-year old men with a family history ranged from 1.6% (10th percentile of genetic risk) to 6.7% (90th percentile of genetic risk). For men without family history, the risk ranged from 0.8% (10th percentile) to 3.4% (90th percentile). Conclusions Our results indicate that incorporating genetic information and family history in prostate cancer risk models can be particularly useful for identifying younger men that might benefit from PSA screening. Impact Although adding genetic risk markers improves model performance, the clinical utility of these genetic risk models is limited. PMID:22237985

  7. Comparison of Nodal Risk Formula and MR Lymphography for Predicting Lymph Node Involvement in Prostate Cancer

    SciTech Connect

    Deserno, Willem M.L.L.G.; Debats, Oscar A.; Rozema, Tom; Fortuin, Ansje S.; Heesakkers, Roel A.M.; Hoogeveen, Yvonne; Peer, Petronella G.M.; Barentsz, Jelle O.; Lin, Emile N.J.T. van

    2011-09-01

    Purpose: To compare the nodal risk formula (NRF) as a predictor for lymph node (LN) metastasis in patients with prostate cancer with magnetic resonance lymphography (MRL) using Ultrasmall Super-Paramagnetic particles of Iron Oxide (USPIO) and with histology as gold standard. Methods and Materials: Logistic regression analysis was performed with the results of histopathological evaluation of the LN as dependent variable and the nodal risk according to the NRF and the result of MRL as independent input variables. Receiver operating characteristic (ROC) analysis was performed to assess the performance of the models. Results: The analysis included 375 patients. In the single-predictor regression models, the NRF and MRL results were both significantly (p <0.001) predictive of the presence of LN metastasis. In the models with both predictors included, NRF was nonsignificant (p = 0.126), but MRL remained significant (p <0.001). For NRF, sensitivity was 0.79 and specificity was 0.38; for MRL, sensitivity was 0.82 and specificity was 0.93. After a negative MRL result, the probability of LN metastasis is 4% regardless of the NRF result. After a positive MRL, the probability of having LN metastasis is 68%. Conclusions: MRL is a better predictor of the presence of LN metastasis than NRF. Using only the NRF can lead to a significant overtreatment on the pelvic LN by radiation therapy. When the MRL result is available, the NRF is no longer of added value.

  8. Predicting the risk of cancer after unprovoked venous thromboembolism: external validation of the RIETE score.

    PubMed

    Bertoletti, Laurent; Robin, Philippe; Jara-Palomare, Luis; Tromeur, Cécile; Pastre, Jean; Prevot-Bitot, Nathalie; Mouneh, Thomas; Le Gal, Grégoire; Salaun, Pierre-Yves

    2017-09-06

    Most recent trials evaluating extensive screening strategies for occult cancer in patients with unprovoked venous thromboembolism failed, among other explanations because of an overall low rate of occult cancer. The RIETE investigators recently proposed a score aiming to identify a sub-group at higher risk. We retrospectively computed the RIETE score for all patients included in the MVTEP study, which evaluated the accuracy of FDG-PET in the screening of occult cancer in patients with unprovoked venous thromboembolism. Performance of the RIETE score was assessed by the proportion of patients classified in each risk group, and the corresponding rate of cancer diagnosis. Among the 386 patients included in the analysis, 136 patients (35.3%) were classified as high risk by the RIETE score. Cancer was diagnosed in 16 (11.8%) of them, while it was diagnosed in 9 (3.6%) of the 250 patients with a low RIETE cancer score: odds ratio 3.6 (95% CI 1.53 to 8.32). The area under the ROC curve was 0.63 (95% CI 0.51 to 0.74). the RIETE score seems to be able to identify a sub-group at high risk for cancer (10%), in our specific data-set of patients with unprovoked venous thromboembolism. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  9. Pancreatic Cancer Risk Factors

    MedlinePlus

    ... Cancer Causes, Risk Factors, and Prevention Pancreatic Cancer Risk Factors A risk factor is anything that affects ... these are risk factors for exocrine pancreatic cancer . Risk factors that can be changed Tobacco use Smoking ...

  10. Predicting cancer risk knowledge and information seeking: the role of social and cognitive factors.

    PubMed

    Hovick, Shelly R; Liang, Ming-Ching; Kahlor, Leeann

    2014-01-01

    This study tests an expanded Structural Influence Model (SIM) to gain a greater understanding of the social and cognitive factors that contribute to disparities in cancer risk knowledge and information seeking. At the core of this expansion is the planned risk information seeking model (PRISM). This study employed an online sample (N = 1,007) of African American, Hispanic, and non-Hispanic White adults. The addition of four cognitive predictors to the SIM substantially increased variance explained in cancer risk knowledge (R(2) = .29) and information seeking (R(2) = .56). Health literacy mediated the effects of social determinants (socioeconomic status [SES] and race/ethnicity) on cancer risk knowledge, while subjective norms mediated their effects on cancer risk information seeking. Social capital and perceived seeking control were also shown to be important mediators of the relationships between SES and cancer communication outcomes. Our results illustrate the social and cognitive mechanisms by which social determinants impact cancer communication outcomes, as well as several points of intervention to reduce communication disparities.

  11. Effect of PSCA gene polymorphisms on gastric cancer risk and survival prediction: A meta-analysis

    PubMed Central

    ZHANG, TAO; CHEN, YUAN-NENG; WANG, ZHEN; CHEN, JUN-QIANG; HUANG, SHI

    2012-01-01

    Previous studies have shown that two single-nucleotide polymorphisms (SNPs) in PSCA (rs2976392 and rs2294008) are associated with gastric cancer (GC), but the results are conflicting. Additionally, the prognostic value of PSCA gene polymorphisms for GC patients is unknown. We performed a meta-analysis using 9 eligible case-control studies to investigate the association between PSCA polymorphisms and GC risk, and additionally investigated the prognostic value of PSCA polymorphisms for GC patients with two eligible studies. The association was measured using random-effect or fixed-effect odds ratios (ORs) combined with 95% confidence intervals (CIs) according to the heterogeneity of the studies. We found that rs2294008 (dominant model: OR, 1.44; 95% CI, 1.16–1.79) and rs2976392 (dominant model: OR, 1.41; 95% CI, 0.98–2.04) polymorphisms were associated with increased risk of GC, although the association of rs2976392 was not statistically significant. For rs2294008, the associations were all consistently significant among the different subgroups stratified by ethnicity and tumor location, but not significant in intestinal or diffuse subtypes. For rs2976392, the associations were consistently significant for the intestinal, diffuse and non-cardia subtypes, but not significant for the cardia subtype. Furthermore, two eligible studies reported inverse results of PCSA in predicting the survival of GC patients (HR, 0.75; 95% CI, 0.59–0.96; and HR, 2.12; 95% CI, 1.22–3.69, respectively). In conclusion, PSCA gene polymorphisms are associated with increased risk of GC and are correlated with the prognosis of GC patients. Future studies are required to evaluate the molecular mechanisms of PSCA polymorphisms in GC and validate the prognostic value in a larger number of patients. PMID:23060941

  12. Lung cancer risk prediction to select smokers for screening CT--a model based on the Italian COSMOS trial.

    PubMed

    Maisonneuve, Patrick; Bagnardi, Vincenzo; Bellomi, Massimo; Spaggiari, Lorenzo; Pelosi, Giuseppe; Rampinelli, Cristiano; Bertolotti, Raffaella; Rotmensz, Nicole; Field, John K; Decensi, Andrea; Veronesi, Giulia

    2011-11-01

    Screening with low-dose helical computed tomography (CT) has been shown to significantly reduce lung cancer mortality but the optimal target population and time interval to subsequent screening are yet to be defined. We developed two models to stratify individual smokers according to risk of developing lung cancer. We first used the number of lung cancers detected at baseline screening CT in the 5,203 asymptomatic participants of the COSMOS trial to recalibrate the Bach model, which we propose using to select smokers for screening. Next, we incorporated lung nodule characteristics and presence of emphysema identified at baseline CT into the Bach model and proposed the resulting multivariable model to predict lung cancer risk in screened smokers after baseline CT. Age and smoking exposure were the main determinants of lung cancer risk. The recalibrated Bach model accurately predicted lung cancers detected during the first year of screening. Presence of nonsolid nodules (RR = 10.1, 95% CI = 5.57-18.5), nodule size more than 8 mm (RR = 9.89, 95% CI = 5.84-16.8), and emphysema (RR = 2.36, 95% CI = 1.59-3.49) at baseline CT were all significant predictors of subsequent lung cancers. Incorporation of these variables into the Bach model increased the predictive value of the multivariable model (c-index = 0.759, internal validation). The recalibrated Bach model seems suitable for selecting the higher risk population for recruitment for large-scale CT screening. The Bach model incorporating CT findings at baseline screening could help defining the time interval to subsequent screening in individual participants. Further studies are necessary to validate these models.

  13. [Value of cardiopulmonary risk index in predicting postoperative short-term prognosis in patients with lung cancer].

    PubMed

    Gu, Yueqing; Gao, Chengxin; Bai, Hao; Liao, Meilin

    2002-06-20

    To determine the value of preoperative cardiopulmonary risk index (CPRI) in predicting the short-term prognosis after lung resection in patients with lung cancer. Preoperative clinical data were used to generate a cardiac risk index (CRI) and a pulmonary risk index (PRI). And the value of cardiopulmonary risk index (CPRI) consisting of CRI and PRI in predicting postoperative prognosis was estimated in patients who underwent lung resection at Shanghai Chest Hospital in 1999. A total of 625 consecutive patients were studied. Postoperative complications occurred in 49 patients (7.8%), including 8 deaths within 30 days of operation. In the total group, CRI, PRI and CPRI scores ranged from 1 to 3, 0 to 5 and 1 to 7, respectively. There were 489 patients with CPRI < 4, and 136 with CPRI≥4. Using CPRI≥4 as a threshold for predicting postoperative complications, the sensitivity, specificity and accuracy rate were 75.5%, 82.8% and 82.2% respectively. The preoperative CPRI is one of the important indexes in predicting the short-term postoperative prognosis for patients with lung cancer. However, it can not completely predict all of postoperative risks, and should be used together with other factors.

  14. Low free and bioavailable testosterone levels may predict pathologically-proven high-risk prostate cancer: a prospective, clinical trial.

    PubMed

    Bayar, Göksel; Şirin, Hakan; Aydın, Mustafa; Özağarı, Ayşim; Tanrıverdi, Orhan; Kadıhasanoğlu, Mustafa; Kendirci, Muammer

    2017-09-01

    To determine the predictive value of free and bioavailable testosterone levels on the detection of high-grade prostate cancer proven by histopathological examination of transrectal prostate biopsy specimens. A total of 405 patients who underwent transrectal prostate biopsy due to high prostatic specific antigen (PSA) (>2.5 ng/mL) and/or abnormal findings at digital rectal examination were included in this study. Blood free and bioavailable testosterone levels were calculated by the formula recommended by International Society for the Study of the Aging Male (ISSAM). The patients were stratified according to the D'Amico classification based on PSA levels and histological outcomes of prostate biopsies as benign, low, intermediate and high-risk prostate cancer. Patients were also divided into five groups according to the percentage of cancerous cores. Prostate cancer was detected in 160 of 405 (39.5%) patients. Total, free and bioavailable testosterone levels did not differ significantly between the patients with benign or malign histology. However, mean free (6.2 vs. 5.2 ng/dL, p=0.02) and bioavailable (151 vs. 125 ng/dL, p=0.001) testosterone levels were found to be significantly different in men with low-intermediate and high-risk prostate cancer. Moreover, a significant correlation was found between free, and bioavailable testosterone levels and percentage of cores with cancer (p=0.002 for free and p=0.016 for bioavailable testosterone, respectively). This prospective clinical study demonstrates that reduced levels of calculated blood free and bioavailable testosterone levels are associated with an increased risk of high-grade prostate cancer. Based on these findings blood free and bioavailable testosterone levels may be be thought to be an adjunctive factor in the prediction of high-risk prostate cancer.

  15. Variations in pelvic dimensions do not predict the risk of circumferential resection margin (CRM) involvement in rectal cancer.

    PubMed

    Salerno, G; Daniels, I R; Brown, G; Norman, A R; Moran, B J; Heald, R J

    2007-06-01

    The objective of this study was to assess the value of preoperative pelvimetry, using magnetic resonance imaging (MRI), in predicting the risk of an involved circumferential resection margin (CRM) in a group of patients with operable rectal cancer. A cohort of 186 patients from the MERCURY study was selected. These patients' histological CRM status was compared against 14 pelvimetry parameters measured from the preoperative MRI. These measurements were taken by one of the investigators (G.S.), who was blinded to the final CRM status. There was no correlation between the pelvimetry and the CRM status. However, there was a difference in the height of the rectal cancer and the positive CRM rate (p = 0.011). Of 61 patients with low rectal cancer, 10 had positive CRM at histology (16.4% with CI 8.2%-22.1%) compared with 5 of 110 patients with mid/upper rectal cancers (4.5% with CI 0.7%-8.4%). Magnetic resonance imaging can predict clear margins in most cases of rectal cancer. Circumferential resection margin positivity cannot be predicted from pelvimetry in patients with rectal cancer selected for curative surgery. The only predictive factor for a positive CRM in the patients studied was tumor height.

  16. Breast cancer surface receptors predict risk for developing brain metastasis and subsequent prognosis

    PubMed Central

    Grewal, Jai; Kesari, Santosh

    2008-01-01

    Determining the status of breast cancer surface receptors (estrogen receptor, progesterone receptor, HER2/neu) has become routine in the care of patients with this disease and has proven to be helpful in guiding treatment. For this reason, breast cancer has become a model for molecularly guided therapy in solid tumors. Emerging data support that these receptors are associated with risk for developing brain metastases. Additionally, once brain metastases have occurred these receptors may also correlate with prognosis. PMID:18373884

  17. 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

  18. Common breast cancer susceptibility alleles and the risk of breast cancer for BRCA1 and BRCA2 mutation carriers: implications for risk prediction

    PubMed Central

    Antoniou, Antonis C; Beesley, Jonathan; McGuffog, Lesley; Sinilnikova, Olga M.; Healey, Sue; Neuhausen, Susan L.; Ding, Yuan Chun; Rebbeck, Timothy R.; Weitzel, Jeffrey N.; Lynch, Henry T.; Isaacs, Claudine; Ganz, Patricia A.; Tomlinson, Gail; Olopade, Olufunmilayo I.; Couch, Fergus J.; Wang, Xianshu; Lindor, Noralane M.; Pankratz, Vernon S.; Radice, Paolo; Manoukian, Siranoush; Peissel, Bernard; Zaffaroni, Daniela; Barile, Monica; Viel, Alessandra; Allavena, Anna; Dall’Olio, Valentina; Peterlongo, Paolo; Szabo, Csilla I.; Zikan, Michal; Claes, Kathleen; Poppe, Bruce; Foretova, Lenka; Mai, Phuong L.; Greene, Mark H.; Rennert, Gad; Lejbkowicz, Flavio; Glendon, Gord; Ozcelik, Hilmi; Andrulis, Irene L.; Thomassen, Mads; Gerdes, Anne-Marie; Sunde, Lone; Cruger, Dorthe; Jensen, Uffe Birk; Caligo, Maria; Friedman, Eitan; Kaufman, Bella; Laitman, Yael; Milgrom, Roni; Dubrovsky, Maya; Cohen, Shimrit; Borg, Ake; Jernström, Helena; Lindblom, Annika; Rantala, Johanna; Stenmark-Askmalm, Marie; Melin, Beatrice; Nathanson, Kate; Domchek, Susan; Jakubowska, Ania; Lubinski, Jan; Huzarski, Tomasz; Osorio, Ana; Lasa, Adriana; Durán, Mercedes; Tejada, Maria-Isabel; Godino, Javier; Benitez, Javier; Hamann, Ute; Kriege, Mieke; Hoogerbrugge, Nicoline; van der Luijt, Rob B; van Asperen, Christi J; Devilee, Peter; Meijers-Heijboer, E.J.; Blok, Marinus J; Aalfs, Cora M.; Hogervorst, Frans; Rookus, Matti; Cook, Margaret; Oliver, Clare; Frost, Debra; Conroy, Don; Evans, D. Gareth; Lalloo, Fiona; Pichert, Gabriella; Davidson, Rosemarie; Cole, Trevor; Cook, Jackie; Paterson, Joan; Hodgson, Shirley; Morrison, Patrick J.; Porteous, Mary E.; Walker, Lisa; Kennedy, M. John; Dorkins, Huw; Peock, Susan; Godwin, Andrew K.; Stoppa-Lyonnet, Dominique; de Pauw, Antoine; Mazoyer, Sylvie; Bonadona, Valérie; Lasset, Christine; Dreyfus, Hélène; Leroux, Dominique; Hardouin, Agnès; Berthet, Pascaline; Faivre, Laurence; Loustalot, Catherine; Noguchi, Tetsuro; Sobol, Hagay; Rouleau, Etienne; Nogues, Catherine; Frénay, Marc; Vénat-Bouvet, Laurence; Hopper, John L.; Daly, Mary B.; Terry, Mary B.; John, Esther M.; Buys, Saundra S.; Yassin, Yosuf; Miron, Alex; Goldgar, David; Singer, Christian F.; Dressler, Anne Catharina; Gschwantler-Kaulich, Daphne; Pfeiler, Georg; Hansen, Thomas V. O.; Jønson, Lars; Agnarsson, Bjarni A.; Kirchhoff, Tomas; Offit, Kenneth; Devlin, Vincent; Dutra-Clarke, Ana; Piedmonte, Marion; Rodriguez, Gustavo C.; Wakeley, Katie; Boggess, John F.; Basil, Jack; Schwartz, Peter E.; Blank, Stephanie V.; Toland, Amanda Ewart; Montagna, Marco; Casella, Cinzia; Imyanitov, Evgeny; Tihomirova, Laima; Blanco, Ignacio; Lazaro, Conxi; Ramus, Susan J.; Sucheston, Lara; Karlan, Beth Y.; Gross, Jenny; Schmutzler, Rita; Wappenschmidt, Barbara; Engel, Christoph; Meindl, Alfons; Lochmann, Magdalena; Arnold, Norbert; Heidemann, Simone; Varon-Mateeva, Raymonda; Niederacher, Dieter; Sutter, Christian; Deissler, Helmut; Gadzicki, Dorothea; Preisler-Adams, Sabine; Kast, Karin; Schönbuchner, Ines; Caldes, Trinidad; de la Hoya, Miguel; Aittomäki, Kristiina; Nevanlinna, Heli; Simard, Jacques; Spurdle, Amanda B.; Holland, Helene; Chen, Xiaoqing; Platte, Radka; Chenevix-Trench, Georgia; Easton, Douglas F.

    2010-01-01

    The known breast cancer (BC) susceptibility polymorphisms in FGFR2, TNRC9/TOX3, MAP3K1,LSP1 and 2q35 confer increased risks of BC for BRCA1 or BRCA2 mutation carriers. We evaluated the associations of three additional SNPs, rs4973768 in SLC4A7/NEK10, rs6504950 in STXBP4/COX11 and rs10941679 at 5p12 and reanalyzed the previous associations using additional carriers in a sample of 12,525 BRCA1 and 7,409 BRCA2 carriers. Additionally, we investigated potential interactions between SNPs and assessed the implications for risk prediction. The minor alleles of rs4973768 and rs10941679 were associated with increased BC risk for BRCA2 carriers (per-allele Hazard Ratio (HR)=1.10, 95%CI:1.03-1.18, p=0.006 and HR=1.09, 95%CI:1.01-1.19, p=0.03, respectively). Neither SNP was associated with BC risk for BRCA1 carriers and rs6504950 was not associated with BC for either BRCA1 or BRCA2 carriers. Of the nine polymorphisms investigated, seven were associated with BC for BRCA2 carriers (FGFR2, TOX3, MAP3K1, LSP1, 2q35, SLC4A7, 5p12, p-values:7×10−11-0.03), but only TOX3 and 2q35 were associated with the risk for BRCA1 carriers (p=0.0049, 0.03 respectively). All risk associated polymorphisms appear to interact multiplicatively on BC risk for mutation carriers. Based on the joint genotype distribution of the seven risk associated SNPs in BRCA2 mutation carriers, the 5% of BRCA2 carriers at highest risk (i.e. between 95th and 100th percentiles) were predicted to have a probability between 80% and 96% of developing BC by age 80, compared with 42-50% for the 5% of carriers at lowest risk. Our findings indicated that these risk differences may be sufficient to influence the clinical management of mutation carriers. PMID:21118973

  19. A risk prediction algorithm based on family history and common genetic variants: application to prostate cancer with potential clinical impact

    PubMed Central

    MacInnis, Robert J; Antoniou, Antonis C; Eeles, Rosalind A; Severi, Gianluca; Olama, Ali Amin Al; McGuffog, Lesley; Kote-Jarai, Zsofia; Guy, Michelle; O'Brien, Lynne T; Hall, Amanda L; Wilkinson, Rosemary A; Sawyer, Emma; Ardern-Jones, Audrey T; Dearnaley, David P.; Horwich, Alan; Khoo, Vincent S.; Parker, Christopher C.; Huddart, Robert A.; Van As, Nicholas; McCredie, Margaret R; English, Dallas R; Giles, Graham G; Hopper, John L; Easton, Douglas F

    2014-01-01

    Genome Wide Association Studies have identified several Single Nucleotide Polymorphisms (SNPs) that are independently associated with small increments in risk of prostate cancer, opening up the possibility for using such variants in risk prediction. Using segregation analysis of population-based samples of 4390 families of prostate cancer patients from the UK and Australia, and assuming all familial aggregation has genetic causes, we previously found that the best model for the genetic susceptibility to prostate cancer was a mixed model of inheritance that included both a recessive major gene component and a polygenic component (P) that represents the effect of a large number of genetic variants each of small effect, where P∼N(0,σP2). Based on published studies of 26 SNPs that are currently known to be associated with prostate cancer, we have extended our model to incorporate these SNPs by decomposing the polygenic component into two parts: a polygenic component due to the known susceptibility SNPs, PK∼N(0,σK2), and the residual polygenic component due to the postulated but as yet unknown genetic variants, PU∼N(0,σU2). The resulting algorithm can be used for predicting the probability of developing prostate cancer in the future based on both SNP profiles and explicit family history information. This approach can be applied to other diseases for which population-based family data and established risk variants exist. PMID:21769933

  20. Invited commentary: use of arsenical skin lesions to predict risk of internal cancer: implications for prevention and future research.

    PubMed

    Ahsan, Habibul; Steinmaus, Craig

    2013-02-01

    Arsenic exposure affects millions of people worldwide, causing substantial mortality and morbidity from cancers and cardiovascular and respiratory diseases. An article in the current issue (Am J Epidemiol. 2013;177(3):202-212) reports that classic dermatological manifestations, typically associated with chronic arsenic exposure, are predictive of internal cancers among Taiwanese decades after the cessation of exposure. Specifically, the risk of lung and urothelial cancers was elevated, which was evident regardless of arsenic dose, smoking, and age. There was also an unexpected elevated risk of prostate cancer. Despite some methodological limitations, these findings underscore the need for assessing whether dermatological manifestations are also predictive of cardiovascular, respiratory, and other arsenic-related, long-term health consequences. Given the emerging evidence of arsenic exposure from dietary sources beyond contaminated drinking water and occupational and environmental settings, and also because the vast majority of diseases and deaths among exposed populations do not show classic dermatological manifestations, larger and more comprehensive investigations of the health effects of arsenic exposure, especially at lower doses, are needed. In parallel, because the risk of known arsenic-related health outcomes remains elevated decades after exposure cessation, research toward identification of early clinical and biological markers of long-term risk as well as avenues for prevention, in addition to policy actions for exposure reductions, is warranted.

  1. Hormone receptor status of a first primary breast cancer predicts contralateral breast cancer risk in the WECARE study population.

    PubMed

    Reiner, Anne S; Lynch, Charles F; Sisti, Julia S; John, Esther M; Brooks, Jennifer D; Bernstein, Leslie; Knight, Julia A; Hsu, Li; Concannon, Patrick; Mellemkjær, Lene; Tischkowitz, Marc; Haile, Robert W; Shen, Ronglai; Malone, Kathleen E; Woods, Meghan; Liang, Xiaolin; Morrow, Monica; Bernstein, Jonine L

    2017-07-19

    Previous population-based studies have described first primary breast cancer tumor characteristics and their association with contralateral breast cancer (CBC) risk. However, information on influential covariates such as treatment, family history of breast cancer, and BRCA1/2 mutation carrier status was not available. In a large, population-based, case-control study, we evaluated whether tumor characteristics of the first primary breast cancer are associated with risk of developing second primary asynchronous CBC, overall and in subgroups of interest, including among BRCA1/2 mutation non-carriers, women who are not treated with tamoxifen, and women without a breast cancer family history. The Women's Environmental Cancer and Radiation Epidemiology Study is a population-based case-control study of 1521 CBC cases and 2212 individually-matched controls with unilateral breast cancer. Detailed information about breast cancer risk factors, treatment for and characteristics of first tumors, including estrogen receptor (ER) and progesterone receptor (PR) status, was obtained by telephone interview and medical record abstraction. Multivariable risk ratios (RRs) and 95% confidence intervals (CIs) were estimated in conditional logistic regression models, adjusting for demographics, treatment, and personal medical and family history. A subset of women was screened for BRCA1/2 mutations. Lobular histology of the first tumor was associated with a 30% increase in CBC risk (95% CI 1.0-1.6). Compared to women with ER+/PR+ first tumors, those with ER-/PR- tumors had increased risk of CBC (RR = 1.4, 95% CI 1.1-1.7). Notably, women with ER-/PR- first tumors were more likely to develop CBC with the ER-/PR- phenotype (RR = 5.4, 95% CI 3.0-9.5), and risk remained elevated in multiple subgroups: BRCA1/2 mutation non-carriers, women younger than 45 years of age, women without a breast cancer family history, and women who were not treated with tamoxifen. Having a hormone receptor

  2. CHRNA5 Risk Variant Predicts Delayed Smoking Cessation and Earlier Lung Cancer Diagnosis—A Meta-Analysis

    PubMed Central

    Hung, Rayjean J.; Baker, Timothy; Horton, Amy; Culverhouse, Rob; Saccone, Nancy; Cheng, Iona; Deng, Bo; Han, Younghun; Hansen, Helen M.; Horsman, Janet; Kim, Claire; Lutz, Sharon; Rosenberger, Albert; Aben, Katja K.; Andrew, Angeline S.; Breslau, Naomi; Chang, Shen-Chih; Dieffenbach, Aida Karina; Dienemann, Hendrik; Frederiksen, Brittni; Han, Jiali; Hatsukami, Dorothy K.; Johnson, Eric O.; Pande, Mala; Wrensch, Margaret R.; McLaughlin, John; Skaug, Vidar; van der Heijden, Henricus F.; Wampfler, Jason; Wenzlaff, Angela; Woll, Penella; Zienolddiny, Shanbeh; Bickeböller, Heike; Brenner, Hermann; Duell, Eric J.; Haugen, Aage; Heinrich, Joachim; Hokanson, John E.; Hunter, David J.; Kiemeney, Lambertus A.; Lazarus, Philip; Le Marchand, Loic; Liu, Geoffrey; Mayordomo, Jose; Risch, Angela; Schwartz, Ann G.; Teare, Dawn; Wu, Xifeng; Wiencke, John K.; Yang, Ping; Zhang, Zuo-Feng; Spitz, Margaret R.; Kraft, Peter; Amos, Christopher I.; Bierut, Laura J.

    2015-01-01

    Background: Recent meta-analyses show strong evidence of associations among genetic variants in CHRNA5 on chromosome 15q25, smoking quantity, and lung cancer. This meta-analysis tests whether the CHRNA5 variant rs16969968 predicts age of smoking cessation and age of lung cancer diagnosis. Methods: Meta-analyses examined associations between rs16969968, age of quitting smoking, and age of lung cancer diagnosis in 24 studies of European ancestry (n = 29 072). In each dataset, we used Cox regression models to evaluate the association between rs16969968 and the two primary phenotypes (age of smoking cessation among ever smokers and age of lung cancer diagnosis among lung cancer case patients) and the secondary phenotype of smoking duration. Heterogeneity across studies was assessed with the Cochran Q test. All statistical tests were two-sided. Results: The rs16969968 allele (A) was associated with a lower likelihood of smoking cessation (hazard ratio [HR] = 0.95, 95% confidence interval [CI] = 0.91 to 0.98, P = .0042), and the AA genotype was associated with a four-year delay in median age of quitting compared with the GG genotype. Among smokers with lung cancer diagnoses, the rs16969968 genotype (AA) was associated with a four-year earlier median age of diagnosis compared with the low-risk genotype (GG) (HR = 1.08, 95% CI = 1.04 to 1.12, P = 1.1*10–5). Conclusion: These data support the clinical significance of the CHRNA5 variant rs16969968. It predicts delayed smoking cessation and an earlier age of lung cancer diagnosis in this meta-analysis. Given the existing evidence that this CHRNA5 variant predicts favorable response to cessation pharmacotherapy, these findings underscore the potential clinical and public health importance of rs16969968 in CHRNA5 in relation to smoking cessation success and lung cancer risk. PMID:25873736

  3. Prediction models in cancer care.

    PubMed

    Vickers, Andrew J

    2011-01-01

    Prediction is ubiquitous across the spectrum of cancer care from screening to hospice. Indeed, oncology is often primarily a prediction problem; many of the early stage cancers cause no symptoms, and treatment is recommended because of a prediction that tumor progression would ultimately threaten a patient's quality of life or survival. Recent years have seen attempts to formalize risk prediction in cancer care. In place of qualitative and implicit prediction algorithms, such as cancer stage, researchers have developed statistical prediction tools that provide a quantitative estimate of the probability of a specific event for an individual patient. Prediction models generally have greater accuracy than reliance on stage or risk groupings, can incorporate novel predictors such as genomic data, and can be used more rationally to make treatment decisions. Several prediction models are now widely used in clinical practice, including the Gail model for breast cancer incidence or the Adjuvant! Online prediction model for breast cancer recurrence. Given the burgeoning complexity of diagnostic and prognostic information, there is simply no realistic alternative to incorporating multiple variables into a single prediction model. As such, the question should not be whether but how prediction models should be used to aid decision-making. Key issues will be integration of models into the electronic health record and more careful evaluation of models, particularly with respect to their effects on clinical outcomes.

  4. Methodology to predict long-term cancer survival from short-term data using Tobacco Cancer Risk and Absolute Cancer Cure models

    NASA Astrophysics Data System (ADS)

    Mould, R. F.; Lederman, M.; Tai, P.; Wong, J. K. M.

    2002-11-01

    Three parametric statistical models have been fully validated for cancer of the larynx for the prediction of long-term 15, 20 and 25 year cancer-specific survival fractions when short-term follow-up data was available for just 1-2 years after the end of treatment of the last patient. In all groups of cases the treatment period was only 5 years. Three disease stage groups were studied, T1N0, T2N0 and T3N0. The models are the Standard Lognormal (SLN) first proposed by Boag (1949 J. R. Stat. Soc. Series B 11 15-53) but only ever fully validated for cancer of the cervix, Mould and Boag (1975 Br. J. Cancer 32 529-50), and two new models which have been termed Tobacco Cancer Risk (TCR) and Absolute Cancer Cure (ACC). In each, the frequency distribution of survival times of defined groups of cancer deaths is lognormally distributed: larynx only (SLN), larynx and lung (TCR) and all cancers (ACC). All models each have three unknown parameters but it was possible to estimate a value for the lognormal parameter S a priori. By reduction to two unknown parameters the model stability has been improved. The material used to validate the methodology consisted of case histories of 965 patients, all treated during the period 1944-1968 by Dr Manuel Lederman of the Royal Marsden Hospital, London, with follow-up to 1988. This provided a follow-up range of 20- 44 years and enabled predicted long-term survival fractions to be compared with the actual survival fractions, calculated by the Kaplan and Meier (1958 J. Am. Stat. Assoc. 53 457-82) method. The TCR and ACC models are better than the SLN model and for a maximum short-term follow-up of 6 years, the 20 and 25 year survival fractions could be predicted. Therefore the numbers of follow-up years saved are respectively 14 years and 19 years. Clinical trial results using the TCR and ACC models can thus be analysed much earlier than currently possible. Absolute cure from cancer was also studied, using not only the prediction models which

  5. Dynamic risk stratification for predicting the recurrence in differentiated thyroid cancer.

    PubMed

    Ozkan, Elgin; Soydal, Cigdem; Nak, Demet; Kucuk, Nuriye O; Kir, Kemal M

    2017-09-27

    To analyze the predictive value of the dynamic risk stratification (DRS) system for assessing the risk of recurrent/persistent disease in our large group of differentiated thyroid carcinoma (DTC) patients. We retrospectively included 2184 consecutive patients who received radioiodine ablation therapy following a total or near total thyroidectomy in our department between 1998 and 2014. The American Thyroid Association (ATA) classification was used for initial risk classification. At the second year of follow-up period after radioiodine ablation therapy, DRS was performed also. The ATA and DRS risk classification results were compared with clinical outcome. According to DRS, more than half of the ATA high-risk patients (73.2%) moved to the DRS low-risk category and the 6.4% of ATA low-risk patients comprised the DRS high-risk category. In comparison of variables within the ATA and the DRS risk groups with clinical outcome, combined use of the ATA and the DRS systems was statistically significant to predict the recurrent/persistent disease (P<0.005). The present study revealed that the DRS system is a necessary stratification system in addition to the initial risk evaluation. The DRS can discriminate those patients who does not require closer follow-up in the long-term period.

  6. Predicting the Mortality Benefit of CT Screening for Second Lung Cancer in a High-Risk Population

    PubMed Central

    Kinsey, C. Matthew; Hamlington, Katharine L.; O’Toole, Jacqueline; Stapleton, Renee; Bates, Jason H. T.

    2016-01-01

    Patients who survive an index lung cancer (ILC) after surgical resection continue to be at significant risk for a metachronous lung cancer (MLC). Indeed, this risk is much higher than the risk of developing an ILC in heavy smokers. There is currently little evidence upon which to base guidelines for screening at-risk patients for MLC, and the risk-reward tradeoffs for screening this patient population are unknown. The goal of this investigation was to estimate the maximum mortality benefit of CT screening for MLC. We developed a computational model to estimate the maximum rates of CT detection of MLC and surgical resection to be expected in a given population as a function of time after resection of an ILC. Applying the model to a hypothetical high-risk population suggests that screening for MLC within 5 years after resection of an ILC may identify only a very small number of treatable cancers. The risk of death from a potentially resectable MLC increases dramatically past this point, however, suggesting that screening after 5 years is imperative. The model also predicts a substantial detection gap for MLC that demonstrates the benefit to be gained as more sensitive screening methods are developed. PMID:27806080

  7. Co-occurring gland angularity in localized subgraphs: predicting biochemical recurrence in intermediate-risk prostate cancer patients.

    PubMed

    Lee, George; Sparks, Rachel; Ali, Sahirzeeshan; Shih, Natalie N C; Feldman, Michael D; Spangler, Elaine; Rebbeck, Timothy; Tomaszewski, John E; Madabhushi, Anant

    2014-01-01

    Quantitative histomorphometry (QH) refers to the application of advanced computational image analysis to reproducibly describe disease appearance on digitized histopathology images. QH thus could serve as an important complementary tool for pathologists in interrogating and interpreting cancer morphology and malignancy. In the US, annually, over 60,000 prostate cancer patients undergo radical prostatectomy treatment. Around 10,000 of these men experience biochemical recurrence within 5 years of surgery, a marker for local or distant disease recurrence. The ability to predict the risk of biochemical recurrence soon after surgery could allow for adjuvant therapies to be prescribed as necessary to improve long term treatment outcomes. The underlying hypothesis with our approach, co-occurring gland angularity (CGA), is that in benign or less aggressive prostate cancer, gland orientations within local neighborhoods are similar to each other but are more chaotically arranged in aggressive disease. By modeling the extent of the disorder, we can differentiate surgically removed prostate tissue sections from (a) benign and malignant regions and (b) more and less aggressive prostate cancer. For a cohort of 40 intermediate-risk (mostly Gleason sum 7) surgically cured prostate cancer patients where half suffered biochemical recurrence, the CGA features were able to predict biochemical recurrence with 73% accuracy. Additionally, for 80 regions of interest chosen from the 40 studies, corresponding to both normal and cancerous cases, the CGA features yielded a 99% accuracy. CGAs were shown to be statistically signicantly ([Formula: see text]) better at predicting BCR compared to state-of-the-art QH methods and postoperative prostate cancer nomograms.

  8. Melanoma Risk Prediction Models

    Cancer.gov

    Developing statistical models that estimate the probability of developing melanoma cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk.

  9. Prediction of Breast Cancer Risk by Aberrant Methylation in Mammary Duct Lavage

    DTIC Science & Technology

    2006-07-01

    Johnson Maddux A, Cler L, Naftalis E, Leitch A, Ashfaq R. Patient And Duct Selection F Duct Lavage. 4th International Santa Barbara Symposium: The...Intraductal Approach to Breast Canc Barbara , CA, 2005. Bushnaq Z, Ashfaq R, Leitch M, Euhus D. Patient Variables Predicting Atypical Cytology by Nipple...Clin Cancer Res 2005;11: 166–72. [13] Hay R, Caputo J, Macy M, Mcclintock P, Reid Y. ATCC cell lines and hybridomas. American Type Culture Collection

  10. Does perceived risk predict breast cancer screening use? Findings from a prospective cohort study of female relatives from the Ontario site of the Breast Cancer Family Registry

    PubMed Central

    Walker, Meghan J.; Mirea, Lucia; Glendon, Gord; Ritvo, Paul; Andrulis, Irene L.; Knight, Julia A.; Chiarelli, Anna M.

    2014-01-01

    Summary Objective While the relationship between perceived risk and adherence to breast cancer screening guidelines has been studied extensively, the majority of studies are cross-sectional. We prospectively examined this relationship among women with familial risk. Materials and Methods The prospective association between perceived risk and screening behaviors was examined in 913 women aged 25 to 72, with varying levels of familial breast cancer risk from the Ontario site of the Breast Cancer Family Registry. Associations between perceived lifetime breast cancer risk and subsequent use of screening mammography, clinical breast examination (CBE) and genetic testing were assessed using logistic regression. Results Overall, perceived risk did not predict subsequent use of screening mammography, CBE or genetic testing. Women at moderate/high familial risk who perceived their risk as greater than 50% were significantly less likely to have a CBE (odds ratio (OR) = 0.52, 95% confidence interval (CI): 0.30–0.91, p=0.04), and less likely to have a mammogram (OR = 0.70, 95% CI: 0.40–1.20, p=0.70) or genetic test (OR = 0.61, 95% CI: 0.34–1.10, p=0.09) compared to women who perceive their risk as 50%. In contrast, women at low familial risk who perceived their risk as greater than 50% were non-significantly more likely to have a mammogram (OR = 1.13, 95% CI: 0.59–2.16, p=0.78), CBE (OR = 1.11, 95% CI: 0.63–1.95, p=0.74) or genetic test (OR = 1.29, 95% CI: 0.50– 3.33, p=0.35) compared to women who perceive their risk as 50%. Conclusion Perceived risk did not significantly predict subsequent screening use overall, however this relationship may be moderated by level of familial risk. Results may inform risk education and management strategies among women with varying levels of familial breast cancer risk. PMID:24821458

  11. External Validation of the Cancer of the Prostate Risk Assessment Postsurgical Score for Prediction of Disease Recurrence after Radical Prostatectomy

    PubMed Central

    Öztürk, Erdem; Güven, Eşref Oğuz; Başar, Halil

    2016-01-01

    Objective. The cancer of the prostate risk assessment (CAPRA-S) postsurgical score predicts recurrence, metastasis, and cancer-specific survival after radical prostatectomy (RP). We evaluated the relation between CAPRA-S score and biochemical recurrence (BCR) in prostate cancer after RP in our clinic. Materials and Methods. This study was performed on 203 patients with prostate carcinoma who underwent open RP and regional lymph node dissection in our clinic between 2008 and 2013. We calculated the CAPRA-S scores including prostate-specific antigen (PSA) at diagnosis, pathology Gleason score, surgical margin, seminal vesicle invasion, extracapsular extension, and lymph node involvement. The patients were divided into 3 risk groups (low, intermediate, and high risk) according to risk scores. Results. Recurrence occurred in 17.8% of the patients (36 patients out of 203 patients) with a median of 11.7-month follow-up. The average recurrence-free survival time is 44.6 months. Surgical margin invasion and seminal vesicle invasion significantly correlated with BCR especially in high risk group (11 and 13 of 15 patients, p < 0.05, resp.). Conclusion. CAPRA-S score can be easily calculated and it is useful in clinical practice in order to timely propose adjuvant therapies after surgery. PMID:27833937

  12. A Risk Score based on histopathological features predicts higher risk of distant recurrence in premenopausal patients with lymph node-negative endocrine-responsive breast cancer

    PubMed Central

    Dellapasqua, Silvia; Bagnardi, Vincenzo; Regan, Meredith M.; Rotmensz, Nicole; Mastropasqua, Mauro G.; Viale, Giuseppe; Maiorano, Eugenio; Price, Karen N.; Gelber, Richard D.; Castiglione-Gertsch, Monica; Goldhirsch, Aron; Colleoni, Marco

    2012-01-01

    SUMMARY Purpose To develop a Risk Score (RS) to predict distant recurrence among premenopausal women with node-negative endocrine-responsive early breast cancer. Methods The Cox model was used to develop the RS using clinical and histopathological features from 378 women participating in the IBCSG Trial VIII who received endocrine therapy alone or following chemotherapy. The performance of the resulting model was validated on a cohort of 1005 patients from a single institution who received endocrine therapy alone. Results In a multivariable analysis, the risk of distant recurrence was associated with tumor size, ER, Ki-67 and peritumoral vascular invasion. In the validation cohort, patients with high RS were at greater risk of distant recurrence compared to patients with low RS (HR, 17.41 ; 95% CI, 5.72 to 52.95). Conclusion In premenopausal women with node-negative endocrine-responsive early breast cancer, the RS identifies patients at higher risk of distant recurrence. PMID:22749924

  13. Histologic changes associated with neoadjuvant chemotherapy are predictive of nodal metastases in patients with high-risk prostate cancer.

    PubMed

    O'Brien, Catherine; True, Lawrence D; Higano, Celestia S; Rademacher, Brooks L S; Garzotto, Mark; Beer, Tomasz M

    2010-04-01

    Clinical trials are evaluating the effect of neoadjuvant chemotherapy on men with high-risk prostate cancer. Little is known about the clinical significance of postchemotherapy tumor histopathologic features. We assessed the prognostic and predictive value of histologic features (intraductal carcinoma, vacuolated cell morphologic features, inconspicuous glands, cribriform architecture, and inconspicuous cancer cells) observed in 50 high-risk prostate cancers treated with preprostatectomy docetaxel and mitoxantrone. At a median follow-up of 65 months, the overall relapse-free survival (RFS) rates at 2 and 5 years were 65% and 49%, respectively. In univariate analyses (using the Kaplan-Meier method and log-rank tests), intraductal (P = .001) and cribriform (P = .014) histologic features were associated with shorter RFS. In multivariate analyses, using the Cox proportional hazards regression, baseline prostate-specific antigen (P = .004), lymph node metastases (P < .001), and cribriform histologic features (P = .007) were associated with shorter RFS. In multivariable logistic regression analysis, only intraductal pattern (P = .007) predicted lymph node metastases. Intraductal and cribriform histologic features apparently predict postchemotherapy outcome.

  14. Histological changes associated with neoadjuvant chemotherapy are predictive of nodal metastases in high-risk prostate cancer patients

    PubMed Central

    O’Brien, Catherine; True, Lawrence D.; Higano, Celestia S.; Rademacher, Brooks L. S.; Garzotto, Mark; Beer, Tomasz M.

    2011-01-01

    Clinical trials are evaluating the effect of neoadjuvant chemotherapy on men with high risk prostate cancer. Little is known about the clinical significance of post-chemotherapy tumor histopathology. We assessed the prognostic and predictive value of histological features (intraductal carcinoma, vacuolated cell morphology, inconspicuous glands, cribriform architecture, and inconspicuous cancer cells) observed in 50 high-risk prostate cancers treated with pre-prostatectomy docetaxel and mitoxantrone. At a median follow-up of 65 months, the overall relapse-free survival (RFS) at 2 and 5 years was 65% and 49%, respectively. In univariate analyses (using Kaplan-Meier method and log-rank tests) intraductal (p=0.001) and cribriform (p=0.014) histologies were associated with shorter RFS. In multivariate analyses, using Cox’s proportional hazards regression, baseline PSA (p=0.004), lymph node metastases (p<0.001), and cribriform histology (p=0.007) were associated with shorter RFS. In multivariable logistic regression analysis, only intraductal pattern (p=0.007) predicted lymph node metastases. Intraductal and cribriform histologies apparently predict post-chemotherapy outcome. PMID:20231619

  15. A proposal of a new nomogram for predicting upstaging in contemporary D'Amico low-risk prostate cancer patients.

    PubMed

    Leyh-Bannurah, Sami-Ramzi; Dell'Oglio, Paolo; Tian, Zhe; Schiffmann, Jonas; Shariat, Shahrokh F; Suardi, Nazareno; Francesco, Montorsi; Alberto, Briganti; Heinzer, Hans; Huland, Hartwig; Graefen, Markus; Budäus, Lars; Karakiewicz, Pierre I

    2017-02-01

    Unfavorable prostate cancer (PCa) disease at final pathology affects at least 10 % of D'Amico low-risk patients. Thus, conservative therapies including active surveillance may be wrongfully applied. The purposes were to assess the rate of upstaging in a contemporary cohort of D'Amico low-risk PCa patients and to develop and externally validate a nomogram as upstaging prediction tool in two European cohorts. Analyses were restricted to 2007 patients who harbored low-risk PCa at ≥10-cores initial biopsy according to D'Amico classification (PSA <10.0 ng/ml, Gleason score <7 and clinical stage ≤T2a). Patients underwent radical prostatectomy at a high-volume center in Hamburg, Germany, from 2010 to 2015. The Hamburg cohort was randomly divided into development (n = 1338) and validation cohorts (n = 669). The development cohort was used to devise a nomogram predicting upstaging, defined as presence of ≥pT3 and/or lymph node invasion. The nomogram was externally validated in two European validation cohorts (Hamburg, n = 669; Milan, n = 465). Upstaging was observed in 187/1338 (14.0 %) of low-risk patients. In multivariable models, four of ten tested variables achieved independent predictor status: age (OR 1.07, 95 % CI 1.04-1.09), PSA (OR 1.21, 95 % CI 1.12-1.31), prostate volume (OR 0.97, 95 % CI 0.96-0.98) and percentage of positive cores (OR 1.02, 95 % CI 1.01-1.03). In external validation, the nomogram demonstrated 70.8 % (Hamburg) and 70.0 % (Milan) accuracy, respectively, with excellent concordance between predicted and observed values. Our proposed nomogram is capable to accurately identify D'Amico low-risk patients at risk of upstaging, utilizing four routinely available clinical variables, age, PSA, prostate volume and percentage of positive biopsy cores. Unfavorable prostate cancer disease at final pathology affects at least 10 % of D'Amico low-risk patients. Thus, we developed and externally validated a new nomogram based on contemporary

  16. Post-bronchoscopy pneumonia in patients suffering from lung cancer: Development and validation of a risk prediction score.

    PubMed

    Takiguchi, Hiroto; Hayama, Naoki; Oguma, Tsuyoshi; Harada, Kazuki; Sato, Masako; Horio, Yukihiro; Tanaka, Jun; Tomomatsu, Hiromi; Tomomatsu, Katsuyoshi; Takihara, Takahisa; Niimi, Kyoko; Nakagawa, Tomoki; Masuda, Ryota; Aoki, Takuya; Urano, Tetsuya; Iwazaki, Masayuki; Asano, Koichiro

    2017-05-01

    The incidence, risk factors, and consequences of pneumonia after flexible bronchoscopy in patients with lung cancer have not been studied in detail. We retrospectively analyzed the data from 237 patients with lung cancer who underwent diagnostic bronchoscopy between April 2012 and July 2013 (derivation sample) and 241 patients diagnosed between August 2013 and July 2014 (validation sample) in a tertiary referral hospital in Japan. A score predictive of post-bronchoscopy pneumonia was developed in the derivation sample and tested in the validation sample. Pneumonia developed after bronchoscopy in 6.3% and 4.1% of patients in the derivation and validation samples, respectively. Patients who developed post-bronchoscopy pneumonia needed to change or cancel their planned cancer therapy more frequently than those without pneumonia (56% vs. 6%, p<0.001). Age ≥70 years, current smoking, and central location of the tumor were independent predictors of pneumonia, which we added to develop our predictive score. The incidence of pneumonia associated with scores=0, 1, and ≥2 was 0, 3.7, and 13.4% respectively in the derivation sample (p=0.003), and 0, 2.9, and 9.7% respectively in the validation sample (p=0.016). The incidence of post-bronchoscopy pneumonia in patients with lung cancer was not rare and associated with adverse effects on the clinical course. A simple 3-point predictive score identified patients with lung cancer at high risk of post-bronchoscopy pneumonia prior to the procedure. Copyright © 2017 The Japanese Respiratory Society. Published by Elsevier B.V. All rights reserved.

  17. Next-Generation Sequencing in Oncology: Genetic Diagnosis, Risk Prediction and Cancer Classification.

    PubMed

    Kamps, Rick; Brandão, Rita D; Bosch, Bianca J van den; Paulussen, Aimee D C; Xanthoulea, Sofia; Blok, Marinus J; Romano, Andrea

    2017-01-31

    Next-generation sequencing (NGS) technology has expanded in the last decades with significant improvements in the reliability, sequencing chemistry, pipeline analyses, data interpretation and costs. Such advances make the use of NGS feasible in clinical practice today. This review describes the recent technological developments in NGS applied to the field of oncology. A number of clinical applications are reviewed, i.e., mutation detection in inherited cancer syndromes based on DNA-sequencing, detection of spliceogenic variants based on RNA-sequencing, DNA-sequencing to identify risk modifiers and application for pre-implantation genetic diagnosis, cancer somatic mutation analysis, pharmacogenetics and liquid biopsy. Conclusive remarks, clinical limitations, implications and ethical considerations that relate to the different applications are provided.

  18. Next-Generation Sequencing in Oncology: Genetic Diagnosis, Risk Prediction and Cancer Classification

    PubMed Central

    Kamps, Rick; Brandão, Rita D.; van den Bosch, Bianca J.; Paulussen, Aimee D. C.; Xanthoulea, Sofia; Blok, Marinus J.; Romano, Andrea

    2017-01-01

    Next-generation sequencing (NGS) technology has expanded in the last decades with significant improvements in the reliability, sequencing chemistry, pipeline analyses, data interpretation and costs. Such advances make the use of NGS feasible in clinical practice today. This review describes the recent technological developments in NGS applied to the field of oncology. A number of clinical applications are reviewed, i.e., mutation detection in inherited cancer syndromes based on DNA-sequencing, detection of spliceogenic variants based on RNA-sequencing, DNA-sequencing to identify risk modifiers and application for pre-implantation genetic diagnosis, cancer somatic mutation analysis, pharmacogenetics and liquid biopsy. Conclusive remarks, clinical limitations, implications and ethical considerations that relate to the different applications are provided. PMID:28146134

  19. Fall Risk in Community Dwelling Elderly Cancer Survivors—A Predictive Model for Gerontological Nurses

    PubMed Central

    Spoelstra, Sandra; Given, Barbara; von Eye, Alexander; Given, Charles

    2015-01-01

    The aim of this predictive study was to test a structural model to establish predictors of fall risk. An aging and nursing model of care was synthesized and used to examine 6912 older adult participants who are low income, using the Minimum Data Set in a community setting in the Midwest. Data analysis established relationships among age, race, a history of a previous fall, depression, pain, and ADLs, IADLs, incontinence, vision, and cognitive status. Factors leading to fall risk can direct nursing activities that have the potential to prevent falls, improving quality of life. PMID:20128528

  20. 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

  1. Does colorectal cancer risk perception predict screening behavior? A systematic review and meta-analysis*

    PubMed Central

    Atkinson, Thomas M.; Salz, Talya; Touza, Kaitlin K.; Li, Yuelin; Hay, Jennifer L.

    2015-01-01

    Objective Although health behavior theories postulate that risk perception should motivate colorectal cancer (CRC) screening, this relationship is unclear. This meta-analysis aims to examine the relationship between CRC risk perception and screening behavior, while considering potential moderators and study quality. Method A search of six databases yielded 58 studies (63 effect sizes) that quantitatively assessed the relationship between CRC risk perception and screening behavior. Results Most included effect sizes (75%) reported a positive association between CRC risk perception and screening behavior. A random effects meta-analysis yielded an overall effect size of z=0.13 (95% CI 0.10–0.16), which was heterogeneous (I2=99%, τ2=0.01). Effect sizes from high-quality studies were significantly lower than those from lower quality studies (z=0.02 vs. 0.16). Conclusions We found a small, positive relationship between CRC risk perception and reported screening behavior, with important identified heterogeneity across moderators. Future studies should focus on high quality study design. PMID:26280755

  2. Salivary Gland Cancer: Risk Factors

    MedlinePlus

    ... Cancer > Salivary Gland Cancer: Risk Factors Request Permissions Salivary Gland Cancer: Risk Factors Approved by the Cancer.Net ... f t k e P Types of Cancer Salivary Gland Cancer Guide Cancer.Net Guide Salivary Gland Cancer ...

  3. Curated microRNAs in urine and blood fail to validate as predictive biomarkers for high-risk prostate cancer.

    PubMed

    Sapre, Nikhil; Hong, Matthew K H; Macintyre, Geoff; Lewis, Heather; Kowalczyk, Adam; Costello, Anthony J; Corcoran, Niall M; Hovens, Christopher M

    2014-01-01

    The purpose of this study was to determine if microRNA profiling of urine and plasma at radical prostatectomy can distinguish potentially lethal from indolent prostate cancer. A panel of microRNAs was profiled in the plasma of 70 patients and the urine of 33 patients collected prior to radical prostatectomy. Expression of microRNAs was correlated to the clinical endpoints at a follow-up time of 3.9 years to identify microRNAs that may predict clinical response after radical prostatectomy. A machine learning approach was applied to test the predictive ability of all microRNAs profiled in urine, plasma, and a combination of both, and global performance assessed using the area under the receiver operator characteristic curve (AUC). Validation of urinary expression of miRNAs was performed on a further independent cohort of 36 patients. The best predictor in plasma using eight miRs yielded only moderate predictive performance (AUC = 0.62). The best predictor of high-risk disease was achieved using miR-16, miR-21 and miR-222 measured in urine (AUC = 0.75). This combination of three microRNAs in urine was a better predictor of high-risk disease than any individual microRNA. Using a different methodology we found that this set of miRNAs was unable to predict high-volume, high-grade disease. Our initial findings suggested that plasma and urinary profiling of microRNAs at radical prostatectomy may allow prognostication of prostate cancer behaviour. However we found that the microRNA expression signature failed to validate in an independent cohort of patients using a different platform for PCR. This highlights the need for independent validation patient cohorts and suggests that urinary microRNA signatures at radical prostatectomy may not be a robust way to predict the course of clinical disease after definitive treatment for prostate cancer.

  4. Assessment of a Four-View Mammographic Image Feature Based Fusion Model to Predict Near-Term Breast Cancer Risk.

    PubMed

    Tan, Maxine; Pu, Jiantao; Cheng, Samuel; Liu, Hong; Zheng, Bin

    2015-10-01

    The purpose of this study was to develop and assess a new quantitative four-view mammographic image feature based fusion model to predict the near-term breast cancer risk of the individual women after a negative screening mammography examination of interest. The dataset included fully-anonymized mammograms acquired on 870 women with two sequential full-field digital mammography examinations. For each woman, the first "prior" examination in the series was interpreted as negative (not recalled) during the original image reading. In the second "current" examination, 430 women were diagnosed with pathology verified cancers and 440 remained negative ("cancer-free"). For each of four bilateral craniocaudal and mediolateral oblique view images of left and right breasts, we computed and analyzed eight groups of global mammographic texture and tissue density image features. A risk prediction model based on three artificial neural networks was developed to fuse image features computed from two bilateral views of four images. The risk model performance was tested using a ten-fold cross-validation method and a number of performance evaluation indices including the area under the receiver operating characteristic curve (AUC) and odds ratio (OR). The highest AUC = 0.725 ± 0.026 was obtained when the model was trained by gray-level run length statistics texture features computed on dense breast regions, which was significantly higher than the AUC values achieved using the model trained by only two bilateral one-view images (p < 0.02). The adjustable OR values monotonically increased from 1.0 to 11.8 as model-generated risk score increased. The regression analysis of OR values also showed a significant increase trend in slope (p < 0.01). As a result, this preliminary study demonstrated that a new four-view mammographic image feature based risk model could provide useful and supplementary image information to help predict the near-term breast cancer risk.

  5. Use of an artificial neural network to predict risk factors of nosocomial infection in lung cancer patients.

    PubMed

    Chen, Jie; Pan, Qin-Shi; Hong, Wan-Dong; Pan, Jingye; Zhang, Wen-Hui; Xu, Gang; Wang, Yu-Min

    2014-01-01

    Statistical methods to analyze and predict the related risk factors of nosocomial infection in lung cancer patients are various, but the results are inconsistent. A total of 609 patients with lung cancer were enrolled to allow factor comparison using Student's t-test or the Mann-Whitney test or the Chi-square test. Variables that were significantly related to the presence of nosocomial infection were selected as candidates for input into the final ANN model. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. The prevalence of nosocomial infection from lung cancer in this entire study population was 20.1% (165/609), nosocomial infections occurring in sputum specimens (85.5%), followed by blood (6.73%), urine (6.0%) and pleural effusions (1.82%). It was shown that long term hospitalization (≥ 22 days, P= 0.000), poor clinical stage (IIIb and IV stage, P=0.002), older age (≥ 61 year old, P=0.023), and use the hormones were linked to nosocomial infection and the ANN model consisted of these four factors .The artificial neural network model with variables consisting of age, clinical stage, time of hospitalization, and use of hormones should be useful for predicting nosocomial infection in lung cancer cases.

  6. Inherited Polymorphisms in Hyaluronan Synthase 1 Predict Risk of Systemic B-Cell Malignancies but Not of Breast Cancer

    PubMed Central

    Kuppusamy, Hemalatha; Ogmundsdottir, Helga M.; Baigorri, Eva; Warkentin, Amanda; Steingrimsdottir, Hlif; Haraldsdottir, Vilhelmina; Mant, Michael J.; Mackey, John; Johnston, James B.; Adamia, Sophia; Belch, Andrew R.; Pilarski, Linda M.

    2014-01-01

    Genetic variations in the hyaluronan synthase 1 gene (HAS1) influence HAS1 aberrant splicing. HAS1 is aberrantly spliced in malignant cells from multiple myeloma (MM) and Waldenstrom macroglobulinemia (WM), but not in their counterparts from healthy donors. The presence of aberrant HAS1 splice variants predicts for poor survival in multiple myeloma (MM). We evaluated the influence of inherited HAS1 single nucleotide polymorphisms (SNP) on the risk of having a systemic B cell malignancy in 1414 individuals compromising 832 patients and 582 healthy controls, including familial analysis of an Icelandic kindred. We sequenced HAS1 gene segments from 181 patients with MM, 98 with monoclonal gammopathy of undetermined significance (MGUS), 72 with Waldenstrom macroglobulinemia (WM), 169 with chronic lymphocytic leukemia (CLL), as well as 34 members of a monoclonal gammopathy-prone Icelandic family, 212 age-matched healthy donors and a case-control cohort of 295 breast cancer patients with 353 healthy controls. Three linked single nucleotide polymorphisms (SNP) in HAS1 intron3 are significantly associated with B-cell malignancies (range p = 0.007 to p = 10−5), but not MGUS or breast cancer, and predict risk in a 34 member Icelandic family (p = 0.005, Odds Ratio = 5.8 (OR)), a relatively homogeneous cohort. In contrast, exon3 SNPs were not significantly different among the study groups. Pooled analyses showed a strong association between the linked HAS1 intron3 SNPs and B-cell malignancies (OR = 1.78), but not for sporadic MGUS or for breast cancer (OR<1.0). The minor allele genotypes of HAS1 SNPs are significantly more frequent in MM, WM, CLL and in affected members of a monoclonal gammopathy-prone family than they are in breast cancer, sporadic MGUS or healthy donors. These inherited changes may increase the risk for systemic B-cell malignancies but not for solid tumors. PMID:24950197

  7. Existing General Population Models Inaccurately Predict Lung Cancer Risk in Patients Referred for Surgical Evaluation

    PubMed Central

    Isbell, James M.; Deppen, Stephen; Putnam, Joe B.; Nesbitt, Jonathan C.; Lambright, Eric S.; Dawes, Aaron; Massion, Pierre P.; Speroff, Theodore; Jones, David R.; Grogan, Eric L.

    2013-01-01

    Background atients undergoing resections for suspicious pulmonary lesions have a 9-55% benign rate. Validated prediction models exist to estimate the probability of malignancy in a general population and current practice guidelines recommend their use. We evaluated these models in a surgical population to determine the accuracy of existing models to predict benign or malignant disease. Methods We conducted a retrospective review of our thoracic surgery quality improvement database (2005-2008) to identify patients who underwent resection of a pulmonary lesion. Patients were stratified into subgroups based on age, smoking status and fluorodeoxyglucose positron emission tomography (PET) results. The probability of malignancy was calculated for each patient using the Mayo and SPN prediction models. Receiver operating characteristic (ROC) and calibration curves were used to measure model performance. Results 89 patients met selection criteria; 73% were malignant. Patients with preoperative PET scans were divided into 4 subgroups based on age, smoking history and nodule PET avidity. Older smokers with PET-avid lesions had a 90% malignancy rate. Patients with PET- non-avid lesions, or PET-avid lesions with age<50 years or never smokers of any age had a 62% malignancy rate. The area under the ROC curve for the Mayo and SPN models was 0.79 and 0.80, respectively; however, the models were poorly calibrated (p<0.001). Conclusions Despite improvements in diagnostic and imaging techniques, current general population models do not accurately predict lung cancer among patients ref erred for surgical evaluation. Prediction models with greater accuracy are needed to identify patients with benign disease to reduce non-therapeutic resections. PMID:21172518

  8. Existing general population models inaccurately predict lung cancer risk in patients referred for surgical evaluation.

    PubMed

    Isbell, James M; Deppen, Stephen; Putnam, Joe B; Nesbitt, Jonathan C; Lambright, Eric S; Dawes, Aaron; Massion, Pierre P; Speroff, Theodore; Jones, David R; Grogan, Eric L

    2011-01-01

    Patients undergoing resections for suspicious pulmonary lesions have a 9% to 55% benign rate. Validated prediction models exist to estimate the probability of malignancy in a general population and current practice guidelines recommend their use. We evaluated these models in a surgical population to determine the accuracy of existing models to predict benign or malignant disease. We conducted a retrospective review of our thoracic surgery quality improvement database (2005 to 2008) to identify patients who underwent resection of a pulmonary lesion. Patients were stratified into subgroups based on age, smoking status, and fluorodeoxyglucose positron emission tomography (PET) results. The probability of malignancy was calculated for each patient using the Mayo and solitary pulmonary nodules prediction models. Receiver operating characteristic and calibration curves were used to measure model performance. A total of 189 patients met selection criteria; 73% were malignant. Patients with preoperative PET scans were divided into four subgroups based on age, smoking history, and nodule PET avidity. Older smokers with PET-avid lesions had a 90% malignancy rate. Patients with PET-nonavid lesions, PET-avid lesions with age less than 50 years, or never smokers of any age had a 62% malignancy rate. The area under the receiver operating characteristic curve for the Mayo and solitary pulmonary nodules models was 0.79 and 0.80, respectively; however, the models were poorly calibrated (p<0.001). Despite improvements in diagnostic and imaging techniques, current general population models do not accurately predict lung cancer among patients referred for surgical evaluation. Prediction models with greater accuracy are needed to identify patients with benign disease to reduce nontherapeutic resections. Copyright © 2011 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

  9. Predicting biochemical recurrence in patients with high-risk prostate cancer using the apparent diffusion coefficient of magnetic resonance imaging

    PubMed Central

    Yoon, Min Young; Park, Juhyun; Cho, Jeong Yeon; Jeong, Chang Wook; Ku, Ja Hyeon; Kim, Hyeon Hoe

    2017-01-01

    Purpose We aimed to investigate whether the apparent diffusion coefficient (ADC) value in diffusion-weighted magnetic resonance imaging predicts the prognoses of patients with high-risk prostate cancer. Materials and Methods A total of 157 patients with high-risk prostate cancer (based on D'Amico's criteria) were included in the analysis. Patients underwent preoperative 3.0 Tesla magnetic resonance imaging within 2 months before radical prostatectomy. Those who received neoadjuvant hormone therapy (33 persons) or radiation therapy (18 persons) were excluded. The ADC of the tumor calculated from 2 b-values (0 and 1,000 s/mm2) was measured. Areas under receiver operating characteristics curves were calculated to maximize the accuracy of the ADC value. Based on the obtained cutoff value, the patients were stratified into 2 groups: Group A consisted of patients with ADC values <746×10−6 mm2/s and group B comprised those with ADC values ≥746×10−6 mm2/s. Results Group A showed higher rate of lymph positive and biochemical recurrence (BCR) rates than group B. Kaplan-Meier analysis showed that the BCR-free survival rate of group A was much lower than that of group B (p<0.001). On Cox proportional regression analyses, ADC group A (hazard ratio [HR], 3.238, p=0.002) and pathologic lymph node positive (HR, 2.242; p=0.009) were independent predictors of BCR. Conclusions In patients with high-risk prostate cancer, ADC value is significantly associated with BCR-free survival. Therefore, the ADC value is a useful tool for predicting the prognoses of these high-risk patients. PMID:28097263

  10. Explaining variance in the cumulus mammographic measures that predict breast cancer risk: a twins and sisters study.

    PubMed

    Nguyen, Tuong L; Schmidt, Daniel F; Makalic, Enes; Dite, Gillian S; Stone, Jennifer; Apicella, Carmel; Bui, Minh; Macinnis, Robert J; Odefrey, Fabrice; Cawson, Jennifer N; Treloar, Susan A; Southey, Melissa C; Giles, Graham G; Hopper, John L

    2013-12-01

    Mammographic density, the area of the mammographic image that appears white or bright, predicts breast cancer risk. We estimated the proportions of variance explained by questionnaire-measured breast cancer risk factors and by unmeasured residual familial factors. For 544 MZ and 339 DZ twin pairs and 1,558 non-twin sisters from 1,564 families, mammographic density was measured using the computer-assisted method Cumulus. We estimated associations using multilevel mixed-effects linear regression and studied familial aspects using a multivariate normal model. The proportions of variance explained by age, body mass index (BMI), and other risk factors, respectively, were 4%, 1%, and 4% for dense area; 7%, 14%, and 4% for percent dense area; and 7%, 40%, and 1% for nondense area. Associations with dense area and percent dense area were in opposite directions than for nondense area. After adjusting for measured factors, the correlations of dense area with percent dense area and nondense area were 0.84 and -0.46, respectively. The MZ, DZ, and sister pair correlations were 0.59, 0.28, and 0.29 for dense area; 0.57, 0.30, and 0.28 for percent dense area; and 0.56, 0.27, and 0.28 for nondense area (SE = 0.02, 0.04, and 0.03, respectively). Under the classic twin model, 50% to 60% (SE = 5%) of the variance of mammographic density measures that predict breast cancer risk are due to undiscovered genetic factors, and the remainder to as yet unknown individual-specific, nongenetic factors. Much remains to be learnt about the genetic and environmental determinants of mammographic density. ©2013 AACR.

  11. Predicting risk of breast cancer recurrence using gene-expression profiling.

    PubMed

    Ignatiadis, Michail; Desmedt, Christine

    2007-01-01

    The molecular profiling of breast tumors using the powerful microarray technology has uncovered the molecular heterogeneity of breast tumors and has offered novel insight into breast tumorigenesis. The estrogen receptor (ER) has been shown to be the most important discriminator dichotomizing breast cancer into two main subsets. At the same time, proliferation, as captured by the recently developed Genomic Grade Index (GGI) has been found to be the most important prognostic factor in breast cancer, far beyond ER status. Interestingly, this index encompasses a significant portion of the predictive power of many published prognostic signatures. The challenge now is to integrate all the prognostic gene signatures available to date towards a comprehensive genomic fingerprint of the primary tumor. In the future, we should be able to offer individualized treatment to our patients based on a clinical decision-making algorithm that takes into account the clinicopathological parameters, the genomic profile of the primary tumor, the presence of micrometastatic cells and pharmacogenetic data for drug response.

  12. Prediction of near-term breast cancer risk using local region-based bilateral asymmetry features in mammography

    NASA Astrophysics Data System (ADS)

    Li, Yane; Fan, Ming; Li, Lihua; Zheng, Bin

    2017-03-01

    This study proposed a near-term breast cancer risk assessment model based on local region bilateral asymmetry features in Mammography. The database includes 566 cases who underwent at least two sequential FFDM examinations. The `prior' examination in the two series all interpreted as negative (not recalled). In the "current" examination, 283 women were diagnosed cancers and 283 remained negative. Age of cancers and negative cases completely matched. These cases were divided into three subgroups according to age: 152 cases among the 37-49 age-bracket, 220 cases in the age-bracket 50- 60, and 194 cases with the 61-86 age-bracket. For each image, two local regions including strip-based regions and difference-of-Gaussian basic element regions were segmented. After that, structural variation features among pixel values and structural similarity features were computed for strip regions. Meanwhile, positional features were extracted for basic element regions. The absolute subtraction value was computed between each feature of the left and right local-regions. Next, a multi-layer perception classifier was implemented to assess performance of features for prediction. Features were then selected according stepwise regression analysis. The AUC achieved 0.72, 0.75 and 0.71 for these 3 age-based subgroups, respectively. The maximum adjustable odds ratios were 12.4, 20.56 and 4.91 for these three groups, respectively. This study demonstrate that the local region-based bilateral asymmetry features extracted from CC-view mammography could provide useful information to predict near-term breast cancer risk.

  13. Risk Stratification For Axillary Lymph Node Metastases in Breast Cancer Patients: What Clinicopathological and Radiological Factors of Primary Breast Cancer Can Predict Preoperatively Axillary Lymph Node Metastases?

    PubMed

    Yun, Seong Jong; Sohn, Yu-Mee; Seo, Mirinae

    2017-03-01

    This study was to investigate clinicopathological features including immunohistochemical subtype and radiological factors of primary breast cancer to predict axillary lymph node metastasis (ALNM) and preoperative risk stratification.From June 2004 to May 2014, 369 breast cancer patients (mean age, 54.7 years; range, 29-82 years) who underwent surgical axillary node sampling were included. Two radiologists retrospectively reviewed clinicopathological features, initial mammography, and initial breast ultrasonography (US). Univariate and multivariate logistic regression analyses were used to evaluate associations between ALNM and variables. Odds ratio with 95% confidence interval and risk of ALNM were calculated.Among 369 patients, 117 (31.7%) had ALNM and 252 (68.3%) had no ALNM revealed surgically. On multivariate analysis, four factors showed positive association with ALNM: the presence of symptoms (P < 0.001), triple-negative breast cancer subtype (P = 0.001), mass size on US (>10 mm, P < 0.001), and Breast Imaging Reporting and Data System category on US (≥4c, P < 0.001). The significant risk of ALNM was particularly seen in patients with two or more factors (2, P = 0.013; 3, P < 0.001; 4, P < 0.001).The estimated risks of ALNM increased in patients with two, three, and four factors with odds ratios of 5.5, 14.3, and 60.0, respectively.The presence of symptoms, triple-negative breast cancer subtype, larger size mass on US (>10 mm), and higher Breast Imaging Reporting and Data System category on US (≥4c) were positively associated with ALNM. Radiologically, US findings are significant factors that can affect the decision making process regarding ALNM. Based on risk stratification, the possibility of ALNM can be better predicted if 2 or more associated factors existed preoperatively.

  14. Selectively predictive calcium supplementation using NCCN risk stratification system after thyroidectomy with differentiated thyroid cancer

    PubMed Central

    Sun, Ronghao; Zhang, Jie; Zhang, Fenghua; Fan, Jinchuan; Yuan, Ying; Li, Chao

    2015-01-01

    Background: Hypocalcemia is a common complication following thyroidectomy. To explore reasonable and simple methods for predicting postoperative hypocalcemia and identify the optimal strategies for selective calcium supplement are meaningful for surgeon. Methods: Based on the NCCN risk stratification system, patients were divided into 4 groups (A-D): low-risk group A, who only underwent limited thyroidectomy (LT) and high-risk groups B, C and D, who had received total thyroidectomy (TT) and selective central and/or lateral neck dissection (SND). After surgery, group C patients were orally given calcium gluconate and group D patients were intravenously given calcium 2 g/day for 7 days, while group B patients did not receive any calcium supplement. Serum calcium and parathyroid hormone (PTH) levels were collected before and after surgery. The incidence of asymptomatic and symptomatic hypocalcemia in each group was recorded. Results: A total of 132 patients with differentiated thyroid carcinoma (DTC) were included who received surgical treatment. No a significant change was observed in serum calcium and PTH levels in group A, while significant decreases in serum calcium and PTH levels were seen in group B (P < 0.05). Intravenous calcium supplement in group D resulted in a more rapid recovery in serum calcium levels (P < 0.05). The incidences of symptomatic hypocalcemia and asymptomatic hypocalcemia were significantly lower in group A and group D respectively compared to the other groups (All P values < 0.05). In group B, a highest asymptomatic and symptomatic hypocalcemia incidence was detected. Conclusion: Selective calcium supplementation for DTC based on NCCN risk stratification system could be recommended for the high-risk patients. PMID:26885165

  15. Selectively predictive calcium supplementation using NCCN risk stratification system after thyroidectomy with differentiated thyroid cancer.

    PubMed

    Sun, Ronghao; Zhang, Jie; Zhang, Fenghua; Fan, Jinchuan; Yuan, Ying; Li, Chao

    2015-01-01

    Hypocalcemia is a common complication following thyroidectomy. To explore reasonable and simple methods for predicting postoperative hypocalcemia and identify the optimal strategies for selective calcium supplement are meaningful for surgeon. Based on the NCCN risk stratification system, patients were divided into 4 groups (A-D): low-risk group A, who only underwent limited thyroidectomy (LT) and high-risk groups B, C and D, who had received total thyroidectomy (TT) and selective central and/or lateral neck dissection (SND). After surgery, group C patients were orally given calcium gluconate and group D patients were intravenously given calcium 2 g/day for 7 days, while group B patients did not receive any calcium supplement. Serum calcium and parathyroid hormone (PTH) levels were collected before and after surgery. The incidence of asymptomatic and symptomatic hypocalcemia in each group was recorded. A total of 132 patients with differentiated thyroid carcinoma (DTC) were included who received surgical treatment. No a significant change was observed in serum calcium and PTH levels in group A, while significant decreases in serum calcium and PTH levels were seen in group B (P < 0.05). Intravenous calcium supplement in group D resulted in a more rapid recovery in serum calcium levels (P < 0.05). The incidences of symptomatic hypocalcemia and asymptomatic hypocalcemia were significantly lower in group A and group D respectively compared to the other groups (All P values < 0.05). In group B, a highest asymptomatic and symptomatic hypocalcemia incidence was detected. Selective calcium supplementation for DTC based on NCCN risk stratification system could be recommended for the high-risk patients.

  16. Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci.

    PubMed

    Clyde, Merlise A; Palmieri Weber, Rachel; Iversen, Edwin S; Poole, Elizabeth M; Doherty, Jennifer A; Goodman, Marc T; Ness, Roberta B; Risch, Harvey A; Rossing, Mary Anne; Terry, Kathryn L; Wentzensen, Nicolas; Whittemore, Alice S; Anton-Culver, Hoda; Bandera, Elisa V; Berchuck, Andrew; Carney, Michael E; Cramer, Daniel W; Cunningham, Julie M; Cushing-Haugen, Kara L; Edwards, Robert P; Fridley, Brooke L; Goode, Ellen L; Lurie, Galina; McGuire, Valerie; Modugno, Francesmary; Moysich, Kirsten B; Olson, Sara H; Pearce, Celeste Leigh; Pike, Malcolm C; Rothstein, Joseph H; Sellers, Thomas A; Sieh, Weiva; Stram, Daniel; Thompson, Pamela J; Vierkant, Robert A; Wicklund, Kristine G; Wu, Anna H; Ziogas, Argyrios; Tworoger, Shelley S; Schildkraut, Joellen M

    2016-10-15

    Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted. © The Author 2016. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  17. Validation of a predictive model that identifies patients at high risk of developing febrile neutropaenia following chemotherapy for breast cancer.

    PubMed

    Jenkins, P; Scaife, J; Freeman, S

    2012-07-01

    We have previously developed a predictive model that identifies patients at increased risk of febrile neutropaenia (FN) following chemotherapy, based on pretreatment haematological indices. This study was designed to validate our earlier findings in a separate cohort of patients undergoing more myelosuppressive chemotherapy supported by growth factors. We conducted a retrospective analysis of 263 patients who had been treated with adjuvant docetaxel, adriamycin and cyclophosphamide (TAC) chemotherapy for breast cancer. All patients received prophylactic pegfilgrastim and the majority also received prophylactic antibiotics. Thirty-one patients (12%) developed FN. Using our previous model, patients in the highest risk group (pretreatment absolute neutrophil count≤3.1 10(9)/l and absolute lymphocyte count≤1.5 10(9)/l) comprised 8% of the total population and had a 33% risk of developing FN. Compared with the rest of the cohort, this group had a 3.4-fold increased risk of developing FN (P=0.001) and a 5.2-fold increased risk of cycle 1 FN (P<0.001). A simple model based on pretreatment differential white blood cell count can be applied to pegfilgrastim-supported patients to identify those who are at higher risk of FN.

  18. Predicting the Risk of New Cerebral Lesions After Stereotactic Radiosurgery (SRS) for Brain Metastases from Breast Cancer.

    PubMed

    Dziggel, Liesa; Dahlke, Markus; Janssen, Stefan; Hornung, Dagmar; Blanck, Oliver; Khoa, Mai Trong; Schild, Steven E; Rades, Dirk

    2015-12-01

    To generate a tool that estimates the probability of developing new cerebral metastases after stereotactic radiosurgery (SRS) in breast cancer patients. SRS dose plus seven characteristics (age, performance score, number of cerebral metastases, maximum diameter of all metastases, location of metastases, extra-cerebral spread and time from breast cancer diagnosis until SRS) were analyzed regarding their ability to predict the probability of new cerebral metastases development following SRS. For those characteristics deemed significant, points of 0 (higher risk of new lesions) or 1 (lower risk) were given. Scores were generated by adding the points of significant characteristics. Performance score (p=0.013) and maximum diameter of all metastases (p=0.022) were associated with development of subsequent brain metastases. Two groups were created, 0-1 and 2 points. Freedom from new cerebral metastases rates were 27% and 92%, respectively, at 15 months (p=0.003). This tool helps select breast cancer with few cerebral metastases receiving SRS who may benefit from additional whole-brain irradiation. Copyright© 2015 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

  19. The value of endorectal MR imaging to predict positive biopsies in clinically intermediate-risk prostate cancer patients.

    PubMed

    Vilanova, J C; Comet, J; Capdevila, A; Barceló, J; Dolz, J L; Huguet, M; Barceló, C; Aldomà, J; Delgado, E

    2001-01-01

    The aim of this study was to assess the effectiveness of endorectal MR imaging in predicting the positive biopsy results in patients with clinically intermediate risk for prostate cancer. We performed a prospective endorectal MR imaging study with 81 patients at intermediate risk to detect prostate cancer between January 1997 and December 1998. Intermediate risk was defined as: prostatic specific antigen (PSA) levels between 4 and 10 ng/ml or PSA levels in the range of 10-20 ng/ml but negative digital rectal examination (DRE) or PSA levels progressively higher (0.75 ng/ml year(-1)). A transrectal sextant biopsy was performed after the endorectal MR exam, and also of the area of suspicion detected by MR imaging. The accuracies were measured, both singly for MR imaging and combined for PSA level and DRE, by calculating the area index of the receiver operating characteristics (ROC) curve. Cancer was detected in 23 patients (28%). Overall sensitivity and specificity of endorectal MRI was 70 and 76%, respectively. Accuracy was 71% estimated from the area under the ROC curve for the total patient group and 84% for the group of patients with PSA level between 10-20 ng/ml. Positive biopsy rate (PBR) was 63% for the group with PSA 10-20 ng/ml and a positive MR imaging, and 15% with a negative MR exam. The PBR was 43% for the group with PSA 4-10 ng/ml and a positive MR study, and 13% with a negative MR imaging examination. We would have avoided 63% of negative biopsies, while missing 30% of cancers for the total group of patients. Endorectal MR imaging was not a sufficient predictor of positive biopsies for patients clinically at intermediate risk for prostate cancer. Although we should not avoid performing systematic biopsies in patients with endorectal MR imaging negative results, as it will miss a significant number of cancers, selected patients with a PSA levels between 10-20 ng/ml or clinical-biopsy disagreement might benefit from endorectal MR imaging.

  20. Applicability of the EORTC risk tables to predict outcomes in non-muscle-invasive bladder cancer in Turkish patients

    PubMed Central

    Kılınç, Muhammet Fatih; Bayar, Göksel; Dalkılıç, Ayhan; Sönmez, Nurettin Cem; Arısan, Serdar; Güney, Soner

    2017-01-01

    Objective To evaluate the consistency of the results of patients who were treated for non-muscle-invasive bladder cancer (NMIBC) in our clinic with the European Organization for Research and Treatment of Cancer (EORTC) risk table. Material and methods Data were retrospectively analyzed from 452 patients who had undergone transurethral resection of bladder tumor (TUR-BT) between the years 2002, and 2010 for primary or recurrent NMIBC. Our study had a retrospective design but based on prospective cohort study. Patients were staged according to the 2002 Tumor Node Metastasis (TNM) classification and the 1973 World Health Organization grading system. Recurrence was defined as non-muscle-invasive or muscle-invasive and progression as muscle-invasive tumor determined based on following cystoscopy and TUR-BT results, and confirmed by histopathologic analysis. Patients in the current study were classified into four groups according to the EORTC risk tables. Time to first recurrence and progression was determined for each risk group. Results Of the 452 patients, 348 were enrolled in this study. The overall mean follow-up period was 55.25 months of all patients. Of 348 patients, 130 (37.4%) and 258 patients (74.1%) had recurrence after treatment at the 1 and 5 year follow-up period, respectively. While 35 (10.1%) and 99 patients (28.4%) progressed to muscle-invasive cancer at the 1 and 5 year follow-up period, respectively. In the multivariate analysis, grade, number, size of the tumor size, and concomitant carcinoma in situ were found to be statistically significant for disease progression and recurrence. Conclusion When EORTC risk tables were comparatively evaluated in our patient population, we can say that EORTC tables predict nearly accurately the clinical course of patients with NMIBC. PMID:28270951

  1. Radiation Dose Predicts for Biochemical Control in Intermediate-Risk Prostate Cancer Patients Treated With Low-Dose-Rate Brachytherapy

    SciTech Connect

    Ho, Alice Y.; Burri, Ryan J.; Cesaretti, Jamie A.; Stone, Nelson N.; Stock, Richard G.

    2009-09-01

    Purpose: To evaluate the influence of patient- and treatment-related factors on freedom from biochemical failure (FFbF) in patients with intermediate-risk prostate cancer. Methods and Materials: From a prospectively collected database of 2250 men treated at Mount Sinai Hospital from 1990 to 2004 with low-dose-rate brachytherapy for prostate cancer, 558 men with either one or more intermediate-risk features (prostate-specific antigen [PSA] level 10-20 ng/mL, Gleason score 7, or Stage T2b) were identified who had a minimum follow-up of 24 months and postimplant CT-based dosimetric analysis. Biologically effective dose (BED) values were calculated to compare doses from different isotopes and treatment regimens. Patients were treated with brachytherapy with or without hormone therapy and/or external-beam radiotherapy. Patient- and treatment-related factors were analyzed with respect to FFbF. The median follow-up was 60 months (range, 24-167 months). Biochemical failure was defined according to the Phoenix definition. Univariate analyses were used to determine whether any variable was predictive of FFbF. A two-sided p value of <0.05 was considered significant. Results: Overall, the actuarial FFbF at 10 years was 86%. Dose (BED <150 Gy{sub 2} vs. {>=}150 Gy{sub 2}) was the only significant predictor of FFbF (p < 0.001). None of the other variables (PSA, external-beam radiotherapy, Gleason score, treatment type, hormones, stage, and number of risk factors) was found to be a statistically significant predictor of 10-year FFbF. Conclusions: Radiation dose is an important predictor of FFbF in intermediate-risk prostate cancer. Treatment should continue to be individualized according to presenting disease characteristics until results from Radiation Therapy Oncology Group trial 0232 become available.

  2. Applicability of the EORTC risk tables to predict outcomes in non-muscle-invasive bladder cancer in Turkish patients.

    PubMed

    Kılınç, Muhammet Fatih; Bayar, Göksel; Dalkılıç, Ayhan; Sönmez, Nurettin Cem; Arısan, Serdar; Güney, Soner

    2017-03-01

    To evaluate the consistency of the results of patients who were treated for non-muscle-invasive bladder cancer (NMIBC) in our clinic with the European Organization for Research and Treatment of Cancer (EORTC) risk table. Data were retrospectively analyzed from 452 patients who had undergone transurethral resection of bladder tumor (TUR-BT) between the years 2002, and 2010 for primary or recurrent NMIBC. Our study had a retrospective design but based on prospective cohort study. Patients were staged according to the 2002 Tumor Node Metastasis (TNM) classification and the 1973 World Health Organization grading system. Recurrence was defined as non-muscle-invasive or muscle-invasive and progression as muscle-invasive tumor determined based on following cystoscopy and TUR-BT results, and confirmed by histopathologic analysis. Patients in the current study were classified into four groups according to the EORTC risk tables. Time to first recurrence and progression was determined for each risk group. Of the 452 patients, 348 were enrolled in this study. The overall mean follow-up period was 55.25 months of all patients. Of 348 patients, 130 (37.4%) and 258 patients (74.1%) had recurrence after treatment at the 1 and 5 year follow-up period, respectively. While 35 (10.1%) and 99 patients (28.4%) progressed to muscle-invasive cancer at the 1 and 5 year follow-up period, respectively. In the multivariate analysis, grade, number, size of the tumor size, and concomitant carcinoma in situ were found to be statistically significant for disease progression and recurrence. When EORTC risk tables were comparatively evaluated in our patient population, we can say that EORTC tables predict nearly accurately the clinical course of patients with NMIBC.

  3. FAM172A expression in circulating tumor cells for prediction of high-risk subgroups of colorectal cancer

    PubMed Central

    Xu, Chang; Zhang, Chunhong; Wang, Haowen; Yang, Han; Li, Gang; Fei, Zhenghua; Li, Wenfeng

    2017-01-01

    Objectives Previous studies used enumerated circulating tumor cells (CTCs) to predict prognosis and therapeutic effect in several types of cancers. However, increasing evidence showed that only enumerated CTCs were not enough to reflect the heterogeneity of tumors. Therefore, we classified different metastasis potentials of CTCs from colorectal cancer (CRC) patients to improve the accuracy of prognosis by CTCs. Methods Blood samples were collected from 45 primary CRC patients. CTCs were enriched by blood filtration, and the RNA in situ hybridization method was used to identify and discriminate subgroups of CTCs. Later, FAM172A expression in individual CTCs was measured. Results Three CTC subgroups (epithelial/biophenotypic/mesenchymal CTCs) were identified using epithelial–mesenchymal transition markers. In our research, mesenchymal CTCs significantly increased along with tumor progression, including developing distant metastasis and vascular invasion. Furthermore, FAM172A expression rate in mesenchymal CTCs was significantly higher than that in epithelial CTCs, which suggested that FAM172A may correlate with tumor malignancy. This hypothesis was further verified by FAM172A expression in mesenchymal CTCs strictly related to tumor aggressiveness factors. Finally, we revealed that mesenchymal CTCs and FAM172A expression may predict high-risk subgroups in stage II CRC. Conclusion Our research proved that CTCs could serve as feasible surrogate samples to detect gene expression as a predictive biomarker for tumor evaluation. PMID:28408845

  4. Computer-aided detection of lung cancer: combining pulmonary nodule detection systems with a tumor risk prediction model

    NASA Astrophysics Data System (ADS)

    Setio, Arnaud A. A.; Jacobs, Colin; Ciompi, Francesco; van Riel, Sarah J.; Winkler Wille, Mathilde M.; Dirksen, Asger; van Rikxoort, Eva M.; van Ginneken, Bram

    2015-03-01

    Computer-Aided Detection (CAD) has been shown to be a promising tool for automatic detection of pulmonary nodules from computed tomography (CT) images. However, the vast majority of detected nodules are benign and do not require any treatment. For effective implementation of lung cancer screening programs, accurate identification of malignant nodules is the key. We investigate strategies to improve the performance of a CAD system in detecting nodules with a high probability of being cancers. Two strategies were proposed: (1) combining CAD detections with a recently published lung cancer risk prediction model and (2) the combination of multiple CAD systems. First, CAD systems were used to detect the nodules. Each CAD system produces markers with a certain degree of suspicion. Next, the malignancy probability was automatically computed for each marker, given nodule characteristics measured by the CAD system. Last, CAD degree of suspicion and malignancy probability were combined using the product rule. We evaluated the method using 62 nodules which were proven to be malignant cancers, from 180 scans of the Danish Lung Cancer Screening Trial. The malignant nodules were considered as positive samples, while all other findings were considered negative. Using a product rule, the best proposed system achieved an improvement in sensitivity, compared to the best individual CAD system, from 41.9% to 72.6% at 2 false positives (FPs)/scan and from 56.5% to 88.7% at 8 FPs/scan. Our experiment shows that combining a nodule malignancy probability with multiple CAD systems can increase the performance of computerized detection of lung cancer.

  5. Predicting cancer outcome

    SciTech Connect

    Gardner, S N; Fernandes, M

    2005-03-24

    We read with interest the paper by Michiels et al on the prediction of cancer with microarrays and the commentary by Ioannidis listing the potential as well as the limitations of this approach (February 5, p 488 and 454). Cancer is a disease characterized by complex, heterogeneous mechanisms and studies to define factors that can direct new drug discovery and use should be encouraged. However, this is easier said than done. Casti teaches that a better understanding does not necessarily extrapolate to better prediction, and that useful prediction is possible without complete understanding (1). To attempt both, explanation and prediction, in a single nonmathematical construct, is a tall order (Figure 1).

  6. Incorporation of a genetic factor into an epidemiologic model for prediction of individual risk of lung cancer: the Liverpool Lung Project.

    PubMed

    Raji, Olaide Y; Agbaje, Olorunsola F; Duffy, Stephen W; Cassidy, Adrian; Field, John K

    2010-05-01

    The Liverpool Lung Project (LLP) has previously developed a risk model for prediction of 5-year absolute risk of lung cancer based on five epidemiologic risk factors. SEZ6L, a Met430IIe polymorphic variant found on 22q12.2 region, has been previously linked with an increased risk of lung cancer in a case-control population. In this article, we quantify the improvement in risk prediction with addition of SEZ6L to the LLP risk model. Data from 388 LLP subjects genotyped for SEZ6L single-nucleotide polymorphism (SNP) were combined with epidemiologic risk factors. Multivariable conditional logistic regression was used to predict 5-year absolute risk of lung cancer with and without this SNP. The improvement in the model associated with the SEZ6L SNP was assessed through pairwise comparison of the area under the receiver operating characteristic curve and the net reclassification improvements (NRI). The extended model showed better calibration compared with the baseline model. There was a statistically significant modest increase in the area under the receiver operating characteristic curve when SEZ6L was added into the baseline model. The NRI also revealed a statistically significant improvement of around 12% for the extended model; this improvement was better for subjects classified into the two intermediate-risk categories by the baseline model (NRI, 27%). Our results suggest that the addition of SEZ6L improved the performance of the LLP risk model, particularly for subjects whose initial absolute risks were unable to discriminate into "low-risk" or "high-risk" group. This work shows an approach to incorporate genetic biomarkers in risk models for predicting an individual's lung cancer risk.

  7. Risk-scoring models for individualized prediction of overall survival in low-grade and high-grade endometrial cancer

    PubMed Central

    AlHilli, Mariam M.; Mariani, Andrea; Bakkum-Gamez, Jamie N.; Dowdy, Sean C.; Weaver, Amy L.; Peethambaram, Preema P.; Keeney, Gary L.; Cliby, William A.; Podratz, Karl C.

    2015-01-01

    Objective Overall survival (OS) in endometrial cancer (EC) is dependent on patient-, disease-, and treatment-specific risk factors. Comprehensive risk-scoring models were developed to estimate OS in low-grade and high-grade EC. Methods Patients undergoing primary surgery for EC from 1999 through 2008 were stratified histologically according to the International Federation of Gynecology and Obstetrics (FIGO) as either (i) low grade: grades 1 and 2 endometrioid EC or (ii) high grade: grade 3, including non-endometrioid EC. Associations between patient-, pathological-, and treatment-specific risk factors and OS starting on postoperative day 30 were assessed using multivariable Cox regression models. Factors independently associated with OS were used to construct nomograms and risk-scoring models. Results Eligible patients (N= 1281) included 925 low-grade and 356 high-grade patients; estimated 5-year OSs were 87.0% and 51.5%, respectively. Among patients alive at last follow-up, median follow-up was 5.0 (low grade) and 4.6 years (high grade), respectively. In low-grade patients, independent factors predictive of compromised OS included age, cardiovascular disease, pulmonary dysfunction, stage, tumor diameter, pelvic lymph node status, and grade 2 or higher 30-day postoperative complications. Among high-grade patients, age, American Society of Anesthesiologists score, stage, lymphovascular space invasion, adjuvant therapy, para-aortic nodal status, and cervical stromal invasion were independent predictors of compromised OS. The two risk-scoring models/nomograms had excellent calibration and discrimination (unbiased c-indices = 0.803 and 0.759). Conclusion Patients with low-grade and high-grade EC can be counseled regarding their predicted OS using the proposed risk-scoring models. This may facilitate institution of personalized treatment algorithms, surveillance strategies, and lifestyle interventions. PMID:24690476

  8. Curated MicroRNAs in Urine and Blood Fail to Validate as Predictive Biomarkers for High-Risk Prostate Cancer

    PubMed Central

    Sapre, Nikhil; Hong, Matthew K. H.; Macintyre, Geoff; Lewis, Heather; Kowalczyk, Adam; Costello, Anthony J.; Corcoran, Niall M.; Hovens, Christopher M.

    2014-01-01

    Purpose The purpose of this study was to determine if microRNA profiling of urine and plasma at radical prostatectomy can distinguish potentially lethal from indolent prostate cancer. Materials and Methods A panel of microRNAs was profiled in the plasma of 70 patients and the urine of 33 patients collected prior to radical prostatectomy. Expression of microRNAs was correlated to the clinical endpoints at a follow-up time of 3.9 years to identify microRNAs that may predict clinical response after radical prostatectomy. A machine learning approach was applied to test the predictive ability of all microRNAs profiled in urine, plasma, and a combination of both, and global performance assessed using the area under the receiver operator characteristic curve (AUC). Validation of urinary expression of miRNAs was performed on a further independent cohort of 36 patients. Results The best predictor in plasma using eight miRs yielded only moderate predictive performance (AUC = 0.62). The best predictor of high-risk disease was achieved using miR-16, miR-21 and miR-222 measured in urine (AUC = 0.75). This combination of three microRNAs in urine was a better predictor of high-risk disease than any individual microRNA. Using a different methodology we found that this set of miRNAs was unable to predict high-volume, high-grade disease. Conclusions Our initial findings suggested that plasma and urinary profiling of microRNAs at radical prostatectomy may allow prognostication of prostate cancer behaviour. However we found that the microRNA expression signature failed to validate in an independent cohort of patients using a different platform for PCR. This highlights the need for independent validation patient cohorts and suggests that urinary microRNA signatures at radical prostatectomy may not be a robust way to predict the course of clinical disease after definitive treatment for prostate cancer. PMID:24705338

  9. A Group of Genome-Based Biomarkers That Add to a Kattan Nomogram for Predicting Progression in Men with High-Risk Prostate Cancer

    PubMed Central

    Paris, Pamela L.; Weinberg, Vivian; Albo, Giancarlo; Roy, Ritu; Burke, Catherine; Simko, Jeffry; Carroll, Peter; Collins, Colin

    2010-01-01

    Purpose The three main treatment options for primary prostate cancer are surgery, radiation, and active surveillance. Surgical and radiation intervention for prostate cancer can be associated with significant morbidity. Therefore, accurate stratification predictive of outcome for prostate cancer patients is essential for appropriate treatment decisions. Nomograms that use clinical and pathologic variables are often used for risk prediction. Favorable outcomes exist even among men classified by nomograms as being at high risk of recurrence. Experimental Design Previously, we identified a set of DNA-based biomarkers termed Genomic Evaluators of Metastatic Prostate Cancer (GEMCaP) and have shown that they can predict risk of recurrence with 80% accuracy. Here, we examined the risk prediction ability of GEMCaP in a high-risk cohort and compared it to a Kattan nomogram. Results We determined that the GEMCaP genotype alone is comparable with the nomogram, and that for a subset of cases with negative lymph nodes improves upon it. Conclusion Thus, GEMCaP shows promise for predicting unfavorable outcomes for negative lymph node high-risk cases, where the nomogram falls short, and suggests that addition of GEMCaP to nomograms may be warranted. PMID:20028763

  10. Race, genetic West African ancestry, and prostate cancer prediction by prostate-specific antigen in prospectively screened high-risk men.

    PubMed

    Giri, Veda N; Egleston, Brian; Ruth, Karen; Uzzo, Robert G; Chen, David Y T; Buyyounouski, Mark; Raysor, Susan; Hooker, Stanley; Torres, Jada Benn; Ramike, Teniel; Mastalski, Kathleen; Kim, Taylor Y; Kittles, Rick

    2009-03-01

    "Race-specific" prostate-specific antigen (PSA) needs evaluation in men at high risk for prostate cancer for optimizing early detection. Baseline PSA and longitudinal prediction for prostate cancer were examined by self-reported race and genetic West African (WA) ancestry in the Prostate Cancer Risk Assessment Program, a prospective high-risk cohort. Eligibility criteria were age 35 to 69 years, family history of prostate cancer, African American race, or BRCA1/2 mutations. Biopsies were done at low PSA values (<4.0 ng/mL). WA ancestry was discerned by genotyping 100 ancestry informative markers. Cox proportional hazards models evaluated baseline PSA, self-reported race, and genetic WA ancestry. Cox models were used for 3-year predictions for prostate cancer. Six hundred forty-six men (63% African American) were analyzed. Individual WA ancestry estimates varied widely among self-reported African American men. Race-specific differences in baseline PSA were not found by self-reported race or genetic WA ancestry. Among men with > or =1 follow-up visit (405 total, 54% African American), 3-year prediction for prostate cancer with a PSA of 1.5 to 4.0 ng/mL was higher in African American men with age in the model (P = 0.025) compared with European American men. Hazard ratios of PSA for prostate cancer were also higher by self-reported race (1.59 for African American versus 1.32 for European American, P = 0.04). There was a trend for increasing prediction for prostate cancer with increasing genetic WA ancestry. "Race-specific" PSA may need to be redefined as higher prediction for prostate cancer at any given PSA in African American men. Large-scale studies are needed to confirm if genetic WA ancestry explains these findings to make progress in personalizing prostate cancer early detection.

  11. Preoperative CA125 and fibrinogen in patients with endometrial cancer: a risk model for predicting lymphovascular space invasion

    PubMed Central

    2017-01-01

    Objective The aim of this study was to build a model to predict the risk of lymphovascular space invasion (LVSI) in women with endometrial cancer (EC). Methods From December 2010 to June 2013, 211 patients with EC undergoing surgery at Shanghai First Maternity and Infant Hospital were enrolled in this retrospective study. Those patients were divided into a positive LVSI group and a negative LVSI group. The clinical and pathological characteristics were compared between the two groups; logistic regression was used to explore risk factors associated with LVSI occurrence. The threshold values of significant factors were calculated to build a risk model and predict LVSI. Results There were 190 patients who were negative for LVSI and 21 patients were positive for LVSI out of 211 patients with EC. It was found that tumor grade, depth of myometrial invasion, number of pelvic lymph nodes, and International Federation of Gynecology and Obstetrics (FIGO) stage (p<0.05) were associated with LVSI occurrence. However, cervical involvement and age (p>0.05) were not associated with LVSI. Receiver operating characteristic (ROC) curves revealed that the threshold values of the following factors were correlated with positive LVSI: 28.1 U/mL of CA19-9, 21.2 U/mL of CA125, 2.58 mg/dL of fibrinogen (Fn), 1.84 U/mL of carcinoembryonic antigen (CEA) and (6.35×109)/L of white blood cell (WBC). Logistic regression analysis indicated that CA125 ≥21.2 (p=0.032) and Fn ≥2.58 mg/dL (p=0.014) were significantly associated with LVSI. Conclusion Positive LVSI could be predicted by CA125 ≥21.2 U/mL and Fn ≥2.58 mg/dL in women with EC. It could help gynecologists better adapt surgical staging and adjuvant therapies. PMID:27894164

  12. Added Value of Serum Hormone Measurements in Risk Prediction Models for Breast Cancer for Women Not Using Exogenous Hormones: Results from the EPIC Cohort.

    PubMed

    Hüsing, Anika; Fortner, Renée T; Kühn, Tilman; Overvad, Kim; Tjønneland, Anne; Olsen, Anja; Boutron-Ruault, Marie-Christine; Severi, Gianluca; Fournier, Agnes; Boeing, Heiner; Trichopoulou, Antonia; Benetou, Vassiliki; Orfanos, Philippos; Masala, Giovanna; Pala, Valeria; Tumino, Rosario; Fasanelli, Francesca; Panico, Salvatore; Bueno de Mesquita, H Bas; Peeters, Petra H; van Gills, Carla H; Quirós, J Ramón; Agudo, Antonio; Sánchez, Maria-Jose; Chirlaque, Maria-Dolores; Barricarte, Aurelio; Amiano, Pilar; Khaw, Kay-Tee; Travis, Ruth C; Dossus, Laure; Li, Kuanrong; Ferrari, Pietro; Merritt, Melissa A; Tzoulaki, Ioanna; Riboli, Elio; Kaaks, Rudolf

    2017-08-01

    Purpose: Circulating hormone concentrations are associated with breast cancer risk, with well-established associations for postmenopausal women. Biomarkers may represent minimally invasive measures to improve risk prediction models.Experimental Design: We evaluated improvements in discrimination gained by adding serum biomarker concentrations to risk estimates derived from risk prediction models developed by Gail and colleagues and Pfeiffer and colleagues using a nested case-control study within the EPIC cohort, including 1,217 breast cancer cases and 1,976 matched controls. Participants were pre- or postmenopausal at blood collection. Circulating sex steroids, prolactin, insulin-like growth factor (IGF) I, IGF-binding protein 3, and sex hormone-binding globulin (SHBG) were evaluated using backward elimination separately in women pre- and postmenopausal at blood collection. Improvement in discrimination was evaluated as the change in concordance statistic (C-statistic) from a modified Gail or Pfeiffer risk score alone versus models, including the biomarkers and risk score. Internal validation with bootstrapping (1,000-fold) was used to adjust for overfitting.Results: Among women postmenopausal at blood collection, estradiol, testosterone, and SHBG were selected into the prediction models. For breast cancer overall, model discrimination after including biomarkers was 5.3 percentage points higher than the modified Gail model alone, and 3.4 percentage points higher than the Pfeiffer model alone, after accounting for overfitting. Discrimination was more markedly improved for estrogen receptor-positive disease (percentage point change in C-statistic: 7.2, Gail; 4.8, Pfeiffer). We observed no improvement in discrimination among women premenopausal at blood collection.Conclusions: Integration of hormone measurements in clinical risk prediction models may represent a strategy to improve breast cancer risk stratification. Clin Cancer Res; 23(15); 4181-9. ©2017 AACR. ©2017

  13. Genetically Predicted Body Mass Index and Breast Cancer Risk: Mendelian Randomization Analyses of Data from 145,000 Women of European Descent.

    PubMed

    Guo, Yan; Warren Andersen, Shaneda; Shu, Xiao-Ou; Michailidou, Kyriaki; Bolla, Manjeet K; Wang, Qin; Garcia-Closas, Montserrat; Milne, Roger L; Schmidt, Marjanka K; Chang-Claude, Jenny; Dunning, Allison; Bojesen, Stig E; Ahsan, Habibul; Aittomäki, Kristiina; Andrulis, Irene L; Anton-Culver, Hoda; Arndt, Volker; Beckmann, Matthias W; Beeghly-Fadiel, Alicia; Benitez, Javier; Bogdanova, Natalia V; Bonanni, Bernardo; Børresen-Dale, Anne-Lise; Brand, Judith; Brauch, Hiltrud; Brenner, Hermann; Brüning, Thomas; Burwinkel, Barbara; Casey, Graham; Chenevix-Trench, Georgia; Couch, Fergus J; Cox, Angela; Cross, Simon S; Czene, Kamila; Devilee, Peter; Dörk, Thilo; Dumont, Martine; Fasching, Peter A; Figueroa, Jonine; Flesch-Janys, Dieter; Fletcher, Olivia; Flyger, Henrik; Fostira, Florentia; Gammon, Marilie; Giles, Graham G; Guénel, Pascal; Haiman, Christopher A; Hamann, Ute; Hooning, Maartje J; Hopper, John L; Jakubowska, Anna; Jasmine, Farzana; Jenkins, Mark; John, Esther M; Johnson, Nichola; Jones, Michael E; Kabisch, Maria; Kibriya, Muhammad; Knight, Julia A; Koppert, Linetta B; Kosma, Veli-Matti; Kristensen, Vessela; Le Marchand, Loic; Lee, Eunjung; Li, Jingmei; Lindblom, Annika; Luben, Robert; Lubinski, Jan; Malone, Kathi E; Mannermaa, Arto; Margolin, Sara; Marme, Frederik; McLean, Catriona; Meijers-Heijboer, Hanne; Meindl, Alfons; Neuhausen, Susan L; Nevanlinna, Heli; Neven, Patrick; Olson, Janet E; Perez, Jose I A; Perkins, Barbara; Peterlongo, Paolo; Phillips, Kelly-Anne; Pylkäs, Katri; Rudolph, Anja; Santella, Regina; Sawyer, Elinor J; Schmutzler, Rita K; Seynaeve, Caroline; Shah, Mitul; Shrubsole, Martha J; Southey, Melissa C; Swerdlow, Anthony J; Toland, Amanda E; Tomlinson, Ian; Torres, Diana; Truong, Thérèse; Ursin, Giske; Van Der Luijt, Rob B; Verhoef, Senno; Whittemore, Alice S; Winqvist, Robert; Zhao, Hui; Zhao, Shilin; Hall, Per; Simard, Jacques; Kraft, Peter; Pharoah, Paul; Hunter, David; Easton, Douglas F; Zheng, Wei

    2016-08-01

    Observational epidemiological studies have shown that high body mass index (BMI) is associated with a reduced risk of breast cancer in premenopausal women but an increased risk in postmenopausal women. It is unclear whether this association is mediated through shared genetic or environmental factors. We applied Mendelian randomization to evaluate the association between BMI and risk of breast cancer occurrence using data from two large breast cancer consortia. We created a weighted BMI genetic score comprising 84 BMI-associated genetic variants to predicted BMI. We evaluated genetically predicted BMI in association with breast cancer risk using individual-level data from the Breast Cancer Association Consortium (BCAC) (cases  =  46,325, controls  =  42,482). We further evaluated the association between genetically predicted BMI and breast cancer risk using summary statistics from 16,003 cases and 41,335 controls from the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) Project. Because most studies measured BMI after cancer diagnosis, we could not conduct a parallel analysis to adequately evaluate the association of measured BMI with breast cancer risk prospectively. In the BCAC data, genetically predicted BMI was found to be inversely associated with breast cancer risk (odds ratio [OR]  =  0.65 per 5 kg/m2 increase, 95% confidence interval [CI]: 0.56-0.75, p = 3.32 × 10-10). The associations were similar for both premenopausal (OR   =   0.44, 95% CI:0.31-0.62, p  =  9.91 × 10-8) and postmenopausal breast cancer (OR  =  0.57, 95% CI: 0.46-0.71, p  =  1.88 × 10-8). This association was replicated in the data from the DRIVE consortium (OR  =  0.72, 95% CI: 0.60-0.84, p   =   1.64 × 10-7). Single marker analyses identified 17 of the 84 BMI-associated single nucleotide polymorphisms (SNPs) in association with breast cancer risk at p < 0.05; for 16 of them, the allele

  14. Genetically Predicted Body Mass Index and Breast Cancer Risk: Mendelian Randomization Analyses of Data from 145,000 Women of European Descent

    PubMed Central

    Guo, Yan; Warren Andersen, Shaneda; Shu, Xiao-Ou; Michailidou, Kyriaki; Bolla, Manjeet K.; Wang, Qin; Garcia-Closas, Montserrat; Milne, Roger L.; Schmidt, Marjanka K.; Chang-Claude, Jenny; Dunning, Allison; Bojesen, Stig E.; Ahsan, Habibul; Aittomäki, Kristiina; Andrulis, Irene L.; Anton-Culver, Hoda; Beckmann, Matthias W.; Beeghly-Fadiel, Alicia; Benitez, Javier; Bogdanova, Natalia V.; Bonanni, Bernardo; Børresen-Dale, Anne-Lise; Brand, Judith; Brauch, Hiltrud; Brenner, Hermann; Brüning, Thomas; Burwinkel, Barbara; Casey, Graham; Chenevix-Trench, Georgia; Couch, Fergus J.; Cross, Simon S.; Czene, Kamila; Dörk, Thilo; Dumont, Martine; Fasching, Peter A.; Figueroa, Jonine; Flesch-Janys, Dieter; Fletcher, Olivia; Flyger, Henrik; Fostira, Florentia; Gammon, Marilie; Giles, Graham G.; Guénel, Pascal; Haiman, Christopher A.; Hamann, Ute; Hooning, Maartje J.; Hopper, John L.; Jakubowska, Anna; Jasmine, Farzana; Jenkins, Mark; John, Esther M.; Johnson, Nichola; Jones, Michael E.; Kabisch, Maria; Knight, Julia A.; Koppert, Linetta B.; Kosma, Veli-Matti; Kristensen, Vessela; Le Marchand, Loic; Lee, Eunjung; Li, Jingmei; Lindblom, Annika; Lubinski, Jan; Malone, Kathi E.; Mannermaa, Arto; Margolin, Sara; McLean, Catriona; Meindl, Alfons; Neuhausen, Susan L.; Nevanlinna, Heli; Neven, Patrick; Olson, Janet E.; Perez, Jose I. A.; Perkins, Barbara; Phillips, Kelly-Anne; Pylkäs, Katri; Rudolph, Anja; Santella, Regina; Sawyer, Elinor J.; Schmutzler, Rita K.; Seynaeve, Caroline; Shah, Mitul; Shrubsole, Martha J.; Southey, Melissa C.; Swerdlow, Anthony J.; Toland, Amanda E.; Tomlinson, Ian; Torres, Diana; Truong, Thérèse; Ursin, Giske; Van Der Luijt, Rob B.; Verhoef, Senno; Whittemore, Alice S.; Winqvist, Robert; Zhao, Hui; Zhao, Shilin; Hall, Per; Simard, Jacques; Kraft, Peter; Hunter, David; Easton, Douglas F.; Zheng, Wei

    2016-01-01

    Background Observational epidemiological studies have shown that high body mass index (BMI) is associated with a reduced risk of breast cancer in premenopausal women but an increased risk in postmenopausal women. It is unclear whether this association is mediated through shared genetic or environmental factors. Methods We applied Mendelian randomization to evaluate the association between BMI and risk of breast cancer occurrence using data from two large breast cancer consortia. We created a weighted BMI genetic score comprising 84 BMI-associated genetic variants to predicted BMI. We evaluated genetically predicted BMI in association with breast cancer risk using individual-level data from the Breast Cancer Association Consortium (BCAC) (cases  =  46,325, controls  =  42,482). We further evaluated the association between genetically predicted BMI and breast cancer risk using summary statistics from 16,003 cases and 41,335 controls from the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) Project. Because most studies measured BMI after cancer diagnosis, we could not conduct a parallel analysis to adequately evaluate the association of measured BMI with breast cancer risk prospectively. Results In the BCAC data, genetically predicted BMI was found to be inversely associated with breast cancer risk (odds ratio [OR]  =  0.65 per 5 kg/m2 increase, 95% confidence interval [CI]: 0.56–0.75, p = 3.32 × 10−10). The associations were similar for both premenopausal (OR   =   0.44, 95% CI:0.31–0.62, p  =  9.91 × 10−8) and postmenopausal breast cancer (OR  =  0.57, 95% CI: 0.46–0.71, p  =  1.88 × 10−8). This association was replicated in the data from the DRIVE consortium (OR  =  0.72, 95% CI: 0.60–0.84, p   =   1.64 × 10−7). Single marker analyses identified 17 of the 84 BMI-associated single nucleotide polymorphisms (SNPs) in association with breast cancer risk at p

  15. Do repeated assessments of performance status improve predictions for risk of death among patients with cancer? A population-based cohort study.

    PubMed

    Su, Jiandong; Barbera, Lisa; Sutradhar, Rinku

    2015-06-01

    Prior work has utilized longitudinal information on performance status to demonstrate its association with risk of death among cancer patients; however, no study has assessed whether such longitudinal information improves the predictions for risk of death. To examine whether the use of repeated performance status assessments improve predictions for risk of death compared to using only performance status assessment at the time of cancer diagnosis. This was a population-based longitudinal study of adult outpatients who had a cancer diagnosis and had at least one assessment of performance status. To account for each patient's changing performance status over time, we implemented a Cox model with a time-varying covariate for performance status. This model was compared to a Cox model using only a time-fixed (baseline) covariate for performance status. The regression coefficients of each model were derived based on a randomly selected 60% of patients, and then, the predictive ability of each model was assessed via concordance probabilities when applied to the remaining 40% of patients. Our study consisted of 15,487 cancer patients with over 53,000 performance status assessments. The utilization of repeated performance status assessments improved predictions for risk of death compared to using only the performance status assessment taken at diagnosis. When studying the hazard of death among patients with cancer, if available, researchers should incorporate changing information on performance status scores, instead of simply baseline information on performance status. © The Author(s) 2015.

  16. An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology

    NASA Astrophysics Data System (ADS)

    Qiu, Yuchen; Wang, Yunzhi; Yan, Shiju; Tan, Maxine; Cheng, Samuel; Liu, Hong; Zheng, Bin

    2016-03-01

    In order to establish a new personalized breast cancer screening paradigm, it is critically important to accurately predict the short-term risk of a woman having image-detectable cancer after a negative mammographic screening. In this study, we developed and tested a novel short-term risk assessment model based on deep learning method. During the experiment, a number of 270 "prior" negative screening cases was assembled. In the next sequential ("current") screening mammography, 135 cases were positive and 135 cases remained negative. These cases were randomly divided into a training set with 200 cases and a testing set with 70 cases. A deep learning based computer-aided diagnosis (CAD) scheme was then developed for the risk assessment, which consists of two modules: adaptive feature identification module and risk prediction module. The adaptive feature identification module is composed of three pairs of convolution-max-pooling layers, which contains 20, 10, and 5 feature maps respectively. The risk prediction module is implemented by a multiple layer perception (MLP) classifier, which produces a risk score to predict the likelihood of the woman developing short-term mammography-detectable cancer. The result shows that the new CAD-based risk model yielded a positive predictive value of 69.2% and a negative predictive value of 74.2%, with a total prediction accuracy of 71.4%. This study demonstrated that applying a new deep learning technology may have significant potential to develop a new short-term risk predicting scheme with improved performance in detecting early abnormal symptom from the negative mammograms.

  17. Avoiding Cancer Risk Information

    PubMed Central

    Emanuel, Amber S.; Kiviniemi, Marc T.; Howell, Jennifer L.; Hay, Jennifer L.; Waters, Erika A.; Orom, Heather; Shepperd, James A.

    2015-01-01

    RATIONALE Perceived risk for health problems such as cancer is a central construct in many models of health decision making and a target for behavior change interventions. However, some portion of the population actively avoids cancer risk information. The prevalence of, explanations for, and consequences of such avoidance are not well understood. OBJECTIVE We examined the prevalence and demographic and psychosocial correlates of cancer risk information avoidance preference in a nationally representative sample. We also examined whether avoidance of cancer risk information corresponds with avoidance of cancer screening. RESULTS Based on our representative sample, 39% of the population indicated that they agreed or strongly agreed that they would “rather not know [their] chance of getting cancer.” This preference was stronger among older participants, female participants, and participants with lower levels of education. Preferring to avoid cancer risk information was stronger among participants who agreed with the beliefs that everything causes cancer, that there’s not much one can do to prevent cancer, and that there are too many recommendations to follow. Finally, the preference to avoid cancer risk information was associated with lower levels of screening for colon cancer. CONCLUSION These findings suggest that cancer risk information avoidance is a multi-determined phenomenon that is associated with demographic characteristics and psychosocial individual differences and also relates to engagement in cancer screening. PMID:26560410

  18. Use of arsenic-induced palmoplantar hyperkeratosis and skin cancers to predict risk of subsequent internal malignancy.

    PubMed

    Hsu, Ling-I; Chen, Gwo-Shing; Lee, Chih-Hung; Yang, Tse-Yen; Chen, Yu-Hsin; Wang, Yuan-Hung; Hsueh, Yu-Mei; Chiou, Hung-Yi; Wu, Meei-Maan; Chen, Chien-Jen

    2013-02-01

    Hyperpigmentation, hyperkeratoses, and Bowen's disease are hallmarks of chronic arsenic exposure. The association between arsenic-induced skin lesions and subsequent internal cancers is examined by using a community-based prospective study. The cohort was enrolled from an arseniasis-endemic area in southwestern Taiwan, where 2,447 residents participated in skin examinations during the late 1980s. The number of participants diagnosed with hyperpigmentation was 673; with hyperkeratosis, 243; and with skin cancer (Bowen's disease or non-melanoma skin cancer), 378. Newly diagnosed internal cancers were ascertained through linkage with National Cancer Registry profiles. Cox regression was performed to estimate hazard ratios with 95% confidence intervals for potential risk predictors. Compared with participants without skin lesions, patients affected with skin cancers had a significantly increased risk of lung cancer (hazard ratio = 4.64, 95% confidence interval: 2.92, 7.38) and urothelial carcinoma (hazard ratio = 2.02, 95% confidence interval: 1.23, 3.30) after adjustment for potential confounders and cumulative arsenic exposure. Hyperkeratosis is significantly associated with an increased lung cancer risk (hazard ratio = 2.76, 95% confidence interval: 1.35, 5.67). A significant interactive effect on lung cancer risk between hyperkeratosis and cigarette smoking was identified, which suggests that patients with hyperkeratosis who have been exposed to arsenic should cease smoking.

  19. A risk prediction algorithm for ovarian cancer incorporating BRCA1, BRCA2, common alleles and other familial effects

    PubMed Central

    Jervis, Sarah; Song, Honglin; Lee, Andrew; Dicks, Ed; Harrington, Patricia; Baynes, Caroline; Manchanda, Ranjit; Easton, Douglas F; Jacobs, Ian; Pharoah, Paul P D; Antoniou, Antonis C

    2015-01-01

    Background Although BRCA1 and BRCA2 mutations account for only ∼27% of the familial aggregation of ovarian cancer (OvC), no OvC risk prediction model currently exists that considers the effects of BRCA1, BRCA2 and other familial factors. Therefore, a currently unresolved problem in clinical genetics is how to counsel women with family history of OvC but no identifiable BRCA1/2 mutations. Methods We used data from 1548 patients with OvC and their relatives from a population-based study, with known BRCA1/2 mutation status, to investigate OvC genetic susceptibility models, using segregation analysis methods. Results The most parsimonious model included the effects of BRCA1/2 mutations, and the residual familial aggregation was accounted for by a polygenic component (SD 1.43, 95% CI 1.10 to 1.86), reflecting the multiplicative effects of a large number of genes with small contributions to the familial risk. We estimated that 1 in 630 individuals carries a BRCA1 mutation and 1 in 195 carries a BRCA2 mutation. We extended this model to incorporate the explicit effects of 17 common alleles that are associated with OvC risk. Based on our models, assuming all of the susceptibility genes could be identified we estimate that the half of the female population at highest genetic risk will account for 92% of all OvCs. Conclusions The resulting model can be used to obtain the risk of developing OvC on the basis of BRCA1/2, explicit family history and common alleles. This is the first model that accounts for all OvC familial aggregation and would be useful in the OvC genetic counselling process. PMID:26025000

  20. Race-specific genetic risk score is more accurate than nonrace-specific genetic risk score for predicting prostate cancer and high-grade diseases.

    PubMed

    Na, Rong; Ye, Dingwei; Qi, Jun; Liu, Fang; Lin, Xiaoling; Helfand, Brian T; Brendler, Charles B; Conran, Carly; Gong, Jian; Wu, Yishuo; Gao, Xu; Chen, Yaqing; Zheng, S Lilly; Mo, Zengnan; Ding, Qiang; Sun, Yinghao; Xu, Jianfeng

    2016-01-01

    Genetic risk score (GRS) based on disease risk-associated single nucleotide polymorphisms (SNPs) is an informative tool that can be used to provide inherited information for specific diseases in addition to family history. However, it is still unknown whether only SNPs that are implicated in a specific racial group should be used when calculating GRSs. The objective of this study is to compare the performance of race-specific GRS and nonrace-specific GRS for predicting prostate cancer (PCa) among 1338 patients underwent prostate biopsy in Shanghai, China. A race-specific GRS was calculated with seven PCa risk-associated SNPs implicated in East Asians (GRS7), and a nonrace-specific GRS was calculated based on 76 PCa risk-associated SNPs implicated in at least one racial group (GRS76). The means of GRS7 and GRS76 were 1.19 and 1.85, respectively, in the study population. Higher GRS7 and GRS76 were independent predictors for PCa and high-grade PCa in univariate and multivariate analyses. GRS7 had a better area under the receiver-operating curve (AUC) than GRS76 for discriminating PCa (0.602 vs 0.573) and high-grade PCa (0.603 vs 0.575) but did not reach statistical significance. GRS7 had a better (up to 13% at different cutoffs) positive predictive value (PPV) than GRS76. In conclusion, a race-specific GRS is more robust and has a better performance when predicting PCa in East Asian men than a GRS calculated using SNPs that are not shown to be associated with East Asians.

  1. Clinical and Genetic Risk Prediction of Subsequent CNS Tumors in Survivors of Childhood Cancer: A Report From the COG ALTE03N1 Study.

    PubMed

    Wang, Xuexia; Sun, Can-Lan; Hageman, Lindsey; Smith, Kandice; Singh, Purnima; Desai, Sunil; Hawkins, Douglas S; Hudson, Melissa M; Mascarenhas, Leo; Neglia, Joseph P; Oeffinger, Kevin C; Ritchey, A Kim; Robison, Leslie L; Villaluna, Doojduen; Landier, Wendy; Bhatia, Smita

    2017-10-04

    Purpose Survivors of childhood cancer treated with cranial radiation therapy are at risk for subsequent CNS tumors. However, significant interindividual variability in risk suggests a role for genetic susceptibility and provides an opportunity to identify survivors of childhood cancer at increased risk for these tumors. Methods We curated candidate genetic variants from previously published studies in adult-onset primary CNS tumors and replicated these in survivors of childhood cancer with and without subsequent CNS tumors (82 participants and 228 matched controls). We developed prediction models to identify survivors at high or low risk for subsequent CNS tumors and validated these models in an independent matched case-control sample (25 participants and 54 controls). Results We demonstrated an association between six previously published single nucleotide polymorphisms (rs15869 [ BRCA2], rs1805389 [ LIG4], rs8079544 [ TP53], rs25489 [ XRCC1], rs1673041 [ POLD1], and rs11615 [ ERCC1]) and subsequent CNS tumors in survivors of childhood cancer. Including genetic variants in a Final Model containing age at primary cancer, sex, and cranial radiation therapy dose yielded an area under the curve of 0.81 (95% CI, 0.76 to 0.86), which was superior ( P = .002) to the Clinical Model (area under the curve, 0.73; 95% CI, 0.66 to 0.80). The prediction model was successfully validated. The sensitivity and specificity of predicting survivors of childhood cancer at highest or lowest risk of subsequent CNS tumors was 87.5% and 83.5%, respectively. Conclusion It is possible to identify survivors of childhood cancer at high or low risk for subsequent CNS tumors on the basis of genetic and clinical information. This information can be used to inform surveillance for early detection of subsequent CNS tumors.

  2. Clinical application of micronucleus test: a case-control study on the prediction of breast cancer risk/susceptibility.

    PubMed

    Bolognesi, Claudia; Bruzzi, Paolo; Gismondi, Viviana; Volpi, Samantha; Viassolo, Valeria; Pedemonte, Simona; Varesco, Liliana

    2014-01-01

    The micronucleus test is a well-established DNA damage assay in human monitoring. The test was proposed as a promising marker of cancer risk/susceptibility mainly on the basis of studies on breast cancer. Our recent meta-analysis showed that the association between micronuclei frequency, either at baseline or after irradiation, and breast cancer risk or susceptibility, has been evaluated in few studies of small size, with inconsistent results. The aim of the present study is to investigate the role of micronucleus assay in evaluating individual breast cancer susceptibility. Two-hundred and twenty untreated breast cancer patients and 295 female controls were enrolled in the study. All women were characterized for cancer family history and 155 subjects were evaluated for the presence of BRCA mutations. Micronuclei frequency was evaluated at baseline and after irradiation with 1-Gy gamma rays from a 137Cs source. The results show a non significant increase of frequency of micronucleated binucleated lymphocytes in cancer patients compared with the controls at baseline (Mean (S.E.): 16.8 (0.7) vs 15.7 (0.5), but not after irradiation (Mean (S.E.): 145.8 (3.0) vs 154.0 (2.6)). Neither a family history of breast cancer nor the presence of a pathogenic mutation in BRCA1/2 genes were associated with an increased micronuclei frequency. Our results do not support a significant role of micronucleus frequency as a biomarker of breast cancer risk/susceptibility.

  3. The PER (Preoperative Esophagectomy Risk) Score: A Simple Risk Score to Predict Short-Term and Long-Term Outcome in Patients with Surgically Treated Esophageal Cancer

    PubMed Central

    Reeh, Matthias; Metze, Johannes; Uzunoglu, Faik G.; Nentwich, Michael; Ghadban, Tarik; Wellner, Ullrich; Bockhorn, Maximilian; Kluge, Stefan; Izbicki, Jakob R.; Vashist, Yogesh K.

    2016-01-01

    Abstract Esophageal resection in patients with esophageal cancer (EC) is still associated with high mortality and morbidity rates. We aimed to develop a simple preoperative risk score for the prediction of short-term and long-term outcomes for patients with EC treated by esophageal resection. In total, 498 patients suffering from esophageal carcinoma, who underwent esophageal resection, were included in this retrospective cohort study. Three preoperative esophagectomy risk (PER) groups were defined based on preoperative functional evaluation of different organ systems by validated tools (revised cardiac risk index, model for end-stage liver disease score, and pulmonary function test). Clinicopathological parameters, morbidity, and mortality as well as disease-free survival (DFS) and overall survival (OS) were correlated to the PER score. The PER score significantly predicted the short-term outcome of patients with EC who underwent esophageal resection. PER 2 and PER 3 patients had at least double the risk of morbidity and mortality compared to PER 1 patients. Furthermore, a higher PER score was associated with shorter DFS (P < 0.001) and OS (P < 0.001). The PER score was identified as an independent predictor of tumor recurrence (hazard ratio [HR] 2.1; P < 0.001) and OS (HR 2.2; P < 0.001). The PER score allows preoperative objective allocation of patients with EC into different risk categories for morbidity, mortality, and long-term outcomes. Thus, multicenter studies are needed for independent validation of the PER score. PMID:26886613

  4. Global trends in nasopharyngeal cancer mortality since 1970 and predictions for 2020: Focus on low-risk areas.

    PubMed

    Carioli, Greta; Negri, Eva; Kawakita, Daisuke; Garavello, Werner; La Vecchia, Carlo; Malvezzi, Matteo

    2017-05-15

    Nasopharyngeal cancer (NPC) mortality shows great disparity between endemic high risk areas, where non-keratinizing carcinoma (NKC) histology is prevalent, and non-endemic low risk regions, where the keratinizing squamous cell carcinoma (KSCC) type is more frequent. We used the World Health Organization database to calculate NPC mortality trends from 1970 to 2014 in several countries worldwide. For the European Union (EU), the United States (US) and Japan, we also predicted trends to 2020. In 2012, the highest age-standardized (world standard) rates were in Hong Kong (4.51/100,000 men and 1.15/100,000 women), followed by selected Eastern European countries. The lowest rates were in Northern Europe and Latin America. EU rates were 0.27/100,000 men and 0.09/100,000 women, US rates were 0.20/100,000 men and 0.08/100,000 women and Japanese rates were 0.16/100,000 men and 0.04/100,000 women. NPC mortality trends were favourable for several countries. The decline was -15% in men and -5% in women between 2002 and 2012 in the EU, -12% in men and -9% in women in the US and about -30% in both sexes in Hong Kong and Japan. The favourable patterns in Europe and the United States are predicted to continue. Changes in salted fish and preserved food consumption account for the fall in NKC. Smoking and alcohol prevalence disparities between sexes and geographic areas may explain the different rates and trends observed for KSCC and partially for NKC. Dietary patterns, as well as improvement in management of the disease, may partly account for the observed trends, too. © 2017 UICC.

  5. The proliferative activity of mammary epithelial cells in normal tissue predicts breast cancer risk in premenopausal women

    PubMed Central

    Huh, Sung Jin; Oh, Hannah; Peterson, Michael A.; Almendro, Vanessa; Hu, Rong; Bowden, Michaela; Lis, Rosina L.; Cotter, Maura B.; Loda, Massimo; Barry, William T.; Polyak, Kornelia; Tamimi, Rulla M.

    2016-01-01

    The frequency and proliferative activity of tissue-specific stem and progenitor cells are suggested to correlate with cancer risk. In this study, we investigated the association between breast cancer risk and the frequency of mammary epithelial cells expressing p27, estrogen receptor (ER), and Ki67 in normal breast tissue. We performed a nested case-control study of 302 women (69 breast cancer cases, 233 controls) who had been initially diagnosed with benign breast disease according to the Nurses’ Health Studies. Immunofluorescence for p27, ER, and Ki67 was performed on tissue microarrays constructed from benign biopsies containing normal mammary epithelium and scored by computational image analysis. We found that the frequency of Ki67+ cells was positively associated with breast cancer risk among premenopausal women (odds ratio [OR]=10.1, 95% confidence interval [CI]=2.12–48.0). Conversely, the frequency of ER+ or p27+ cells was inversely, but not significantly, associated with subsequent breast cancer risk (ER+: OR=0.70, 95% CI=0.33–1.50; p27+: OR=0.89, 95% CI=0.45–1.75). Notably, high Ki67+/low p27+ and high Ki67+/low ER+ cell frequencies were significantly associated with a 5-fold higher risk of breast cancer compared to low Ki67+/low p27+ and low Ki67+/low ER+ cell frequencies, respectively, among premenopausal women (Ki67hi/p27lo: OR=5.08, 95% CI=1.43–18.1; Ki67hi/ERlo: OR=4.68, 95% CI=1.63–13.5). Taken together, our data suggest that the fraction of actively cycling cells in normal breast tissue may represent a marker for breast cancer risk assessment, which may therefore impact the frequency of screening procedures in at-risk women. PMID:26941287

  6. Risk Prediction for Breast, Endometrial, and Ovarian Cancer in White Women Aged 50 y or Older: Derivation and Validation from Population-Based Cohort Studies

    PubMed Central

    Pfeiffer, Ruth M.; Park, Yikyung; Kreimer, Aimée R.; Lacey, James V.; Pee, David; Greenlee, Robert T.; Buys, Saundra S.; Hollenbeck, Albert; Rosner, Bernard; Gail, Mitchell H.; Hartge, Patricia

    2013-01-01

    Background Breast, endometrial, and ovarian cancers share some hormonal and epidemiologic risk factors. While several models predict absolute risk of breast cancer, there are few models for ovarian cancer in the general population, and none for endometrial cancer. Methods and Findings Using data on white, non-Hispanic women aged 50+ y from two large population-based cohorts (the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial [PLCO] and the National Institutes of Health–AARP Diet and Health Study [NIH-AARP]), we estimated relative and attributable risks and combined them with age-specific US-population incidence and competing mortality rates. All models included parity. The breast cancer model additionally included estrogen and progestin menopausal hormone therapy (MHT) use, other MHT use, age at first live birth, menopausal status, age at menopause, family history of breast or ovarian cancer, benign breast disease/biopsies, alcohol consumption, and body mass index (BMI); the endometrial model included menopausal status, age at menopause, BMI, smoking, oral contraceptive use, MHT use, and an interaction term between BMI and MHT use; the ovarian model included oral contraceptive use, MHT use, and family history or breast or ovarian cancer. In independent validation data (Nurses' Health Study cohort) the breast and ovarian cancer models were well calibrated; expected to observed cancer ratios were 1.00 (95% confidence interval [CI]: 0.96–1.04) for breast cancer and 1.08 (95% CI: 0.97–1.19) for ovarian cancer. The number of endometrial cancers was significantly overestimated, expected/observed = 1.20 (95% CI: 1.11–1.29). The areas under the receiver operating characteristic curves (AUCs; discriminatory power) were 0.58 (95% CI: 0.57–0.59), 0.59 (95% CI: 0.56–0.63), and 0.68 (95% CI: 0.66–0.70) for the breast, ovarian, and endometrial models, respectively. Conclusions These models predict absolute risks for breast, endometrial, and

  7. A single-center study on predicting outcomes of primary androgen deprivation therapy for prostate cancer using the Japan Cancer of the Prostate Risk Assessment (J-CAPRA) score.

    PubMed

    Yamaguchi, Yuichiro; Hayashi, Yujiro; Ishizuya, Yu; Takeda, Ken; Nakai, Yasutomo; Arai, Yasuyuki; Nakayama, Masashi; Kakimoto, Ken-ichi; Nishimura, Kazuo

    2015-02-01

    Japan Cancer of the Prostate Risk Assessment scores are reportedly useful for predicting progression-free survival after primary androgen deprivation therapy of prostate cancer patients. This study validated the risk assessment at a single institution. We studied 255 prostate cancer patients given primary androgen deprivation therapy. Progression-free survival, cause-specific survival and overall survival were analyzed according to Japan Cancer of the Prostate Risk Assessment score-based risk classification. Cases with lymph node or distant metastases were subdivided by the risk classification. Ages ranged from 50 to 90 years (median: 76.5). Observation periods were 2-199 (median: 46.5) months. Primary androgen deprivation therapy includes combined androgen blockade in 150 cases (58.8%), uncombined luteinizing hormone-releasing hormone agonist in 97 (38.0%) and uncombined anti-androgenic agent in 8 (3.2%). Risk classified by Japan Cancer of the Prostate Risk Assessment scores was low in 104 cases (40.8%), intermediate in 86 (33.7%) and high in 65 (25.5%). The 5-year/10-year progression-free survival rates were 100%/80.8% in the low-risk, 82.3%/69.5% in the intermediate-risk and 34.7%/16.5% in the high-risk group. The 5-year/10-year cause-specific survival rates were 100%/100% in the low-risk, 90.7%/58.2% in the intermediate-risk and 63%/30.8% in the high-risk group. The 5-year/10-year overall survival rates were 87.5%/78.5% in the low-risk, 76.2%/43.1% in the intermediate-risk and 54.9%/25.4% in the high-risk group. For lymph node metastasis, cause-specific survival differed minimally between the intermediate- and high-risk groups (P = 0.1118). For distant metastasis, cause-specific survival differed significantly between the intermediate- and high-risk groups (P = 0.0264). Japan Cancer of the Prostate Risk Assessment score-based risk classification is useful for predicting post-primary androgen deprivation therapy progression-free survival, cause-specific survival

  8. AutoDensity: an automated method to measure mammographic breast density that predicts breast cancer risk and screening outcomes

    PubMed Central

    2013-01-01

    Introduction While Cumulus – a semi-automated method for measuring breast density – is utilised extensively in research, it is labour-intensive and unsuitable for screening programmes that require an efficient and valid measure on which to base screening recommendations. We develop an automated method to measure breast density (AutoDensity) and compare it to Cumulus in terms of association with breast cancer risk and breast cancer screening outcomes. Methods AutoDensity automatically identifies the breast area in the mammogram and classifies breast density in a similar way to Cumulus, through a fast, stand-alone Windows or Linux program. Our sample comprised 985 women with screen-detected cancers, 367 women with interval cancers and 4,975 controls (women who did not have cancer), sampled from first and subsequent screening rounds of a film mammography screening programme. To test the validity of AutoDensity, we compared the effect estimates using AutoDensity with those using Cumulus from logistic regression models that tested the association between breast density and breast cancer risk, risk of small and large screen-detected cancers and interval cancers, and screening programme sensitivity (the proportion of cancers that are screen-detected). As a secondary analysis, we report on correlation between AutoDensity and Cumulus measures. Results AutoDensity performed similarly to Cumulus in all associations tested. For example, using AutoDensity, the odds ratios for women in the highest decile of breast density compared to women in the lowest quintile for invasive breast cancer, interval cancers, large and small screen-detected cancers were 3.2 (95% CI 2.5 to 4.1), 4.7 (95% CI 3.0 to 7.4), 6.4 (95% CI 3.7 to 11.1) and 2.2 (95% CI 1.6 to 3.0) respectively. For Cumulus the corresponding odds ratios were: 2.4 (95% CI 1.9 to 3.1), 4.1 (95% CI 2.6 to 6.3), 6.6 (95% CI 3.7 to 11.7) and 1.3 (95% CI 0.9 to 1.8). Correlation between Cumulus and AutoDensity measures was 0

  9. Understanding the role of family dynamics, perceived norms, and lung cancer worry in predicting second-hand smoke avoidance among high-risk lung cancer families.

    PubMed

    Manning, Mark; Wojda, Mark; Hamel, Lauren; Salkowski, Alicia; Schwartz, Ann G; Harper, Felicity Wk

    2017-10-01

    Reducing exposure to environmental tobacco smoke significantly reduces lung cancer risk. We used family communication patterns theory and the theory of planned behavior to examine whether perceived norms and lung cancer worry more strongly influenced intentions to avoid environmental tobacco smoke in families higher in conformity and conversation orientations. Results from 52 individuals in 17 high-risk lung cancer families showed injunctive norms were positively related to intentions when families conformed and conversed more. Lung cancer worry was positively related to intentions in high conformity families and negatively related to intentions in low conformity families. Findings can benefit interventions to reduce environmental tobacco smoke exposure.

  10. Potential usefulness of a topic model-based categorization of lung cancers as quantitative CT biomarkers for predicting the recurrence risk after curative resection

    NASA Astrophysics Data System (ADS)

    Kawata, Y.; Niki, N.; Ohmatsu, H.; Satake, M.; Kusumoto, M.; Tsuchida, T.; Aokage, K.; Eguchi, K.; Kaneko, M.; Moriyama, N.

    2014-03-01

    In this work, we investigate a potential usefulness of a topic model-based categorization of lung cancers as quantitative CT biomarkers for predicting the recurrence risk after curative resection. The elucidation of the subcategorization of a pulmonary nodule type in CT images is an important preliminary step towards developing the nodule managements that are specific to each patient. We categorize lung cancers by analyzing volumetric distributions of CT values within lung cancers via a topic model such as latent Dirichlet allocation. Through applying our scheme to 3D CT images of nonsmall- cell lung cancer (maximum lesion size of 3 cm) , we demonstrate the potential usefulness of the topic model-based categorization of lung cancers as quantitative CT biomarkers.

  11. A new molecular prognostic score for predicting the risk of distant metastasis in patients with HR+/HER2− early breast cancer

    PubMed Central

    Gong, Gyungyub; Kwon, Mi Jeong; Han, Jinil; Lee, Hee Jin; Lee, Se Kyung; Lee, Jeong Eon; Lee, Seon-Heui; Park, Sarah; Choi, Jong-Sun; Cho, Soo Youn; Ahn, Sei Hyun; Lee, Jong Won; Cho, Sang Rae; Moon, Youngho; Nam, Byung-Ho; Nam, Seok Jin; Choi, Yoon-La; Shin, Young Kee

    2017-01-01

    To make an optimal treatment decision for early stage breast cancer, it is important to identify risk of recurrence. Here, we developed and validated a new prognostic model for predicting the risk of distant metastasis in patients with pN0-N1, hormone receptor-positive, HER2-negative (HR+/HER2−) breast cancer treated with hormone therapy alone. RNA was extracted from formalin-fixed, paraffin-embedded tumor tissues and gene expression was measured by quantitative real-time reverse transcription-PCR. The relative expression of six novel prognostic genes was combined with two clinical variables (nodal status and tumor size) to calculate a risk score (BCT score). In the validation cohort treated with hormone therapy alone, the 10 year rate of distant metastasis in the high-risk group (26.3%) according to BCT score was significantly higher than that in the low-risk group (3.8%) (P < 0.001). Multivariate analysis adjusted for clinical variables revealed that BCT score is an independent predictor of distant metastasis. Moreover, the C-index estimate revealed that BCT score has a prognostic power superior to that of prognostic models based on clinicopathological parameters. The BCT score outperforms prognostic models based on traditional clinicopathological factors and predicts the risk of distant metastasis in patients with HR+/HER2− early breast cancer. PMID:28350001

  12. Faecal haemoglobin concentration influences risk prediction of interval cancers resulting from inadequate colonoscopy quality: analysis of the Taiwanese Nationwide Colorectal Cancer Screening Program.

    PubMed

    Chiu, Sherry Yueh-Hsia; Chuang, Shu-Ling; Chen, Sam Li-Sheng; Yen, Amy Ming-Fang; Fann, Jean Ching-Yuan; Chang, Dun-Cheng; Lee, Yi-Chia; Wu, Ming-Shiang; Chou, Chu-Kuang; Hsu, Wen-Feng; Chiou, Shu-Ti; Chiu, Han-Mo

    2017-02-01

    Interval colorectal cancer (CRC) after colonoscopy may affect effectiveness and cost-effectiveness of screening programmes. We aimed to investigate whether and how faecal haemoglobin concentration (FHbC) of faecal immunochemical testing (FIT) affected the risk prediction of interval cancer (IC) caused by inadequate colonoscopy quality in a FIT-based population screening programme. From 2004 to 2009, 29 969 subjects underwent complete colonoscopy after positive FIT in the Taiwanese Nationwide CRC Screening Program. The IC rate was traced until the end of 2012. The incidence of IC was calculated in relation to patient characteristics, endoscopy-related factors (such adenoma detection rate (ADR)) and FHbC. Poisson regression analysis was performed to assess the potential risk factors for colonoscopy IC. One hundred and sixty-two ICs developed after an index colonoscopy and the estimated incidence was 1.14 per 1000 person-years of observation for the entire cohort. Increased risk of IC was most remarkable in the uptake of colonoscopy in settings with ADR lower than 15% (adjusted relative risk (aRR)=3.09, 95% CI 1.55 to 6.18) and then higher FHbC (μg Hb/g faeces) (100-149: aRR=2.55, 95% CI 1.52 to 4.29, ≥150: aRR=2.74, 95% CI 1.84 to 4.09) with adjustment for older age and colorectal neoplasm detected at baseline colonoscopy. Similar findings were observed for subjects with negative index colonoscopy. Colonoscopy ICs arising from FIT-based population screening programmes were mainly influenced by inadequate colonoscopy quality and independently predicted by FHbC that is associated with a priori chance of advanced neoplasm. This finding is helpful for future modification of screening logistics based on FHbC. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  13. Predicting the Risk of Non–organ-confined Prostate Cancer When Perineural Invasion Is Found on Biopsy

    PubMed Central

    Gorin, Michael A.; Chalfin, Heather J.; Epstein, Jonathan I.; Feng, Zhaoyong; Partin, Alan W.; Trock, Bruce J.

    2015-01-01

    OBJECTIVE To more precisely define the risk of non–organ-confined (non-OC) prostate cancer among men with perineural invasion (PNI) identified on prostate biopsy. MATERIALS AND METHODS The Johns Hopkins radical prostatectomy database was queried for men with PNI reported on prostate biopsy. Patients with and without non-OC disease were compared for differences in preoperative clinical and pathologic characteristics, including three biopsy-based measures of tumor volume (number of cores with cancer, percentage of cores with cancer, and maximum percent core involvement with cancer). After evaluating the different preoperative variables in univariate analyses, a multivariable logistic regression model was generated, and bootstrap estimates of the risk of non-OC disease were calculated. RESULTS In total, 556 patients with PNI were analyzed, 279 (50.2%) of whom were found to have non-OC prostate cancer. In univariate analyses, preoperative prostate-specific antigen, clinical T stage, biopsy Gleason sum, and the three biopsy-based measures of tumor volume were significantly associated with non-OC disease. Of the three measures of tumor volume, the best fit to the data and highest degree of model discrimination were obtained using maximum percent core involvement with cancer. Incorporating this variable, preoperative prostate-specific antigen, clinical T stage, and biopsy Gleason sum into a multivariable model, the estimated risk of non-OC disease was found to range from 13.8% to 94.4% (bootstrap corrected c-index = 0.735). CONCLUSION Men with PNI on prostate biopsy are at a wide range of risk for non-OC disease. Preoperative estimation of this risk is improved by considering readily available biopsy estimates of tumor volume. PMID:24655556

  14. Interleukin-1 Gene Cluster Polymorphisms and its Haplotypes may Predict the Risk to Develop Cervical Cancer in Tunisia.

    PubMed

    Zidi, Sabrina; Sghaier, Ikram; Zouidi, Ferjeni; Benahmed, Amira; Stayoussef, Mouna; Kochkar, Radhia; Gazouani, Ezzedine; Mezlini, Amel; Yacoubi-Loueslati, Besma

    2015-09-01

    Our study aimed to evaluate the association between IL-1α (4845 G/T), IL-1β (-511C/T) and IL-1RN (VNTR) polymorphisms and risk of cervical cancer. This case-control study investigates three polymorphisms in 130 patients and 260 controls by PCR-restriction fragment length polymorphism (RFLP). The IL-1RN (VNTR) A1/A3 genotype appear as a cervical cancer risk factor (p = 0.048; OR = 2.92; 95 % CI = 1.00-8.74), moreover, the L/2* decreased the risk (p = 0.011; OR = 0.47; 95 % CI = 0.25-0.88) and may be a protective factor against this pathology. Stratified analysis according to the FIGO stage subgroup revealed that the IL-1β-511 T/T genotype and T allele may be a protective factors against cervical cancer development for patients with early stage (p = 0.030; OR = 0.46; 95 % CI = 0.22-0.96) (p = 0.020; OR = 0.68; 95 % CI = 0.48-0.97). However, for the patients with advanced FIGO stage, IL-1RN-VNTR L/2* genotype appear as a protective factor for this pathology (p = 0.023; OR = 0.29; 95 % CI = 0.08-0.99). The (G-T-L) haplotype showed a significant decreased frequency in cervical cancer patients as compared to controls (p = 0.032; OR = 0.53; 95 % CI = 0.29-0.95). In contrast, the (T-T-2*) combination appear a risk factor for the development of cervical cancer (p = 0.018; OR = 1.57; 95 % CI = 1.07-2.30). Our study suggested that IL1 cluster polymorphisms and haplotypes may be a genetic risk factor for cervical cancer.

  15. Melanoma risk prediction models.

    PubMed

    Nikolić, Jelena; Loncar-Turukalo, Tatjana; Sladojević, Srdan; Marinković, Marija; Janjić, Zlata

    2014-08-01

    The lack of effective therapy for advanced stages of melanoma emphasizes the importance of preventive measures and screenings of population at risk. Identifying individuals at high risk should allow targeted screenings and follow-up involving those who would benefit most. The aim of this study was to identify most significant factors for melanoma prediction in our population and to create prognostic models for identification and differentiation of individuals at risk. This case-control study included 697 participants (341 patients and 356 controls) that underwent extensive interview and skin examination in order to check risk factors for melanoma. Pairwise univariate statistical comparison was used for the coarse selection of the most significant risk factors. These factors were fed into logistic regression (LR) and alternating decision trees (ADT) prognostic models that were assessed for their usefulness in identification of patients at risk to develop melanoma. Validation of the LR model was done by Hosmer and Lemeshow test, whereas the ADT was validated by 10-fold cross-validation. The achieved sensitivity, specificity, accuracy and AUC for both models were calculated. The melanoma risk score (MRS) based on the outcome of the LR model was presented. The LR model showed that the following risk factors were associated with melanoma: sunbeds (OR = 4.018; 95% CI 1.724-9.366 for those that sometimes used sunbeds), solar damage of the skin (OR = 8.274; 95% CI 2.661-25.730 for those with severe solar damage), hair color (OR = 3.222; 95% CI 1.984-5.231 for light brown/blond hair), the number of common naevi (over 100 naevi had OR = 3.57; 95% CI 1.427-8.931), the number of dysplastic naevi (from 1 to 10 dysplastic naevi OR was 2.672; 95% CI 1.572-4.540; for more than 10 naevi OR was 6.487; 95%; CI 1.993-21.119), Fitzpatricks phototype and the presence of congenital naevi. Red hair, phototype I and large congenital naevi were only present in melanoma patients and thus

  16. Diabetes mellitus: influences on cancer risk.

    PubMed

    Szablewski, Leszek

    2014-10-01

    Diabetes mellitus and cancer are common conditions, and their co-diagnosis in the same individual is not infrequent. The relative risks associated with type 2 diabetes are greater than twofold for hepatic, pancreatic, and endometrial cancers. The relative risk is somewhat lower, at 1.2-1.5-fold for colorectal, breast, and bladder cancers. In comparison, the relative risk of lung cancer is less than 1. The evidence for other malignancies (e.g. kidney, non-Hodgkin lymphoma) is inconclusive, whereas prostatic cancer occurs less frequently in male patients with diabetes. The potential biologic links between the two diseases are incompletely understood. Evidence from observational studies suggests that some medications used to treat hyperglycemia are associated with either increased or reduced risk of cancer. Whereas anti-diabetic drugs have a minor influence on cancer risk, drugs used to treat cancer may either cause diabetes or worsen pre-existing diabetes. If hyperinsulinemia acts as a critical link between the observed increased cancer risk and type 2 diabetes, one would predict that patients with type 1 diabetes would have a different cancer risk pattern than patients with type 2 diabetes because the former patients are exposed to lower levels of exogenous administered insulin. Obtained results showed that patients with type 1 diabetes had elevated risks of cancers of the stomach, cervix, and endometrium. Type 1 diabetes is associated with a modest excess cancer risk overall and risks of specific cancers that differ from those associated with type 2 diabetes.

  17. Can the conventional sextant prostate biopsy accurately predict unilateral prostate cancer in low-risk, localized, prostate cancer?

    PubMed

    Mayes, Janice M; Mouraviev, Vladimir; Sun, Leon; Tsivian, Matvey; Madden, John F; Polascik, Thomas J

    2011-01-01

    We evaluate the reliability of routine sextant prostate biopsy to detect unilateral lesions. A total of 365 men with complete records including all clinical and pathologic variables who underwent a preoperative sextant biopsy and subsequent radical prostatectomy (RP) for clinically localized prostate cancer at our medical center between January 1996 and December 2006 were identified. When the sextant biopsy detects unilateral disease, according to RP results, the NPV is high (91%) with a low false negative rate (9%). However, the sextant biopsy has a PPV of 28% with a high false positive rate (72%). Therefore, a routine sextant prostate biopsy cannot provide reliable, accurate information about the unilaterality of tumor lesion(s). Copyright © 2011 Elsevier Inc. All rights reserved.

  18. Supervised Classification by Filter Methods and Recursive Feature Elimination Predicts Risk of Radiotherapy-Related Fatigue in Patients with Prostate Cancer

    PubMed Central

    Saligan, Leorey N; Fernández-Martínez, Juan Luis; deAndrés-Galiana, Enrique J; Sonis, Stephen

    2014-01-01

    BACKGROUND Fatigue is a common side effect of cancer (CA) treatment. We used a novel analytical method to identify and validate a specific gene cluster that is predictive of fatigue risk in prostate cancer patients (PCP) treated with radiotherapy (RT). METHODS A total of 44 PCP were categorized into high-fatigue (HF) and low-fatigue (LF) cohorts based on fatigue score change from baseline to RT completion. Fold-change differential and Fisher’s linear discriminant analyses (LDA) from 27 subjects with gene expression data at baseline and RT completion generated a reduced base of most discriminatory genes (learning phase). A nearest-neighbor risk (k-NN) prediction model was developed based on small-scale prognostic signatures. The predictive model validity was tested in another 17 subjects using baseline gene expression data (validation phase). RESULT The model generated in the learning phase predicted HF classification at RT completion in the validation phase with 76.5% accuracy. CONCLUSION The results suggest that a novel analytical algorithm that incorporates fold-change differential analysis, LDA, and a k-NN may have applicability in predicting regimen-related toxicity in cancer patients with high reliability, if we take into account these results and the limited amount of data that we had at disposal. It is expected that the accuracy will be improved by increasing data sampling in the learning phase. PMID:25506196

  19. Estimating Radiogenic Cancer Risks

    EPA Pesticide Factsheets

    This document presents a revised methodology for EPA's estimation of cancer risks due to low-LET radiation exposures developed in light of information that has become available, especially new information on the Japanese atomic bomb survivors.

  20. Age and Cancer Risk

    PubMed Central

    White, Mary C.; Holman, Dawn M.; Boehm, Jennifer E.; Peipins, Lucy A.; Grossman, Melissa; Henley, S. Jane

    2015-01-01

    This article challenges the idea that cancer cannot be prevented among older adults by examining different aspects of the relationship between age and cancer. Although the sequential patterns of aging cannot be changed, several age-related factors that contribute to disease risk can be. For most adults, age is coincidentally associated with preventable chronic conditions, avoidable exposures, and modifiable risk behaviors that are causally associated with cancer. Midlife is a period of life when the prevalence of multiple cancer risk factors is high and incidence rates begin to increase for many types of cancer. However, current evidence suggests that for most adults, cancer does not have to be an inevitable consequence of growing older. Interventions that support healthy environments, help people manage chronic conditions, and promote healthy behaviors may help people make a healthier transition from midlife to older age and reduce the likelihood of developing cancer. Because the number of adults reaching older ages is increasing rapidly, the number of new cancer cases will also increase if current incidence rates remain unchanged. Thus, the need to translate the available research into practice to promote cancer prevention, especially for adults at midlife, has never been greater. PMID:24512933

  1. Prevalence of delirium among patients at a cancer ward: Clinical risk factors and prediction by bedside cognitive tests.

    PubMed

    Grandahl, Mia Gall; Nielsen, Svend Erik; Koerner, Ejnar Alex; Schultz, Helga Holm; Arnfred, Sidse Marie

    2016-08-01

    Background Delirium is a frequent psychiatric complication to cancer, but rarely recognized by oncologists. Aims 1. To estimate the prevalence of delirium among inpatients admitted at an oncological cancer ward 2. To investigate whether simple clinical factors predict delirium 3. To examine the value of cognitive testing in the assessment of delirium. Methods On five different days, we interviewed and assessed patients admitted to a Danish cancer ward. The World Health Organization International Classification of Diseases Version 10, WHO ICD-10 Diagnostic System and the Confusion Assessment Method (CAM) were used for diagnostic categorization. Clinical information was gathered from medical records and all patients were tested with Mini Cognitive Test, The Clock Drawing Test, and the Digit Span Test. Results 81 cancer patients were assessed and 33% were diagnosed with delirium. All delirious participants were CAM positive. Poor performance on the cognitive tests was associated with delirium. Medical records describing CNS metastases, benzodiazepine or morphine treatment were associated with delirium. Conclusions Delirium is prevalent among cancer inpatients. The Mini Cognitive Test, The Clock Drawing Test, and the Digit Span Test can be used as screening tools for delirium among inpatients with cancer, but even in synergy, they lack specificity. Combining cognitive testing and attention to nurses' records might improve detection, yet further studies are needed to create a more detailed patient profile for the detection of delirium.

  2. Understanding your colon cancer risk

    MedlinePlus

    Colon cancer - prevention; Colon cancer - screening ... We do not know what causes colon cancer, but we do know some of the things that may increase the risk of getting it, such as: Age. Your risk increases after ...

  3. Patient-specific driver gene prediction and risk assessment through integrated network analysis of cancer omics profiles

    PubMed Central

    Bertrand, Denis; Chng, Kern Rei; Sherbaf, Faranak Ghazi; Kiesel, Anja; Chia, Burton K. H.; Sia, Yee Yen; Huang, Sharon K.; Hoon, Dave S.B.; Liu, Edison T.; Hillmer, Axel; Nagarajan, Niranjan

    2015-01-01

    Extensive and multi-dimensional data sets generated from recent cancer omics profiling projects have presented new challenges and opportunities for unraveling the complexity of cancer genome landscapes. In particular, distinguishing the unique complement of genes that drive tumorigenesis in each patient from a sea of passenger mutations is necessary for translating the full benefit of cancer genome sequencing into the clinic. We address this need by presenting a data integration framework (OncoIMPACT) to nominate patient-specific driver genes based on their phenotypic impact. Extensive in silico and in vitro validation helped establish OncoIMPACT's robustness, improved precision over competing approaches and verifiable patient and cell line specific predictions (2/2 and 6/7 true positives and negatives, respectively). In particular, we computationally predicted and experimentally validated the gene TRIM24 as a putative novel amplified driver in a melanoma patient. Applying OncoIMPACT to more than 1000 tumor samples, we generated patient-specific driver gene lists in five different cancer types to identify modes of synergistic action. We also provide the first demonstration that computationally derived driver mutation signatures can be overall superior to single gene and gene expression based signatures in enabling patient stratification and prognostication. Source code and executables for OncoIMPACT are freely available from http://sourceforge.net/projects/oncoimpact. PMID:25572314

  4. Colorectal Cancer Risk Assessment Tool

    MedlinePlus

    ... Colorectal Cancer Risk Factors Download SAS and Gauss Code Page Options Print Page Quick Links Colon and Rectal Cancer Home Page Colon and Rectal Cancer: Prevention, Genetics, Causes Tests to Detect Colorectal Cancer and Polyps ...

  5. Cancer Risk Assessment Primer.

    ERIC Educational Resources Information Center

    Aidala, Jim

    1985-01-01

    Describes the scientific basis of cancer risk assessment, outlining the dominant controversies surrounding the use of different methods for identifying carcinogens (short-term tests, animal bioassays, and epidemiological studies). Points out that risk assessment is as much an art as it is a science. (DH)

  6. Cancer Risk Assessment Primer.

    ERIC Educational Resources Information Center

    Aidala, Jim

    1985-01-01

    Describes the scientific basis of cancer risk assessment, outlining the dominant controversies surrounding the use of different methods for identifying carcinogens (short-term tests, animal bioassays, and epidemiological studies). Points out that risk assessment is as much an art as it is a science. (DH)

  7. Distinguishing Low-Risk Luminal A Breast Cancer Subtypes with Ki-67 and p53 Is More Predictive of Long-Term Survival.

    PubMed

    Lee, Se Kyung; Bae, Soo Youn; Lee, Jun Ho; Lee, Hyun-Chul; Yi, Hawoo; Kil, Won Ho; Lee, Jeong Eon; Kim, Seok Won; Nam, Seok Jin

    2015-01-01

    Overexpression of p53 is the most frequent genetic alteration in breast cancer. Recently, many studies have shown that the expression of mutant p53 differs for each subtype of breast cancer and is associated with different prognoses. In this study, we aimed to determine the suitable cut-off value to predict the clinical outcome of p53 overexpression and its usefulness as a prognostic factor in each subtype of breast cancer, especially in luminal A breast cancer. Approval was granted by the Institutional Review Board of Samsung Medical Center. We analyzed a total of 7,739 patients who were surgically treated for invasive breast cancer at Samsung Medical Center between Dec 1995 and Apr 2013. Luminal A subtype was defined as ER&PR + and HER2- and was further subclassified according to Ki-67 and p53 expression as follows: luminal A (Ki-67-,p53-), luminal A (Ki-67+, p53-), luminal A (Ki-67 -, p53+) and luminal A (Ki-67+, p53+). Low-risk luminal A subtype was defined as negative for both Ki-67 and p53 (luminal A [ki-67-, p53-]), and others subtypes were considered to be high-risk luminal A breast cancer. A cut-off value of 10% for p53 was a good predictor of clinical outcome in all patients and luminal A breast cancer patients. The prognostic role of p53 overexpression for OS and DFS was only significant in luminal A subtype. The combination of p53 and Ki-67 has been shown to have the best predictive power as calculated by the area under curve (AUC), especially for long-term overall survival. In this study, we have shown that overexpression of p53 and Ki-67 could be used to discriminate low-risk luminal A subtype in breast cancer. Therefore, using the combination of p53 and Ki-67 expression in discriminating low-risk luminal A breast cancer may improve the prognostic power and provide the greatest clinical utility.

  8. Comparison of risk classification between EndoPredict and MammaPrint in ER-positive/HER2-negative primary invasive breast cancer.

    PubMed

    Peláez-García, Alberto; Yébenes, Laura; Berjón, Alberto; Angulo, Antonia; Zamora, Pilar; Sánchez-Méndez, José Ignacio; Espinosa, Enrique; Redondo, Andrés; Heredia-Soto, Victoria; Mendiola, Marta; Feliú, Jaime; Hardisson, David

    2017-01-01

    To compare the concordance in risk classification between the EndoPredict and the MammaPrint scores obtained for the same cancer samples on 40 estrogen-receptor positive/HER2-negative breast carcinomas. Formalin-fixed, paraffin-embedded invasive breast carcinoma tissues that were previously analyzed with MammaPrint as part of routine care of the patients, and were classified as high-risk (20 patients) and low-risk (20 patients), were selected to be analyzed by the EndoPredict assay, a second generation gene expression test that combines expression of 8 genes (EP score) with two clinicopathological variables (tumor size and nodal status, EPclin score). The EP score classified 15 patients as low-risk and 25 patients as high-risk. EPclin re-classified 5 of the 25 EP high-risk patients into low-risk, resulting in a total of 20 high-risk and 20 low-risk tumors. EP score and MammaPrint score were significantly correlated (p = 0.008). Twelve of 20 samples classified as low-risk by MammaPrint were also low-risk by EP score (60%). 17 of 20 MammaPrint high-risk tumors were also high-risk by EP score. The overall concordance between EP score and MammaPrint was 72.5% (κ = 0.45, (95% CI, 0.182 to 0.718)). EPclin score also correlated with MammaPrint results (p = 0.004). Discrepancies between both tests occurred in 10 cases: 5 MammaPrint low-risk patients were classified as EPclin high-risk and 5 high-risk MammaPrint were classified as low-risk by EPclin and overall concordance of 75% (κ = 0.5, (95% CI, 0.232 to 0.768)). This pilot study demonstrates a limited concordance between MammaPrint and EndoPredict. Differences in results could be explained by the inclusion of different gene sets in each platform, the use of different methodology, and the inclusion of clinicopathological parameters, such as tumor size and nodal status, in the EndoPredict test.

  9. Comparison of risk classification between EndoPredict and MammaPrint in ER-positive/HER2-negative primary invasive breast cancer

    PubMed Central

    Peláez-García, Alberto; Yébenes, Laura; Berjón, Alberto; Angulo, Antonia; Zamora, Pilar; Sánchez-Méndez, José Ignacio; Espinosa, Enrique; Redondo, Andrés; Heredia-Soto, Victoria; Mendiola, Marta; Feliú, Jaime

    2017-01-01

    Purpose To compare the concordance in risk classification between the EndoPredict and the MammaPrint scores obtained for the same cancer samples on 40 estrogen-receptor positive/HER2-negative breast carcinomas. Methods Formalin-fixed, paraffin-embedded invasive breast carcinoma tissues that were previously analyzed with MammaPrint as part of routine care of the patients, and were classified as high-risk (20 patients) and low-risk (20 patients), were selected to be analyzed by the EndoPredict assay, a second generation gene expression test that combines expression of 8 genes (EP score) with two clinicopathological variables (tumor size and nodal status, EPclin score). Results The EP score classified 15 patients as low-risk and 25 patients as high-risk. EPclin re-classified 5 of the 25 EP high-risk patients into low-risk, resulting in a total of 20 high-risk and 20 low-risk tumors. EP score and MammaPrint score were significantly correlated (p = 0.008). Twelve of 20 samples classified as low-risk by MammaPrint were also low-risk by EP score (60%). 17 of 20 MammaPrint high-risk tumors were also high-risk by EP score. The overall concordance between EP score and MammaPrint was 72.5% (κ = 0.45, (95% CI, 0.182 to 0.718)). EPclin score also correlated with MammaPrint results (p = 0.004). Discrepancies between both tests occurred in 10 cases: 5 MammaPrint low-risk patients were classified as EPclin high-risk and 5 high-risk MammaPrint were classified as low-risk by EPclin and overall concordance of 75% (κ = 0.5, (95% CI, 0.232 to 0.768)). Conclusions This pilot study demonstrates a limited concordance between MammaPrint and EndoPredict. Differences in results could be explained by the inclusion of different gene sets in each platform, the use of different methodology, and the inclusion of clinicopathological parameters, such as tumor size and nodal status, in the EndoPredict test. PMID:28886093

  10. Prediction of long-term cumulative incidences based on short-term parametric model for competing risks: application in early breast cancer.

    PubMed

    Cabarrou, B; Belin, L; Somda, S M; Falcou, M C; Pierga, J Y; Kirova, Y; Delord, J P; Asselain, B; Filleron, T

    2016-04-01

    Use of parametric statistical models can be a solution to reduce the follow-up period time required to estimate long-term survival. Mould and Boag were the first to use the lognormal model. Competing risks methodology seems more suitable when a particular event type is of interest than classical survival analysis. The objective was to evaluate the ability of the Jeong and Fine model to predict long-term cumulative incidence. Survival data recorded by Institut Curie (Paris) from 4761 breast cancer patients treated and followed between 1981 and 2013 were used. Long-term cumulative incidence rates predicted by the model using short-term follow-up data were compared to non-parametric estimation using complete follow-up data. 20- or 25-year cumulative incidence rates for loco-regional recurrence and distant metastasis predicted by the model using a maximum of 10 years of follow-up data had a maximum difference of around 6 % compared to non-parametric estimation. Prediction rates were underestimated for the third and composite event (contralateral or second cancer or death). Predictive ability of Jeong and Fine model on breast cancer data was generally good considering the short follow-up period time used for the estimation especially when a proportion of patient did not experience loco-regional recurrence or distant metastasis.

  11. Prediction of prostate cancer from prostate biopsy in Chinese men using a genetic score derived from 24 prostate cancer risk-associated SNPs.

    PubMed

    Jiang, Haowen; Liu, Fang; Wang, Zhong; Na, Rong; Zhang, Limin; Wu, Yishuo; Zheng, Jie; Lin, Xiaoling; Jiang, Deke; Sun, Jielin; Zheng, S Lilly; Ding, Qiang; Xu, Jianfeng

    2013-11-01

    Twenty-four prostate cancer (PCa) risk-associated single nucleotide polymorphisms (SNPs) in Chinese men have been cataloged. We evaluated whether these SNPs can independently predict outcomes of prostate biopsy, and improve the predictive performance of existing clinical variables. Three hundred eight consecutive patients that underwent prostate biopsy for detection of PCa at Huashan Hospital, Shanghai, China between April 2011 and August 2012 were recruited. Clinical variables such as serum prostate-specific antigen (PSA) levels and peripheral blood samples were collected prior to a 10-core biopsy. A genetic score based on these 24 PCa associated SNPs was calculated for each individual. Among 308 patients underwent prostate biopsy, 141 (45.8%) were diagnosed with PCa. Genetic score was significantly higher in patients with PCa (median = 1.30) than without (median = 0.89), P = 3.81 × 10(-6). The difference remained significant after adjusting for age and total PSA, P = 0.007. The PCa detection rate increased with increasing genetic score; 26.3%, 43.2%, and 60.0% for men with lower (<0.5), average (0.5-1.5), and higher (>1.5) genetic score, respectively, P(-trend)  = 0.0003. For patients with moderately elevated PSA levels (1.6-20 ng/ml), the PCa detection rate was 31.2% overall and was 16.7%, 31.2%, and 40.9% for men with lower (<0.5), average (0.5-1.5), and higher (>1.5) genetic score, respectively, P(-trend)  = 0.03. For patients with PSA ≥ 20 ng/ml, however, the PCa detection rates were high (>69%) regardless of genetic score. A genetic score based on PCa risk-associated SNPs is an independent predictor of prostate biopsy outcomes in Chinese men and may be helpful to determine the need for prostate biopsy among patients within a "gray zone" of PCa risk. © 2013 Wiley Periodicals, Inc.

  12. Understanding your prostate cancer risk

    MedlinePlus

    ... medlineplus.gov/ency/patientinstructions/000931.htm Understanding your prostate cancer risk To use the sharing features on this ... enable JavaScript. Are you at risk for developing prostate cancer in your lifetime? Learn about the risk factors ...

  13. Understanding your breast cancer risk

    MedlinePlus

    ... ency/patientinstructions/000830.htm Understanding your breast cancer risk To use the sharing features on this page, ... you can do to help prevent breast cancer. Risk Factors You Cannot Control Risk factors you cannot ...

  14. 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.

  15. 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

  16. A risk score model predictive of the presence of additional disease in the axilla in early-breast cancer patients with one or two metastatic sentinel lymph nodes.

    PubMed

    Canavese, G; Bruzzi, P; Catturich, A; Vecchio, C; Tomei, D; Del Mastro, L; Carli, F; Guenzi, M; Lacopo, F; Dozin, B

    2014-07-01

    Axillary lymph node dissection (ALND) in early-breast cancer patients with positive sentinel node (SLN+) may not always be necessary. To predict the finding of ≥1 metastatic axillary node in addition to SLN+(s); to discriminate between patients who would or not benefit from ALND. Records of 397 consecutive patients with 1-2 SLN+s receiving ALND were reviewed. Clinico-pathological features were used in univariate and multivariate analyses to develop a logistic regression model predictive of the risk of ≥1 additional axillary node involved. The discrimination power of the model was quantified by the area under the receiver operating characteristic curve (AUC) and validated using an independent set of 83 patients. In univariate analyses, the risk of ≥1 additional node involved was correlated with tumor size, grade, HER-2 and Ki-67 over-expression, number of SLN+s. All factors, but Ki-67, retained in multivariate regressions were used to generate a predictive model with good discriminating power on both the training and the validation sets (AUC 0.73 and 0.75, respectively). Three patient groups were defined based on their risk to present additional axillary burden. The model identifies SLN+-patients at low risk (≤15%) who could reasonably be spared ALND and those at high risk (>75%) who should receive ALND. For patients at intermediate risk, ALND appropriateness could be individually evaluated based on other clinico-pathological parameters. Copyright © 2014 Elsevier Ltd. All rights reserved.

  17. Cancer risk modelling and radiological protection.

    PubMed

    Wakeford, Richard

    2012-03-01

    Statistical models describing how the radiation-related risks of particular types of cancer vary with the doses of radiation received by specific tissues are derived from data gathered in epidemiological studies of exposed groups of people, guided by an incomplete understanding of radiobiological mechanisms gleaned from experimental studies. Cancer risk models have been developed for a dozen or so different types of cancer, and take account of the effect of important risk modifying factors such as age at exposure and time since exposure. Of primary importance in the development of cancer risk models is the experience of the Japanese atomic bomb survivors, but other exposed groups contribute information, including those exposed to radiation from internally deposited radioactive material, such as inhaled radon. Cancer risk models predict that at low doses or low dose rates the excess risk of cancer is directly proportional to the dose of radiation received, with no threshold dose--the linear no threshold (LNT) dose-response model--and the inferred summary estimate of the overall average lifetime excess risk of developing a serious cancer is ∼ 5%/Sv. It is these cancer risk models and this inferred nominal risk estimate that provide the technical basis of radiological protection. Although it is difficult to definitively test the LNT model at low doses or low dose rates, because the predicted excess risk is small compared with fluctuations in the baseline risk, evidence exists that a small risk of cancer results from low-level exposure to radiation and that the excess risk is around that predicted by current risk models.

  18. SU-E-T-628: Predicted Risk of Post-Irradiation Cerebral Necrosis in Pediatric Brain Cancer Patients: A Treatment Planning Comparison of Proton Vs. Photon Therapy

    SciTech Connect

    Freund, D; Zhang, R; Sanders, M; Newhauser, W

    2015-06-15

    Purpose: Post-irradiation cerebral necrosis (PICN) is a severe late effect that can Result from brain cancers treatment using radiation therapy. The purpose of this study was to compare the treatment plans and predicted risk of PICN after volumetric modulated arc therapy (VMAT) to the risk after passively scattered proton therapy (PSPT) and intensity modulated proton therapy (IMPT) in a cohort of pediatric patients. Methods: Thirteen pediatric patients with varying age and sex were selected for this study. A clinical treatment volume (CTV) was constructed for 8 glioma patients and 5 ependymoma patients. Prescribed dose was 54 Gy over 30 fractions to the planning volume. Dosimetric endpoints were compared between VMAT and proton plans. The normal tissue complication probability (NTCP) following VMAT and proton therapy planning was also calculated using PICN as the biological endpoint. Sensitivity tests were performed to determine if predicted risk of PICN was sensitive to positional errors, proton range errors and selection of risk models. Results: Both PSPT and IMPT plans resulted in a significant increase in the maximum dose and reduction in the total brain volume irradiated to low doses compared with the VMAT plans. The average ratios of NTCP between PSPT and VMAT were 0.56 and 0.38 for glioma and ependymoma patients respectively and the average ratios of NTCP between IMPT and VMAT were 0.67 and 0.68 for glioma and ependymoma plans respectively. Sensitivity test revealed that predicted ratios of risk were insensitive to range and positional errors but varied with risk model selection. Conclusion: Both PSPT and IMPT plans resulted in a decrease in the predictive risk of necrosis for the pediatric plans studied in this work. Sensitivity analysis upheld the qualitative findings of the risk models used in this study, however more accurate models that take into account dose and volume are needed.

  19. Severe diarrhea in patients with advanced-stage colorectal cancer receiving FOLFOX or FOLFIRI chemotherapy: the development of a risk prediction tool.

    PubMed

    Dranitsaris, George; Shah, Amil; Spirovski, Biljana; Vincent, Mark

    2007-01-01

    FOLFOX (oxaliplatin/leucovorin/5-fluorouracil) and FOLFIRI (irinotecan/leucovorin/5-fluorouracil) are important regimens for the treatment of advanced-stage colorectal cancer (CRC). However, both are associated with severe diarrhea, leading to hospitalization, dose reductions/delays, and even death. In this study, the development of a prediction model for severe diarrhea is described. The records of 200 patients with CRC who had received FOLFOX or FOLFIRI in 3 Canadian cancer centers were reviewed. Clinical and biochemistry parameters potentially associated with diarrhea were abstracted. Logistic regression analysis was applied to develop the final risk model. A risk scoring system, ranging from 0 to 15, was then created from the regression parameters. A receiver operative characteristic curve analysis was done to measure the accuracy of the scoring system. Important predictors for severe diarrhea included existing comorbidity, patient performance status, an increased baseline bilirubin level, resection of the primary tumor, FOLFOX chemotherapy, metastatic or advanced locoregional versus resected stage IV disease, and the initiation of treatment in the summer months. The receiver operative characteristic analysis had an area under the curve of 0.80 (95% confidence interval, 0.74-0.87). An overall risk score of > or = 7 for a given patient was identified as being the optimal cutoff to maximize the sensitivity (61.4%) and specificity (89.6%) of the prediction tool. We developed a prediction tool for severe diarrhea in patients with CRC receiving FOLFOX or FOLFIRI chemotherapy. To make the model available for easy use and access, we have incorporated it on to our risk prediction Web site: www.PredictPatientEvents.com. Prospective external validation is also being planned.

  20. Cancer risk communication, predictive testing and management in France, Germany, the Netherlands and the UK: general practitioners' and breast surgeons' current practice and preferred practice responsibilities.

    PubMed

    Nippert, Irmgard; Julian-Reynier, Claire; Harris, Hilary; Evans, Gareth; van Asperen, Christi J; Tibben, Aad; Schmidtke, Jörg

    2014-01-01

    Genetic testing has its greatest public health value when it identifies individuals who will benefit from specific interventions based upon their risk. This paradigm is the basis for the use of predictive tests, such as BRCA1/BRCA2 testing which has become part of clinical practice for more than a decade. Currently predictive BRCA1/BRCA2 testing is offered to women using low, moderate and high risk based upon family history as cut-off levels. Non-genetic health professionals such as general practitioners (GPs) and breast surgeons (BS) are seen as gatekeepers to manage demand and/or facilitate access to appropriate services for high-risk patients. Data about current practices are lacking. The paper presents data on the current practice of GPs' and BS' cancer risk assessment, referral practices and preferred practice responsibilities for women at risk for familial breast cancer in France, Germany, the Netherlands and the UK derived by a self-administered questionnaire send to a representative sample of GPs and BS in the four countries. One thousand one hundred ninety-seven GPs and 1,223 BS completed the questionnaire. Both GPs and BS reported that they are consulted by a considerable number of patients presenting with concerns about a family history of cancer. Both commonalities and striking differences could be observed between GPs and BS from the four participating countries. GPs from France and Germany reported significantly higher proportions taking a family history of cancer including the extended family than GPs from the Netherlands and the UK. Most GPs from France, Germany and the Netherlands stated their willingness for providing risk assessment for an unaffected (high-risk) woman with a family history of breast cancer and the vast majority of BS from all four countries reported that they themselves would provide risk assessment for an unaffected (high-risk) woman with a family history of breast cancer. However, a substantial number of both GPs and BS would

  1. Predicting the risk of local recurrence in patients with breast cancer: an approach to a new computer-based predictive tool.

    PubMed

    Sanghani, Mona; Balk, Ethan; Cady, Blake; Wazer, David

    2007-10-01

    To develop a new web-based tool, designated IBTR!, which integrates prognostic factors for local recurrence (LR) into a model to predict the 10-year risk of LR after breast conserving surgery (BCS) with or without radiation therapy (RT) with the goal of assisting with patient counseling and medical decision-making. All available randomized trials of BCS alone versus BCS plus RT, meta-analyses, and institutional reports were reviewed to identify the principal prognostic factors for LR after breast-conserving therapy. Patient age, margin status, lymphovascular invasion (LVI), tumor size, tumor grade, use of chemotherapy, and use of hormonal therapy were found to consistently and significantly impact LR across multiple studies. Based upon a composite analysis of the relevant published randomized and nonrandomized studies, relative risk (RR) ratios were estimated and assigned to each prognostic category. These RR ratios were entered into a mathematical model with the 10-year baseline rates of recurrence with and without RT, 7% and 24%, respectively, to predict patient-specific LR risk. Individual data entered into this computer model with regards to patient age, margin status, LVI, tumor size, tumor grade, use of chemotherapy, and use of hormonal therapy will generate patient-specific predicted 10-year LR risk with and without RT. A graphic representation of the relative risk reduction with RT will also be displayed alongside the numerical display. IBTR! is a first attempt at a computer model incorporating LR prognostic factors in an evidence-based fashion to predict individual LR risk and the potential additional benefit from RT.

  2. The DGAV risk calculator: development and validation of statistical models for a web-based instrument predicting complications of colorectal cancer surgery.

    PubMed

    Crispin, Alexander; Klinger, Carsten; Rieger, Anna; Strahwald, Brigitte; Lehmann, Kai; Buhr, Heinz-Johannes; Mansmann, Ulrich

    2017-08-10

    The purpose of this study is to provide a web-based calculator predicting complication probabilities of patients undergoing colorectal cancer (CRC) surgery in Germany. Analyses were based on records of first-time CRC surgery between 2010 and February 2017, documented in the database of the Study, Documentation, and Quality Center (StuDoQ) of the Deutsche Gesellschaft für Allgemein- und Viszeralchirurgie (DGAV), a registry of CRC surgery in hospitals throughout Germany, covering demography, medical history, tumor features, comorbidity, behavioral risk factors, surgical procedures, and outcomes. Using logistic ridge regression, separate models were developed in learning samples of 6729 colon and 4381 rectum cancer patients and evaluated in validation samples of sizes 2407 and 1287. Discrimination was assessed using c statistics. Calibration was examined graphically by plotting observed versus predicted complication probabilities and numerically using Brier scores. We report validation results regarding 15 outcomes such as any major complication, surgical site infection, anastomotic leakage, bladder voiding disturbance after rectal surgery, abdominal wall dehiscence, various internistic complications, 30-day readmission, 30-day reoperation rate, and 30-day mortality. When applied to the validation samples, c statistics ranged between 0.60 for anastomosis leakage and 0.85 for mortality after rectum cancer surgery. Brier scores ranged from 0.003 to 0.127. While most models showed satisfactory discrimination and calibration, this does not preclude overly optimistic or pessimistic individual predictions. To avoid misinterpretation, one has to understand the basic principles of risk calculation and risk communication. An e-learning tool outlining the appropriate use of the risk calculator is provided.

  3. Cancer risk from inorganics

    SciTech Connect

    Swierenga, S.H.; Gilman, J.P.; McLean, J.R.

    1987-01-01

    Inorganic metals and minerals for which there is evidence of carcinogenicity are identified. The risk of cancer from contact with them in the work place, the general environment, and under conditions of clinical (medical) exposure is discussed. The evidence indicates that minerals and metals most often influence cancer development through their action as cocarcinogens. The relationship between the physical form of mineral fibers, smoking and carcinogenic risk is emphasized. Metals are categorized as established (As, Be, Cr, Ni), suspected (Cd, Pb) and possible carcinogens, based on the existing in vitro, animal experimental and human epidemiological data. Cancer risk and possible modes of action of elements in each class are discussed. Views on mechanisms that may be responsible for the carcinogenicity of metals are updated and analysed. Some specific examples of cancer risks associated with the clinical use of potentially carcinogenic metals and from radioactive pharmaceuticals used in therapy and diagnosis are presented. Questions are raised as to the effectiveness of conventional dosimetry in accurately measuring risk from radiopharmaceuticals. 302 references.

  4. Evaluation of BRCA1 and BRCA2 mutations and risk-prediction models in a typical Asian country (Malaysia) with a relatively low incidence of breast cancer.

    PubMed

    Thirthagiri, E; Lee, S Y; Kang, P; Lee, D S; Toh, G T; Selamat, S; Yoon, S-Y; Taib, N A Mohd; Thong, M K; Yip, C H; Teo, S H

    2008-01-01

    The cost of genetic testing and the limited knowledge about the BRCA1 and BRCA2 genes in different ethnic groups has limited its availability in medium- and low-resource countries, including Malaysia. In addition, the applicability of many risk-assessment tools, such as the Manchester Scoring System and BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) which were developed based on mutation rates observed primarily in Caucasian populations using data from multiplex families, and in populations where the rate of breast cancer is higher, has not been widely tested in Asia or in Asians living elsewhere. Here, we report the results of genetic testing for mutations in the BRCA1 or BRCA2 genes in a series of families with breast cancer in the multi-ethnic population (Malay, Chinese and Indian) of Malaysia. A total of 187 breast cancer patients with either early-onset breast cancer (at age cancer were comprehensively tested by full sequencing of both BRCA1 and BRCA2. Two algorithms to predict the presence of mutations, the Manchester Scoring System and BOADICEA, were evaluated. Twenty-seven deleterious mutations were detected (14 in BRCA1 and 13 in BRCA2), only one of which was found in two unrelated individuals (BRCA2 490 delCT). In addition, 47 variants of uncertain clinical significance were identified (16 in BRCA1 and 31 in BRCA2). Notably, many mutations are novel (13 of the 30 BRCA1 mutations and 24 of the 44 BRCA2). We report that while there were an equal proportion of BRCA1 and BRCA2 mutations in the Chinese population in our study, there were significantly more BRCA2 mutations among the Malays. In addition, we show that the predictive power of the BOADICEA risk-prediction model and the Manchester Scoring System was significantly better for BRCA1 than BRCA2, but that the overall sensitivity, specificity and positive-predictive value was lower in this

  5. 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.

  6. Breastfeeding and breast cancer risk.

    PubMed

    Brinton, L A; Potischman, N A; Swanson, C A; Schoenberg, J B; Coates, R J; Gammon, M D; Malone, K E; Stanford, J L; Daling, J R

    1995-05-01

    A population-based case-control study of breast cancer with a focus on premenopausal women under 45 years of age, conducted in three geographic regions of the United States, enabled the evaluation of risk in relation to varying breastfeeding practices. Among premenopausal parous women (1,211 cases, 1,120 random-digit-dialing controls), a history of breastfeeding for two or more weeks was associated with a relative risk (RR) of 0.87 (95 percent confidence interval [CI] = 0.7-1.0). This relationship was not altered substantially by removing from the reference group women who had problems with breastfeeding in the first two weeks, including those with insufficient milk production. Risk was not related substantially to number of children breastfed or length of breastfeeding, although a relatively low risk was observed among those breastfeeding for the longest duration examined (RR = 0.67, CI = 0.4-1.1 for an average period per child of 72 or more weeks). Women who began to breastfeed at a young age (< 22 years) experienced the greatest reduction in risk, but other timing parameters (e.g., interval since first or last breastfeeding) were not predictive of risk. Risks were not modified substantially by age or menopause status, although the number of menopausal subjects examined was limited. Use of medications to stop breast milk was unrelated to risk (RR = 1.04). The results of this study do not support the notion that breastfeeding substantially reduces breast cancer risk; however, this may reflect the fact that most of our study subjects breastfed only for limited periods of time (average breastfeeding per child of 30 weeks). Further studies are needed to clarify the relationship of breastfeeding to breast cancer risk, and to determine possible etiologic mechanisms underlying any observed associations.

  7. Risk determination and prevention of breast cancer.

    PubMed

    Howell, Anthony; Anderson, Annie S; Clarke, Robert B; Duffy, Stephen W; Evans, D Gareth; Garcia-Closas, Montserat; Gescher, Andy J; Key, Timothy J; Saxton, John M; Harvie, Michelle N

    2014-09-28

    Breast cancer is an increasing public health problem. Substantial advances have been made in the treatment of breast cancer, but the introduction of methods to predict women at elevated risk and prevent the disease has been less successful. Here, we summarize recent data on newer approaches to risk prediction, available approaches to prevention, how new approaches may be made, and the difficult problem of using what we already know to prevent breast cancer in populations. During 2012, the Breast Cancer Campaign facilitated a series of workshops, each covering a specialty area of breast cancer to identify gaps in our knowledge. The risk-and-prevention panel involved in this exercise was asked to expand and update its report and review recent relevant peer-reviewed literature. The enlarged position paper presented here highlights the key gaps in risk-and-prevention research that were identified, together with recommendations for action. The panel estimated from the relevant literature that potentially 50% of breast cancer could be prevented in the subgroup of women at high and moderate risk of breast cancer by using current chemoprevention (tamoxifen, raloxifene, exemestane, and anastrozole) and that, in all women, lifestyle measures, including weight control, exercise, and moderating alcohol intake, could reduce breast cancer risk by about 30%. Risk may be estimated by standard models potentially with the addition of, for example, mammographic density and appropriate single-nucleotide polymorphisms. This review expands on four areas: (a) the prediction of breast cancer risk, (b) the evidence for the effectiveness of preventive therapy and lifestyle approaches to prevention, (c) how understanding the biology of the breast may lead to new targets for prevention, and (d) a summary of published guidelines for preventive approaches and measures required for their implementation. We hope that efforts to fill these and other gaps will lead to considerable advances in our

  8. Lifestyle and cancer risk.

    PubMed

    Weiderpass, Elisabete

    2010-11-01

    The main behavioural and environmental risk factors for cancer mortality in the world are related to diet and physical inactivity, use of addictive substances, sexual and reproductive health, exposure to air pollution and use of contaminated needles. The population attributable fraction for all cancer sites worldwide considering the joint effect of these factors is about 35% (34 % for low-and middle-income countries and 37% for high-income countries). Seventy-one percent(71%) of lung cancer deaths are caused by tobacco use (lung cancer is the leading cause of cancer death globally). The combined effects of tobacco use, low fruit and vegetable intake, urban air pollution, and indoor smoke from household use of solid fuels cause 76% of lung cancer deaths. Exposure to these behavioural and environmental factors is preventable; modifications in lifestyle could have a large impact in reducing the cancer burden worldwide (WHO, 2009). The evidence of association between lifestyle factors and cancer, as well as the main international recommendations for prevention are briefly reviewed and commented upon here.

  9. Common Breast Cancer Susceptibility Variants in LSP1 and RAD51L1 Are Associated with Mammographic Density Measures that Predict Breast Cancer Risk

    PubMed Central

    Vachon, Celine M.; Scott, Christopher G.; Fasching, Peter A.; Hall, Per; Tamimi, Rulla M.; Li, Jingmei; Stone, Jennifer; Apicella, Carmel; Odefrey, Fabrice; Gierach, Gretchen L.; Jud, Sebastian M.; Heusinger, Katharina; Beckmann, Matthias W.; Pollan, Marina; Fernández-Navarro, Pablo; González-Neira, Anna; Benítez, Javier; van Gils, Carla H.; Lokate, Mariëtte; Onland-Moret, N. Charlotte; Peeters, Petra H.M.; Brown, Judith; Leyland, Jean; Varghese, Jajini S.; Easton, Douglas F.; Thompson, Deborah J.; Luben, Robert N.; Warren, Ruth ML; Wareham, Nicholas J.; Loos, Ruth JF; Khaw, Kay-Tee; Ursin, Giske; Lee, Eunjung; Gayther, Simon A.; Ramus, Susan J.; Eeles, Rosalind A.; Leach, Martin O.; Kwan-Lim, Gek; Couch, Fergus J.; Giles, Graham G.; Baglietto, Laura; Krishnan, Kavitha; Southey, Melissa C.; Le Marchand, Loic; Kolonel, Laurence N.; Woolcott, Christy; Maskarinec, Gertraud; Haiman, Christopher A; Walker, Kate; Johnson, Nichola; McCormack, Valerie A.; Biong, Margarethe; Alnæs, Grethe I.G.; Gram, Inger Torhild; Kristensen, Vessela N.; Børresen-Dale, Anne-Lise; Lindström, Sara; Hankinson, Susan E.; Hunter, David J.; Andrulis, Irene L.; Knight, Julia A.; Boyd, Norman F.; Figueroa, Jonine D.; Lissowska, Jolanta; Wesolowska, Ewa; Peplonska, Beata; Bukowska, Agnieszka; Reszka, Edyta; Liu, JianJun; Eriksson, Louise; Czene, Kamila; Audley, Tina; Wu, Anna H.; Pankratz, V. Shane; Hopper, John L.; dos-Santos-Silva, Isabel

    2013-01-01

    Background Mammographic density adjusted for age and body mass index (BMI) is a heritable marker of breast cancer susceptibility. Little is known about the biological mechanisms underlying the association between mammographic density and breast cancer risk. We examined whether common low-penetrance breast cancer susceptibility variants contribute to inter-individual differences in mammographic density measures. Methods We established an international consortium (DENSNP) of 19 studies from 10 countries, comprising 16,895 Caucasian women, to conduct a pooled cross-sectional analysis of common breast cancer susceptibility variants in 14 independent loci and mammographic density measures. Dense and non-dense areas, and percent density, were measured using interactive-thresholding techniques. Mixed linear models were used to assess the association between genetic variants and the square roots of mammographic density measures adjusted for study, age, case status, body mass index (BMI) and menopausal status. Results Consistent with their breast cancer associations, the C-allele of rs3817198 in LSP1 was positively associated with both adjusted dense area (p=0.00005) and adjusted percent density (p=0.001) whereas the A-allele of rs10483813 in RAD51L1 was inversely associated with adjusted percent density (p=0.003), but not with adjusted dense area (p=0.07). Conclusion We identified two common breast cancer susceptibility variants associated with mammographic measures of radio-dense tissue in the breast gland. Impact We examined the association of 14 established breast cancer susceptibility loci with mammographic density phenotypes within a large genetic consortium and identified two breast cancer susceptibility variants, LSP1-rs3817198 and RAD51L1-rs10483813, associated with mammographic measures and in the same direction as the breast cancer association. PMID:22454379

  10. Post-traumatic Stress Symptoms and Post-traumatic Growth in 223 Childhood Cancer Survivors: Predictive Risk Factors.

    PubMed

    Tremolada, Marta; Bonichini, Sabrina; Basso, Giuseppe; Pillon, Marta

    2016-01-01

    With modern therapies and supportive care, survival rates of childhood cancer have increased considerably. However, there are long-term psychological sequelae of these treatments that may not manifest until pediatric survivors are into adulthood. The prevalence of post-traumatic stress disorder in young adult survivors of childhood cancer ranges from 6.2 to 22%; associated risk factors are young age at the assessment, female gender, low education level, and some disease-related factors. The aim of this study was to investigate, in adolescent and young adult (AYA) survivors of childhood cancer, the incidence and severity of post-traumatic stress symptoms (PTSSs), and to identify the risk factors and the associated post-traumatic growth (PTG) index. Participants were 223 AYA cancer survivors recruited during follow-up visits in the Oncohematology Clinic of the Department of Child and Woman's Health, University of Padua. Data were collected from self-report questionnaires on PTSS incidence, PTG mean score, perceived social support, and medical and socio-demographic factors. Ex-patients' mean age at the assessment was 19.33 years (SD = 3.01, 15-25), 123 males and 100 females, with a mean of years off-therapy of 9.64 (SD = 4.17). Most (52.5%) had survived an hematological disorder and 47.5% a solid tumor when they were aged, on average, 8.02 years (SD = 4.40). The main results indicated a moderate presence of clinical (≥9 symptoms: 9.4%) and sub-clinical PTSS (6-8 symptoms: 11.2%), with the avoidance criterion most often encountered. Re-experience symptoms and PTG mean score were significantly associated (r = 0.24; p = 0.0001). A hierarchical regression model (R (2) = 0.08; F = 1.46; p = 0.05) identified female gender (β = 0.16; p = 0.05) and less perceived social support (β = -0.43; p = 0.05) as risk factors to developing PTSS. Another hierarchical regression model assessed the possible predictors of the PTG total score (R (2) = 0.36; F = 9.1; p = 0.0001), with

  11. Post-traumatic Stress Symptoms and Post-traumatic Growth in 223 Childhood Cancer Survivors: Predictive Risk Factors

    PubMed Central

    Tremolada, Marta; Bonichini, Sabrina; Basso, Giuseppe; Pillon, Marta

    2016-01-01

    With modern therapies and supportive care, survival rates of childhood cancer have increased considerably. However, there are long-term psychological sequelae of these treatments that may not manifest until pediatric survivors are into adulthood. The prevalence of post-traumatic stress disorder in young adult survivors of childhood cancer ranges from 6.2 to 22%; associated risk factors are young age at the assessment, female gender, low education level, and some disease-related factors. The aim of this study was to investigate, in adolescent and young adult (AYA) survivors of childhood cancer, the incidence and severity of post-traumatic stress symptoms (PTSSs), and to identify the risk factors and the associated post-traumatic growth (PTG) index. Participants were 223 AYA cancer survivors recruited during follow-up visits in the Oncohematology Clinic of the Department of Child and Woman’s Health, University of Padua. Data were collected from self-report questionnaires on PTSS incidence, PTG mean score, perceived social support, and medical and socio-demographic factors. Ex-patients’ mean age at the assessment was 19.33 years (SD = 3.01, 15–25), 123 males and 100 females, with a mean of years off-therapy of 9.64 (SD = 4.17). Most (52.5%) had survived an hematological disorder and 47.5% a solid tumor when they were aged, on average, 8.02 years (SD = 4.40). The main results indicated a moderate presence of clinical (≥9 symptoms: 9.4%) and sub-clinical PTSS (6–8 symptoms: 11.2%), with the avoidance criterion most often encountered. Re-experience symptoms and PTG mean score were significantly associated (r = 0.24; p = 0.0001). A hierarchical regression model (R2 = 0.08; F = 1.46; p = 0.05) identified female gender (β = 0.16; p = 0.05) and less perceived social support (β = -0.43; p = 0.05) as risk factors to developing PTSS. Another hierarchical regression model assessed the possible predictors of the PTG total score (R2 = 0.36; F = 9.1; p = 0.0001), with

  12. Comparative economics of a 12-gene assay for predicting risk of recurrence in stage II colon cancer.

    PubMed

    Alberts, Steven R; Yu, Tiffany M; Behrens, Robert J; Renfro, Lindsay A; Srivastava, Geetika; Soori, Gamini S; Dakhil, Shaker R; Mowat, Rex B; Kuebler, John P; Kim, George P; Mazurczak, Miroslaw A; Hornberger, John

    2014-12-01

    Prior economic analysis that compared the 12-gene assay to published patterns of care predicted the assay would improve outcomes while lowering medical costs for stage II, T3, mismatch-repair-proficient (MMR-P) colon cancer patients. This study assessed the validity of those findings with real-world adjuvant chemotherapy (aCT) recommendations from the US third-party payer perspective. Costs and quality-adjusted life-years (QALYs) were estimated for stage II, T3, MMR-P colon cancer patients using guideline-compliant, state-transition probability estimation methods in a Markov model. A study of 141 patients from 17 sites in the Mayo Clinic Cancer Research Consortium provided aCT recommendations before and after knowledge of the 12-gene assay results. Progression and adverse events data with aCT regimens were based on published literature. Drug and administration costs for aCT were obtained from 2014 Medicare Fee Schedule. Sensitivity analyses evaluated the drivers and robustness of the primary outcomes. After receiving the 12-gene assay results, physician recommendations in favor of aCT decreased 22 %; fluoropyrimidine monotherapy and FOLFOX recommendations each declined 11 %. Average per-patient drugs, administration, and adverse events costs decreased $US2,339, $US733, and $US3,211, respectively. Average total direct medical costs decreased $US991. Average patient well-being improved by 0.114 QALYs. Savings are expected to persist even if the cost of oxaliplatin drops by >75 % due to generic substitution. This study provides evidence that real-world changes in aCT recommendations due to the 12-gene assay are likely to reduce direct medical costs and improve well-being for stage II, T3, MMR-P colon cancer patients.

  13. Toxicogenomic outcomes predictive of forestomach carcinogenesis following exposure to benzo(a)pyrene: Relevance to human cancer risk

    SciTech Connect

    Labib, Sarah Guo, Charles H. Williams, Andrew Yauk, Carole L. White, Paul A. Halappanavar, Sabina

    2013-12-01

    Forestomach tumors are observed in mice exposed to environmental carcinogens. However, the relevance of this data to humans is controversial because humans lack a forestomach. We hypothesize that an understanding of early molecular changes after exposure to a carcinogen in the forestomach will provide mode-of-action information to evaluate the applicability of forestomach cancers to human cancer risk assessment. In the present study we exposed mice to benzo(a)pyrene (BaP), an environmental carcinogen commonly associated with tumors of the rodent forestomach. Toxicogenomic tools were used to profile gene expression response in the forestomach. Adult Muta™Mouse males were orally exposed to 25, 50, and 75 mg BaP/kg-body-weight/day for 28 consecutive days. The forestomach was collected three days post-exposure. DNA microarrays, real-time RT-qPCR arrays, and protein analyses were employed to characterize responses in the forestomach. Microarray results showed altered expression of 414 genes across all treatment groups (± 1.5 fold; false discovery rate adjusted P ≤ 0.05). Significant downregulation of genes associated with phase II xenobiotic metabolism and increased expression of genes implicated in antigen processing and presentation, immune response, chemotaxis, and keratinocyte differentiation were observed in treated groups in a dose-dependent manner. A systematic comparison of the differentially expressed genes in the forestomach from the present study to differentially expressed genes identified in human diseases including human gastrointestinal tract cancers using the NextBio Human Disease Atlas showed significant commonalities between the two models. Our results provide molecular evidence supporting the use of the mouse forestomach model to evaluate chemically-induced gastrointestinal carcinogenesis in humans. - Highlights: • Benzo(a)pyrene-mediated transcriptomic response in the forestomach was examined. • The immunoproteosome subunits and MHC class I

  14. Cancer risk and PCOS.

    PubMed

    Dumesic, Daniel A; Lobo, Rogerio A

    2013-08-01

    Women with polycystic ovary syndrome (PCOS) have a 2.7-fold increased risk for developing endometrial cancer. A major factor for this increased malignancy risk is prolonged exposure of the endometrium to unopposed estrogen that results from anovulation. Additionally, secretory endometrium of some women with PCOS undergoing ovulation induction or receiving exogenous progestin exhibits progesterone resistance accompanied by dysregulation of gene expression controlling steroid action and cell proliferation. Endometrial surveillance includes transvaginal ultrasound and/or endometrial biopsy to assess thickened endometrium, prolonged amenorrhea, unopposed estrogen exposure or abnormal vaginal bleeding. Medical management for abnormal vaginal bleeding or endometrial hyperplasia consists of estrogen-progestin oral contraceptives, cyclic or continuous progestins or a levonorgestrel-releasing (Mirena) intrauterine device. Lifestyle modification with caloric restriction and exercise is appropriate to treat obesity as a concomitant risk factor for developing endometrial disease. An increased risk of ovarian cancer may also exist in some women with PCOS. There are strong data to suggest that oral contraceptive use is protective against ovarian cancer and increases with the duration of therapy. The mechanism of this protection may be through suppression of gonadotropin secretion rather than the prevention of "incessant ovulation". There is no apparent association of PCOS with breast cancer, although the high prevalence of metabolic dysfunction from obesity is a common denominator for both conditions. Recent data suggest that the use of metformin may be protective for both endometrial and breast cancer. There are insufficient data to evaluate any association between PCOS and vaginal, vulvar and cervical cancer or uterine leiomyosarcoma. Copyright © 2013 Elsevier Inc. All rights reserved.

  15. Human respiratory tract cancer risks of inhaled formaldehyde: dose-response predictions derived from biologically-motivated computational modeling of a combined rodent and human dataset.

    PubMed

    Conolly, Rory B; Kimbell, Julia S; Janszen, Derek; Schlosser, Paul M; Kalisak, Darin; Preston, Julian; Miller, Frederick J

    2004-11-01

    Formaldehyde inhalation at 6 ppm and above causes nasal squamous cell carcinoma (SCC) in F344 rats. The quantitative implications of the rat tumors for human cancer risk are of interest, since epidemiological studies have provided only equivocal evidence that formaldehyde is a human carcinogen. Conolly et al. (Toxicol. Sci. 75, 432-447, 2003) analyzed the rat tumor dose-response assuming that both DNA-reactive and cytotoxic effects of formaldehyde contribute to SCC development. The key elements of their approach were: (1) use of a three-dimensional computer reconstruction of the rat nasal passages and computational fluid dynamics (CFD) modeling to predict regional dosimetry of formaldehyde; (2) association of the flux of formaldehyde into the nasal mucosa, as predicted by the CFD model, with formation of DNA-protein cross-links (DPX) and with cytolethality/regenerative cellular proliferation (CRCP); and (3) use of a two-stage clonal growth model to link DPX and CRCP with tumor formation. With this structure, the prediction of the tumor dose response was extremely sensitive to cell kinetics. The raw dose-response data for CRCP are J-shaped, and use of these data led to a predicted J-shaped dose response for tumors, notwithstanding a concurrent low-dose-linear, directly mutagenic effect of formaldehyde mediated by DPX. In the present work the modeling approach used by Conolly et al. (ibid.) was extended to humans. Regional dosimetry predictions for the entire respiratory tract were obtained by merging a three-dimensional CFD model for the human nose with a one-dimensional typical path model for the lower respiratory tract. In other respects, the human model was structurally identical to the rat model. The predicted human dose response for DPX was obtained by scale-up of a computational model for DPX calibrated against rat and rhesus monkey data. The rat dose response for CRCP was used "as is" for the human model, since no preferable alternative was identified. Three

  16. [Environment and cancer risk].

    PubMed

    Boffetta, Paolo

    2013-10-01

    Several environmental factors, defined as pollutants present in air, water or other media, have been shown to be carcinogenic, including residential exposure to asbestos and radon, second-hand tobacco smoke, diesel engine emissions, and arsenic contamination of drinking water. Other factors, such as outdoor air pollution and water chlorination byproducts, are suspected carcinogens. In the case of pesticides and electromagnetic fields, including the use of cell phones, the available evidence does not suggest an increased risk of cancer. Overall, environmental causes of cancer are responsible for a limited proportion of the total burden of cancer in France and other high-income countries. Because of the involuntary nature of the exposure and the possibility to implement preventive measures, research into environmental cancer remains an important priority.

  17. 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

  18. Calibrated predictions for multivariate competing risks models.

    PubMed

    Gorfine, Malka; Hsu, Li; Zucker, David M; Parmigiani, Giovanni

    2014-04-01

    Prediction models for time-to-event data play a prominent role in assessing the individual risk of a disease, such as cancer. Accurate disease prediction models provide an efficient tool for identifying individuals at high risk, and provide the groundwork for estimating the population burden and cost of disease and for developing patient care guidelines. We focus on risk prediction of a disease in which family history is an important risk factor that reflects inherited genetic susceptibility, shared environment, and common behavior patterns. In this work family history is accommodated using frailty models, with the main novel feature being allowing for competing risks, such as other diseases or mortality. We show through a simulation study that naively treating competing risks as independent right censoring events results in non-calibrated predictions, with the expected number of events overestimated. Discrimination performance is not affected by ignoring competing risks. Our proposed prediction methodologies correctly account for competing events, are very well calibrated, and easy to implement.

  19. Quantifying prognosis with risk predictions.

    PubMed

    Pace, Nathan L; Eberhart, Leopold H J; Kranke, Peter R

    2012-01-01

    Prognosis is a forecast, based on present observations in a patient, of their probable outcome from disease, surgery and so on. Research methods for the development of risk probabilities may not be familiar to some anaesthesiologists. We briefly describe methods for identifying risk factors and risk scores. A probability prediction rule assigns a risk probability to a patient for the occurrence of a specific event. Probability reflects the continuum between absolute certainty (Pi = 1) and certified impossibility (Pi = 0). Biomarkers and clinical covariates that modify risk are known as risk factors. The Pi as modified by risk factors can be estimated by identifying the risk factors and their weighting; these are usually obtained by stepwise logistic regression. The accuracy of probabilistic predictors can be separated into the concepts of 'overall performance', 'discrimination' and 'calibration'. Overall performance is the mathematical distance between predictions and outcomes. Discrimination is the ability of the predictor to rank order observations with different outcomes. Calibration is the correctness of prediction probabilities on an absolute scale. Statistical methods include the Brier score, coefficient of determination (Nagelkerke R2), C-statistic and regression calibration. External validation is the comparison of the actual outcomes to the predicted outcomes in a new and independent patient sample. External validation uses the statistical methods of overall performance, discrimination and calibration and is uniformly recommended before acceptance of the prediction model. Evidence from randomised controlled clinical trials should be obtained to show the effectiveness of risk scores for altering patient management and patient outcomes.

  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. Automated assessment of imaging biomarkers for the PanCan lung cancer risk prediction model with validation on NLST data

    NASA Astrophysics Data System (ADS)

    Wiemker, Rafael; Sevenster, Merlijn; MacMahon, Heber; Li, Feng; Dalal, Sandeep; Tahmasebi, Amir; Klinder, Tobias

    2017-03-01

    The imaging biomarkers EmphysemaPresence and NoduleSpiculation are crucial inputs for most models aiming to predict the risk of indeterminate pulmonary nodules detected at CT screening. To increase reproducibility and to accelerate screening workflow it is desirable to assess these biomarkers automatically. Validation on NLST images indicates that standard histogram measures are not sufficient to assess EmphysemaPresence in screenees. However, automatic scoring of bulla-resembling low attenuation areas can achieve agreement with experts with close to 80% sensitivity and specificity. NoduleSpiculation can be automatically assessed with similar accuracy. We find a dedicated spiculi tracing score to slightly outperform generic combinations of texture features with classifiers.

  2. Space Radiation Cancer Risks

    NASA Technical Reports Server (NTRS)

    Cucinotta, Francis A.

    2007-01-01

    Space radiation presents major challenges to astronauts on the International Space Station and for future missions to the Earth s moon or Mars. Methods used to project risks on Earth need to be modified because of the large uncertainties in projecting cancer risks from space radiation, and thus impact safety factors. We describe NASA s unique approach to radiation safety that applies uncertainty based criteria within the occupational health program for astronauts: The two terrestrial criteria of a point estimate of maximum acceptable level of risk and application of the principle of As Low As Reasonably Achievable (ALARA) are supplemented by a third requirement that protects against risk projection uncertainties using the upper 95% confidence level (CL) in the radiation cancer projection model. NASA s acceptable level of risk for ISS and their new lunar program have been set at the point-estimate of a 3-percent risk of exposure induced death (REID). Tissue-averaged organ dose-equivalents are combined with age at exposure and gender-dependent risk coefficients to project the cumulative occupational radiation risks incurred by astronauts. The 95% CL criteria in practice is a stronger criterion than ALARA, but not an absolute cut-off as is applied to a point projection of a 3% REID. We describe the most recent astronaut dose limits, and present a historical review of astronaut organ doses estimates from the Mercury through the current ISS program, and future projections for lunar and Mars missions. NASA s 95% CL criteria is linked to a vibrant ground based radiobiology program investigating the radiobiology of high-energy protons and heavy ions. The near-term goal of research is new knowledge leading to the reduction of uncertainties in projection models. Risk projections involve a product of many biological and physical factors, each of which has a differential range of uncertainty due to lack of data and knowledge. The current model for projecting space radiation

  3. Space Radiation Cancer Risks

    NASA Technical Reports Server (NTRS)

    Cucinotta, Francis A.

    2007-01-01

    Space radiation presents major challenges to astronauts on the International Space Station and for future missions to the Earth s moon or Mars. Methods used to project risks on Earth need to be modified because of the large uncertainties in projecting cancer risks from space radiation, and thus impact safety factors. We describe NASA s unique approach to radiation safety that applies uncertainty based criteria within the occupational health program for astronauts: The two terrestrial criteria of a point estimate of maximum acceptable level of risk and application of the principle of As Low As Reasonably Achievable (ALARA) are supplemented by a third requirement that protects against risk projection uncertainties using the upper 95% confidence level (CL) in the radiation cancer projection model. NASA s acceptable level of risk for ISS and their new lunar program have been set at the point-estimate of a 3-percent risk of exposure induced death (REID). Tissue-averaged organ dose-equivalents are combined with age at exposure and gender-dependent risk coefficients to project the cumulative occupational radiation risks incurred by astronauts. The 95% CL criteria in practice is a stronger criterion than ALARA, but not an absolute cut-off as is applied to a point projection of a 3% REID. We describe the most recent astronaut dose limits, and present a historical review of astronaut organ doses estimates from the Mercury through the current ISS program, and future projections for lunar and Mars missions. NASA s 95% CL criteria is linked to a vibrant ground based radiobiology program investigating the radiobiology of high-energy protons and heavy ions. The near-term goal of research is new knowledge leading to the reduction of uncertainties in projection models. Risk projections involve a product of many biological and physical factors, each of which has a differential range of uncertainty due to lack of data and knowledge. The current model for projecting space radiation

  4. MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays

    PubMed Central

    Li, Hui; Zhu, Yitan; Burnside, Elizabeth S.; Drukker, Karen; Hoadley, Katherine A.; Fan, Cheng; Conzen, Suzanne D.; Whitman, Gary J.; Sutton, Elizabeth J.; Net, Jose M.; Ganott, Marie; Huang, Erich; Morris, Elizabeth A.; Perou, Charles M.; Ji, Yuan; Giger, Maryellen L.

    2016-01-01

    Purpose To investigate relationships between computer-extracted breast magnetic resonance (MR) imaging phenotypes with multigene assays of MammaPrint, Oncotype DX, and PAM50 to assess the role of radiomics in evaluating the risk of breast cancer recurrence. Materials and Methods Analysis was conducted on an institutional review board–approved retrospective data set of 84 deidentified, multi-institutional breast MR examinations from the National Cancer Institute Cancer Imaging Archive, along with clinical, histopathologic, and genomic data from The Cancer Genome Atlas. The data set of biopsy-proven invasive breast cancers included 74 (88%) ductal, eight (10%) lobular, and two (2%) mixed cancers. Of these, 73 (87%) were estrogen receptor positive, 67 (80%) were progesterone receptor positive, and 19 (23%) were human epidermal growth factor receptor 2 positive. For each case, computerized radiomics of the MR images yielded computer-extracted tumor phenotypes of size, shape, margin morphology, enhancement texture, and kinetic assessment. Regression and receiver operating characteristic analysis were conducted to assess the predictive ability of the MR radiomics features relative to the multigene assay classifications. Results Multiple linear regression analyses demonstrated significant associations (R2 = 0.25–0.32, r = 0.5–0.56, P < .0001) between radiomics signatures and multigene assay recurrence scores. Important radiomics features included tumor size and enhancement texture, which indicated tumor heterogeneity. Use of radiomics in the task of distinguishing between good and poor prognosis yielded area under the receiver operating characteristic curve values of 0.88 (standard error, 0.05), 0.76 (standard error, 0.06), 0.68 (standard error, 0.08), and 0.55 (standard error, 0.09) for MammaPrint, Oncotype DX, PAM50 risk of relapse based on subtype, and PAM50 risk of relapse based on subtype and proliferation, respectively, with all but the latter showing

  5. MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays.

    PubMed

    Li, Hui; Zhu, Yitan; Burnside, Elizabeth S; Drukker, Karen; Hoadley, Katherine A; Fan, Cheng; Conzen, Suzanne D; Whitman, Gary J; Sutton, Elizabeth J; Net, Jose M; Ganott, Marie; Huang, Erich; Morris, Elizabeth A; Perou, Charles M; Ji, Yuan; Giger, Maryellen L

    2016-11-01

    Purpose To investigate relationships between computer-extracted breast magnetic resonance (MR) imaging phenotypes with multigene assays of MammaPrint, Oncotype DX, and PAM50 to assess the role of radiomics in evaluating the risk of breast cancer recurrence. Materials and Methods Analysis was conducted on an institutional review board-approved retrospective data set of 84 deidentified, multi-institutional breast MR examinations from the National Cancer Institute Cancer Imaging Archive, along with clinical, histopathologic, and genomic data from The Cancer Genome Atlas. The data set of biopsy-proven invasive breast cancers included 74 (88%) ductal, eight (10%) lobular, and two (2%) mixed cancers. Of these, 73 (87%) were estrogen receptor positive, 67 (80%) were progesterone receptor positive, and 19 (23%) were human epidermal growth factor receptor 2 positive. For each case, computerized radiomics of the MR images yielded computer-extracted tumor phenotypes of size, shape, margin morphology, enhancement texture, and kinetic assessment. Regression and receiver operating characteristic analysis were conducted to assess the predictive ability of the MR radiomics features relative to the multigene assay classifications. Results Multiple linear regression analyses demonstrated significant associations (R(2) = 0.25-0.32, r = 0.5-0.56, P < .0001) between radiomics signatures and multigene assay recurrence scores. Important radiomics features included tumor size and enhancement texture, which indicated tumor heterogeneity. Use of radiomics in the task of distinguishing between good and poor prognosis yielded area under the receiver operating characteristic curve values of 0.88 (standard error, 0.05), 0.76 (standard error, 0.06), 0.68 (standard error, 0.08), and 0.55 (standard error, 0.09) for MammaPrint, Oncotype DX, PAM50 risk of relapse based on subtype, and PAM50 risk of relapse based on subtype and proliferation, respectively, with all but the latter showing statistical

  6. Toxicogenomic outcomes predictive of forestomach carcinogenesis following exposure to benzo(a)pyrene: relevance to human cancer risk.

    PubMed

    Labib, Sarah; Guo, Charles H; Williams, Andrew; Yauk, Carole L; White, Paul A; Halappanavar, Sabina

    2013-12-01

    Forestomach tumors are observed in mice exposed to environmental carcinogens. However, the relevance of this data to humans is controversial because humans lack a forestomach. We hypothesize that an understanding of early molecular changes after exposure to a carcinogen in the forestomach will provide mode-of-action information to evaluate the applicability of forestomach cancers to human cancer risk assessment. In the present study we exposed mice to benzo(a)pyrene (BaP), an environmental carcinogen commonly associated with tumors of the rodent forestomach. Toxicogenomic tools were used to profile gene expression response in the forestomach. Adult Muta™Mouse males were orally exposed to 25, 50, and 75 mgBaP/kg-body-weight/day for 28 consecutive days. The forestomach was collected three days post-exposure. DNA microarrays, real-time RT-qPCR arrays, and protein analyses were employed to characterize responses in the forestomach. Microarray results showed altered expression of 414 genes across all treatment groups (± 1.5 fold; false discovery rate adjusted P ≤ 0.05). Significant downregulation of genes associated with phase II xenobiotic metabolism and increased expression of genes implicated in antigen processing and presentation, immune response, chemotaxis, and keratinocyte differentiation were observed in treated groups in a dose-dependent manner. A systematic comparison of the differentially expressed genes in the forestomach from the present study to differentially expressed genes identified in human diseases including human gastrointestinal tract cancers using the NextBio Human Disease Atlas showed significant commonalities between the two models. Our results provide molecular evidence supporting the use of the mouse forestomach model to evaluate chemically-induced gastrointestinal carcinogenesis in humans.

  7. Autoantibody Signature Enhances the Positive Predictive Power of Computed Tomography and Nodule-Based Risk Models for Detection of Lung Cancer

    PubMed Central

    Massion, Pierre P.; Healey, Graham F.; Peek, Laura J.; Fredericks, Lynn; Sewell, Herb F.; Murray, Andrea; Robertson, John F. R.

    2017-01-01

    Introduction The incidence of pulmonary nodules is increasing with the movement toward screening for lung cancer by low-dose computed tomography. Given the large number of benign nodules detected by computed tomography, an adjunctive test capable of distinguishing malignant from benign nodules would benefit practitioners. The ability of the EarlyCDT-Lung blood test (Oncimmune Ltd., Nottingham, United Kingdom) to make this distinction by measuring autoantibodies to seven tumor-associated antigens was evaluated in a prospective registry. Methods Of the members of a cohort of 1987 individuals with Health Insurance Portability and Accountability Act authorization, those with pulmonary nodules detected, imaging, and pathology reports were reviewed. All patients for whom a nodule was identified within 6 months of testing by EarlyCDT-Lung were included. The additivity of the test to nodule size and nodule-based risk models was explored. Results A total of 451 patients (32%) had at least one nodule, leading to 296 eligible patients after exclusions, with a lung cancer prevalence of 25%. In 4- to 20-mm nodules, a positive test result represented a greater than twofold increased relative risk for development of lung cancer as compared with a negative test result. Also, when the “both-positive rule” for combining binary tests was used, adding EarlyCDT-Lung to risk models improved diagnostic performance with high specificity (>92%) and positive predictive value (>70%). Conclusions A positive autoantibody test result reflects a significant increased risk for malignancy in lung nodules 4 to 20 mm in largest diameter. These data confirm that EarlyCDT-Lung may add value to the armamentarium of the practitioner in assessing the risk for malignancy in indeterminate pulmonary nodules. PMID:27615397

  8. CYP2C8*3 predicts benefit/risk profile in breast cancer patients receiving neoadjuvant paclitaxel

    PubMed Central

    Motsinger-Reif, Alison A.; Drobish, Amy; Winham, Stacey J.; McLeod, Howard L.; Carey, Lisa A.; Dees, E. Claire

    2013-01-01

    Paclitaxel is one of the most frequently used chemotherapeutic agents for the treatment of breast cancer patients. Using a candidate gene approach, we hypothesized that polymorphisms in genes relevant to the metabolism and transport of paclitaxel are associated with treatment efficacy and toxicity. Patient and tumor characteristics and treatment outcomes were collected prospectively for breast cancer patients treated with paclitaxel-containing regimens in the neoadjuvant setting. Treatment response was measured before and after each phase of treatment by clinical tumor measurement and categorized according to RECIST criteria, while toxicity data were collected from physician notes. The primary endpoint was achievement of clinical complete response (cCR) and secondary endpoints included clinical response rate (complete response + partial response) and grade 3+ peripheral neuropathy. The genotypes and haplotypes assessed were CYP1B1*3, CYP2C8*3, CYP3A4*1B/CYP3A5*3C, and ABCB1*2. A total of 111 patients were included in this study. Overall, cCR was 30.1 % to the paclitaxel component. CYP2C8*3 carriers (23/111, 20.7 %) had higher rates of cCR (55 % vs. 23 %; OR = 3.92 [95 % CI: 1.46–10.48], corrected p = 0.046). In the secondary toxicity analysis, we observed a trend toward greater risk of severe neuropathy (22 % vs. 8 %; OR = 3.13 [95 % CI: 0.89–11.01], uncorrected p = 0.075) in subjects carrying the CYP2C8*3 variant. Other polymorphisms interrogated were not significantly associated with response or toxicity. Patients carrying CYP2C8*3 are more likely to achieve clinical complete response from neoadjuvant paclitaxel treatment, but may also be at increased risk of experiencing severe peripheral neurotoxicity. PMID:22527101

  9. Predicted Risk of Radiation-Induced Cancers After Involved Field and Involved Node Radiotherapy With or Without Intensity Modulation for Early-Stage Hodgkin Lymphoma in Female Patients

    SciTech Connect

    Weber, Damien C.; Johanson, Safora; Peguret, Nicolas; Cozzi, Luca; Olsen, Dag R.

    2011-10-01

    Purpose: To assess the excess relative risk (ERR) of radiation-induced cancers (RIC) in female patients with Hodgkin lymphoma (HL) female patients treated with conformal (3DCRT), intensity modulated (IMRT), or volumetric modulated arc (RA) radiation therapy. Methods and Materials: Plans for 10 early-stage HL female patients were computed for 3DCRT, IMRT, and RA with involved field RT (IFRT) and involvednode RT (INRT) radiation fields. Organs at risk dose--volume histograms were computed and inter-compared for IFRT vs. INRT and 3DCRT vs. IMRT/RA, respectively. The ERR for cancer induction in breasts, lungs, and thyroid was estimated using both linear and nonlinear models. Results: The mean estimated ERR for breast, lung, and thyroid were significantly lower (p < 0.01) with INRT than with IFRT planning, regardless of the radiation delivery technique used, assuming a linear dose-risk relationship. We found that using the nonlinear model, the mean ERR values were significantly (p < 0.01) increased with IMRT or RA compared to those with 3DCRT planning for the breast, lung, and thyroid, using an IFRT paradigm. After INRT planning, IMRT or RA increased the risk of RIC for lung and thyroid only. Conclusions: In this comparative planning study, using a nonlinear dose--risk model, IMRT or RA increased the estimated risk of RIC for breast, lung, and thyroid for HL female patients. This study also suggests that INRT planning, compared to IFRT planning, may reduce the ERR of RIC when risk is predicted using a linear model. Observing the opposite effect, with a nonlinear model, however, questions the validity of these biologically parameterized models.

  10. A Predictive Model Using Histopathologic Characteristics of Early-Stage Type 1 Endometrial Cancer to Identify Patients at High Risk for Lymph Node Metastasis.

    PubMed

    Bendifallah, Sofiane; Canlorbe, Geoffroy; Laas, Enora; Huguet, Florence; Coutant, Charles; Hudry, Delphine; Graesslin, Olivier; Raimond, Emilie; Touboul, Cyril; Collinet, Pierre; Cortez, Annie; Bleu, Géraldine; Daraï, Emile; Ballester, Marcos

    2015-12-01

    This study aimed to develop a predictive model using histopathologic characteristics of early-stage type 1 endometrial cancer (EC) to identify patients at high risk for lymph node (LN) metastases. The data of 523 patients who received primary surgical treatment between January 2001 and December 2012 were abstracted from a prospective multicenter database (training set). A multivariate logistic regression analysis of selected prognostic features was performed to develop a nomogram predicting LN metastases. To assess its accuracy, an internal validation technique with a bootstrap approach was adopted. The optimal threshold in terms of clinical utility, sensitivity, specificity, negative predictive values (NPVs), and positive predictive values (PPVs) was evaluated by the receiver-operating characteristics (ROC) curve area and the Youden Index. Overall, the LN metastasis rate was 12.4 % (65/523). Lymph node metastases were associated with histologic grade, tumor diameter, depth of myometrial invasion, and lymphovascular space involvement status. These variables were included in the nomogram. Discrimination of the model was 0.83 [95 % confidence interval (CI) 0.80-0.85] in the training set. The area under the curve ROC for predicting LN metastases after internal validation was 0.82 (95 % CI 0.80-0.84). The Youden Index provided a value of 0.2, corresponding to a cutoff of 140 points (total score in the algorithm). At this threshold, the model had a sensitivity of 0.73 (95 % CI 0.62-0.83), a specificity of 0.84 (95 % CI 0.82-0.85), a PPV of 0.40 (95 % CI 0.34-0.45), and an NPV of 0.95 (95 % CI 0.94-0.97). The results show that the risk of LN metastases can be predicted correctly so that patients at high risk can benefit from adapted surgical treatment.

  11. A Nomogram to Predict Recurrence and Survival of High-Risk Patients Undergoing Sublobar Resection for Lung Cancer: An Analysis of a Multicenter Prospective Study (ACOSOG Z4032).

    PubMed

    Kent, Michael S; Mandrekar, Sumithra J; Landreneau, Rodney; Nichols, Francis; Foster, Nathan R; DiPetrillo, Thomas A; Meyers, Bryan; Heron, Dwight E; Jones, David R; Tan, Angelina D; Starnes, Sandra; Putnam, Joe B; Fernando, Hiran C

    2016-07-01

    Individualized prediction of outcomes may help with therapy decisions for patients with non-small cell lung cancer. We developed a nomogram by analyzing 17 clinical factors and outcomes from a randomized study of sublobar resection for non-small cell lung cancer in high-risk operable patients. The study compared sublobar resection alone with sublobar resection with brachytherapy. There were no differences in primary and secondary outcomes between the study arms, and they were therefore combined for this analysis. The clinical factors of interest (considered as continuous variables) were assessed in a univariate Cox proportional hazards model for significance at the 0.10 level for their impact on overall survival (OS), local recurrence-free survival (LRFS), and any recurrence-free survival (RFS). The final multivariable model was developed using a stepwise model selection. Of 212 patients, 173 had complete data on all 17 risk factors. Median follow-up was 4.94 years (range, 0.04 to 6.22). The 5-year OS, LRFS, and RFS were 58.4%, 53.2%, and 47.4%, respectively. Age, baseline percent diffusing capacity of lung for carbon monoxide, and maximum tumor diameter were significant predictors for OS, LRFS, and RFS in the multivariable model. Nomograms were subsequently developed for predicting 5-year OS, LRFS, and RFS. Age, baseline percent diffusing capacity of lung for carbon monoxide, and maximum tumor diameter significantly predicted outcomes after sublobar resection. Such nomograms may be helpful for treatment planning in early stage non-small cell lung cancer and to guide future studies. Copyright © 2016 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

  12. Ecological risk assessment, prediction, and assessing risk predictions.

    PubMed

    Gibbs, Mark

    2011-11-01

    Ecological risk assessment embodied in an adaptive management framework is becoming the global standard approach for formally assessing and managing the ecological risks of technology and development. Ensuring the continual improvement of ecological risk assessment approaches is partly achieved through the dissemination of not only the types of risk assessment approaches used, but also their efficacy. While there is an increasing body of literature describing the results of general comparisons between alternate risk assessment methods and models, there is a paucity of literature that post hoc assesses the performance of specific predictions based on an assessment of risk and the effectiveness of the particular model used to predict the risk. This is especially the case where risk assessments have been used to grant consent or approval for the construction of major infrastructure projects. While postconstruction environmental monitoring is increasingly commonplace, it is not common for a postconstruction assessment of the accuracy and performance of the ecological risk assessment and underpinning model to be undertaken. Without this "assessment of the assessment," it is difficult for other practitioners to gain insight into the performance of the approach and models used and therefore, as argued here, this limits the rate of improvement of risk assessment approaches.

  13. Critical review of prostate cancer predictive tools.

    PubMed

    Shariat, Shahrokh F; Kattan, Michael W; Vickers, Andrew J; Karakiewicz, Pierre I; Scardino, Peter T

    2009-12-01

    Prostate cancer is a very complex disease, and the decision-making process requires the clinician to balance clinical benefits, life expectancy, comorbidities and potential treatment-related side effects. Accurate prediction of clinical outcomes may help in the difficult process of making decisions related to prostate cancer. In this review, we discuss attributes of predictive tools and systematically review those available for prostate cancer. Types of tools include probability formulas, look-up and propensity scoring tables, risk-class stratification prediction tools, classification and regression tree analysis, nomograms and artificial neural networks. Criteria to evaluate tools include discrimination, calibration, generalizability, level of complexity, decision analysis and ability to account for competing risks and conditional probabilities. The available predictive tools and their features, with a focus on nomograms, are described. While some tools are well-calibrated, few have been externally validated or directly compared with other tools. In addition, the clinical consequences of applying predictive tools need thorough assessment. Nevertheless, predictive tools can facilitate medical decision-making by showing patients tailored predictions of their outcomes with various alternatives. Additionally, accurate tools may improve clinical trial design.

  14. CRITICAL REVIEW OF PROSTATE CANCER PREDICTIVE TOOLS

    PubMed Central

    Shariat, Shahrokh F.; Kattan, Michael W.; Vickers, Andrew J.; Karakiewicz, Pierre I.; Scardino, Peter T.

    2010-01-01

    Summary Prostate cancer is a very complex disease, and the decision-making process requires the clinician to balance clinical benefits, life expectancy, comorbidities, and potential treatment related side effects. Accurate prediction of clinical outcomes may help in the difficult process of making decisions related to prostate cancer. In this review, we discuss attributes of predictive tools and systematically review those available for prostate cancer. Types of tools include probability formulas, look-up and propensity scoring tables, risk-class stratification prediction tools, classification and regression tree analysis, nomograms, and artificial neural networks. Criteria to evaluate tools include discrimination, calibration, generalizability, level of complexity, decision analysis, and ability to account for competing risks and conditional probabilities. We describe the available predictive tools and their features, focusing on nomograms. While some tools are well-calibrated, few have been externally validated or directly compared to other tools. In addition, the clinical consequences of applying predictive tools need thorough assessment. Nevertheless, predictive tools can facilitate medical decision-making by showing patients tailored predictions of their outcomes with various alternatives. Additionally, accurate tools may improve clinical trial design. PMID:20001796

  15. Comparing GIS-based measures in access to mammography and their validity in predicting neighborhood risk of late-stage breast cancer.

    PubMed

    Lian, Min; Struthers, James; Schootman, Mario

    2012-01-01

    Assessing neighborhood environment in access to mammography remains a challenge when investigating its contextual effect on breast cancer-related outcomes. Studies using different Geographic Information Systems (GIS)-based measures reported inconsistent findings. We compared GIS-based measures (travel time, service density, and a two-Step Floating Catchment Area method [2SFCA]) of access to FDA-accredited mammography facilities in terms of their Spearman correlation, agreement (Kappa) and spatial patterns. As an indicator of predictive validity, we examined their association with the odds of late-stage breast cancer using cancer registry data. The accessibility measures indicated considerable variation in correlation, Kappa and spatial pattern. Measures using shortest travel time (or average) and service density showed low correlations, no agreement, and different spatial patterns. Both types of measures showed low correlations and little agreement with the 2SFCA measures. Of all measures, only the two measures using 6-timezone-weighted 2SFCA method were associated with increased odds of late-stage breast cancer (quick-distance-decay: odds ratio [OR] = 1.15, 95% confidence interval [CI] = 1.01-1.32; slow-distance-decay: OR = 1.19, 95% CI = 1.03-1.37) after controlling for demographics and neighborhood socioeconomic deprivation. Various GIS-based measures of access to mammography facilities exist and are not identical in principle and their association with late-stage breast cancer risk. Only the two measures using the 2SFCA method with 6-timezone weighting were associated with increased odds of late-stage breast cancer. These measures incorporate both travel barriers and service competition. Studies may observe different results depending on the measure of accessibility used.

  16. A Radio-genomics Approach for Identifying High Risk Estrogen Receptor-positive Breast Cancers on DCE-MRI: Preliminary Results in Predicting OncotypeDX Risk Scores

    NASA Astrophysics Data System (ADS)

    Wan, Tao; Bloch, B. Nicolas; Plecha, Donna; Thompson, Cheryi L.; Gilmore, Hannah; Jaffe, Carl; Harris, Lyndsay; Madabhushi, Anant

    2016-02-01

    To identify computer extracted imaging features for estrogen receptor (ER)-positive breast cancers on dynamic contrast en-hanced (DCE)-MRI that are correlated with the low and high OncotypeDX risk categories. We collected 96 ER-positivebreast lesions with low (<18, N = 55) and high (>30, N = 41) OncotypeDX recurrence scores. Each lesion was quantitatively charac-terize via 6 shape features, 3 pharmacokinetics, 4 enhancement kinetics, 4 intensity kinetics, 148 textural kinetics, 5 dynamic histogram of oriented gradient (DHoG), and 6 dynamic local binary pattern (DLBP) features. The extracted features were evaluated by a linear discriminant analysis (LDA) classifier in terms of their ability to distinguish low and high OncotypeDX risk categories. Classification performance was evaluated by area under the receiver operator characteristic curve (Az). The DHoG and DLBP achieved Az values of 0.84 and 0.80, respectively. The 6 top features identified via feature selection were subsequently combined with the LDA classifier to yield an Az of 0.87. The correlation analysis showed that DHoG (ρ = 0.85, P < 0.001) and DLBP (ρ = 0.83, P < 0.01) were significantly associated with the low and high risk classifications from the OncotypeDX assay. Our results indicated that computer extracted texture features of DCE-MRI were highly correlated with the high and low OncotypeDX risk categories for ER-positive cancers.

  17. Risk Factor Modification and Projections of Absolute Breast Cancer Risk

    PubMed Central

    Decarli, Adriano; Schairer, Catherine; Pfeiffer, Ruth M.; Pee, David; Masala, Giovanna; Palli, Domenico

    2011-01-01

    Background Although modifiable risk factors have been included in previous models that estimate or project breast cancer risk, there remains a need to estimate the effects of changes in modifiable risk factors on the absolute risk of breast cancer. Methods Using data from a case–control study of women in Italy (2569 case patients and 2588 control subjects studied from June 1, 1991, to April 1, 1994) and incidence and mortality data from the Florence Registries, we developed a model to predict the absolute risk of breast cancer that included five non-modifiable risk factors (reproductive characteristics, education, occupational activity, family history, and biopsy history) and three modifiable risk factors (alcohol consumption, leisure physical activity, and body mass index). The model was validated using independent data, and the percent risk reduction was calculated in high-risk subgroups identified by use of the Lorenz curve. Results The model was reasonably well calibrated (ratio of expected to observed cancers = 1.10, 95% confidence interval [CI] = 0.96 to 1.26), but the discriminatory accuracy was modest. The absolute risk reduction from exposure modifications was nearly proportional to the risk before modifying the risk factors and increased with age and risk projection time span. Mean 20-year reductions in absolute risk among women aged 65 years were 1.6% (95% CI = 0.9% to 2.3%) in the entire population, 3.2% (95% CI = 1.8% to 4.8%) among women with a positive family history of breast cancer, and 4.1% (95% CI = 2.5% to 6.8%) among women who accounted for the highest 10% of the total population risk, as determined from the Lorenz curve. Conclusions These data give perspective on the potential reductions in absolute breast cancer risk from preventative strategies based on lifestyle changes. Our methods are also useful for calculating sample sizes required for trials to test lifestyle interventions. PMID:21705679

  18. Risk factor modification and projections of absolute breast cancer risk.

    PubMed

    Petracci, Elisabetta; Decarli, Adriano; Schairer, Catherine; Pfeiffer, Ruth M; Pee, David; Masala, Giovanna; Palli, Domenico; Gail, Mitchell H

    2011-07-06

    Although modifiable risk factors have been included in previous models that estimate or project breast cancer risk, there remains a need to estimate the effects of changes in modifiable risk factors on the absolute risk of breast cancer. Using data from a case-control study of women in Italy (2569 case patients and 2588 control subjects studied from June 1, 1991, to April 1, 1994) and incidence and mortality data from the Florence Registries, we developed a model to predict the absolute risk of breast cancer that included five non-modifiable risk factors (reproductive characteristics, education, occupational activity, family history, and biopsy history) and three modifiable risk factors (alcohol consumption, leisure physical activity, and body mass index). The model was validated using independent data, and the percent risk reduction was calculated in high-risk subgroups identified by use of the Lorenz curve. The model was reasonably well calibrated (ratio of expected to observed cancers = 1.10, 95% confidence interval [CI] = 0.96 to 1.26), but the discriminatory accuracy was modest. The absolute risk reduction from exposure modifications was nearly proportional to the risk before modifying the risk factors and increased with age and risk projection time span. Mean 20-year reductions in absolute risk among women aged 65 years were 1.6% (95% CI = 0.9% to 2.3%) in the entire population, 3.2% (95% CI = 1.8% to 4.8%) among women with a positive family history of breast cancer, and 4.1% (95% CI = 2.5% to 6.8%) among women who accounted for the highest 10% of the total population risk, as determined from the Lorenz curve. These data give perspective on the potential reductions in absolute breast cancer risk from preventative strategies based on lifestyle changes. Our methods are also useful for calculating sample sizes required for trials to test lifestyle interventions.

  19. Cognitive and emotional factors predicting decisional conflict among high-risk breast cancer survivors who receive uninformative BRCA1/2 results.

    PubMed

    Rini, Christine; O'Neill, Suzanne C; Valdimarsdottir, Heiddis; Goldsmith, Rachel E; Jandorf, Lina; Brown, Karen; DeMarco, Tiffani A; Peshkin, Beth N; Schwartz, Marc D

    2009-09-01

    To investigate high-risk breast cancer survivors' risk reduction decision making and decisional conflict after an uninformative BRCA1/2 test. Prospective, longitudinal study of 182 probands undergoing BRCA1/2 testing, with assessments 1-, 6-, and 12-months postdisclosure. Primary predictors were health beliefs and emotional responses to testing assessed 1-month postdisclosure. Main outcomes included women's perception of whether they had made a final risk management decision (decision status) and decisional conflict related to this issue. There were four patterns of decision making, depending on how long it took women to make a final decision and the stability of their decision status across assessments. Late decision makers and nondecision makers reported the highest decisional conflict; however, substantial numbers of women--even early and intermediate decision makers--reported elevated decisional conflict. Analyses predicting decisional conflict 1- and 12-months postdisclosure found that, after accounting for control variables and decision status, health beliefs and emotional factors predicted decisional conflict at different timepoints, with health beliefs more important 1 month after test disclosure and emotional factors more important 1 year later. Many of these women may benefit from decision making assistance. Copyright 2009 APA, all rights reserved.

  20. Breast cancer risk factors

    PubMed Central

    Ciszewski, Tomasz; Łopacka-Szatan, Karolina; Miotła, Paweł; Starosławska, Elżbieta

    2015-01-01

    Breast cancer is the most frequently diagnosed neoplastic disease in women around menopause often leading to a significant reduction of these women's ability to function normally in everyday life. The increased breast cancer incidence observed in epidemiological studies in a group of women actively participating in social and professional life implicates the necessity of conducting multidirectional studies in order to identify risk factors associated with the occurrence of this type of neoplasm. Taking the possibility of influencing the neoplastic transformation process in individuals as a criterion, all the risk factors initiating the process can be divided into two groups. The first group would include inherent factors such as age, sex, race, genetic makeup promoting familial occurrence of the neoplastic disease or the occurrence of benign proliferative lesions of the mammary gland. They all constitute independent parameters and do not undergo simple modification in the course of an individual's life. The second group would include extrinsic factors conditioned by lifestyle, diet or long-term medical intervention such as using oral hormonal contraceptives or hormonal replacement therapy and their influence on the neoplastic process may be modified to a certain degree. Identification of modifiable factors may contribute to development of prevention strategies decreasing breast cancer incidence. PMID:26528110

  1. Abortion, Miscarriage, and Breast Cancer Risk

    MedlinePlus

    ... Cancers Breast Cancer Screening Research Abortion, Miscarriage, and Breast Cancer Risk: 2003 Workshop In February 2003, the National ... the development of breast cancer. Important Information about Breast Cancer Risk Factors At present, the factors known to ...

  2. Breast Cancer Risk in American Women

    MedlinePlus

    ... of Breast & Gynecologic Cancers Breast Cancer Screening Research Breast Cancer Risk in American Women On This Page What ... risk of developing the disease. Personal history of breast cancer : Women who have had breast cancer are more ...

  3. Overall rate, location, and predictive factors for positive surgical margins after robot-assisted laparoscopic radical prostatectomy for high-risk prostate cancer

    PubMed Central

    Kang, Sung Gu; Schatloff, Oscar; Haidar, Abdul Muhsin; Samavedi, Srinivas; Palmer, Kenneth J; Cheon, Jun; Patel, Vipul R

    2016-01-01

    We report the overall rate, locations and predictive factors of positive surgical margins (PSMs) in 271 patients with high-risk prostate cancer. Between April 2008 and October 2011, we prospectively collected data from patients classified as D’Amico high-risk who underwent robot-assisted laparoscopic radical prostatectomy. Overall rate and location of PSMs were reported. Stepwise logistic regression models were fitted to assess predictive factors of PSM. The overall rate of PSMs was 25.1% (68 of 271 patients). Of these PSM, 38.2% (26 of 68) were posterolateral (PL), 26.5% (18 of 68) multifocal, 16.2% (11 of 68) in the apex, 14.7% (10 of 68) in the bladder neck, and 4.4% (3/68) in other locations. The PSM rate of patients with pathological stage pT2 was 8.6% (12 of 140), 26.6% (17 of 64) of pT3a, 53.3% (32/60) of pT3b, and 100% (7 of 7) of pT4. In a logistic regression model including pre-, intra-, and post-operative parameters, body mass index (odds ratio [OR]: 1.09; 95% confidence interval [CI]: 1.01–1.19, P= 0.029), pathological stage (pT3b or higher vs pT2; OR: 5.14; 95% CI: 1.92–13.78; P = 0.001) and percentage of the tumor (OR: 46.71; 95% CI: 6.37–342.57; P< 0.001) were independent predictive factors for PSMs. The most common location of PSMs in patients at high-risk was the PL aspect, which reflects the reported tumor aggressiveness. The only significant predictive factors of PSMs were pathological outcomes, such as percentage of the tumor in the specimen and pathological stage. PMID:25966623

  4. The PREMM1,2,6 Model Predicts Risk of MLH1, MSH2, and MSH6 Germline Mutations Based on Cancer History

    PubMed Central

    Kastrinos, Fay; Steyerberg, Ewout W.; Mercado, Rowena; Balmaña, Judith; Holter, Spring; Gallinger, Steven; Siegmund, Kimberly D.; Church, James M.; Jenkins, Mark A.; Lindor, Noralane M.; Thibodeau, Stephen N.; Burbidge, Lynn Anne; Wenstrup, Richard J.; Syngal, Sapna

    2011-01-01

    BACKGROUND & AIMS We developed and validated a model to estimate the risks for mutations in the mismatch repair (MMR) genes MLH1, MSH2, and MSH6 based on personal and family history of cancer. METHODS Data were analyzed from 4539 probands tested for mutations in MLH1, MSH2, and MSH6. A multivariable polytomous logistic regression model (PREMM1,2,6) was developed to predict the overall risk of MMR gene mutations and the risk of mutation in each of the 3 genes. The model’s discriminative ability was validated in 1827 population-based CRC cases. RESULTS Twelve percent of the original cohort carried pathogenic mutations (204 in MLH1, 250 in MSH2, and 71 in MSH6). The PREMM1,2,6 model incorporated the following factors from the probands and first- and second-degree relatives (odds ratio; 95% confidence intervals [CI]): male sex (1.9; 1.5–2.4), a CRC (4.3; 3.3–5.6), multiple CRCs (13.7; 8.5–22), endometrial cancer (6.1; 4.6–8.2), and extracolonic cancers (3.3; 2.4–4.6). The areas under the receiver operating characteristic curves were 0.86 (95% CI: 0.82–0.91) for MLH1 mutation carriers, 0.87 (95% CI: 0.83–0.92) for MSH2, and 0.81 (95% CI: 0.69– 0.93) for MSH6; in validation, they were 0.88 for the overall cohort (95% CI: 0.86–0.90) and the population-based cases (95% CI: 0.83–0.92). CONCLUSIONS We developed the PREMM1,2,6 model that incorporates information on cancer history from probands and their relatives to estimate an individual’s risk for mutations in the MMR genes MLH1, MSH2, and MSH6. This web-based decision making tool can be used to assess risk for hereditary CRC and guide clinical management. PMID:20727894

  5. Environmental cancer risks

    NASA Astrophysics Data System (ADS)

    Bell, Peter M.

    In a long-awaited report (‘Assessment of Technologies for Determining Cancer Risks From the Environment’), the U.S. Office of Technology Assessment (OTA) has evaluated the role of environmental factors in cancer diseases. Environment is interpreted broadly as encompassing anything that interacts with humans, including the natural environment, food, radiation, the workplace, etc. Geologic factors range from geographic location to radiation and specific minerals. The report, however, is based on an inadequate data base in most instances, and its major recommendations are related to the establishment of a national cancer registry to record cancer statistics, as is done for many other diseases. Presently, hard statistics are lacking in the establishment of some association between the cause-effect relationship of most environmental factors and most carcinogens. Of particular interest, but unfortunately based on unreliable data, are the effects of mineral substances such as ‘asbestos.’ USGS mineralogist Malcolm Ross will review asbestos and its effects on human health in the forthcoming Mineralogical Society of America's Short Course on the Amphiboles (Reviews in Mineralogy, 9, in press, 1981).

  6. Neuroanatomy Predicts Individual Risk Attitudes

    PubMed Central

    Gilaie-Dotan, Sharon; Tymula, Agnieszka; Cooper, Nicole; Kable, Joseph W.; Glimcher, Paul W.

    2014-01-01

    Over the course of the last decade a multitude of studies have investigated the relationship between neural activations and individual human decision-making. Here we asked whether the anatomical features of individual human brains could be used to predict the fundamental preferences of human choosers. To that end, we quantified the risk attitudes of human decision-makers using standard economic tools and quantified the gray matter cortical volume in all brain areas using standard neurobiological tools. Our whole-brain analysis revealed that the gray matter volume of a region in the right posterior parietal cortex was significantly predictive of individual risk attitudes. Participants with higher gray matter volume in this region exhibited less risk aversion. To test the robustness of this finding we examined a second group of participants and used econometric tools to test the ex ante hypothesis that gray matter volume in this area predicts individual risk attitudes. Our finding was confirmed in this second group. Our results, while being silent about causal relationships, identify what might be considered the first stable biomarker for financial risk-attitude. If these results, gathered in a population of midlife northeast American adults, hold in the general population, they will provide constraints on the possible neural mechanisms underlying risk attitudes. The results will also provide a simple measurement of risk attitudes that could be easily extracted from abundance of existing medical brain scans, and could potentially provide a characteristic distribution of these attitudes for policy makers. PMID:25209279

  7. HIV Infection and Cancer Risk

    MedlinePlus

    ... Other Funding Find NCI funding for small business innovation, technology transfer, and contracts Training Cancer Training at ... Engels EA, Pfeiffer RM, Goedert JJ, et al. Trends in cancer risk among people with AIDS in ...

  8. Developmental dyslexia: predicting individual risk.

    PubMed

    Thompson, Paul A; Hulme, Charles; Nash, Hannah M; Gooch, Debbie; Hayiou-Thomas, Emma; Snowling, Margaret J

    2015-09-01

    Causal theories of dyslexia suggest that it is a heritable disorder, which is the outcome of multiple risk factors. However, whether early screening for dyslexia is viable is not yet known. The study followed children at high risk of dyslexia from preschool through the early primary years assessing them from age 3 years and 6 months (T1) at approximately annual intervals on tasks tapping cognitive, language, and executive-motor skills. The children were recruited to three groups: children at family risk of dyslexia, children with concerns regarding speech, and language development at 3;06 years and controls considered to be typically developing. At 8 years, children were classified as 'dyslexic' or not. Logistic regression models were used to predict the individual risk of dyslexia and to investigate how risk factors accumulate to predict poor literacy outcomes. Family-risk status was a stronger predictor of dyslexia at 8 years than low language in preschool. Additional predictors in the preschool years include letter knowledge, phonological awareness, rapid automatized naming, and executive skills. At the time of school entry, language skills become significant predictors, and motor skills add a small but significant increase to the prediction probability. We present classification accuracy using different probability cutoffs for logistic regression models and ROC curves to highlight the accumulation of risk factors at the individual level. Dyslexia is the outcome of multiple risk factors and children with language difficulties at school entry are at high risk. Family history of dyslexia is a predictor of literacy outcome from the preschool years. However, screening does not reach an acceptable clinical level until close to school entry when letter knowledge, phonological awareness, and RAN, rather than family risk, together provide good sensitivity and specificity as a screening battery. © 2015 The Authors. Journal of Child Psychology and Psychiatry published by

  9. Developmental dyslexia: predicting individual risk

    PubMed Central

    Thompson, Paul A; Hulme, Charles; Nash, Hannah M; Gooch, Debbie; Hayiou-Thomas, Emma; Snowling, Margaret J

    2015-01-01

    Background Causal theories of dyslexia suggest that it is a heritable disorder, which is the outcome of multiple risk factors. However, whether early screening for dyslexia is viable is not yet known. Methods The study followed children at high risk of dyslexia from preschool through the early primary years assessing them from age 3 years and 6 months (T1) at approximately annual intervals on tasks tapping cognitive, language, and executive-motor skills. The children were recruited to three groups: children at family risk of dyslexia, children with concerns regarding speech, and language development at 3;06 years and controls considered to be typically developing. At 8 years, children were classified as ‘dyslexic’ or not. Logistic regression models were used to predict the individual risk of dyslexia and to investigate how risk factors accumulate to predict poor literacy outcomes. Results Family-risk status was a stronger predictor of dyslexia at 8 years than low language in preschool. Additional predictors in the preschool years include letter knowledge, phonological awareness, rapid automatized naming, and executive skills. At the time of school entry, language skills become significant predictors, and motor skills add a small but significant increase to the prediction probability. We present classification accuracy using different probability cutoffs for logistic regression models and ROC curves to highlight the accumulation of risk factors at the individual level. Conclusions Dyslexia is the outcome of multiple risk factors and children with language difficulties at school entry are at high risk. Family history of dyslexia is a predictor of literacy outcome from the preschool years. However, screening does not reach an acceptable clinical level until close to school entry when letter knowledge, phonological awareness, and RAN, rather than family risk, together provide good sensitivity and specificity as a screening battery. PMID:25832320

  10. A Preliminary Study of the Ability of the 4Kscore test, the Prostate Cancer Prevention Trial-Risk Calculator and the European Research Screening Prostate-Risk Calculator for Predicting High-Grade Prostate Cancer.

    PubMed

    Borque-Fernando, Á; Esteban-Escaño, L M; Rubio-Briones, J; Lou-Mercadé, A C; García-Ruiz, R; Tejero-Sánchez, A; Muñoz-Rivero, M V; Cabañuz-Plo, T; Alfaro-Torres, J; Marquina-Ibáñez, I M; Hakim-Alonso, S; Mejía-Urbáez, E; Gil-Fabra, J; Gil-Martínez, P; Ávarez-Alegret, R; Sanz, G; Gil-Sanz, M J

    2016-04-01

    To prevent the overdiagnosis and overtreatment of prostate cancer (PC), therapeutic strategies have been established such as active surveillance and focal therapy, as well as methods for clarifying the diagnosis of high-grade prostate cancer (HGPC) (defined as a Gleason score ≥7), such as multiparametric magnetic resonance imaging and new markers such as the 4Kscore test (4KsT). By means of a pilot study, we aim to test the ability of the 4KsT to identify HGPC in prostate biopsies (Bx) and compare the test with other multivariate prognostic models such as the Prostate Cancer Prevention Trial Risk Calculator 2.0 (PCPTRC 2.0) and the European Research Screening Prostate Cancer Risk Calculator 4 (ERSPC-RC 4). Fifty-one patients underwent a prostate Bx according to standard clinical practice, with a minimum of 10 cores. The diagnosis of HGPC was agreed upon by 4 uropathologists. We compared the predictions from the various models by using the Mann-Whitney U test, area under the ROC curve (AUC) (DeLong test), probability density function (PDF), box plots and clinical utility curves. Forty-three percent of the patients had PC, and 23.5% had HGPC. The medians of probability for the 4KsT, PCPTRC 2.0 and ERSPC-RC 4 were significantly different between the patients with HGPC and those without HGPC (p≤.022) and were more differentiated in the case of 4KsT (51.5% for HGPC [25-75 percentile: 25-80.5%] vs. 16% [P 25-75: 8-26.5%] for non-HGPC; p=.002). All models presented AUCs above 0.7, with no significant differences between any of them and 4KsT (p≥.20). The PDF and box plots showed good discriminative ability, especially in the ERSPC-RC 4 and 4KsT models. The utility curves showed how a cutoff of 9% for 4KsT identified all cases of HGPC and provided a 22% savings in biopsies, which is similar to what occurs with the ERSPC-RC 4 models and a cutoff of 3%. The assessed predictive models offer good discriminative ability for HGPCs in Bx. The 4KsT is a good classification

  11. A Risk Model for Lung Cancer Incidence

    PubMed Central

    Hoggart, Clive; Brennan, Paul; Tjonneland, Anne; Vogel, Ulla; Overvad, Kim; Østergaard, Jane Nautrup; Kaaks, Rudolf; Canzian, Federico; Boeing, Heiner; Steffen, Annika; Trichopoulou, Antonia; Bamia, Christina; Trichopoulos, Dimitrios; Johansson, Mattias; Palli, Domenico; Krogh, Vittorio; Tumino, Rosario; Sacerdote, Carlotta; Panico, Salvatore; Boshuizen, Hendriek; Bueno-de-Mesquita, H. Bas; Peeters, Petra H.M.; Lund, Eiliv; Gram, Inger Torhild; Braaten, Tonje; Rodríguez, Laudina; Agudo, Antonio; Sanchez-Cantalejo, Emilio; Arriola, Larraitz; Chirlaque, Maria-Dolores; Barricarte, Aurelio; Rasmuson, Torgny; Khaw, Kay-Tee; Wareham, Nicholas; Allen, Naomi E.; Riboli, Elio; Vineis, Paolo

    2015-01-01

    Risk models for lung cancer incidence would be useful for prioritizing individuals for screening and participation in clinical trials of chemoprevention. We present a risk model for lung cancer built using prospective cohort data from a general population which predicts individual incidence in a given time period. We build separate risk models for current and former smokers using 169,035 ever smokers from the multicenter European Prospective Investigation into Cancer and Nutrition (EPIC) and considered a model for never smokers. The data set was split into independent training and test sets. Lung cancer incidence was modeled using survival analysis, stratifying by age started smoking, and for former smokers, also smoking duration. Other risk factors considered were smoking intensity, 10 occupational/environmental exposures previously implicated with lung cancer, and single-nucleotide polymorphisms at two loci identified by genome-wide association studies of lung cancer. Individual risk in the test set was measured by the predicted probability of lung cancer incidence in the year preceding last follow-up time, predictive accuracy was measured by the area under the receiver operator characteristic curve (AUC). Using smoking information alone gave good predictive accuracy: the AUC and 95% confidence interval in ever smokers was 0.843 (0.810–0.875), the Bach model applied to the same data gave an AUC of 0.775 (0.737–0.813). Other risk factors had negligible effect on the AUC, including never smokers for whom prediction was poor. Our model is generalizable and straightforward to implement. Its accuracy can be attributed to its modeling of lifetime exposure to smoking. PMID:22496387

  12. Prediction of lateral pelvic lymph node metastasis from lower rectal cancer using magnetic resonance imaging and risk factors for metastasis: Multicenter study of the Lymph Node Committee of the Japanese Society for Cancer of the Colon and Rectum.

    PubMed

    Ogawa, Shimpei; Hida, Jin-Ichi; Ike, Hideyuki; Kinugasa, Tetsushi; Ota, Mitsuyoshi; Shinto, Eiji; Itabashi, Michio; Okamoto, Takahiro; Yamamoto, Masakazu; Sugihara, Kenichi; Watanabe, Toshiaki

    2017-07-31

    The goal of the study was to examine prediction of lateral pelvic lymph node (LPLN) metastasis from lower rectal cancer using a logistic model including risk factors for LPLN metastasis and magnetic resonance imaging (MRI) clinical LPLN (cLPLN) status, compared to prediction based on MRI alone. The subjects were 272 patients with lower rectal cancer who underwent MRI prior to mesorectal excision combined with LPLN dissection (LPLD) at six institutes. No patients received neoadjuvant therapy. Prediction models for right and left pathological LPLN (pLPLN) metastasis were developed using cLPLN status, histopathological grade, and perirectal lymph node (PRLN) status. For evaluation, data for patients with left LPLD were substituted into the right-side equation and vice versa. Left LPLN metastasis was predicted using the right-side model with accuracy of 86.5%, sensitivity 56.4%, specificity 92.7%, positive predictive value (PPV) 61.1%, and negative predictive value (NPV) 91.2%, while these data using MRI cLPLN status alone were 80.4, 76.9, 81.2, 45.5, and 94.5%, respectively. Similarly, right LPLN metastasis was predicted using the left-side equation with accuracy of 83.8%, sensitivity 57.8%, specificity 90.4%, PPV 60.5%, and NPV 89.4%, and the equivalent data using MRI alone were 78.4, 68.9, 80.8, 47.7, and 91.1%, respectively. The AUCs for the right- and left-side equations were significantly higher than the equivalent AUCs for MRI cLPLN status alone. A logistic model including risk factors for LPLN metastasis and MRI findings had significantly better performance for prediction of LPLN metastasis compared with a model based on MRI findings alone.

  13. Relevance of Spatial Heterogeneity of Immune Infiltration for Predicting Risk of Recurrence After Endocrine Therapy of ER+ Breast Cancer.

    PubMed

    Heindl, Andreas; Sestak, Ivana; Naidoo, Kalnisha; Cuzick, Jack; Dowsett, Mitchell; Yuan, Yinyin

    2018-02-01

    Despite increasing evidence supporting the clinical utility of immune infiltration in the estrogen receptor-negative (ER-) subtype, the prognostic value of immune infiltration for ER+ disease is not well defined. Quantitative immune scores of cell abundance and spatial heterogeneity were computed using a fully automated hematoxylin and eosin-stained image analysis algorithm and spatial statistics for 1178 postmenopausal patients with ER+ breast cancer treated with five years' tamoxifen or anastrozole. The prognostic significance of immune scores was compared with Oncotype DX 21-gene recurrence score (RS), PAM50 risk of recurrence (ROR) score, IHC4, and clinical treatment score, available for 963 patients. Statistical tests were two-sided. Scores of immune cell abundance were not associated with recurrence-free survival. In contrast, high immune spatial scores indicating increased cell spatial clustering were associated with poor 10-year, early (0-5 years), and late (5-10 years) recurrence-free survival (Immune Hotspot: LR-χ2 = 14.06, P < .001, for 0-10 years; LR-χ2 = 6.24, P = .01, for 0-5 years; LR-χ2 = 7.89, P = .005, for 5-10 years). The prognostic value of spatial scores for late recurrence was similar to that of IHC4 and RS in both populations, but was not as strong as other tests in comparison for recurrence across 10 years. These results provide a missing link between tumor immunity and disease outcome in ER+ disease by examining tumor spatial architecture. The association between spatial scores and late recurrence suggests a lasting memory of protumor immunity that may impact disease progression and evolution of endocrine treatment resistance, which may be exploited for therapeutic advances.

  14. [Infertility and risk of cancer].

    PubMed

    Hippeläinen, Maritta

    2012-01-01

    Ovulation problems, ovarian endometriosis and impaired sperm quality may be factors underlying infertility and possibly predisposing to cancer diseases. Infertility therapies utilize products that alter the hormonal balance and may in theory increase the risk of cancer. Handling of gametes in the laboratory is also likely to influence gene regulation. Ovulation induction therapies may increase the risk of uterine cancer, and in vitro fertilization (IVF) therapies may increase ovarian tumors. Children born after IVF therapies seem to have a statistically elevated risk of cancer. Instead of risk ratios, the use of clear figures is recommended in patient information.

  15. The c-MET Network as Novel Prognostic Marker for Predicting Bladder Cancer Patients with an Increased Risk of Developing Aggressive Disease.

    PubMed

    Kim, Young-Won; Yun, Seok Joong; Jeong, Phildu; Kim, Seon-Kyu; Kim, Seon-Young; Yan, Chunri; Seo, Sung Phil; Lee, Sang Keun; Kim, Jayoung; Kim, Wun-Jae

    2015-01-01

    Previous studies have shown that c-MET is overexpressed in cases of aggressive bladder cancer (BCa). Identification of crosstalk between c-MET and other RTKs such as AXL and PDGFR suggest that c-MET network genes (c-MET-AXL-PDGFR) may be clinically relevant to BCa. Here, we examine whether expression of c-MET network genes can be used to identify BCa patients at increased risk of developing aggressive disease. In vitro analysis, c-MET knockdown suppressed cell proliferation, invasion, and migration, and increased sensitivity to cisplatin-induced apoptosis. In addition, c-MET network gene (c-MET, AXL, and PDGFR) expression allowed discrimination of BCa tissues from normal control tissues and appeared to predict poor disease progression in non-muscle invasive BCa patients and poor overall survival in muscle invasive BCa patients. These results suggest that c-MET network gene expression is a novel prognostic marker for predicting which BCa patients have an increased risk of developing aggressive disease. These genes might be a useful marker for co-targeting therapy, and are expected to play an important role in improving both response to treatment and survival of BCa patients.

  16. The c-MET Network as Novel Prognostic Marker for Predicting Bladder Cancer Patients with an Increased Risk of Developing Aggressive Disease

    PubMed Central

    Jeong, Phildu; Kim, Seon-Kyu; Kim, Seon-Young; Yan, Chunri; Seo, Sung Phil; Lee, Sang Keun; Kim, Jayoung; Kim, Wun-Jae

    2015-01-01

    Previous studies have shown that c-MET is overexpressed in cases of aggressive bladder cancer (BCa). Identification of crosstalk between c-MET and other RTKs such as AXL and PDGFR suggest that c-MET network genes (c-MET-AXL-PDGFR) may be clinically relevant to BCa. Here, we examine whether expression of c-MET network genes can be used to identify BCa patients at increased risk of developing aggressive disease. In vitro analysis, c-MET knockdown suppressed cell proliferation, invasion, and migration, and increased sensitivity to cisplatin-induced apoptosis. In addition, c-MET network gene (c-MET, AXL, and PDGFR) expression allowed discrimination of BCa tissues from normal control tissues and appeared to predict poor disease progression in non-muscle invasive BCa patients and poor overall survival in muscle invasive BCa patients. These results suggest that c-MET network gene expression is a novel prognostic marker for predicting which BCa patients have an increased risk of developing aggressive disease. These genes might be a useful marker for co-targeting therapy, and are expected to play an important role in improving both response to treatment and survival of BCa patients. PMID:26225770

  17. Loss of Nuclear Localized and Tyrosine Phosphorylated Stat5 in Breast Cancer Predicts Poor Clinical Outcome and Increased Risk of Antiestrogen Therapy Failure

    PubMed Central

    Peck, Amy R.; Witkiewicz, Agnieszka K.; Liu, Chengbao; Stringer, Ginger A.; Klimowicz, Alexander C.; Pequignot, Edward; Freydin, Boris; Tran, Thai H.; Yang, Ning; Rosenberg, Anne L.; Hooke, Jeffrey A.; Kovatich, Albert J.; Nevalainen, Marja T.; Shriver, Craig D.; Hyslop, Terry; Sauter, Guido; Rimm, David L.; Magliocco, Anthony M.; Rui, Hallgeir

    2011-01-01

    Purpose To investigate nuclear localized and tyrosine phosphorylated Stat5 (Nuc-pYStat5) as a marker of prognosis in node-negative breast cancer and as a predictor of response to antiestrogen therapy. Patients and Methods Levels of Nuc-pYStat5 were analyzed in five archival cohorts of breast cancer by traditional diaminobenzidine-chromogen immunostaining and pathologist scoring of whole tissue sections or by immunofluorescence and automated quantitative analysis (AQUA) of tissue microarrays. Results Nuc-pYStat5 was an independent prognostic marker as measured by cancer-specific survival (CSS) in patients with node-negative breast cancer who did not receive systemic adjuvant therapy, when adjusted for common pathology parameters in multivariate analyses both by standard chromogen detection with pathologist scoring of whole tissue sections (cohort I; n = 233) and quantitative immunofluorescence of a tissue microarray (cohort II; n = 291). Two distinct monoclonal antibodies gave concordant results. A progression array (cohort III; n = 180) revealed frequent loss of Nuc-pYStat5 in invasive carcinoma compared to normal breast epithelia or ductal carcinoma in situ, and general loss of Nuc-pYStat5 in lymph node metastases. In cohort IV (n = 221), loss of Nuc-pYStat5 was associated with increased risk of antiestrogen therapy failure as measured by univariate CSS and time to recurrence (TTR). More sensitive AQUA quantification of Nuc-pYStat5 in antiestrogen-treated patients (cohort V; n = 97) identified by multivariate analysis patients with low Nuc-pYStat5 at elevated risk for therapy failure (CSS hazard ratio [HR], 21.55; 95% CI, 5.61 to 82.77; P < .001; TTR HR, 7.30; 95% CI, 2.34 to 22.78; P = .001). Conclusion Nuc-pYStat5 is an independent prognostic marker in node-negative breast cancer. If confirmed in prospective studies, Nuc-pYStat5 may become a useful predictive marker of response to adjuvant hormone therapy. PMID:21576635

  18. Predictive Models of Liver Cancer

    EPA Science Inventory

    Predictive models of chemical-induced liver cancer face the challenge of bridging causative molecular mechanisms to adverse clinical outcomes. The latent sequence of intervening events from chemical insult to toxicity are poorly understood because they span multiple levels of bio...

  19. Predictive Models of Liver Cancer

    EPA Science Inventory

    Predictive models of chemical-induced liver cancer face the challenge of bridging causative molecular mechanisms to adverse clinical outcomes. The latent sequence of intervening events from chemical insult to toxicity are poorly understood because they span multiple levels of bio...

  20. Estimates of radiogenic cancer risks.

    PubMed

    Puskin, J S; Nelson, C B

    1995-07-01

    A methodology recently developed by the U.S. EPA for estimating the carcinogenic risks from ionizing radiation is described. For most cancer sites, the risk model is one in which age-specific, relative risk coefficients are obtained by taking a geometric mean of the coefficients derived from the atomic bomb survivor data using two different methods for transporting risks from the Japanese to the U.S. population. The risk models are applied to estimate organ-specific risks per unit dose for a stationary population with mortality rates governed by 1980 U.S. vital statistics. With the exception of breast cancer, low-LET radiogenic cancer risk estimates are reduced by a factor of 2 at low doses and dose rates compared to acute high dose exposure conditions. For low dose (or dose rate) conditions, the risk of inducing a premature cancer death from uniform, whole body, low-LET irradiation is calculated to be 5.1 x 10(-2) Gy-1. Neglecting nonfatal skin cancers, the corresponding incidence risk is 7.6 x 10(-2) Gy-1. High-LET (alpha particle) risks are presumed to increase linearly with dose and to be independent of dose rate. High-LET risks are estimated to be 20 times the low-LET risks estimated under low dose rate conditions, except for leukemia and breast cancer where RBEs of 1 and 10 are adopted, respectively.

  1. Risk Stratification for Second Primary Lung Cancer.

    PubMed

    Han, Summer S; Rivera, Gabriel A; Tammemägi, Martin C; Plevritis, Sylvia K; Gomez, Scarlett L; Cheng, Iona; Wakelee, Heather A

    2017-09-01

    Purpose This study estimated the 10-year risk of developing second primary lung cancer (SPLC) among survivors of initial primary lung cancer (IPLC) and evaluated the clinical utility of the risk prediction model for selecting eligibility criteria for screening. Methods SEER data were used to identify a population-based cohort of 20,032 participants diagnosed with IPLC between 1988 and 2003 and who survived ≥ 5 years after the initial diagnosis. We used a proportional subdistribution hazards model to estimate the 10-year risk of developing SPLC among survivors of lung cancer LC in the presence of competing risks. Considered predictors included age, sex, race, treatment, histology, stage, and extent of disease. We examined the risk-stratification ability of the prediction model and performed decision curve analysis to evaluate the clinical utility of the model by calculating its net benefit in varied risk thresholds for screening. Results Although the median 10-year risk of SPLC among survivors of LC was 8.36%, the estimated risk varied substantially (range, 0.56% to 14.3%) when stratified by age, histology, and extent of IPLC in the final prediction model. The stratification by deciles of estimated risk showed that the observed incidence of SPLC was significantly higher in the tenth-decile group (12.5%) versus the first-decile group (2.9%; P < 10(-10)). The decision curve analysis yielded a range of risk thresholds (1% to 11.5%) at which the clinical net benefit of the risk model was larger than those in hypothetical all-screening or no-screening scenarios. Conclusion The risk stratification approach in SPLC can be potentially useful for identifying survivors of LC to be screened by computed tomography. More comprehensive environmental and genetic data may help enhance the predictability and stratification ability of the risk model for SPLC.

  2. Cancer of the Prostate Risk Assessment (CAPRA) Preoperative Score Versus Postoperative Score (CAPRA-S): ability to predict cancer progression and decision-making regarding adjuvant therapy after radical prostatectomy.

    PubMed

    Seo, Won Ik; Kang, Pil Moon; Kang, Dong Il; Yoon, Jang Ho; Kim, Wansuk; Chung, Jae Il

    2014-09-01

    The University of California, San Francisco, announced in 2011 Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) score which included pathologic data, but there were no results for comparing preoperative predictors with the CAPRA-S score. We evaluated the validation of the CAPRA-S score in our institution and compare the result with the preoperative progression predictor, CAPRA score. Data of 130 patients were reviewed who underwent radical prostatectomy for localized prostate cancer from 2008 to 2013. Performance of CAPRA-S score in predicting progression free probabilities was assessed through Kaplan Meier analysis and Cox proportional hazards regression test. Additionally, prediction probability was compared with preoperative CAPRA score by logistic regression analysis. Comparing CAPRA score, the CAPRA-S score showed improved prediction ability for 5 yr progression free survival (concordance index 0.80, P = 0.04). After risk group stratification, 3 group model of CAPRA-S was superior than 3 group model of CAPRA for 3-yr progression free survival and 5-yr progression free survival (concordance index 0.74 vs. 0.70, 0.77 vs. 0.71, P < 0.001). Finally the CAPRA-S score was the more ideal predictor concerned with adjuvant therapy than the CAPRA score through decision curve analysis. The CPARA-S score is a useful predictor for disease progression after radical prostatectomy.

  3. ProPSA and the Prostate Health Index as predictive markers for aggressiveness in low-risk prostate cancer-results from an international multicenter study.

    PubMed

    Heidegger, I; Klocker, H; Pichler, R; Pircher, A; Prokop, W; Steiner, E; Ladurner, C; Comploj, E; Lunacek, A; Djordjevic, D; Pycha, A; Plas, E; Horninger, W; Bektic, J

    2017-09-01

    One of the major challenges in prostate cancer (PCa) treatment is distinguishing insignificant PCa from those forms that need active treatment. We evaluated the impact of PSA isoforms on risk stratification in patients with low-risk PCa as well as in active surveillance (AS) candidates who underwent radical prostatectomy. A total of 112 patients with biopsy confirmed Gleason score (GS) 6 PCa of four different international institutions were prospectively enrolled in the study. Blood withdrawal was performed the day before radical prostatectomy. In addition, patients were classified according to the EAU and NCCN criteria for AS candidates. PSA, free PSA (fPSA) and proPSA were measured using dual monoclonal antibody sandwich immunoassays. In addition, the Prostate Health Index (PHI=proPSA/fPSA × √PSA) was calculated. Final histology of the radical prostatectomy specimens was correlated to PSA, its isoforms and PHI. Serum proPSA levels were significantly elevated in those patients with an upgrade in final histology (GS⩾7). In addition, higher proPSA levels were predictive for extraprostatic extension (⩾pT3a) as well as for positive surgical margins. Interestingly, PHI had an even higher predictive power when compared with proPSA alone concerning GS upgrading, extraprostatic extension and surgical margins in both the total and the AS patient group. We showed in a multicenter study that proPSA is a valuable biomarker to detect patients with aggressive PCa in a cohort of GS 6 patients, who would benefit from active tumor therapy. Combining proPSA with the standard markers PSA and fPSA using PHI further increases the predictive accuracy significantly. Moreover, our data support the use of PHI for monitoring PCa patients under AS.

  4. Long-term outcome prediction by clinicopathological risk classification algorithms in node-negative breast cancer--comparison between Adjuvant!, St Gallen, and a novel risk algorithm used in the prospective randomized Node-Negative-Breast Cancer-3 (NNBC-3) trial.

    PubMed

    Schmidt, M; Victor, A; Bratzel, D; Boehm, D; Cotarelo, C; Lebrecht, A; Siggelkow, W; Hengstler, J G; Elsässer, A; Gehrmann, M; Lehr, H-A; Koelbl, H; von Minckwitz, G; Harbeck, N; Thomssen, C

    2009-02-01

    Defining risk categories in breast cancer is of considerable clinical significance. We have developed a novel risk classification algorithm and compared its prognostic utility to the Web-based tool Adjuvant! and to the St Gallen risk classification. After a median follow-up of 10 years, we retrospectively analyzed 410 consecutive node-negative breast cancer patients who had not received adjuvant systemic therapy. High risk was defined by any of the following criteria: (i) age <35 years, (ii) grade 3, (iii) human epithelial growth factor receptor-2 positivity, (iv) vascular invasion, (v) progesterone receptor negativity, (vi) grade 2 tumors >2 cm. All patients were also characterized using Adjuvant! and the St Gallen 2007 risk categories. We analyzed disease-free survival (DFS) and overall survival (OS). The Node-Negative-Breast Cancer-3 (NNBC-3) algorithm enlarged the low-risk group to 37% as compared with Adjuvant! (17%) and St Gallen (18%), respectively. In multivariate analysis, both Adjuvant! [P = 0.027, hazard ratio (HR) 3.81, 96% confidence interval (CI) 1.16-12.47] and the NNBC-3 risk classification (P = 0.049, HR 1.95, 95% CI 1.00-3.81) significantly predicted OS, but only the NNBC-3 algorithm retained its prognostic significance in multivariate analysis for DFS (P < 0.0005). The novel NNBC-3 risk algorithm is the only clinicopathological risk classification algorithm significantly predicting DFS as well as OS.

  5. Familial risk for lung cancer

    PubMed Central

    Kanwal, Madiha; Ding, Xiao-Ji; Cao, Yi

    2017-01-01

    Lung cancer, which has a low survival rate, is a leading cause of cancer-associated mortality worldwide. Smoking and air pollution are the major causes of lung cancer; however, numerous studies have demonstrated that genetic factors also contribute to the development of lung cancer. A family history of lung cancer increases the risk for the disease in both smokers and never-smokers. This review focuses on familial lung cancer, in particular on the familial aggregation of lung cancer. The development of familial lung cancer involves shared environmental and genetic factors among family members. Familial lung cancer represents a good model for investigating the association between environmental and genetic factors, as well as for identifying susceptibility genes for lung cancer. In addition, studies on familial lung cancer may help to elucidate the etiology and mechanism of lung cancer, and may identify novel biomarkers for early detection and diagnosis, targeted therapy and improved prevention strategies. This review presents the aetiology and molecular biology of lung cancer and then systematically introduces and discusses several aspects of familial lung cancer, including the characteristics of familial lung cancer, population-based studies on familial lung cancer and the genetics of familial lung cancer. PMID:28356926

  6. Wnt pathway activation predicts increased risk of tumor recurrence in patients with stage I nonsmall cell lung cancer.

    PubMed

    Shapiro, Mark; Akiri, Gal; Chin, Cynthia; Wisnivesky, Juan P; Beasley, Mary B; Weiser, Todd S; Swanson, Scott J; Aaronson, Stuart A

    2013-03-01

    To determine the incidence of Wnt pathway activation in patients with stage I NSCLC and its influence on lung cancer recurrence. Despite resection, the 5-year recurrence with localized stage I nonsmall cell lung cancer (NSCLC) is 18.4%-24%. Aberrant Wnt signaling activation plays an important role in a wide variety of tumor types. However, there is not much known about the role the Wnt pathway plays in patients with stage I lung cancer. Tumor and normal lung tissues from 55 patients following resection for stage I NSCLC were subjected to glutathione S-transferase (GST) E-cadherin pulldown and immunoblot analysis to assess levels of uncomplexed β-catenin, a reliable measure of Wnt signaling activation. The β-catenin gene was also screened for oncogenic mutations in tumors with activated Wnt signaling. Cancer recurrence rates were correlated in a blinded manner in patients with Wnt pathway-positive and -negative tumors. Tumors in 20 patients (36.4%) scored as Wnt positive, with only 1 exhibiting a β-catenin oncogenic mutation. Patients with Wnt-positive tumors experienced a significantly higher rate of overall cancer recurrence than those with Wnt-negative tumors (30.0% vs. 5.7%, P = 0.02), with 25.0% exhibiting distal tumor recurrence compared with 2.9% in the Wnt-negative group (P = 0.02). Wnt pathway activation occurred in a substantial fraction of Stage I NSCLCs, which was rarely due to mutations. Moreover, Wnt pathway activation was associated with a significantly higher rate of tumor recurrence. These findings suggest that Wnt pathway activation reflects a more aggressive tumor phenotype and identifies patients who may benefit from more aggressive therapy in addition to resection.

  7. Infective Endocarditis and Cancer Risk

    PubMed Central

    Sun, Li-Min; Wu, Jung-Nan; Lin, Cheng-Li; Day, Jen-Der; Liang, Ji-An; Liou, Li-Ren; Kao, Chia-Hung

    2016-01-01

    Abstract This study investigated the possible relationship between endocarditis and overall and individual cancer risk among study participants in Taiwan. We used data from the National Health Insurance program of Taiwan to conduct a population-based, observational, and retrospective cohort study. The case group consisted of 14,534 patients who were diagnosed with endocarditis between January 1, 2000 and December 31, 2010. For the control group, 4 patients without endocarditis were frequency matched to each endocarditis patient according to age, sex, and index year. Competing risks regression analysis was conducted to determine the effect of endocarditis on cancer risk. A large difference was noted in Charlson comorbidity index between endocarditis and nonendocarditis patients. In patients with endocarditis, the risk for developing overall cancer was significant and 119% higher than in patients without endocarditis (adjusted subhazard ratio = 2.19, 95% confidence interval = 1.98–2.42). Regarding individual cancers, in addition to head and neck, uterus, female breast and hematological malignancies, the risks of developing colorectal cancer, and some digestive tract cancers were significantly higher. Additional analyses determined that the association of cancer with endocarditis is stronger within the 1st 5 years after endocarditis diagnosis. This population-based cohort study found that patients with endocarditis are at a higher risk for colorectal cancer and other cancers in Taiwan. The risk was even higher within the 1st 5 years after endocarditis diagnosis. It suggested that endocarditis is an early marker of colorectal cancer and other cancers. The underlying mechanisms must still be explored and may account for a shared risk factor of infection in both endocarditis and malignancy. PMID:27015220

  8. Competing risks to breast cancer mortality.

    PubMed

    Rosenberg, Marjorie A

    2006-01-01

    Simulation models analyzing the impact of treatment interventions and screening on the level of breast cancer mortality require an input of mortality from causes other than breast cancer, or competing risks. This chapter presents an actuarial method of creating cohort life tables using published data that removes breast cancer as a cause of death. Mortality from causes other than breast cancer as a percentage of all-cause mortality is smallest for women in their forties and fifties, as small as 85% of the all-cause rate, although the level and percentage of the impact varies by birth cohort. This method produces life tables by birth cohort and by age that are easily included as a common input by the various CISNET modeling groups to predict mortality from other causes. Attention to removing breast cancer mortality from all-cause mortality is worthwhile, because breast cancer mortality can be as high as 15% at some ages.

  9. Predictive diagnosis of the risk of breast cancer recurrence after surgery by single-particle quantum dot imaging

    PubMed Central

    Gonda, Kohsuke; Miyashita, Minoru; Higuchi, Hideo; Tada, Hiroshi; Watanabe, Tomonobu M.; Watanabe, Mika; Ishida, Takanori; Ohuchi, Noriaki

    2015-01-01

    In breast cancer, the prognosis of human epidermal growth factor receptor 2 (HER2)-positive patients (20–25%) has been dramatically improved by the clinical application of the anti-HER2 antibody drugs trastuzumab and pertuzumab. However, the clinical outcomes of HER2-negative cases with a poor prognosis have not improved, and novel therapeutic antibody drugs or diagnostic molecular markers of prognosis are urgently needed. Here, we targeted protease-activated receptor 1 (PAR1) as a new biomarker for HER2-negative patients. The developed anti-PAR1 antibody inhibited PAR1 activation by matrix metalloprotease 1 and thereby prevented cancer-cell migration and invasion. To estimate PAR1 expression levels in HER2-negative patient tissues using the antibody, user-friendly immunohistochemistry with fluorescence nanoparticles or quantum dots (QDs) was developed. Previously, immunohistochemistry with QDs was affected by tissue autofluorescence, making quantitative measurement extremely difficult. We significantly improved the quantitative sensitivity of immunohistochemistry with QDs by using an autofluorescence-subtracted image and single-QD imaging. The immunohistochemistry showed that PAR1 expression was strongly correlated with relapse-free survival time in HER2-negative breast cancer patients. Therefore, the developed anti-PAR1 antibody is a strong candidate for use as an anticancer drug and a prognostic biomarker for HER2-negative patients. PMID:26392299

  10. Evaluation of BRCA1 and BRCA2 mutation prevalence, risk prediction models and a multistep testing approach in French‐Canadian families with high risk of breast and ovarian cancer

    PubMed Central

    Simard, Jacques; Dumont, Martine; Moisan, Anne‐Marie; Gaborieau, Valérie; Vézina, Hélène; Durocher, Francine; Chiquette, Jocelyne; Plante, Marie; Avard, Denise; Bessette, Paul; Brousseau, Claire; Dorval, Michel; Godard, Béatrice; Houde, Louis; Joly, Yann; Lajoie, Marie‐Andrée; Leblanc, Gilles; Lépine, Jean; Lespérance, Bernard; Malouin, Hélène; Parboosingh, Jillian; Pichette, Roxane; Provencher, Louise; Rhéaume, Josée; Sinnett, Daniel; Samson, Carolle; Simard, Jean‐Claude; Tranchant, Martine; Voyer, Patricia; BRCAs, INHERIT; Easton, Douglas; Tavtigian, Sean V; Knoppers, Bartha‐Maria; Laframboise, Rachel; Bridge, Peter; Goldgar, David

    2007-01-01

    Background and objective In clinical settings with fixed resources allocated to predictive genetic testing for high‐risk cancer predisposition genes, optimal strategies for mutation screening programmes are critically important. These depend on the mutation spectrum found in the population under consideration and the frequency of mutations detected as a function of the personal and family history of cancer, which are both affected by the presence of founder mutations and demographic characteristics of the underlying population. The results of multistep genetic testing for mutations in BRCA1 or BRCA2 in a large series of families with breast cancer in the French‐Canadian population of Quebec, Canada are reported. Methods A total of 256 high‐risk families were ascertained from regional familial cancer clinics throughout the province of Quebec. Initially, families were tested for a panel of specific mutations known to occur in this population. Families in which no mutation was identified were then comprehensively tested. Three algorithms to predict the presence of mutations were evaluated, including the prevalence tables provided by Myriad Genetics Laboratories, the Manchester Scoring System and a logistic regression approach based on the data from this study. Results 8 of the 15 distinct mutations found in 62 BRCA1/BRCA2‐positive families had never been previously reported in this population, whereas 82% carried 1 of the 4 mutations currently observed in ⩾2 families. In the subset of 191 families in which at least 1 affected individual was tested, 29% carried a mutation. Of these 27 BRCA1‐positive and 29 BRCA2‐positive families, 48 (86%) were found to harbour a mutation detected by the initial test. Among the remaining 143 inconclusive families, all 8 families found to have a mutation after complete sequencing had Manchester Scores ⩾18. The logistic regression and Manchester Scores provided equal predictive power, and both were significantly better

  11. Risk stratification in prostate cancer screening.

    PubMed

    Roobol, Monique J; Carlsson, Sigrid V

    2013-01-01

    Screening for prostate cancer is a controversial topic within the field of urology. The US Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial did not demonstrate any difference in prostate-cancer-related mortality rates between men screened annually rather than on an 'opportunistic' basis. However, in the world's largest trial to date--the European Randomised Study of Screening for Prostate Cancer--screening every 2-4 years was associated with a 21% reduction in prostate-cancer-related mortality rate after 11 years. Citing the uncertain ratio between potential harm and potential benefit, the US Preventive Services Task Force recently recommended against serum PSA screening. Although this ratio has yet to be elucidated, PSA testing--and early tumour detection--is undoubtedly beneficial for some individuals. Instead of adopting a 'one size fits all' approach, physicians are likely to perform personalized risk assessment to minimize the risk of negative consequences, such as anxiety, unnecessary testing and biopsies, overdiagnosis, and overtreatment. The PSA test needs to be combined with other predictive factors or be used in a more thoughtful way to identify men at risk of symptomatic or life-threatening cancer, without overdiagnosing indolent disease. A risk-adapted approach is needed, whereby PSA testing is tailored to individual risk.

  12. Reproduction and Breast Cancer Risk

    PubMed Central

    Hanf, Volker; Hanf, Dorothea

    2014-01-01

    Summary Reproduction is doubtlessly one of the main biological meanings of life. It is therefore not surprising that various aspects of reproduction impact on breast cancer risk. Various developmental levels may become targets of breast tumorigenesis. This review follows the chronologic sequence of events in the life of a female at risk, starting with the intrauterine development. Furthermore, the influence of both contraceptive measures and fertility treatment on breast cancer development is dealt with, as well as various pregnancy-associated factors, events, and perinatal outcomes. Finally, the contribution of breast feeding to a reduced breast cancer risk is discussed. PMID:25759622

  13. Experiences of predictive testing in young people at risk of Huntington's disease, familial cardiomyopathy or hereditary breast and ovarian cancer.

    PubMed

    MacLeod, Rhona; Beach, Anna; Henriques, Sasha; Knopp, Jasmin; Nelson, Katie; Kerzin-Storrar, Lauren

    2014-03-01

    While debate has focused on whether testing of minors for late onset genetic disorders should be carried out if there is no medical benefit, less is known about the impact on young people (<25 years) who have had predictive testing often many years before the likely onset of symptoms. We looked at the experiences of young people who had had predictive testing for a range of conditions with variable ages at onset and options for screening and treatment. A consecutive series of 61 young people who had a predictive test aged 15-25 years at the Clinical Genetic Service, Manchester, for HD, HBOC (BrCa 1 or 2) or FCM (Hypertrophic Cardiomyopathy or Dilated Cardiomyopathy), were invited to participate. Thirty-six (36/61; 59%) agreed to participate (10 HD, 16 HBOC and 10 FCM) and telephone interviews were audiotaped, transcribed and analysed using Interpretative Phenomenological Analysis. None of the participants expressed regret at having the test at a young age. Participants saw the value of pretest counselling not in facilitating a decision, but rather as a source of information and support. Differences emerged among the three groups in parent/family involvement in the decision to be tested. Parents in FCM families were a strong influence in favour of testing, in HBOC the decision was autonomous but usually congruent with the views of parents, whereas in HD the decision was autonomous and sometimes went against the opinions of parents/grandparents. Participants from all three groups proposed more tailoring of predictive test counselling to the needs of young people.

  14. Number of Unfavorable Intermediate-Risk Factors Predicts Pathologic Upstaging and Prostate Cancer-Specific Mortality Following Radical Prostatectomy: Results From the SEARCH Database.

    PubMed

    Zumsteg, Zachary S; Chen, Zinan; Howard, Lauren E; Amling, Christopher L; Aronson, William J; Cooperberg, Matthew R; Kane, Christopher J; Terris, Martha K; Spratt, Daniel E; Sandler, Howard M; Freedland, Stephen J

    2017-02-01

    To validate and further improve the stratification of intermediate risk prostate cancer into favorable and unfavorable subgroups for patients undergoing radical prostatectomy. The SEARCH database was queried for IR patients undergoing radical prostatectomy without adjuvant radiotherapy. UIR disease was defined any patient with at least one unfavorable risk factor (URF), including primary Gleason pattern 4, 50% of more biopsy cores containing cancer, or multiple National Comprehensive Cancer Network IR factors. One thousand five hundred eighty-six patients with IR prostate cancer comprised the study cohort. Median follow-up was 62 months. Patients classified as UIR were significantly more likely to have pathologic high-risk features, such as Gleason score 8 - 10, pT3-4 disease, or lymph node metastases, than FIR patients (P < 0.001). Furthermore, UIR patients had significantly higher rates of PSA-relapse (PSA, hazard ratio [HR] = 1.89, P < 0.001) and distant metastasis (DM, HR = 2.92, P = 0.001), but no difference in prostate cancer-specific mortality (PCSM) or all-cause mortality in multivariable analysis. On secondary analysis, patients with ≥2 URF had significantly worse PSA-RFS, DM, and PCSM than those with 0 or 1 URF. Moreover, 40% of patients with ≥2 URF had high-risk pathologic features. Patients with UIR prostate cancer are at increased risk of PSA relapse, DM, and pathologic upstaging following prostatectomy. However, increased risk of PCSM was only detected in those with ≥2 URF. This suggests that further refinement of the UIR subgroup may improve risk stratification. Prostate Prostate 77:154-163, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  15. Risks of Skin Cancer Screening

    MedlinePlus

    ... the body's largest organ . It protects against heat, sunlight, injury, and infection . Skin also helps control body ... cancer risk factors include: Being exposed to natural sunlight or artificial sunlight (such as from tanning beds) ...

  16. Cancer risks after radiation exposures

    SciTech Connect

    Voelz, G.L.

    1980-01-01

    A general overview of the effects of ionizing radiation on cancer induction is presented. The relationship between the degree of risk and absorbed dose is examined. Mortality from radiation-induced cancer in the US is estimated and percentages attributable to various sources are given. (ACR)

  17. HEALTHY EATING INDEX AND OVARIAN CANCER RISK

    PubMed Central

    Chandran, Urmila; Bandera, Elisa V.; Williams-King, Melony G.; Paddock, Lisa E.; Rodriguez-Rodriguez, Lorna; Lu, Shou-En; Faulkner, Shameka; Pulick, Katherine; Olson, Sara H.

    2011-01-01

    The evidence for a role of diet on ovarian cancer prevention remains inconclusive. While many studies have evaluated individual foods and food groups, the evaluation of a comprehensive dietary quality index for predicting cancer risk has received little attention. This study investigates the association between the Healthy Eating Index (HEI), which reflects adherence to the current USDA Dietary Guidelines for Americans, and ovarian cancer risk in a population-based case-control study in New Jersey. A total of 205 cases and 390 controls completed the Block 98.2 Food Frequency Questionnaire (FFQ) in addition to reporting on potential risk factors for ovarian cancer. FFQ data were then utilized to calculate the HEI score, and cup, ounce, gram, or caloric equivalents for the 12 different food groups comprising the index. In multivariate models the OR for the highest tertile of the HEI score compared to the lowest (reflecting a better diet compared to a worse diet) was 0.90 (95% CI: 0.55–1.47). There was limited evidence for a statistically significant association between any of the 12 individual food components and ovarian cancer risk. Based on this study’s results, neither individual food groups nor dietary quality showed potential for preventing ovarian cancer. PMID:21286802

  18. Androgen receptor profiling predicts prostate cancer outcome

    PubMed Central

    Stelloo, Suzan; Nevedomskaya, Ekaterina; van der Poel, Henk G; de Jong, Jeroen; van Leenders, Geert JLH; Jenster, Guido; Wessels, Lodewyk FA; Bergman, Andries M; Zwart, Wilbert

    2015-01-01

    Prostate cancer is the second most prevalent malignancy in men. Biomarkers for outcome prediction are urgently needed, so that high-risk patients could be monitored more closely postoperatively. To identify prognostic markers and to determine causal players in prostate cancer progression, we assessed changes in chromatin state during tumor development and progression. Based on this, we assessed genomewide androgen receptor/chromatin binding and identified a distinct androgen receptor/chromatin binding profile between primary prostate cancers and tumors with an acquired resistance to therapy. These differential androgen receptor/chromatin interactions dictated expression of a distinct gene signature with strong prognostic potential. Further refinement of the signature provided us with a concise list of nine genes that hallmark prostate cancer outcome in multiple independent validation series. In this report, we identified a novel gene expression signature for prostate cancer outcome through generation of multilevel genomic data on chromatin accessibility and transcriptional regulation and integration with publically available transcriptomic and clinical datastreams. By combining existing technologies, we propose a novel pipeline for biomarker discovery that is easily implementable in other fields of oncology. PMID:26412853

  19. Risk Stratification System for Oral Cancer Screening.

    PubMed

    Pereira, Lutécia H Mateus; Reis, Isildinha M; Reategui, Erika P; Gordon, Claudia; Saint-Victor, Sandra; Duncan, Robert; Gomez, Carmen; Bayers, Stephanie; Fisher, Penelope; Perez, Aymee; Goodwin, W Jarrard; Hu, Jennifer J; Franzmann, Elizabeth J

    2016-06-01

    Oral cavity and oropharyngeal cancer (oral cancer) is a deadly disease that is increasing in incidence. Worldwide 5-year survival is only 50% due to delayed intervention with more than half of the diagnoses at stage III and IV, whereas earlier detection (stage I and II) yields survival rates up to 80% to 90%. Salivary soluble CD44 (CD44), a tumor-initiating marker, and total protein levels may facilitate oral cancer risk assessment and early intervention. This study used a hospital-based design with 150 cases and 150 frequency-matched controls to determine whether CD44 and total protein levels in oral rinses were associated with oral cancer independent of age, gender, race, ethnicity, tobacco and alcohol use, and socioeconomic status (SES). High-risk subjects receiving oral cancer prevention interventions as part of a community-based program (n = 150) were followed over 1 year to determine marker specificity and variation. CD44 ≥5.33 ng/mL was highly associated with case status [adjusted OR 14.489; 95% confidence interval (CI), 5.973-35.145; P < .0001, vs. reference group CD44 <2.22 ng/mL and protein <1.23 mg/mL]. Total protein aided prediction above CD44 alone. Sensitivity and specificity in the frequency-matched study was 80% and 48.7%, respectively. However, controls were not representative of the target screening population due, in part, to a high rate of prior cancer. In contrast, specificity in the high-risk community was 74% and reached 95% after annual retesting. Simple and inexpensive salivary CD44 and total protein measurements may help identify individuals at heightened risk for oral cancer from the millions who partake in risky behaviors. Cancer Prev Res; 9(6); 445-55. ©2016 AACR. ©2016 American Association for Cancer Research.

  20. Interval to Biochemical Failure Predicts Clinical Outcomes in Patients With High-Risk Prostate Cancer Treated by Combined-Modality Radiation Therapy

    SciTech Connect

    Shilkrut, Mark; McLaughlin, P. William; Merrick, Gregory S.; Vainshtein, Jeffrey M.; Feng, Felix Y.; Hamstra, Daniel A.

    2013-07-15

    Purpose: To validate the prognostic value of interval to biochemical failure (IBF) in patients with high-risk prostate cancer (HiRPCa) treated with combined-modality radiation therapy (CMRT) with or without androgen deprivation therapy (ADT). Methods and Materials: We conducted a retrospective review of HiRPCa (prostate-specific antigen >20 ng/mL, Gleason score [GS] 8-10, or clinical T stage T3-T4) treated with either dose-escalated external beam radiation therapy (EBRT) or CMRT. Interval to biochemical failure was classified as ≤18 or >18 months from the end of all therapy to the date of biochemical failure (BF). Kaplan-Meier methods and Cox proportional hazards regression were used to evaluate the prognostic value of IBF ≤18 months for distant metastasis (DM) and prostate cancer-specific mortality (PCSM). Results: Of 958 patients with a median follow-up of 63.2 months, 175 patients experienced BF. In those with BF, there were no differences in pretreatment clinical characteristics between the EBRT and CMRT groups, except for a higher proportion of patients with GS 8-10 in the CMRT group (70% vs 52%, P=.02). Median IBF after all therapy was 24.0 months (interquartile range 9.6-46.0) in the EBRT group and 18.9 months (interquartile range 9.2-34.5) in the CMRT group (P=.055). On univariate analysis, IBF ≤18 months was associated with increased risk of DM and PCSM in the entire cohort and the individual EBRT and CMRT groups. On multivariate analysis, only GS 9-10 and IBF ≤18 months, but not the radiation therapy regimen or ADT use, predicted DM (hazard ratio [HR] 3.7, P<.01, 95% confidence interval [CI] 1.4-10.3 for GS 9-10; HR 3.9, P<.0001, 95% CI 2.4-6.5 for IBF ≤18 months) and PCSM (HR 14.8, P<.009, 95% CI 2.0-110 for GS 9-10; HR 4.4, P<.0001, 95% CI 2.4-8.1 for IBF ≤18 months). Conclusions: Short IBF was highly prognostic for higher DM and PCSM in patients with HiRPCa. The prognostic value of IBF for DM and PCSM was not affected by the radiation

  1. Classical risk factors, but not HPV status, predict survival after chemoradiotherapy in advanced head and neck cancer patients.

    PubMed

    Descamps, Géraldine; Karaca, Yasemin; Lechien, Jérôme R; Kindt, Nadège; Decaestecker, Christine; Remmelink, Myriam; Larsimont, Denis; Andry, Guy; Hassid, Samantha; Rodriguez, Alexandra; Khalife, Mohammad; Journe, Fabrice; Saussez, Sven

    2016-10-01

    Despite the advent of concomitant chemoradiotherapy (CCRT), the prognosis of advanced head and neck squamous cell carcinoma (HNSCC) patients remains particularly poor. Classically, HNSCC, especially oropharyngeal carcinomas, associated with human papillomavirus (HPV) exhibits better treatment outcomes than HNSCCs in non-infected patients, eliciting a call for the de-escalation of current therapies. To improve the management of HNSCC patients, we aimed to determine the impact of active HPV infection on patient response, recurrence and survival after CCRT in a population of heavy tobacco and alcohol consumers. Paraffin-embedded samples from 218 advanced HNSCC patients, mostly smokers and/or drinkers treated by CCRT, were tested for the presence of HPV DNA by surrogate type-specific E6/E7 qPCR and p16 immunohistochemistry. Associations between the response to CCRT and patient outcomes according to HPV status and clinical data were evaluated by Kaplan-Meier analysis and both univariate and multivariate Cox regression. Type-specific E6/E7 PCR demonstrated HPV positivity in 20 % of HNSCC. Regarding HPV status, we did not find any significant relation with response to therapy in terms of progression-free survival or overall survival. However, we observed a significantly worse prognosis for consumers of alcohol and tobacco compared to nondrinkers (p = 0.003) and non-smokers (p = 0.03). Survival analyses also revealed that the outcome is compromised in stage IV patients (p = 0.007) and, in particular, for oral cavity, hypopharynx and oropharynx carcinoma patients (p = 0.001). The risk of death from HNSCC significantly increases when patients are exposed to tobacco and alcohol during their therapy, regardless of HPV status.

  2. Occupational risk for laryngeal cancer

    SciTech Connect

    Flanders, W.D.; Rothman, K.J.

    1982-04-01

    In a case-control analysis, we studied the effects of type of employment on laryngeal cancer risk using the interview data from the Third National Cancer Survey. Effects were measured relative to the risk for those employed in a group of arbitrarily defined industries and occupations with low risk. We excluded females and controlled for age, tobacco use, alcohol use, and race in the analysis. We found ratio estimates above 3.0 for workers in the railroad industry and the lumber industry; and for sheetmetal workers, grinding wheel operators, and automobile mechanics.

  3. Occupational risk for laryngeal cancer.

    PubMed

    Flanders, W D; Rothman, K J

    1982-04-01

    In a case-control analysis, we studied the effects of type of employment on laryngeal cancer risk using the interview data from the Third National Cancer Survey. Effects were measured relative to the risk for those employed in a group of arbitrarily defined industries and occupations with low risk. We excluded females and controlled for age, tobacco use, alcohol use, and race in the analysis. We found ratio estimates above 3.0 for workers in the railroad industry and the lumber industry; and for sheetmetal workers, grinding wheel operators, and automobile mechanics.

  4. Pelvic Lymph Node Status Assessed by 18F-Fluorodeoxyglucose Positron Emission Tomography Predicts Low-Risk Group for Distant Recurrence in Locally Advanced Cervical Cancer: A Prospective Study

    SciTech Connect

    Kang, Sokbom; Park, Jung-Yeol; Lim, Myung-Chul; Song, Yong-Joong; Park, Se-Hyun; Kim, Seok-Ki; Chung, Dae-Chul; Seo, Sang-Soo; Kim, Joo-Young; Park, Sang-Yoon

    2011-03-01

    Purpose: To develop a prediction model to identify a low-risk group for distant recurrence in patients with locally advanced cervical cancer treated by concurrent chemoradiation. Methods and Materials: Prospectively, 62 patients with locally advanced cervical cancer were recruited as a training cohort. Clinical variables and parameters obtained from positron emission tomography (PET) and magnetic resonance imaging were analyzed by logistic regression. For the test set, 54 patients were recruited independently. To identify the low-risk group, negative likelihood ratio (LR) less than 0.2 was set to be a cutoff. Results: Among the training cohort, multivariate logistic analysis revealed that advanced International Federation of Gynecology and Obstetrics (FIGO) stage and a high serum squamous cancer cell (SCC) antigen level were significant risk factors (p = 0.015 and 0.025, respectively). Using the two parameters, criteria to determine a low-risk subset for distant recurrence were postulated: (1) FIGO Stage IIB or less and (2) pretreatment SCC < 2.4 (Model A). Positive pelvic node on PET completely predicted all cases with distant recurrence and thus was considered as another prediction model (Model B). In the test cohort, although Model A did not showed diagnostic performance, Model B completely predicted all cases with distant recurrence and showed a sensitivity of 100% with negative LR of 0. Across the training and test cohort (n = 116), the false negative rate was 0 (95% confidence interval 0%-7.6%). Conclusions: Positive pelvic node on PET is a useful marker in prediction of distant recurrence in patients with locally advanced cervical cancer who are treated with concurrent chemoradiation.

  5. Alcohol and Cancer Risk

    MedlinePlus

    ... oral cavity (excluding the lips), pharynx (throat), and larynx (voice box) ( 4 ). People who consume 50 or ... developing cancers of the oral cavity , pharynx (throat), larynx , and esophagus than people who use either alcohol ...

  6. Circulating Adiponectin and Risk of Endometrial Cancer

    PubMed Central

    Zheng, Qiaoli; Wu, Haijian; Cao, Jiang

    2015-01-01

    Background Adiponectin is an insulin-sensitizing hormone produced by adipocytes. It has been suggested to be involved in endometrial tumorigenesis. Published data have shown inconsistent results for the association between circulating adiponectin levels and endometrial cancer. In this study, we conducted a meta-analysis to evaluate the predictive value of circulating adiponectin levels on the development of endometrial cancer. Methods PubMed, Embase, ISI web of knowledge, and Cochrane databases were searched for all eligible studies, and the summary relative risk (SRR) was calculated. Additionally, we performed dose-response analysis with eight eligible studies. Results A total of 1,955 cases and 3,458 controls from 12 studies were included. The SRR for the ‘highest’ vs ‘lowest’ adiponectin levels indicated high adiponectin level reduced the risk of endometrial cancer [SRR = 0.40, 95% confidence interval (CI), 0.33–0.66]. Results from the subgroup analyses were consistent with the overall analysis. The SRR for each 1 µg/ml increase of adiponectin indicated a 3% reduction in endometrial cancer risk (95% CI: 2%–4%), and a 14% reduction for each increase of 5 µg/ml (95% CI: 9%–19%). No evidence of publication bias was found. Conclusions This meta-analysis demonstrates that low level of circulating adiponectin is a risk factor for endometrial cancer. PMID:26030130

  7. Performance characteristics of prostate-specific antigen density and biopsy core details to predict oncological outcome in patients with intermediate to high-risk prostate cancer underwent robot-assisted radical prostatectomy.

    PubMed

    Yashi, Masahiro; Nukui, Akinori; Tokura, Yuumi; Takei, Kohei; Suzuki, Issei; Sakamoto, Kazumasa; Yuki, Hideo; Kambara, Tsunehito; Betsunoh, Hironori; Abe, Hideyuki; Fukabori, Yoshitatsu; Nakazato, Yoshimasa; Kaji, Yasushi; Kamai, Takao

    2017-06-23

    Many urologic surgeons refer to biopsy core details for decision making in cases of localized prostate cancer (PCa) to determine whether an extended resection and/or lymph node dissection should be performed. Furthermore, recent reports emphasize the predictive value of prostate-specific antigen density (PSAD) for further risk stratification, not only for low-risk PCa, but also for intermediate- and high-risk PCa. This study focused on these parameters and compared respective predictive impact on oncologic outcomes in Japanese PCa patients. Two-hundred and fifty patients with intermediate- and high-risk PCa according to the National Comprehensive Cancer Network (NCCN) classification, that underwent robot-assisted radical prostatectomy at a single institution, and with observation periods of longer than 6 months were enrolled. None of the patients received hormonal treatments including antiandrogens, luteinizing hormone-releasing hormone analogues, or 5-alpha reductase inhibitors preoperatively. PSAD and biopsy core details, including the percentage of positive cores and the maximum percentage of cancer extent in each positive core, were analyzed in association with unfavorable pathologic results of prostatectomy specimens, and further with biochemical recurrence. The cut-off values of potential predictive factors were set through receiver-operating characteristic curve analyses. In the entire cohort, a higher PSAD, the percentage of positive cores, and maximum percentage of cancer extent in each positive core were independently associated with advanced tumor stage ≥ pT3 and an increased index tumor volume > 0.718 ml. NCCN classification showed an association with a tumor stage ≥ pT3 and a Gleason score ≥8, and the attribution of biochemical recurrence was also sustained. In each NCCN risk group, these preoperative factors showed various associations with unfavorable pathological results. In the intermediate-risk group, the percentage of positive cores showed

  8. Genetic polymorphisms in the microRNA binding-sites of the thymidylate synthase gene predict risk and survival in gastric cancer.

    PubMed

    Shen, Rong; Liu, Hongliang; Wen, Juyi; Liu, Zhensheng; Wang, Li-E; Wang, Qiming; Tan, Dongfeng; Ajani, Jaffer A; Wei, Qingyi

    2015-09-01

    Thymidylate synthase (TYMS) plays a crucial role in folate metabolism as well as DNA synthesis and repair. We hypothesized that functional polymorphisms in the 3' UTR of TYMS are associated with gastric cancer risk and survival. In the present study, we tested our hypothesis by genotyping three potentially functional (at miRNA binding sites) TYMS SNPs (rs16430 6bp del/ins, rs2790 A>G and rs1059394 C>T) in 379 gastric cancer patients and 431 cancer-free controls. Compared with the rs16430 6bp/6bp + 6bp/0bp genotypes, the 0bp/0bp genotype was associated with significantly increased gastric cancer risk (adjusted OR = 1.72, 95% CI = 1.15-2.58). Similarly, rs2790 GG and rs1059394 TT genotypes were also associated with significantly increased risk (adjusted OR = 2.52, 95% CI = 1.25-5.10 and adjusted OR = 1.57, 95% CI = 1.04-2.35, respectively), compared with AA + AG and CC + CT genotypes, respectively. In the haplotype analysis, the T-G-0bp haplotype was associated with significantly increased gastric cancer risk, compared with the C-A-6bp haplotype (adjusted OR = 1.34, 95% CI = 1.05-1.72). Survival analysis revealed that rs16430 0bp/0bp and rs1059394 TT genotypes were also associated with poor survival in gastric cancer patients who received chemotherapy treatment (adjusted HR = 1.61, 95% CI = 1.05-2.48 and adjusted HR = 1.59, 95% CI = 1.02-2.48, respectively). These results suggest that these three variants in the miRNA binding sites of TYMS may be associated with cancer risk and survival of gastric cancer patients. Larger population studies are warranted to verify these findings.

  9. Breast cancer risk assessment using genetic variants and risk factors in a Singapore Chinese population

    PubMed Central

    2014-01-01

    Introduction Genetic variants for breast cancer risk identified in genome-wide association studies (GWAS) in Western populations require further testing in Asian populations. A risk assessment model incorporating both validated genetic variants and established risk factors may improve its performance in risk prediction of Asian women. Methods A nested case-control study of female breast cancer (411 cases and 1,212 controls) within the Singapore Chinese Health Study was conducted to investigate the effects of 51 genetic variants identified in previous GWAS on breast cancer risk. The independent effect of these genetic variants was assessed by creating a summed genetic risk score (GRS) after adjustment for body mass index and the Gail model risk factors for breast cancer. Results The GRS was an independent predictor of breast cancer risk in Chinese women. The multivariate-adjusted odds ratios (95% confidence intervals) of breast cancer for the second, third, and fourth quartiles of the GRS were 1.26 (0.90 to 1.76), 1.47 (1.06 to 2.04) and 1.75 (1.27 to 2.41) respectively (P for trend <0.001). In addition to established risk factors, the GRS improved the classification of 6.2% of women for their absolute risk of breast cancer in the next five years. Conclusions Genetic variants on top of conventional risk factors can improve the risk prediction of breast cancer in Chinese women. PMID:24941967

  10. Bone metastasis risk factors in breast cancer

    PubMed Central

    Pulido, Catarina; Vendrell, Inês; Ferreira, Arlindo R; Casimiro, Sandra; Mansinho, André; Alho, Irina; Costa, Luís

    2017-01-01

    Bone is the single most frequent site for bone metastasis in breast cancer patients. Patients with bone-only metastasis have a fairly good prognosis when compared with patients with visceral disease. Nevertheless, cancer-induced bone disease carries an important risk of developing skeletal related events that impact quality of life (QoL). It is therefore particularly important to stratify patients according to their risk of developing bone metastasis. In this context, several risk factors have been studied, including demographic, clinicopathological, genetic, and metabolic factors. Most of them show conflicting or non-definitive associations and are not validated for clinical use. Nonetheless, tumour intrinsic subtype is widely accepted as a major risk factor for bone metastasis development and luminal breast cancer carries an increased risk for bone disease. Other factors such as gene signatures, expression of specific cytokines (such as bone sialoprotein and bone morphogenetic protein 7) or components of the extracellular matrix (like bone crosslinked C-telopeptide) might also influence the development of bone metastasis. Knowledge of risk factors related with bone disease is of paramount importance as it might be a prediction tool for triggering the use of targeted agents and allow for better patient selection for future clinical trials. PMID:28194227

  11. Cancer risk assessment of toxaphene.

    PubMed

    Buranatrevedh, Surasak

    2004-07-01

    The primary purpose is to do cancer risk assessment of toxaphene by using four steps of risk assessment proposed by the United States National Academy of Sciences/National Research Council (NAS/NRC). Four steps of risk assessment including hazard identification, dose-response relationship, exposure assessment, and risk characterization were used to evaluate cancer risk of toxaphene. Toxaphene was the most heavily used insecticide in many parts of the world before it was banned in 1982. It increased incidence of neoplasms of liver and uterus in mice and increased incidence of neoplasms of endocrine organs, thyroid, pituitary, adrenal, mammary glands, and reproductive systems in rats. From mice's and rats' study, slope factor for toxaphene is 0.8557 (mg/ kg/day)(-1). Lifetime average daily dose (LADD) of toxaphene from ambient air, surface water, soil, and fish were 1.08 x 10(-6), 5.71 x 10(-6), 3.43 x 10(-7), and 7.96 x 10(-5) mg/kg/day, respectively. Cancer risk of toxaphene for average exposure is 7.42 x 10(-5). From this study, toxaphene might have carcinogenic risk among humans.

  12. A Phase II Study of Stereotactic Body Radiation Therapy for Low-Intermediate-High-Risk Prostate Cancer Using Helical Tomotherapy: Dose-Volumetric Parameters Predicting Early Toxicity

    PubMed Central

    Macias, Victor A.; Blanco, Manuel L.; Barrera, Inmaculada; Garcia, Rafael

    2014-01-01

    Endpoint: To assess early urinary (GU) and rectal (GI) toxicities after helical tomotherapy Stereotactic body radiation therapy (SBRT), and to determine their predictive factors. Methods: Since May 2012, 45 prostate cancer patients were treated with eight fractions of 5.48 (low risk, 29%) or 5.65 Gy (intermediate-high risk, 71%) on alternative days over 2.5 weeks. The exclusion criteria were Gleason score 9–10, PSA >40 ng/mL, cT3b-4, IPSS ≥20, and history of acute urinary retention. During the follow-up, a set of potential prognostic factors was correlated with urinary or rectal toxicity. Results: The median follow-up was 13.8 months (2–25 months). There were no grade ≥3 toxicities. Acute grade 2 GU complications were found in a 22.7% of men, but in 2.3% of patients at 1 month, 0% at 6 months, and 0% at 12 months. The correspondent figures for grade 2 GI toxicities were 20.4% (acute), 2.3% (1 month), 3.6% (6 months), and 5% (12 months). Acute GI toxicity was significantly correlated with the rectal volume (>15 cm3) receiving 28 Gy, only when expressed as absolute volume. The age (>72 years old) was a predictor of GI toxicity after 1 month of treatment. No correlation was found, however, between urinary toxicity and the other analyzed variables. IPSS increased significantly at the time of the last fraction and within the first month, returning to the baseline at sixth month. Urinary-related quality of life (IPSS question 8 score), it was not significantly worsen during radiotherapy returning to the baseline levels 1 month after the treatment. At 12 months follow-up patient’s perception of their urinary function improved significantly in comparison with the baseline. Conclusion: Our scheme of eight fractions on alternative days delivered using helical tomotherapy is well tolerated. We recommend using actual volume instead of percentual volume in the treatment planning, and not to exceed 15 cm3 of rectal volume receiving

  13. Development of a prediction model and estimation of cumulative risk for upper aerodigestive tract cancer on the basis of the aldehyde dehydrogenase 2 genotype and alcohol consumption in a Japanese population

    PubMed Central

    Koyanagi, Yuriko N.; Ito, Hidemi; Oze, Isao; Hosono, Satoyo; Tanaka, Hideo; Abe, Tetsuya; Shimizu, Yasuhiro; Hasegawa, Yasuhisa

    2017-01-01

    Alcohol consumption and the aldehyde dehydrogenase 2 (ALDH2) polymorphism are associated with the risk of upper aerodigestive tract cancer, and a significant gene–environment interaction between the two has been confirmed in a Japanese population. To aid the development of a personalized prevention strategy, we developed a risk-prediction model and estimated absolute risks stratified by a combination of the ALDH2 genotype and alcohol consumption. We carried out two age-matched and sex-matched case–control studies: one (630 cases and 1260 controls) for model derivation and the second (654 cases and 654 controls) for external validation. On the basis of data from the derivation study, a prediction model was developed by fitting a conditional logistic regression model using the following predictors: age, sex, smoking, drinking, and the ALDH2 genotype. The risk model, including a combination of the ALDH2 genotype and alcohol consumption, provided high discriminatory accuracy and good calibration in both the derivation and the validation studies: C statistics were 0.82 (95% confidence interval 0.80–0.84) and 0.83 (95% confidence interval 0.81–0.85), respectively, and the calibration plots of both studies remained close to the ideal calibration line. Cumulative risks were obtained by combining odds ratios estimated from the risk model with the age-specific incidence rate and population size. For heavy drinkers with a heterozygous genotype, the cumulative risk at age 80 was above 20%. In contrast, risk in the other groups was less than 5%. In conclusion, modification of alcohol consumption according to the ALDH2 genotype will have a major impact on upper aerodigestive tract cancer prevention. These findings represent a simple and practical model for personalized cancer prevention. PMID:26862830

  14. Providing access to risk prediction tools via the HL7 XML-formatted risk web service.

    PubMed

    Chipman, Jonathan; Drohan, Brian; Blackford, Amanda; Parmigiani, Giovanni; Hughes, Kevin; Bosinoff, Phil

    2013-07-01

    Cancer risk prediction tools provide valuable information to clinicians but remain computationally challenging. Many clinics find that CaGene or HughesRiskApps fit their needs for easy- and ready-to-use software to obtain cancer risks; however, these resources may not fit all clinics' needs. The HughesRiskApps Group and BayesMendel Lab therefore developed a web service, called "Risk Service", which may be integrated into any client software to quickly obtain standardized and up-to-date risk predictions for BayesMendel tools (BRCAPRO, MMRpro, PancPRO, and MelaPRO), the Tyrer-Cuzick IBIS Breast Cancer Risk Evaluation Tool, and the Colorectal Cancer Risk Assessment Tool. Software clients that can convert their local structured data into the HL7 XML-formatted family and clinical patient history (Pedigree model) may integrate with the Risk Service. The Risk Service uses Apache Tomcat and Apache Axis2 technologies to provide an all Java web service. The software client sends HL7 XML information containing anonymized family and clinical history to a Dana-Farber Cancer Institute (DFCI) server, where it is parsed, interpreted, and processed by multiple risk tools. The Risk Service then formats the results into an HL7 style message and returns the risk predictions to the originating software client. Upon consent, users may allow DFCI to maintain the data for future research. The Risk Service implementation is exemplified through HughesRiskApps. The Risk Service broadens the availability of valuable, up-to-date cancer risk tools and allows clinics and researchers to integrate risk prediction tools into their own software interface designed for their needs. Each software package can collect risk data using its own interface, and display the results using its own interface, while using a central, up-to-date risk calculator. This allows users to choose from multiple interfaces while always getting the latest risk calculations. Consenting users contribute their data for future

  15. From palmistry to anthropometry: can 2nd to 4th digit length (2D:4D) predict the risk of prostate cancer?

    PubMed

    Salomão, Layla; Figueiredo, Rui Teófilo; Oliveira Santos, Rafael; Damião, Ronaldo; da Silva, Eloisio Alexsandro

    2014-01-01

    The 2nd to 4th digit length (2D:4D) is inversely related to androgen exposure during the fetal period, which may represent a risk factor for several steroid-related diseases. We aimed to evaluate the relationship between 2D:4D ratio and the risk of developing prostate cancer (PCa). We assessed the 2D:4D ratio of 474 men >40 years old, stratified into three groups: group 1 (n = 222) patients with PCa, group 2 (n = 82) subjects with high risk of PCa, and group 3 (n = 170) men with low risk of PCa. Subjects were submitted to a digital picture of the ventral surface of the right hand and 2nd and 4th fingers measurements were determined by the distance from the proximal crease to the tip using computer-assisted analysis. The mean serum prostate-specific antigen level was 7.5 ng/ml in the high-risk group and 0.92 ng/ml in the low-risk group (p < 0.05). The mean 2D:4D ratios were 0.96 ± 0.04, 0.97 ± 0.04 and 0.96 ± 0.04 for the PCa, high-risk and low-risk groups, respectively, and no difference was found among the three groups (p = 0.12). Anthropometry of the hand using the 2D:4D ratio is not a predictor of PCa. 2013 S. Karger AG, Basel.

  16. Hair Dyes and Cancer Risk

    MedlinePlus

    ... Research Areas Cancer Biology Cancer Genomics Causes of Cancer Diagnosis Prevention Screening & Early Detection Treatment Cancer & Public Health ... Research Areas Cancer Biology Cancer Genomics Causes of Cancer Diagnosis Prevention Screening & Early Detection Treatment Cancer & Public Health ...

  17. Survivin Expression as a Predictive Marker for Local Control in Patients With High-Risk T1 Bladder Cancer Treated With Transurethral Resection and Radiochemotherapy

    SciTech Connect

    Weiss, Christian; Krause, Steffen F.; Sauer, Rolf; Roedel, Claus; Roedel, Franz

    2009-08-01

    Purpose: The objectives of this study were to investigate the expression of survivin in tumor samples from patients with high-risk T1 bladder cancer and to correlate its expression with clinicopathologic features as well as clinical outcomes after initial transurethral resection (TURBT) followed by radiotherapy (RT) or radiochemotherapy (RCT). Methods and Materials: Survivin protein expression was evaluated by immunohistochemistry on tumor specimen (n = 48) from the initial TURBT, and was correlated with clinical and histopathologic characteristics as well as with 5-year rates of local failure, tumor progression, and death from urothelial cancer after primary bladder sparring treatment with RT/RCT. Results: Survivin was not expressed in normal bladder urothelium but was overexpressed in 67% of T1 tumors. No association between survivin expression and clinicopathologic factors (age, gender, grading, multifocality, associated carcinoma in situ) could be shown. With a median follow-up of 27 months (range, 3-140 months), elevated survivin expression was significantly associated with an increased probability of local failure after TURBT and RCT/RT (p = 0.003). There was also a clear trend toward a higher risk of tumor progression (p = 0.07) and lower disease-specific survival (p = 0.10). Conclusions: High survivin expression is a marker of tumor aggressiveness and may help to identify a subgroup of patients with T1 bladder cancer at a high risk for recurrence when treated with primary organ-sparing approaches such as TURBT and RCT.

  18. Alcohol Metabolism and Cancer Risk

    PubMed Central

    Seitz, Helmut K.; Becker, Peter

    2007-01-01

    Chronic alcohol consumption increases the risk for cancer of the organs and tissues of the respiratory tract and the upper digestive tract (i.e., upper aerodigestive tract), liver, colon, rectum, and breast. Various factors may contribute to the development (i.e., pathogenesis) of alcohol-associated cancer, including the actions of acetaldehyde, the first and most toxic metabolite of alcohol metabolism. The main enzymes involved in alcohol and acetaldehyde metabolism are alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH), which are encoded by multiple genes. Because some of these genes exist in several variants (i.e., are polymorphic), and the enzymes encoded by certain variants may result in elevated acetaldehyde levels, the presence of these variants may predispose to certain cancers. Several mechanisms may contribute to alcohol-related cancer development. Acetaldehyde itself is a cancer-causing substance in experimental animals and reacts with DNA to form cancer-promoting compounds. In addition, highly reactive, oxygen-containing molecules that are generated during certain pathways of alcohol metabolism can damage the DNA, thus also inducing tumor development. Together with other factors related to chronic alcohol consumption, these metabolism-related factors may increase tumor risk in chronic heavy drinkers. PMID:17718399

  19. CANCER RISK ASSESSMENT FOR CHLOROFORM

    EPA Science Inventory

    Chloroform is a common chlorination by-product in drinking water. EPA has regulated chloroform as a probable human carcinogen under the Safe Drinking Water Act. The cancer risk estimate via ingestion was based on the 1985 Jorgenson study identifying kidney tumors in male Osborne ...

  20. CANCER RISK ASSESSMENT FOR CHLOROFORM

    EPA Science Inventory

    Chloroform is a common chlorination by-product in drinking water. EPA has regulated chloroform as a probable human carcinogen under the Safe Drinking Water Act. The cancer risk estimate via ingestion was based on the 1985 Jorgenson study identifying kidney tumors in male Osborne ...

  1. Can Smog Raise Breast Cancer Risk?

    MedlinePlus

    ... gov/news/fullstory_164495.html Can Smog Raise Breast Cancer Risk? Exposure to fine-particle air pollution linked ... have dense breasts, a known risk factor for breast cancer, new research suggests. "It appears that women who ...

  2. Suicide Risk Quadruples After Lung Cancer Diagnosis

    MedlinePlus

    ... news/fullstory_165864.html Suicide Risk Quadruples After Lung Cancer Diagnosis Doctors, loved ones need to be on ... TUESDAY, May 23, 2017 (HealthDay News) -- People with lung cancer have a strikingly higher-than-normal risk of ...

  3. Colon Cancer Risk Assessment - Gauss Program

    Cancer.gov

    An executable file (in GAUSS) that projects absolute colon cancer risk (with confidence intervals) according to NCI’s Colorectal Cancer Risk Assessment Tool (CCRAT) algorithm. GAUSS is not needed to run the program.

  4. A link prediction approach to cancer drug sensitivity prediction.

    PubMed

    Turki, Turki; Wei, Zhi

    2017-10-03

    Predicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine. In this paper, we model cancer drug sensitivity as a link prediction, which is shown to be an effective technique. We evaluate our proposed link prediction algorithms and compare them with an existing drug sensitivity prediction approach based on clinical trial data. The experimental results based on the clinical trial data show the stability of our link prediction algorithms, which yield the highest area under the ROC curve (AUC) and are statistically significant. We propose a link prediction approach to obtain new feature representation. Compared with an existing approach, the results show that incorporating the new feature representation to the link prediction algorithms has significantly improved the performance.

  5. What Are the Risk Factors for Thymus Cancer?

    MedlinePlus

    ... and Prevention What Are the Risk Factors for Thymus Cancer? A risk factor is anything that affects ... Cancer? Can Thymus Cancer Be Prevented? More In Thymus Cancer About Thymus Cancer Causes, Risk Factors, and ...

  6. Blood Epigenetic Age may Predict Cancer Incidence and Mortality.

    PubMed

    Zheng, Yinan; Joyce, Brian T; Colicino, Elena; Liu, Lei; Zhang, Wei; Dai, Qi; Shrubsole, Martha J; Kibbe, Warren A; Gao, Tao; Zhang, Zhou; Jafari, Nadereh; Vokonas, Pantel; Schwartz, Joel; Baccarelli, Andrea A; Hou, Lifang

    2016-03-01

    Biological measures of aging are important for understanding the health of an aging population, with epigenetics particularly promising. Previous studies found that tumor tissue is epigenetically older than its donors are chronologically. We examined whether blood Δage (the discrepancy between epigenetic and chronological ages) can predict cancer incidence or mortality, thus assessing its potential as a cancer biomarker. In a prospective cohort, Δage and its rate of change over time were calculated in 834 blood leukocyte samples collected from 442 participants free of cancer at blood draw. About 3-5 years before cancer onset or death, Δage was associated with cancer risks in a dose-responsive manner (P = 0.02) and a one-year increase in Δage was associated with cancer incidence (HR: 1.06, 95% CI: 1.02-1.10) and mortality (HR: 1.17, 95% CI: 1.07-1.28). Participants with smaller Δage and decelerated epigenetic aging over time had the lowest risks of cancer incidence (P = 0.003) and mortality (P = 0.02). Δage was associated with cancer incidence in a 'J-shaped' manner for subjects examined pre-2003, and with cancer mortality in a time-varying manner. We conclude that blood epigenetic age may mirror epigenetic abnormalities related to cancer development, potentially serving as a minimally invasive biomarker for cancer early detection.

  7. A Biopsy-based 17-gene Genomic Prostate Score Predicts Recurrence After Radical Prostatectomy and Adverse Surgical Pathology in a Racially Diverse Population of Men with Clinically Low- and Intermediate-risk Prostate Cancer.

    PubMed

    Cullen, Jennifer; Rosner, Inger L; Brand, Timothy C; Zhang, Nan; Tsiatis, Athanasios C; Moncur, Joel; Ali, Amina; Chen, Yongmei; Knezevic, Dejan; Maddala, Tara; Lawrence, H Jeffrey; Febbo, Phillip G; Srivastava, Shiv; Sesterhenn, Isabell A; McLeod, David G

    2015-07-01

    Biomarkers that are validated in independent cohorts are needed to improve risk assessment for prostate cancer (PCa). A racially diverse cohort of men (20% African American [AA]) was used to evaluate the association of the clinically validated 17-gene Genomic Prostate Score (GPS) with recurrence after radical prostatectomy and adverse pathology (AP) at surgery. Biopsies from 431 men treated for National Comprehensive Cancer Network (NCCN) very low-, low-, or intermediate-risk PCa between 1990 and 2011 at two US military medical centers were tested to validate the association between GPS and biochemical recurrence (BCR) and to confirm the association with AP. Metastatic recurrence (MR) was also evaluated. Cox proportional hazards models were used for BCR and MR, and logistic regression was used for AP. Central pathology review was performed by one uropathologist. AP was defined as primary Gleason pattern 4 or any pattern 5 and/or pT3 disease. GPS results (scale: 0-100) were obtained in 402 cases (93%); 62 men (15%) experienced BCR, 5 developed metastases, and 163 had AP. Median follow-up was 5.2 yr. GPS predicted time to BCR in univariable analysis (hazard ratio per 20 GPS units [HR/20 units]: 2.9; p<0.001) and after adjusting for NCCN risk group (HR/20 units: 2.7; p<0.001). GPS also predicted time to metastases (HR/20 units: 3.8; p=0.032), although the event rate was low (n=5). GPS was strongly associated with AP (odds ratio per 20 GPS units: 3.3; p<0.001), adjusted for NCCN risk group. In AA and Caucasian men, the median GPS was 30.3 for both, the distributions of GPS results were similar, and GPS was similarly predictive of outcome. The association of GPS with near- and long-term clinical end points establishes the assay as a strong independent measure of PCa aggressiveness. Tumor aggressiveness, as measured by GPS, and outcomes were similar in AA and Caucasian men in this equal-access health care system. Predicting outcomes in men with newly diagnosed prostate

  8. Risk of Ovarian Cancer Relapse Score

    PubMed Central

    Rizzuto, Ivana; Stavraka, Chara; Chatterjee, Jayanta; Borley, Jane; Hopkins, Thomas Glass; Gabra, Hani; Ghaem-Maghami, Sadaf; Huson, Les; Blagden, Sarah P.

    2015-01-01

    Objective The aim of this study was to construct a prognostic index that predicts risk of relapse in women who have completed first-line treatment for ovarian cancer (OC). Methods A database of OC cases from 2000 to 2010 was interrogated for International Federation of Gynecology and Obstetrics stage, grade and histological subtype of cancer, preoperative and posttreatment CA-125 level, presence or absence of residual disease after cytoreductive surgery and on postchemotherapy computed tomography scan, and time to progression and death. The strongest predictors of relapse were included into an algorithm, the Risk of Ovarian Cancer Relapse (ROVAR) score. Results Three hundred fifty-four cases of OC were analyzed to generate the ROVAR score. Factors selected were preoperative serum CA-125, International Federation of Gynecology and Obstetrics stage and grade of cancer, and presence of residual disease at posttreatment computed tomography scan. In the validation data set, the ROVAR score had a sensitivity and specificity of 94% and 61%, respectively. The concordance index for the validation data set was 0.91 (95% confidence interval, 0.85-0.96). The score allows patient stratification into low (<0.33), intermediate (0.34–0.67), and high (>0.67) probability of relapse. Conclusions The ROVAR score stratifies patients according to their risk of relapse following first-line treatment for OC. This can broadly facilitate the appropriate tailoring of posttreatment care and support. PMID:25647256

  9. Risks of Breast Cancer Screening

    MedlinePlus

    ... of Breast & Gynecologic Cancers Breast Cancer Screening Research Breast Cancer Screening (PDQ®)–Patient Version What is screening? Go ... cancer screening: Cancer Screening Overview General Information About Breast Cancer Key Points Breast cancer is a disease in ...

  10. Epidemiology of endocrine-related risk factors for breast cancer.

    PubMed

    Bernstein, Leslie

    2002-01-01

    Ovarian and other hormones are major determinants of breast cancer risk. Particularly important is the accumulative exposure of the breast to circulating levels of the ovarian hormones estradiol and progesterone. A number of breast cancer risk factors can be understood in light of how they affect women's hormone profiles. Age is a marker for the onset and cessation of ovarian activity. Racial differences in hormone profiles correlate with breast cancer incidence patterns. Age at menarche not only serves as the chronological indicator of the onset of ovarian activity, but as a predictor of ovulatory frequency during adolescence and hormone levels in young adults, and has a long-lasting influence on risk. Age at menopause, another established breast cancer risk factor, marks the cessation of ovarian activity. Pregnancy history and lactation experience also are hormonal markers of breast cancer risk. Postmenopausal obesity, which is associated with higher levels of estrogen following cessation of ovarian activity, increases breast cancer risk, whereas physical activity, which can limit menstrual function, reduces risk. A relatively recent area of investigation is prenatal exposures like preeclampsia and low birth weight; both may be associated with lower in utero exposure to estrogen and also may predict lower breast cancer risk as an adult. Improved understanding of these exposures and their potential interactions with breast cancer susceptibility genes may, in the future, improve our prospects for breast cancer prevention.

  11. Cancer risks after radiation exposure in middle age.

    PubMed

    Shuryak, Igor; Sachs, Rainer K; Brenner, David J

    2010-11-03

    Epidemiological data show that radiation exposure during childhood is associated with larger cancer risks compared with exposure at older ages. For exposures in adulthood, however, the relative risks of radiation-induced cancer in Japanese atomic bomb survivors generally do not decrease monotonically with increasing age of adult exposure. These observations are inconsistent with most standard models of radiation-induced cancer, which predict that relative risks decrease monotonically with increasing age at exposure, at all ages. We analyzed observed cancer risk patterns as a function of age at exposure in Japanese atomic bomb survivors by using a biologically based quantitative model of radiation carcinogenesis that incorporates both radiation induction of premalignant cells (initiation) and radiation-induced promotion of premalignant damage. This approach emphasizes the kinetics of radiation-induced initiation and promotion, and tracks the yields of premalignant cells before, during, shortly after, and long after radiation exposure. Radiation risks after exposure in younger individuals are dominated by initiation processes, whereas radiation risks after exposure at later ages are more influenced by promotion of preexisting premalignant cells. Thus, the cancer site-dependent balance between initiation and promotion determines the dependence of cancer risk on age at radiation exposure. For example, in terms of radiation induction of premalignant cells, a quantitative measure of the relative contribution of initiation vs promotion is 10-fold larger for breast cancer than for lung cancer. Reflecting this difference, radiation-induced breast cancer risks decrease with age at exposure at all ages, whereas radiation-induced lung cancer risks do not. For radiation exposure in middle age, most radiation-induced cancer risks do not, as often assumed, decrease with increasing age at exposure. This observation suggests that promotional processes in radiation carcinogenesis

  12. Predictions of space radiation fatality risk for exploration missions

    NASA Astrophysics Data System (ADS)

    Cucinotta, Francis A.; To, Khiet; Cacao, Eliedonna

    2017-05-01

    In this paper we describe revisions to the NASA Space Cancer Risk (NSCR) model focusing on updates to probability distribution functions (PDF) representing the uncertainties in the radiation quality factor (QF) model parameters and the dose and dose-rate reduction effectiveness factor (DDREF). We integrate recent heavy ion data on liver, colorectal, intestinal, lung, and Harderian gland tumors with other data from fission neutron experiments into the model analysis. In an earlier work we introduced distinct QFs for leukemia and solid cancer risk predictions, and here we consider liver cancer risks separately because of the higher RBE's reported in mouse experiments compared to other tumors types, and distinct risk factors for liver cancer for astronauts compared to the U.S. population. The revised model is used to make predictions of fatal cancer and circulatory disease risks for 1-year deep space and International Space Station (ISS) missions, and a 940 day Mars mission. We analyzed the contribution of the various model parameter uncertainties to the overall uncertainty, which shows that the uncertainties in relative biological effectiveness (RBE) factors at high LET due to statistical uncertainties and differences across tissue types and mouse strains are the dominant uncertainty. NASA's exposure limits are approached or exceeded for each mission scenario considered. Two main conclusions are made: 1) Reducing the current estimate of about a 3-fold uncertainty to a 2-fold or lower uncertainty will require much more expansive animal carcinogenesis studies in order to reduce statistical uncertainties and understand tissue, sex and genetic variations. 2) Alternative model assumptions such as non-targeted effects, increased tumor lethality and decreased latency at high LET, and non-cancer mortality risks from circulatory diseases could significantly increase risk estimates to several times higher than the NASA limits.

  13. Predictions of space radiation fatality risk for exploration missions.

    PubMed

    Cucinotta, Francis A; To, Khiet; Cacao, Eliedonna

    2017-05-01

    In this paper we describe revisions to the NASA Space Cancer Risk (NSCR) model focusing on updates to probability distribution functions (PDF) representing the uncertainties in the radiation quality factor (QF) model parameters and the dose and dose-rate reduction effectiveness factor (DDREF). We integrate recent heavy ion data on liver, colorectal, intestinal, lung, and Harderian gland tumors with other data from fission neutron experiments into the model analysis. In an earlier work we introduced distinct QFs for leukemia and solid cancer risk predictions, and here we consider liver cancer risks separately because of the higher RBE's reported in mouse experiments compared to other tumors types, and distinct risk factors for liver cancer for astronauts compared to the U.S. The revised model is used to make predictions of fatal cancer and circulatory disease risks for 1-year deep space and International Space Station (ISS) missions, and a 940 day Mars mission. We analyzed the contribution of the various model parameter uncertainties to the overall uncertainty, which shows that the uncertainties in relative biological effectiveness (RBE) factors at high LET due to statistical uncertainties and differences across tissue types and mouse strains are the dominant uncertainty. NASA's exposure limits are approached or exceeded for each mission scenario considered. Two main conclusions are made: 1) Reducing the current estimate of about a 3-fold uncertainty to a 2-fold or lower uncertainty will require much more expansive animal carcinogenesis studies in order to reduce statistical uncertainties and understand tissue, sex and genetic variations. 2) Alternative model assumptions such as non-targeted effects, increased tumor lethality and decreased latency at high LET, and non-cancer mortality risks from circulatory diseases could significantly increase risk estimates to several times higher than the NASA limits. Copyright © 2017 The Committee on Space Research (COSPAR

  14. Developmental Dyslexia: Predicting Individual Risk

    ERIC Educational Resources Information Center

    Thompson, Paul A.; Hulme, Charles; Nash, Hannah M.; Gooch, Debbie; Hayiou-Thomas, Emma; Snowling, Margaret J.

    2015-01-01

    Background: Causal theories of dyslexia suggest that it is a heritable disorder, which is the outcome of multiple risk factors. However, whether early screening for dyslexia is viable is not yet known. Methods: The study followed children at high risk of dyslexia from preschool through the early primary years assessing them from age 3 years and 6…

  15. Developmental Dyslexia: Predicting Individual Risk

    ERIC Educational Resources Information Center

    Thompson, Paul A.; Hulme, Charles; Nash, Hannah M.; Gooch, Debbie; Hayiou-Thomas, Emma; Snowling, Margaret J.

    2015-01-01

    Background: Causal theories of dyslexia suggest that it is a heritable disorder, which is the outcome of multiple risk factors. However, whether early screening for dyslexia is viable is not yet known. Methods: The study followed children at high risk of dyslexia from preschool through the early primary years assessing them from age 3 years and 6…

  16. Family Factors Predicting Categories of Suicide Risk

    ERIC Educational Resources Information Center

    Randell, Brooke P.; Wang, Wen-Ling; Herting, Jerald R.; Eggert, Leona L.

    2006-01-01

    We compared family risk and protective factors among potential high school dropouts with and without suicide-risk behaviors (SRB) and examined the extent to which these factors predict categories of SRB. Subjects were randomly selected from among potential dropouts in 14 high schools. Based upon suicide-risk status, 1,083 potential high school…

  17. Lesion volume predicts prostate cancer risk and aggressiveness: validation of its value alone and matched with prostate imaging reporting and data system score.

    PubMed

    Martorana, Eugenio; Pirola, Giacomo Maria; Scialpi, Michele; Micali, Salvatore; Iseppi, Andrea; Bonetti, Luca Reggiani; Kaleci, Shaniko; Torricelli, Pietro; Bianchi, Giampaolo

    2017-07-01

    To demonstrate the association between magnetic resonance imaging (MRI) estimated lesion volume (LV), prostate cancer detection and tumour clinical significance, evaluating this variable alone and matched with Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) score. We retrospectively analysed 157 consecutive patients, with at least one prior negative systematic prostatic biopsy, who underwent transperineal prostate MRI/ultrasonography fusion-targeted biopsy between January 2014 and February 2016. Suspicious lesions were delineated using a 'region of interest' and the system calculated prostate volume and LV. Patients were divided in groups considering LV (≤0.5, 0.5-1, ≥1 mL) and PI-RADS score (1-5). We considered clinically significant prostate cancer as all cancers with a Gleason score of ≥3 + 4 as suggested by PI-RADS v2. A direct comparison between MRI estimated LV (MRI LV) and histological tumour volume (HTV) was done in 23 patients who underwent radical prostatectomy during the study period. Differences between MRI LV and HTV were assessed using the paired sample t-test. MRI LV and HTV concordance was verified using a Bland-Altman plot. The chi-squared test and logistic and ordinal regression models were used to evaluate difference in frequencies. The MRI LV and PI-RADS score were associated both with prostate cancer detection (both P < 0.001) and with significant prostate cancer detection (P < 0.001 and P = 0.008, respectively). When the two variables were matched, increasing LV increased the risk within each PI-RADS group. Prostate cancer detection was 1.4-times higher for LVs of 0.5-1 mL and 1.8-times higher for LVs of ≥1 mL; significant prostate cancer detection was 2.6-times for LVs of 0.5-1 mL and 4-times for LVs of ≥1 mL. There was a positive correlation between MRI LV and HTV (r = 0.9876, P < 0.001). Finally, Bland-Altman analysis showed that MRI LV was underestimated by 4.2% compared to HTV. Study limitations include its

  18. Dosimetric parameters as predictive factors for biochemical control in patients with higher risk prostate cancer treated with Pd-103 and supplemental beam radiation

    SciTech Connect

    Orio, Peter; Wallner, Kent . E-mail: kent.Wallner@med.va.gov; Merrick, Gregory; Herstein, Andrew; Mitsuyama, Paul; Thornton, Ken; Butler, Wayne; Sutlief, Steven

    2007-02-01

    Purpose: To analyze the role of dosimetric quality parameters in maximizing cancer eradication in higher risk prostate cancer patients treated with palladium (Pd)-103 and supplemental beam radiation. Methods: One-hundred-seventy-nine patients treated with Pd-103 and supplemental beam radiation, with minimum 2 years follow-up prostate-specific antigen (PSA) values and posttreatment computed tomography scans were analyzed. Dosimetric parameters included the V100 (percent of the postimplant volume covered by the prescription dose), the D90 (the minimum dose that covered 90% of the post implant volume), and the treatment margins (the radial distance between the prostatic edge and the prescription isodose). Treatment margins (TMs) were calculated using premarket software. Results: Freedom from biochemical failure was 79% at 3 years, with 92 of the 179 patients (51%) followed beyond 3 years. In comparing patients who did or did not achieve biochemical control, the most striking differences were in biologic factors of pretreatment PSA and Gleason score. The V100, D90, and average TM all showed nonsignificant trends to higher values in patients with biochemical control. In multivariate analysis of each of the three dosimetric parameters against PSA and Gleason score, TM showed the strongest correlation with biochemical control (p = 0.19). Conclusions: For patients with intermediate and high-risk prostate cancer treated with Pd-103 brachytherapy and external beam radiation, biologic factors (PSA and Gleason score) were the most important determinants of cancer eradication. However, there is a trend to better outcomes among patients with higher quality implant parameters, suggesting that attention to implant quality will maximize the likelihood of cure.

  19. Cancer Risk Assessment for Space Radiation

    NASA Technical Reports Server (NTRS)

    Richmond, Robert C.; Curreri, Peter A. (Technical Monitor)

    2002-01-01

    Predicting the occurrence of human cancer following exposure to any agent causing genetic damage is a difficult task. This is because the uncertainty of uniform exposure to the damaging agent, and the uncertainty of uniform processing of that damage within a complex set of biological variables, degrade the confidence of predicting the delayed expression of cancer as a relatively rare event within any given clinically normal individual. The radiation health research priorities for enabling long-duration human exploration of space were established in the 1996 NRC Report entitled "Radiation Hazards to Crews of Interplanetary Missions: Biological Issues and Research Strategies". This report emphasized that a 15-fold uncertainty in predicting radiation-induced cancer incidence must be reduced before NASA can commit humans to extended interplanetary missions. That report concluded that the great majority of this uncertainty is biologically based, while a minority is physically based due to uncertainties in radiation dosimetry and radiation transport codes. Since that report, the biologically based uncertainty has remained large, and the relatively small uncertainty associated with radiation dosimetry has increased due to the considerations raised by concepts of microdosimetry. In a practical sense, however, the additional uncertainties introduced by microdosimetry are encouraging since they are in a direction of lowered effective dose absorbed through infrequent interactions of any given cell with the high energy particle component of space radiation. The biological uncertainty in predicting cancer risk for space radiation derives from two primary facts. 1) One animal tumor study has been reported that includes a relevant spectrum of particle radiation energies, and that is the Harderian gland model in mice. Fact #1: Extension of cancer risk from animal models, and especially from a single study in an animal model, to humans is inherently uncertain. 2) One human database

  20. Cancer Risk Assessment for Space Radiation

    NASA Technical Reports Server (NTRS)

    Richmond, Robert C.; Curreri, Peter A. (Technical Monitor)

    2002-01-01

    Predicting the occurrence of human cancer following exposure to any agent causing genetic damage is a difficult task. This is because the uncertainty of uniform exposure to the damaging agent, and the uncertainty of uniform processing of that damage within a complex set of biological variables, degrade the confidence of predicting the delayed expression of cancer as a relatively rare event within any given clinically normal individual. The radiation health research priorities for enabling long-duration human exploration of space were established in the 1996 NRC Report entitled "Radiation Hazards to Crews of Interplanetary Missions: Biological Issues and Research Strategies". This report emphasized that a 15-fold uncertainty in predicting radiation-induced cancer incidence must be reduced before NASA can commit humans to extended interplanetary missions. That report concluded that the great majority of this uncertainty is biologically based, while a minority is physically based due to uncertainties in radiation dosimetry and radiation transport codes. Since that report, the biologically based uncertainty has remained large, and the relatively small uncertainty associated with radiation dosimetry has increased due to the considerations raised by concepts of microdosimetry. In a practical sense, however, the additional uncertainties introduced by microdosimetry are encouraging since they are in a direction of lowered effective dose absorbed through infrequent interactions of any given cell with the high energy particle component of space radiation. The biological uncertainty in predicting cancer risk for space radiation derives from two primary facts. 1) One animal tumor study has been reported that includes a relevant spectrum of particle radiation energies, and that is the Harderian gland model in mice. Fact #1: Extension of cancer risk from animal models, and especially from a single study in an animal model, to humans is inherently uncertain. 2) One human database

  1. Prediction of fracture risk. II: Other risk factors.

    PubMed

    Ross, P D

    1996-12-01

    Many osteoporotic fractures are probably preventable-by definition, prevention requires identification of those at risk prior to fracture. There is a continuum in fracture risk and a very wide range in risk among individuals. Bone density, previous fractures, and the frequency and types of falls are important risk factors for fractures. There are also many other risk factors for bone loss, falls, and fractures. People with multiple risk factors are at greater risk than those with either a single risk factor or none. Identification of risk factors can help when planning interventions. For example, dietary deficiencies are amenable to dietary modification or supplementation; however, the effects of many risk factors have not been quantified separately, making it difficult to determine the importance. In addition, it is not possible to accurately predict current bone density and fracture risk from risk factors for bone loss; bone density should always be measured directly.

  2. Novel predictive biomarkers for cervical cancer prognosis

    PubMed Central

    Moreno-Acosta, Pablo; Carrillo, Schyrly; Gamboa, Oscar; Romero-Rojas, Alfredo; Acosta, Jinneth; Molano, Monica; Balart-Serra, Joseph; Cotes, Martha; Rancoule, Chloé; Magné, Nicolas

    2016-01-01

    High hypoxic, glycolytic and acidosis metabolisms characterize cervical cancer tumors and have been described to be involved in chemoradioresistance mechanisms. Based on these observations, the present study assessed four selected novel biomarkers on the prognosis of locally advanced cervical carcinoma. A total of 66 patients with stage IIB/IIIB cervical cancer were retrospectively included. The protein expression levels of glucose transporter 1 (GLUT1), carbonic anhydrase 9 (CAIX) and hexokinase 1 (HKII) were investigated by immunohistochemistry on tumor biopsies, hemoglobin was measured and the disease outcome was monitored. A total of 53 patients (80.3%) presented a complete response. For these patients, the protein expression levels of GLUT1, CAIX and HKII were overexpressed. A significant difference was observed (P=0.0127) for hemoglobin levels (≤11 g/dl) in responsive compared with non-responsive patients. The expression of GLUT1 is associated with a lower rate of both overall and disease-free survival, with a trend of decreased risk of 1.1x and 1.5x, respectively. Co-expression of GLUT1 and HKII is associated with a decreased trend risk of 1.6x for overall survival. Patients with hemoglobin levels ≤11 g/dl had a 4.3-fold risk (P=0.02) in decreasing both to the rate of overall and disease-free survival. The presence of anemic hypoxia (hemoglobin ≤11 g/dl) and the expression of GLUT1 and/or HKII influence treatment response and are associated with a lower overall and disease-free survival. The present results demonstrated that these biomarkers may be used as predictive markers and suggested that these metabolic pathways can be used as potential novel therapeutic targets. PMID:28101358

  3. External validation of EORTC risk scores to predict recurrence after transurethral resection of brazilian patients with non-muscle invasive bladder cancer stages Ta and T1

    PubMed Central

    Almeida, Gilberto L.; Busato, Wilson F. S.; Ribas, Carmen Marcondes; Ribas-Filho, Jurandir Marcondes; Cobelli, Ottavio De

    2016-01-01

    ABSTRACT Validate the EORTC risk tables in Brazilian patients with NMIBC. Methods: 205 patients were analyzed. The 6 parameters analyzed were: histologic grading, pathologic stage, size and number of tumors, previous recurrence rate and concomitant CIS. The time for first recurrence (TFR), risk score and probability of recurrence were calculated and compared to the probabilities obtained from EORTC risk tables. C-index was calculated and accuracy of EORTC tables was analyzed. Results: pTa was presented in 91 (44.4%) patients and pT1 in 114 (55.6%). Ninety-seven (47.3%) patients had solitary tumor, and 108 (52.7%) multiple tumors. One hundred and three (50.2%) patients had tumors smaller than 3 cm and 102 (40.8%) had bigger than 3 cm. Concomitant CIS was observed in 21 (10.2%) patients. Low grade was presented in 95 (46.3%) patients, and high grade in 110 (53.7%). Intravesical therapy was utilized in 105 (56.1%) patients. Recurrence was observed in 117 (57.1%) patients and the mean TFR was 14,2 ± 7,3 months. C-index was 0,72 for 1 year and 0,7 for 5 years. The recurrence risk was 28,8% in 1 year and 57,1% in 5 years, independently of the scoring risk. In our population, the EORTC risk tables overestimated the risk of recurrence in 1 year and underestimated in 5 years. Conclusion: The validation of the EORTC risk tables in Brazilian patients with NMIBC was satisfactory and should be stimulated to predict recurrence, although these may overestimated the risk of recurrence in 1 year and underestimated in 5 years. PMID:27509372

  4. Pernicious anaemia and cancer risk in Denmark.

    PubMed Central

    Mellemkjaer, L.; Gridley, G.; Møller, H.; Hsing, A. W.; Linet, M. S.; Brinton, L. A.; Olsen, J. H.

    1996-01-01

    A cohort of 5072 patients with pernicious anaemia was identified in the Danish Hospital Discharge Register from 1977 to 1989 and, through linkage to the Danish Cancer Registry, the occurrence of cancer in the cohort was determined up to 1991. Observed numbers of cancer cases during 1-15 years of follow-up were compared with expected numbers based on national incidence rates. Besides the well-established increased risk for stomach cancer, the analysis also revealed a 2-fold increase in the relative risk for cancer of the buccal cavity and pharynx among pernicious anaemia patients in accordance with previous studies; previously reported elevated risks for other digestive tract cancers were not confirmed. There was a non-significantly increased risk for lymphatic and haematological malignancy but the risk tended to disappear after 5 years of follow-up, indicating a possible selection bias. Decreased risks for cervical cancer and non-melanoma skin cancer were also seen. PMID:8611439

  5. Cancer: a family at risk

    PubMed Central

    Iżycki, Dariusz

    2014-01-01

    The diagnosis of cancer is a family experience that changes the lives of all its members, bringing an immense amount of stress and many challenging situations. The daily routine, common activities and distribution of duties all have to change. Family members follow the phases of the disease, very often suffering comparable or greater distress than the patient. They use various coping methods which aim at helping both the sick relative and themselves. These methods, together with emotional responses, change over time according to the phase of the disease. Cancer puts the family at risk since it imposes an alternation in the relations among family members. It affects the couple's relationship, their sex life, and it can also be a cause of major trauma among their children and adolescents. The diagnosis of cancer brings also individual risks for the family members in terms of psychological and physical health impairment. Family caregivers often feel overloaded with the additional obligations and roles they have to pick up. They find it increasingly burdening to care full-time for the household and provide emotional support for the patient. The family's problems and the way family members regard the disease may be also a result of the family system they are in. This article describes the nature of caregiving to a patient with cancer and the biggest concerns for the family. PMID:26327863

  6. Association of Breast Cancer Risk loci with Breast Cancer Survival

    PubMed Central

    Barrdahl, Myrto; Canzian, Federico; Lindström, Sara; Shui, Irene; Black, Amanda; Hoover, Robert N.; Ziegler, Regina G.; Buring, Julie E.; Chanock, Stephen J.; Diver, W. Ryan; Gapstur, Susan M.; Gaudet, Mia M.; Giles, Graham G.; Haiman, Christopher; Henderson, Brian E.; Hankinson, Susan; Hunter, David J.; Joshi, Amit D.; Kraft, Peter; Lee, I-Min; Le Marchand, Loic; Milne, Roger L.; Southey, Melissa C.; Willett, Walter; Gunter, Marc; Panico, Salvatore; Sund, Malin; Weiderpass, Elisabete; Sánchez, María-José; Overvad, Kim; Dossus, Laure; Peeters, Petra H; Khaw, Kay-Tee; Trichopoulos, Dimitrios; Kaaks, Rudolf; Campa, Daniele

    2015-01-01

    The survival of breast cancer patients is largely influenced by tumor characteristics, such as TNM stage, tumor grade and hormone receptor status. However, there is growing evidence that inherited genetic variation might affect the disease prognosis and response to treatment. Several lines of evidence suggest that alleles influencing breast cancer risk might also be associated with breast cancer survival. We examined the associations between 35 breast cancer susceptibility loci and the disease over-all survival (OS) in 10,255 breast cancer patients from the National Cancer Institute Breast and Prostate Cancer Cohort Consortium (BPC3) of which 1,379 died, including 754 of breast cancer. We also conducted a meta-analysis of almost 35,000 patients and 5,000 deaths, combining results from BPC3 and the Breast Cancer Association Consortium (BCAC) and performed in silico analyses of SNPs with significant associations. In BPC3, the C allele of LSP1-rs3817198 was significantly associated with improved OS (HRper-allele=0.70; 95% CI: 0.58–0.85; Ptrend=2.84×10−4; HRheterozygotes=0.71; 95% CI: 0.55–0.92; HRhomozygotes=0.48; 95% CI: 0.31–0.76; P2DF=1.45×10−3). In silico, the C allele of LSP1-rs3817198 was predicted to increase expression of the tumor suppressor cyclin-dependent kinase inhibitor 1C (CDKN1C). In the meta-analysis, TNRC9-rs3803662 was significantly associated with increased death hazard (HRMETA =1.09; 95% CI: 1.04–1.15; Ptrend=6.6×10−4; HRheterozygotes=0.96 95% CI: 0.90–1.03; HRhomozygotes= 1.21; 95% CI: 1.09–1.35; P2DF=1.25×10−4). In conclusion, we show that there is little overlap between the breast cancer risk single nucleotide polymorphisms (SNPs) identified so far and the SNPs associated with breast cancer prognosis, with the possible exceptions of LSP1-rs3817198 and TNRC9-rs3803662. PMID:25611573

  7. Risk and the physics of clinical prediction.

    PubMed

    McEvoy, John W; Diamond, George A; Detrano, Robert C; Kaul, Sanjay; Blaha, Michael J; Blumenthal, Roger S; Jones, Steven R

    2014-04-15

    The current paradigm of primary prevention in cardiology uses traditional risk factors to estimate future cardiovascular risk. These risk estimates are based on prediction models derived from prospective cohort studies and are incorporated into guideline-based initiation algorithms for commonly used preventive pharmacologic treatments, such as aspirin and statins. However, risk estimates are more accurate for populations of similar patients than they are for any individual patient. It may be hazardous to presume that the point estimate of risk derived from a population model represents the most accurate estimate for a given patient. In this review, we exploit principles derived from physics as a metaphor for the distinction between predictions regarding populations versus patients. We identify the following: (1) predictions of risk are accurate at the level of populations but do not translate directly to patients, (2) perfect accuracy of individual risk estimation is unobtainable even with the addition of multiple novel risk factors, and (3) direct measurement of subclinical disease (screening) affords far greater certainty regarding the personalized treatment of patients, whereas risk estimates often remain uncertain for patients. In conclusion, shifting our focus from prediction of events to detection of disease could improve personalized decision-making and outcomes. We also discuss innovative future strategies for risk estimation and treatment allocation in preventive cardiology. Copyright © 2014 Elsevier Inc. All rights reserved.

  8. 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

  9. Sun Protection Motivational Stages and Behavior: Skin Cancer Risk Profiles

    ERIC Educational Resources Information Center

    Pagoto, Sherry L.; McChargue, Dennis E.; Schneider, Kristin; Cook, Jessica Werth

    2004-01-01

    Objective: To create skin cancer risk profiles that could be used to predict sun protection among Midwest beachgoers. Method: Cluster analysis was used with study participants (N=239), who provided information about sun protection motivation and behavior, perceived risk, burn potential, and tan importance. Participants were clustered according to…

  10. Sun Protection Motivational Stages and Behavior: Skin Cancer Risk Profiles

    ERIC Educational Resources Information Center

    Pagoto, Sherry L.; McChargue, Dennis E.; Schneider, Kristin; Cook, Jessica Werth

    2004-01-01

    Objective: To create skin cancer risk profiles that could be used to predict sun protection among Midwest beachgoers. Method: Cluster analysis was used with study participants (N=239), who provided information about sun protection motivation and behavior, perceived risk, burn potential, and tan importance. Participants were clustered according to…

  11. Early Life and Risk of Breast Cancer

    DTIC Science & Technology

    2004-08-01

    birth weight and of growth during childhood and adolescence on risk of breast cancer. We used a unique material of school charts with information on...childhood and adolescence influence breast cancer risk. 14. SUBJECT TERMS 15. NUMBER OF PAGES Epidemiology, Etiology, Risk Factors, Weight, Growth 132 16...childhood and adolescence on risk of breast cancer in a cohort of more than 150,000 girls on whom information on birth weight and between 6 and 8

  12. Predictive and therapeutic markers in ovarian cancer

    DOEpatents

    Gray, Joe W.; Guan, Yinghui; Kuo, Wen-Lin; Fridlyand, Jane; Mills, Gordon B.

    2013-03-26

    Cancer markers may be developed to detect diseases characterized by increased expression of apoptosis-suppressing genes, such as aggressive cancers. Genes in the human chromosomal regions, 8q24, 11q13, 20q11-q13, were found to be amplified indicating in vivo drug resistance in diseases such as ovarian cancer. Diagnosis and assessment of amplification levels certain genes shown to be amplified, including PVT1, can be useful in prediction of poor outcome of patient's response and drug resistance in ovarian cancer patients with low survival rates. Certain genes were found to be high priority therapeutic targets by the identification of recurrent aberrations involving genome sequence, copy number and/or gene expression are associated with reduced survival duration in certain diseases and cancers, specifically ovarian cancer. Therapeutics to inhibit amplification and inhibitors of one of these genes, PVT1, target drug resistance in ovarian cancer patients with low survival rates is described.

  13. Use of dose-dependent absorption into target tissues to more accurately predict cancer risk at low oral doses of hexavalent chromium.

    PubMed

    Haney, J

    2015-02-01

    The mouse dose at the lowest water concentration used in the National Toxicology Program hexavalent chromium (CrVI) drinking water study (NTP, 2008) is about 74,500 times higher than the approximate human dose corresponding to the 35-city geometric mean reported in EWG (2010) and over 1000 times higher than that based on the highest reported tap water concentration. With experimental and environmental doses differing greatly, it is a regulatory challenge to extrapolate high-dose results to environmental doses orders of magnitude lower in a meaningful and toxicologically predictive manner. This seems particularly true for the low-dose extrapolation of results for oral CrVI-induced carcinogenesis since dose-dependent differences in the dose fraction absorbed by mouse target tissues are apparent (Kirman et al., 2012). These data can be used for a straightforward adjustment of the USEPA (2010) draft oral slope factor (SFo) to be more predictive of risk at environmentally-relevant doses. More specifically, the evaluation of observed and modeled differences in the fraction of dose absorbed by target tissues at the point-of-departure for the draft SFo calculation versus lower doses suggests that the draft SFo be divided by a dose-specific adjustment factor of at least an order of magnitude to be less over-predictive of risk at more environmentally-relevant doses.

  14. Erlotinib and the Risk of Oral Cancer

    PubMed Central

    William, William N.; Papadimitrakopoulou, Vassiliki; Lee, J. Jack; Mao, Li; Cohen, Ezra E.W.; Lin, Heather Y.; Gillenwater, Ann M.; Martin, Jack W.; Lingen, Mark W.; Boyle, Jay O.; Shin, Dong M.; Vigneswaran, Nadarajah; Shinn, Nancy; Heymach, John V.; Wistuba, Ignacio I.; Tang, Ximing; Kim, Edward S.; Saintigny, Pierre; Blair, Elizabeth A.; Meiller, Timothy; Gutkind, J. Silvio; Myers, Jeffrey; El-Naggar, Adel; Lippman, Scott M.

    2016-01-01

    IMPORTANCE Standard molecularly based strategies to predict and/or prevent oral cancer development in patients with oral premalignant lesions (OPLs) are lacking. OBJECTIVE To test if the epidermal growth factor receptor inhibitor erlotinib would reduce oral cancer development in patients with high-risk OPLs defined by specific loss of heterozygosity (LOH) profiles. Secondary objectives included prospective determination of LOH as a prognostic marker in OPLs. DESIGN The Erlotinib Prevention of Oral Cancer (EPOC) study was a randomized, placebo-controlled, double-bind trial. Accrual occurred from November 2006 through July 2012, with a median follow-up time of 35 months in an ambulatory care setting in 5 US academic referral institutions. Patients with OPLs were enrolled in the protocol, and each underwent LOH profiling (N = 379); they were classified as high-risk (LOH-positive) or low-risk (LOH-negative) patients based on their LOH profiles and oral cancer history. The randomized sample consisted of 150 LOH-positive patients. INTERVENTIONS Oral erlotinib treatment (150mg/d) or placebo for 12 months. MAIN OUTCOMES AND MEASURES Oral cancer–free survival (CFS). RESULTS A total of 395 participants were classified with LOH profiles, and 254 were classified LOH positive. Of these, 150 (59%) were randomized, 75 each to the placebo and erlotinib groups. The 3-year CFS rates in placebo- and erlotinib-treated patients were 74%and 70%, respectively (hazard ratio [HR], 1.27; 95%CI, 0.68–2.38; P = .45). The 3-year CFS was significantly lower for LOH-positive compared with LOH-negative groups (74%vs 87%, HR, 2.19; 95%CI, 1.25–3.83; P = .01). Increased EGFR gene copy number correlated with LOH-positive status (P < .001) and lower CFS (P = .01). The EGFR gene copy number was not predictive of erlotinib efficacy. Erlotinib-induced skin rash was associated with improved CFS (P = .01). CONCLUSIONS AND RELEVANCE In this trial, LOH was validated as a marker of oral cancer risk and

  15. Breast cancer risk assessment in primary care.

    PubMed

    Brown, Shannon Lynn; Kartoz, Connie

    2014-01-01

    Breast cancer is the most common cancer (when excluding skin cancers) in women and the second most common cause of cancer death in women, with a lifetime prevalence of 12.5% (, ; ). Breast cancer screening reduces risk of cancer death, thereby increasing rate of survival to up to 89% for women with stage 1 and 2 breast cancer (; ). Despite these data, undue harm may occur with unnecessary screening because overidentification of risk, and excessive, costly biopsies may result. Costs and benefits of screening must be weighed. Nurses at all levels can play a pivotal role in promotion of appropriate breast cancer screening and subsequently breast cancer prevention by using accurate screening tools, such as the Tyrer-Cuzick model. Although there are some limitations with this tool, screening at the primary care level has demonstrated improved clinical outcomes (). Its use can help nurses accurately assess a woman's breast cancer risk, by promoting appropriate screening at the primary care level ().

  16. Height and Prostate Cancer Risk

    PubMed Central

    Zuccolo, Luisa; Harris, Ross; Gunnell, David; Oliver, Steven; Lane, Jane Athene; Davis, Michael; Donovan, Jenny; Neal, David; Hamdy, Freddie; Beynon, Rebecca; Savovic, Jelena; Martin, Richard Michael

    2008-01-01

    Background Height, a marker of childhood environmental exposures, is positively associated with prostate cancer risk, perhaps through the insulin-like growth factor system. We investigated the relationship of prostate cancer with height and its components (leg and trunk length) in a nested case-control study and with height in a dose-response meta-analysis. Methods We nested a case-control study within a population-based randomized controlled trial evaluating treatments for localized prostate cancer in British men ages 50 to 69 years, including 1,357 cases detected through prostate-specific antigen testing and 7,990 controls (matched on age, general practice, assessment date). Nine bibliographic databases were searched systematically for studies on the height-prostate cancer association that were pooled in a meta-analysis. Results Based on the nested case-control, the odds ratio (OR) of prostate-specific antigen-detected prostate cancer per 10 cm increase in height was 1.06 [95% confidence interval (95% CI): 0.97-1.16; ptrend = 0.2]. There was stronger evidence of an association of height with high-grade prostate cancer (OR: 1.23; 95% CI: 1.06-1.43), mainly due to the leg component, but not with low-grade disease (OR: 0.99; 95% CI: 0.90-1.10). In general, associations with leg or trunk length were similar. A meta-analysis of 58 studies found evidence that height is positively associated with prostate cancer (random-effects OR per 10 cm: 1.06; 95% CI: 1.03-1.09), with a stronger effect for prospective studies of more advanced/aggressive cancers (random-effects OR: 1.12; 95% CI: 1.05-1.19). Conclusion These data indicate a limited role for childhood environmental exposures—as indexed by adult height—on prostate cancer incidence, while suggesting a greater role for progression, through mechanisms requiring further investigation. PMID:18768501

  17. Cell Phones and Cancer Risk

    MedlinePlus

    ... Caregivers Questions to Ask about Advanced Cancer Research Managing Cancer Care Finding Health Care Services Costs & Medical ... Feelings Planning for Advanced Cancer Advanced Cancer & Caregivers Managing Cancer Care Finding Health Care Services Managing Costs ...

  18. Risks of Prostate Cancer Screening

    MedlinePlus

    ... Genetics of Prostate Cancer Prostate Cancer Screening Research Prostate Cancer Screening (PDQ®)–Patient Version What is screening? Go ... These are called diagnostic tests . General Information About Prostate Cancer Key Points Prostate cancer is a disease in ...

  19. Risks of Lung Cancer Screening

    MedlinePlus

    ... Treatment Lung Cancer Prevention Lung Cancer Screening Research Lung Cancer Screening (PDQ®)–Patient Version What is screening? ... These are called diagnostic tests . General Information About Lung Cancer Key Points Lung cancer is a disease ...

  20. Does the level of prostate cancer risk affect cancer prevention with finasteride?

    PubMed Central

    Thompson, Ian M.; Tangen, Catherine M.; Parnes, Howard L.; Lippman, Scott M.; Coltman, Charles A.

    2009-01-01

    Objectives Finasteride reduced the risk of prostate cancer by 24.8% in the Prostate Cancer Prevention Trial; whether this represents treatment or prevention and who is most likely to benefit are unknown. We sought to clarify these issues by this investigation. Methods We fit a logistic regression model to men in the placebo group of the PCPT using risk factors for prostate cancer at entry to predict prostate cancer during the subsequent 7 years of study. Men in the two treatment groups were categorized into quintiles of risk of prostate cancer based on the predictive logistic model. A second model was fit evaluating finasteride’s effect on prostate cancer for each subgroup defined by quartiles of baseline PSA. The magnitude of the prevention effect of finasteride on prostate cancer was then evaluated across risk and PSA strata. Results Finasteride significantly reduced prostate cancer risk for all risk quintiles. For quintiles 1 through 5, odds ratios were 0.72, 0.52, 0.64, 0.66, and 0.71, respectively (all p≤0.05). For quartiles of risk of entry PSA (< 0.7 ng/mL, 0.7–1.1 ng/mL, 1.1–1.7 ng/mL, and 1.8–3.0 ng/mL), odds ratios increased (smaller treatment effect) as PSA increased: 0.60, 0.62, 0.66, and 0.69 respectively, but remained significant for all strata (each p<0.001). Conclusions Finasteride significantly reduced prostate cancer risk, regardless of the level of this risk, estimated either by multivariable risk or by PSA stratum; this suggests that finasteride exerts both treatment and preventive effects. All men undergoing PSA screening should be informed of the potential for finasteride to reduce their risk of prostate cancer. PMID:18455628

  1. Risks of Stomach (Gastric) Cancer Screening

    MedlinePlus

    ... finding cancer before it causes symptoms ) decreases a person's chance of dying from the disease. For some types of cancer, ... Studies showed that screening a large number of people for stomach cancer using these tests did not decrease the risk of dying from stomach cancer. More studies are needed to ...

  2. Overview of entry risk predictions

    NASA Astrophysics Data System (ADS)

    Mrozinski, R.; Mendeck, G.; Cutri-Kohart, R.

    Risk to people on the ground from uncontrolled entries of spacecraft is a primary concern when analyzing end-of-life disposal options for satellites. Countries must balance this risk with the need to mitigate an exponentially growing space debris population. Currently the United States does this via guidelines that call for a satellite to be disposed of in a controlled manner if an uncontrolled entry would be too risky to people on the ground. This risk is measured by a quantity called "casualty expectation", or E , where casualty expectation is defined as the expectedc number of people suffering death or injury due to a spacecraft entry event. If Ec exceeds 1 in 10,000, U. S. guidelines state that the entry should be controlled rather than uncontrolled. Since this guideline can have serious impacts on the cost, lifetime, and even the mission and functionality of a satellite, it is critical that this quantity be estimated well, and decision makers understand all assumptions and limitations inherent in the resulting value. This paper discusses several issues regarding estimates of casualty expectation, beginning with an overview of relevant United States policies and guidelines. The equation the space industry typically uses to estimate casualty expectation is presented, along with a look at the sensitivity of the results to the typical assumptions, models, and initial condition uncertainties. Differences in these modeling issues with respect to launch failure Ec estimates are included in the discussion. An alternate quantity to assess risks due to spacecraft entries is introduced. "Probability of casualty", or Pc , is defined as the probability of one or more instances of people suffering death or injury due to a spacecraft entry event. The equation to estimate Pc is derived, where the same assumptions, modeling, and initial condition issues for Ec apply. Several examples are then given of both Ec and Pc estimate calculations. Due to the difficult issues in

  3. Biomarkers for prediction of venous thromboembolism in cancer.

    PubMed

    Pabinger, Ingrid; Thaler, Johannes; Ay, Cihan

    2013-09-19

    Cancer patients are at increased risk of deep vein thrombosis and pulmonary embolism. The incidence among different groups of cancer patients varies considerably depending on clinical factors, the most important being tumor entity and stage. Biomarkers have been specifically investigated for their capacity of predicting venous thromboembolism (VTE) during the course of disease. Parameters of blood count analysis (elevated leukocyte and platelet count and decreased hemoglobin) have turned out to be useful in risk prediction. Associations between elevated levels and future VTE have been found for d-dimer, prothrombin fragment 1+2, and soluble P-selectin and also for clotting factor VIII and the thrombin generation potential. The results for tissue factor-bearing microparticles are heterogeneous: an association with occurrence of VTE in pancreatic cancer might be present, whereas in other cancer entities, such as glioblastoma, colorectal, or gastric carcinoma, this could not be confirmed. Risk assessment models were developed that include clinical and laboratory markers. In the high-risk categories, patient groups with up to a >20% VTE rate within 6 months can be identified. A further improvement in risk stratification would allow better identification of patients for primary VTE prevention using indirect or novel direct anticoagulants.

  4. Improving Prediction of Prostate Cancer Recurrence using Chemical Imaging

    NASA Astrophysics Data System (ADS)

    Kwak, Jin Tae; Kajdacsy-Balla, André; Macias, Virgilia; Walsh, Michael; Sinha, Saurabh; Bhargava, Rohit

    2015-03-01

    Precise Outcome prediction is crucial to providing optimal cancer care across the spectrum of solid cancers. Clinically-useful tools to predict risk of adverse events (metastases, recurrence), however, remain deficient. Here, we report an approach to predict the risk of prostate cancer recurrence, at the time of initial diagnosis, using a combination of emerging chemical imaging, a diagnostic protocol that focuses simultaneously on the tumor and its microenvironment, and data analysis of frequent patterns in molecular expression. Fourier transform infrared (FT-IR) spectroscopic imaging was employed to record the structure and molecular content from tumors prostatectomy. We analyzed data from a patient cohort that is mid-grade dominant - which is the largest cohort of patients in the modern era and in whom prognostic methods are largely ineffective. Our approach outperforms the two widely used tools, Kattan nomogram and CAPRA-S score in a head-to-head comparison for predicting risk of recurrence. Importantly, the approach provides a histologic basis to the prediction that identifies chemical and morphologic features in the tumor microenvironment that is independent of conventional clinical information, opening the door to similar advances in other solid tumors.

  5. Risk Profiling May Improve Lung Cancer Screening

    Cancer.gov

    A new modeling study suggests that individualized, risk-based selection of ever-smokers for lung cancer screening may prevent more lung cancer deaths and improve the effectiveness and efficiency of screening compared with current screening recommendations

  6. Predictive risk models for proximal aortic surgery

    PubMed Central

    Díaz, Rocío; Pascual, Isaac; Álvarez, Rubén; Alperi, Alberto; Rozado, Jose; Morales, Carlos; Silva, Jacobo; Morís, César

    2017-01-01

    Predictive risk models help improve decision making, information to our patients and quality control comparing results between surgeons and between institutions. The use of these models promotes competitiveness and led to increasingly better results. All these virtues are of utmost importance when the surgical operation entails high-risk. Although proximal aortic surgery is less frequent than other cardiac surgery operations, this procedure itself is more challenging and technically demanding than other common cardiac surgery techniques. The aim of this study is to review the current status of predictive risk models for patients who undergo proximal aortic surgery, which means aortic root replacement, supracoronary ascending aortic replacement or aortic arch surgery. PMID:28616348

  7. Predictive risk models for proximal aortic surgery.

    PubMed

    Hernandez-Vaquero, Daniel; Díaz, Rocío; Pascual, Isaac; Álvarez, Rubén; Alperi, Alberto; Rozado, Jose; Morales, Carlos; Silva, Jacobo; Morís, César

    2017-05-01

    Predictive risk models help improve decision making, information to our patients and quality control comparing results between surgeons and between institutions. The use of these models promotes competitiveness and led to increasingly better results. All these virtues are of utmost importance when the surgical operation entails high-risk. Although proximal aortic surgery is less frequent than other cardiac surgery operations, this procedure itself is more challenging and technically demanding than other common cardiac surgery techniques. The aim of this study is to review the current status of predictive risk models for patients who undergo proximal aortic surgery, which means aortic root replacement, supracoronary ascending aortic replacement or aortic arch surgery.

  8. ATM, radiation, and the risk of second primary breast cancer.

    PubMed

    Bernstein, Jonine L; Concannon, Patrick

    2017-07-27

    It was first suggested more than 40 years ago that heterozygous carriers for the human autosomal recessive disorder Ataxia-Telangiectasia (A-T) might also be at increased risk for cancer. Subsequent studies have identified the responsible gene, Ataxia-Telangiectasia Mutated (ATM), characterized genetic variation at this locus in A-T and a variety of different cancers, and described the functions of the ATM protein with regard to cellular DNA damage responses. However, an overall model of how ATM contributes to cancer risk, and in particular, the role of DNA damage in this process, remains lacking. This review considers these questions in the context of contralateral breast cancer (CBC). Heterozygous carriers of loss of function mutations in ATM that are A-T causing, are at increased risk of breast cancer. However, examination of a range of genetic variants, both rare and common, across multiple cancers, suggests that ATM may have additional effects on cancer risk that are allele-dependent. In the case of CBC, selected common alleles at ATM are associated with a reduced incidence of CBC, while other rare and predicted deleterious variants may act jointly with radiation exposure to increase risk. Further studies that characterize germline and somatic ATM mutations in breast cancer and relate the detected genetic changes to functional outcomes, particularly with regard to radiation responses, are needed to gain a complete picture of the complex relationship between ATM, radiation and breast cancer.

  9. Risk terrain modeling predicts child maltreatment.

    PubMed

    Daley, Dyann; Bachmann, Michael; Bachmann, Brittany A; Pedigo, Christian; Bui, Minh-Thuy; Coffman, Jamye

    2016-12-01

    As indicated by research on the long-term effects of adverse childhood experiences (ACEs), maltreatment has far-reaching consequences for affected children. Effective prevention measures have been elusive, partly due to difficulty in identifying vulnerable children before they are harmed. This study employs Risk Terrain Modeling (RTM), an analysis of the cumulative effect of environmental factors thought to be conducive for child maltreatment, to create a highly accurate prediction model for future substantiated child maltreatment cases in the City of Fort Worth, Texas. The model is superior to commonly used hotspot predictions and more beneficial in aiding prevention efforts in a number of ways: 1) it identifies the highest risk areas for future instances of child maltreatment with improved precision and accuracy; 2) it aids the prioritization of risk-mitigating efforts by informing about the relative importance of the most significant contributing risk factors; 3) since predictions are modeled as a function of easily obtainable data, practitioners do not have to undergo the difficult process of obtaining official child maltreatment data to apply it; 4) the inclusion of a multitude of environmental risk factors creates a more robust model with higher predictive validity; and, 5) the model does not rely on a retrospective examination of past instances of child maltreatment, but adapts predictions to changing environmental conditions. The present study introduces and examines the predictive power of this new tool to aid prevention efforts seeking to improve the safety, health, and wellbeing of vulnerable children.

  10. Risk-optimized proton therapy to minimize radiogenic second cancers

    NASA Astrophysics Data System (ADS)

    Rechner, Laura A.; Eley, John G.; Howell, Rebecca M.; Zhang, Rui; Mirkovic, Dragan; Newhauser, Wayne D.

    2015-05-01

    Proton therapy confers substantially lower predicted risk of second cancer compared with photon therapy. However, no previous studies have used an algorithmic approach to optimize beam angle or fluence-modulation for proton therapy to minimize those risks. The objectives of this study were to demonstrate the feasibility of risk-optimized proton therapy and to determine the combination of beam angles and fluence weights that minimizes the risk of second cancer in the bladder and rectum for a prostate cancer patient. We used 6 risk models to predict excess relative risk of second cancer. Treatment planning utilized a combination of a commercial treatment planning system and an in-house risk-optimization algorithm. When normal-tissue dose constraints were incorporated in treatment planning, the risk model that incorporated the effects of fractionation, initiation, inactivation, repopulation and promotion selected a combination of anterior and lateral beams, which lowered the relative risk by 21% for the bladder and 30% for the rectum compared to the lateral-opposed beam arrangement. Other results were found for other risk models.

  11. Non Melanoma Skin Cancer and Subsequent Cancer Risk

    PubMed Central

    Rees, Judy R.; Zens, M. Scot; Gui, Jiang; Celaya, Maria O.; Riddle, Bruce L.; Karagas, Margaret R.

    2014-01-01

    Introduction Several studies have shown an increased risk of cancer after non melanoma skin cancers (NMSC) but the individual risk factors underlying this risk have not been elucidated, especially in relation to sun exposure and skin sensitivity to sunlight. Purpose The aim of this study was to examine the individual risk factors associated with the development of subsequent cancers after non melanoma skin cancer. Methods Participants in the population-based New Hampshire Skin Cancer Study provided detailed risk factor data, and su