Cancer Epidemiology and Prevention Biomarkers最新文献

{"title":"Abstract PR07: Comparison of risk model recommendations for women at high-risk of breast cancer based on clinical thresholds using the Prospective Family Study Cohort (ProF-SC)","authors":"M. Terry, K. Phillips, Y. Liao, R. MacInnis, G. Dite, M. Daly, E. John, I. Andrulis, S. Buys, R. Buchsbaum, J. Hopper","doi":"10.1158/1538-7755.CARISK16-PR07","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-PR07","url":null,"abstract":"Background: Clinical guidelines for classifying women as high-risk for breast cancer when considering chemoprevention and/or MRI screening options include thresholds of remaining lifetime risk (RLR) of 20% or more and/or a fixed time interval (e.g., 5-year risk of 1.67 or higher, 10-year risk of 3.34 or higher). Although clinicians have noted differences in risk estimates from the existing risk models, there have been few prospective validations using large cohorts to describe the magnitude of the discordancies between these models. Methods: We prospectively followed 16,285 women without breast cancer at baseline for an average of 10.2 years to compare the RLR and 10-year risk assigned by two commonly used risk estimation models for high risk women: 1) The International Breast Cancer Intervention Study tool (IBIS); and 2) the Breast Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA). We compared the model-assigned 10-year risks with subsequent incidence of breast cancer in the cohort. We used chi-square statistics to assess calibration and the area under the receiver operating characteristic curve (AUC) to assess discrimination. Results: We observed differences between risk models in terms of the proportion of women classified as high-risk based on 20% or more RLR (IBIS=56% vs BOADICEA=23%). Only 21% of women were classified as high risk by both models, 35% of women were classified as high risk by IBIS only and 2% of women were classified as high risk by BOADICEA only. The difference was not evident (IBIS=52% vs BOADICEA=51%) when using a 10-year risk threshold of 3.34%. Using this 10-year threshold, 43% of women were classified as high risk by both models, 9% of women were classified as high risk by IBIS only and 8% of women were classified as high risk by BOADICEA only. IBIS risk predictions (mean=4.9%) were better calibrated to observed breast cancer incidence (5.8%, 95% confidence interval (CI)=5.4% to 6.2%) than were those based on BOADICEA (mean=4.2%). When we compared the magnitude of the discordancy between IBIS and BOADICEA by age, race/ethnicity, and number of relatives affected, we observed the extent of discordancy (e.g. one model resulted in a woman being above the clinical threshold when the other did not) depended on age. Specifically, for women under the age of 40 years, only 3.1% of women were high risk with IBIS but not BOADICEA compared with 7.5% classified as high risk by BOADICEA but not IBIS. Both models gave similar predictions of high risk with same proportion discordant for women over 50, and the same proportion discordant by race/ethnicity. When we compared the discordancy by those unaffected and affected with breast cancer after ten years of follow-up, 51% of unaffected women were high risk by IBIS using the 10-year threshold and 50% by BOADICEA with only 8% discordant (high risk on only one model). For women who were diagnosed with breast cancer prospectively after baseline, 75% were clas","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":"113 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78419886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract IA05: Modeling genetic susceptibility to breast cancer","authors":"A. Antoniou","doi":"10.1158/1538-7755.CARISK16-IA05","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-IA05","url":null,"abstract":"Several breast cancer genetic susceptibility variants have been identified to date. These include mutations in the high risk BRCA1 and BRCA2 genes, other rare genetic variants conferring intermediate to high risks (e.g. PALB2, CHEK2, ATM and others) and >150 common alleles (SNPs) conferring low risks. The presentation will provide an overview of the latest developments and challenges in understanding the penetrance of mutations in BRCA1, BRCA2 and PALB2. Genetic counseling of women with BRCA1 and BRCA2 mutations currently relies on average cancer risk estimates obtained from retrospective penetrance studies. The talk will present penetrance estimates from ongoing prospective analyses, based on data from the International BRCA1/2 Carrier Cohort Study, the largest prospective cohort of BRCA1/2 mutation carriers that includes ~10,000 BRCA1/2 mutation carriers with follow-up information. The talk will also review the latest efforts and results from the Consortium of Investigators of Modifiers of BRCA1/2 to identify and characterise genetic modifiers of cancer risks for BRCA1 and BRCA2 mutation carriers and to provide individualized cancer risks for women with BRCA1 and BRCA2 mutations. Finally, the presentation will describe the efforts to develop a comprehensive risk prediction model for breast cancer, specifically the BOADICEA model that includes the explicit effects of mutations in BRCA1, BRCA2, PALB2, CHEK2, ATM, and of the common breast cancer susceptibility variants identified through genome-wide association studies. Citation Format: Antonis C. Antoniou. Modeling genetic susceptibility to breast cancer. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr IA05.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84871884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract B14: Modulating effects of green and black tea on biomarkers of chronic inflammation by gender and smoking status","authors":"I. Hakim, S. Aldaham, J. Foote, H. Chow","doi":"10.1158/1538-7755.CARISK16-B14","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-B14","url":null,"abstract":"Background/Purpose: Epidemiologic data implies that there are gender differences in lung cancer pathogenesis and possibly increased susceptibility to lung cancer in women. Chronic inflammation has been implicated as important modulator of human health by playing a significant role in both disease prevention and disease development. Several studies have demonstrated increased interleukin 6 (IL-6) and C-reactive protein (CRP) in the blood of smokers. The overall goal of this study was to develop a feasible tea intervention that will serve as a model for the chemoprevention of a wide range of tobacco-related diseases. Our immediate goal, that was addressed over a 4-year study period, was to determine the effects of high tea consumption on biological markers of chronic inflammation that mediate lung cancer risk, including, IL-6, CRP and antioxidant enzyme activities. Methods: We completed a 6-month randomized, controlled, double-blinded trial in a group of current and former smokers. Participants were stratified on smoking status and gender, and were randomized to green or black tea preparations or a control intervention (matching placebo). Levels of urinary iIL-6 and CRP are used to measure chronic inflammation and levels of superoxide dismutase (SOD) in red blood cells are used to measure antioxidant enzymes. Results: The study protocol was approved by all parties. A total of 138 participants (78 females and 60 males) completed the study. Our data show that il6 is significantly correlated with years of smoking and pack/year among smokers and former smokers. At the end of the 6-month intervention, female smokers in the green tea group showed a significant decrease in IL-6 (P=0.036) while male former smokers in the black tea group showed a significant decrease in CRP levels (P=0.012). There were no significant changes in dietary and serum antioxidant levels between the different groups. Conclusion: This data implies that smokers are more likely to benefit from green tea intake while the beneficial effects of black tea are observed among former smokers. Note: This abstract was not presented at the conference. Citation Format: Iman A. Hakim, Sami A. Aldaham, Janet Foote, H-H Sherry Chow. Modulating effects of green and black tea on biomarkers of chronic inflammation by gender and smoking status. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr B14.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81319314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract B26: Pre- and post-diagnostic use of nonsteroidal anti-inflammatory drugs and prostate cancer mortality among men diagnosed with prostate cancer in the NIH-AARP and PLCO cohorts","authors":"C. Zhou, S. Daugherty, A. Black, L. Liao, N. Freedman, C. Abnet, R. Pfeiffer, M. Cook","doi":"10.1158/1538-7755.CARISK16-B26","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-B26","url":null,"abstract":"Background: Prostate cancer is the second leading cause of cancer death in American men, but few modifiable risk factors have been established for prostate cancer progression and survival. Experimental studies have suggested that nonsteroidal anti-inflammatory drugs (NSAIDs) may improve prostate cancer survival through anti-thrombotic and anti-inflammation mechanisms. However, previous observational studies have shown mixed results. No study has examined over-the-counter non-aspirin NSAIDs in relation to prostate cancer survival. Few studies have assessed aspirin use before prostate cancer diagnosis in relation to prostate cancer survival, and whether any etiologically relevant time window of exposure exists remains unclear. Methods: We assessed two cohorts of prostate cancer cases from two large prospective studies in the United States NIH-AARP Diet and Health Study and Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial to investigate associations of aspirin and other nonselective non-aspirin NSAID use before and after prostate cancer diagnosis with prostate cancer-specific and all-cause mortality. Cox proportional hazards models with age as the time metric were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Results across the two studies were meta-analyzed in a fixed effects model if consistent associations were observed. Results: We did not find statistically significant associations of pre- or post-diagnostic NSAID use with prostate cancer-specific mortality. However, aspirin users versus nonusers five years or more before prostate cancer diagnosis had a 14% (95%CI=0.74 to 1.00) and a 16% (95%CI=0.78 to 0.89) reduced prostate cancer-specific and all-cause mortality when combining the two studies. Post-diagnostic occasional (less than once per day) and daily aspirin use were associated with 17% (95%CI=0.72 to 0.95) and 25% (95%CI=0.66 to 0.86) reductions in all-cause mortality independent of pre-diagnostic use, comparing with no use. Conclusions: This analysis suggests a modest delayed survival benefit of aspirin use before prostate cancer diagnosis and highlights the importance of comorbidity prevention among prostate cancer survivors. Citation Format: Cindy Ke Zhou, Sarah E. Daugherty, Amanda Black, Linda M. Liao, Neal D. Freedman, Christian C. Abnet, Ruth Pfeiffer, Michael B. Cook. Pre- and post-diagnostic use of nonsteroidal anti-inflammatory drugs and prostate cancer mortality among men diagnosed with prostate cancer in the NIH-AARP and PLCO cohorts. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr B26.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":"143 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80288308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract B20: Portinari: Communicating personalized risk in cervical cancer screening using data exploration","authors":"S. Sen, Manoel Horta Ribeiro, M. Nygård","doi":"10.1158/1538-7755.CARISK16-B20","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-B20","url":null,"abstract":"Background: Cervical cancer incidence rate has significantly decreased in countries that established organized screening programs. The program invites women in the age group 25 to 69 years for screening exams based on a set of guidelines. The guidelines aim to reduce over-screening of individuals at a very low-risk while effectively detecting and treating individuals at a high-risk of developing cervical cancer. However, risk determined by the screening program for a woman is often different from perception of their own risk. This contributes to a wide range of screening behavior as seen in the data collected by the Cancer Registry of Norway from 1992 to 2014. Some women get screened very frequently while some others consider themselves to be at a low-risk. Women make their own choices and we can see many patterns in their screening trajectories. Furthermore, implementation of new biomarkers may improve efficiency of the screening and requires adjustment of the guidelines. Therefore, we ask, can we use the complete set of cervical cancer screening related data collected from 1.8 million women in Norway to communicate personalized risk and concurrently evaluate performance of existing screening guidelines? Objective: Development and demonstration of a data exploration tool Portinari to communicate personalized risk of a patient based on historical data of a population. Methods and Results: We developed Portinari, a web-based, user friendly, data exploration tool for (non-)experts to query and visualize personalized risk of patients who have undergone a specific sequence of exams S. The sequence of exams of a patient is specified using a user-friendly visual editor. This visual representation is automatically transformed to a graph query. The query is executed on a graph database of screening data, which is an intuitive data structure to store and query trajectories of exams and their respective diagnosis taken over 22 years from the entire Norwegian female population. Matches for the graph query of a specific individual9s exams is a collection of identical sequences found in other patients in the database which forms the basis for risk visualization. The patient9s personal prognosis is presented by summarizing the future of all matching patients found in the database. The summary is presented as a Sankey diagram that shows arrows, representing patients flowing from one diagnosis to another with the origin being the last exam in S. The width of the arrows is proportional to the size of the represented flow which is the number of patients in our case. The Sankey diagram allows a patient to visualize both frequently taken paths taken by patients and also outliers to help them make an informed choice. We will demonstrate the use of Portinari to a) evaluate screening guidelines b) communicate personalized risk using various example scenarios. Citation Format: Sagar Sen, Manoel Horta Ribeiro, Mari Nygard. Portinari: Communicating personalized risk in ce","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75602822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract A12: Urinary levels of PGE-M and estrogens are independently associated with postmenopausal breast cancer risk","authors":"Sangmi Kim, Jeff Campbell, Wonsuk Yoo, Jack A. Taylor, D. Sandler","doi":"10.1158/1538-7755.CARISK16-A12","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-A12","url":null,"abstract":"Prostaglandin E 2 (PGE 2 ) induces aromatase expression in adipose tissue leading to increased estrogen production that may promote the development and progression of breast cancer. However, few studies have simultaneously investigated systemic levels of PGE 2 and estrogen in relation to postmenopausal breast cancer risk. In a case-cohort study of postmenopausal women (295 cases and 294 subcohort) we previously reported that high levels of PGE-M, a major metabolite of PGE 2 , were associated with an increased risk of breast cancer among postmenopausal women who did not regularly use nonsteroidal anti-inflammatory drugs (NSAIDs). Here we determined urinary estrogen metabolites (EMs) using mass spectrometry in the same case-cohort set and using linear regression estimated the effect of PGE-M on EMs. Hazard ratios (HRs) for the risk of developing breast cancer in relation to PGE-M and EMs were evaluated in Cox regression models with and without mutual adjustment. PGE-M was a significant predictor of estrone (E1), but not estradiol (E2) levels in multivariable analysis. Elevated E2 levels were associated with an increased risk of developing breast cancer (HR Q5vs.Q1 =1.54, 95% CI: 1.01-2.35), and this association remained unchanged after adjustment for PGE-M (HR Q5vs.Q1 =1.52, 95% CI: 0.99-2.33). Similarly, elevated levels of PGE-M were associated with increased risk of developing breast cancer (HR Q4vs.Q1 =2.01, 95% CI: 1.01-4.29), and this association was only nominally changed after consideration of E1 or E2 levels. Urinary levels of PGE-M and parent estrogens were independently associated with future risk of developing breast cancer among these postmenopausal women. Increased breast cancer risk associated with PGE-M might be attributable both to PGE 2 -mediated increases in estrogens, and to additional effects related to inflammation. Note: This abstract was not presented at the conference. Citation Format: Sangmi Kim, Jeff Campbell, Wonsuk Yoo, Jack A. Taylor, Dale P. Sandler. Urinary levels of PGE-M and estrogens are independently associated with postmenopausal breast cancer risk. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr A12.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80192322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract B29: Terahertz spectral imaging and scanning for early detection of skin cancer","authors":"A. Rahman, Aunik K. Rahman, B. Rao","doi":"10.1158/1538-7755.CARISK16-B29","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-B29","url":null,"abstract":"A terahertz diagnosis tool has been developed to identify early stage skin cancer at the cellular level. Here, three different techniques are used where each technique independently identifies a given disease condition compared to healthy skin specimen; thus, collectively forms a diagnostic procedure with minimal falls alarm. Namely, terahertz sub-surface spectral imaging, terahertz absorbance spectroscopy and skin thickness profiling have been used. Terahertz radiation is non-ionizing, therefore, save for in-vivo investigations. It is also more sensitive than other forms of probing energies. In the present work, biopsies from three skin disease conditions have been compared with a healthy skin sample. It was found that the terahertz images clearly visualize healthy skin cells where a regular cellular pattern is visible. In contrast, cancerous skin specimen images exhibit deterioration from regular cellular pattern indicating abnormal conditions. For example, the skin specimen excised by Mohs microsurgery and diagnosed for basal cell carcinoma exhibits cell agglomeration indicating the onset of tumor formation. Similarly, other skin conditions such as squamous cell carcinoma and lentigo maligna exhibit their characteristic images without a regular cell pattern. Since the skin is a layered structure, a thickness profile of the healthy skin clearly exhibits this layering pattern while the layering is significantly diminished for cancerous skin samples. Thus a diminished layering profile is an indication of skin abnormalities. In addition, spectral analyses also exhibit distinguishable differences between different cancer conditions compared to healthy skin spectrum. Details of the methodology and results will be discussed. Note: This abstract was not presented at the conference. Citation Format: Anis Rahman, Aunik K. Rahman, Babar Rao. Terahertz spectral imaging and scanning for early detection of skin cancer. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr B29.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85459127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract IA24: Developing and evaluating cancer risk assessment tools for primary care","authors":"J. Emery","doi":"10.1158/1538-7755.CARISK16-IA24","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-IA24","url":null,"abstract":"Numerous risk assessment tools have been developed which predict either current or future risk of a cancer diagnosis yet very few are used in routine clinical practice. These tools could be used for tailored disease prevention, more efficient use of cancer screening tests and to promote behavioural change to reduce cancer risk. We have a growing number of cancer risk-prediction models which incorporate phenotypic, behavioural and, increasingly, genomic variables; these models require simple-to-use risk assessment tools for their implementation into clinical practice, and in particular ones which can be incorporated into primary care. In this presentation I will present a recent systematic review of RCTs in primary care of cancer risk assessment tools. This will highlight some of the key issues which remain for successful implementation of these tools into primary care practice. Selecting which cancer risk prediction model to incorporate into a tool will depend not only the predictive utility of the model but also the feasibility of collecting more complex predictive variables in clinical practice. We should design tools that can be incorporated into the clinical consultation, and which present cancer risks in meaningful ways that are more likely to lead to appropriate behaviour change. I will present research on the development of the CRISP tool to demonstrate how we are applying these principles and trialing its effect on risk-stratified colorectal cancer screening in Australian primary care. Citation Format: Jon Emery. Developing and evaluating cancer risk assessment tools for primary care. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr IA24.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89857237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract A10: Analysis of gene expression and DNA methylation profiles of Notch signaling pathway genes in human glioblastoma","authors":"Madhuri G. S. Aithal, R. Narayanappa","doi":"10.1158/1538-7755.CARISK16-A10","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-A10","url":null,"abstract":"In cancer, DNA methylation affects important signal transduction pathways leading to altered receptor function, disruption of normal cell-cell interaction, etc. Since methylation occurs at a very early stage, hypermethylated promoters hold great promise as biomarkers for early detection and an effective drug target for gene reactivation. The Notch signaling pathway is one such developmental pathway governing cell fate decisions. Dysregulated Notch signaling is found to have a prominent role in the development of various cancers. Glioblastoma is the most common primary brain tumor with a very poor prognosis. Therefore it is important to study genetic and epigenetic events leading to gliomagenesis and to guide new treatment strategies. The aim of this study was to detect Notch pathway genes potentially regulated by promoter methylation in human glioblastoma. We used real-time PCR and methylation-specific PCR to study gene expression and methylation status of seven Notch pathway genes (Notch1, Notch2, Notch3, Notch4, JAG1, JAG2 and DLL3) from human glioblastoma formalin fixed paraffin embedded sections. We identified Notch3 and JAG2 promoters as methylated and Notch4 with both methylated and unmethylated promoter. Despite methylation, Notch3 gene showed robust gene expression suggesting its partial dependency on promoter methylation and the presence of alternative regulatory mechanisms. However, low gene expression of JAG2 and the absence of Notch4 gene expression suggest a possibility of epigenetic silencing. This study for the first time provides gene expression and DNA methylation profiles of Notch pathway genes from glioblastoma patient samples. We have identified genes whose expression may be regulated by epigenetic mechanisms and thus can be used as markers that may guide treatment decisions. Note: This abstract was not presented at the conference. Citation Format: Madhuri G S Aithal, Rajeswari Narayanappa. Analysis of gene expression and DNA methylation profiles of Notch signaling pathway genes in human glioblastoma. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr A10.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":"602 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77361264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract PR02: Validation of breast cancer risk prediction model using Nurses Health and Nurse Health II Studies","authors":"Chi Gao, P. Choudhury, P. Maas, R. Tamimi, H. Eliassen, N. Chatterjee, M. García-Closas, P. Kraft","doi":"10.1158/1538-7755.CARISK16-PR02","DOIUrl":"https://doi.org/10.1158/1538-7755.CARISK16-PR02","url":null,"abstract":"Background: Adding genetic and other biomarkers to breast cancer risk prediction models could markedly improve model discrimination; however, these expanded models have not been validated in a range of populations. In particular, the calibration of these new models how well the predicted absolute risks match observed risks has not been established. Good calibration is essential to confirm the utility of these risk models in precision prevention and treatment programs. Large cohort studies provide an ideal setting to validate risk models, as they can be used to validate both relative and absolute risks. However, in practice, genetic and biomarker data are often not available in the full cohort, but only on a sub sample of cases and controls. When the rules for sampling cases and controls into the sub sample are known, inverse-probability-of-sampling (IPW) weights can be used to estimate empirical absolute risks. When the sampling rules are unknown or complicated, the IPW weights can be estimated by regressing selection into the sub sample on matching and other inclusion criteria. Methods: We evaluated the performance of recently published breast cancer risk prediction models [Maas et al. JAMA Oncol 2016] in the Nurses Health Study (NHS) and Nurses Health Study II (NHSII). We first assess a prediction model that only includes questionnaire data (BMI, hormone replacement therapy (HRT), alcohol consumption, smoking status, height, parity, age at menarche and menopause, age at first birth, and family history of breast cancer). These data are available on all subjects in the NHS and NHSII blood subcohorts: 32,826 women in NHS (with disease follow-up from 1990-2012) and 29,611 women in NHS II (1999-2013). We will then validate a model that includes both questionnaire data and a polygenic risk score based on 92 established risk SNPs. Genetic data are available on case-control samples nested within the blood subcohorts: 2308 breast cancer cases and 3344 controls from NHS and 612 breast cancer cases and 933 controls from NHSII. We estimated IPW weights among controls using logistic regression in the blood subcohorts, with sampling as control being the outcome and the following predictors: age at baseline, menopausal status, HRT, length of HRT use for premenopausal women at baseline, and length of follow up time. We used the iCARE software package (Maas P, Chatterjee N, Wheeler W et al. 2015) to calculate predicted 5 and 10-year absolute risks of breast cancer based on the published models, empirical 5 and 10-year incidence across deciles of predicted risk, and Hosmer-Lemeshow goodness of fit and AUC statistics. Results: For the risk model without genetic information, predicted risks in the blood subcohorts ranged from 6.5/1,000 (1st decile) to 20.1/1,000 (10th decile) for NHS. Although empirical risks increased across deciles at approximately the same rate as predicted rates, empirical risks were higher than predicted (Hosmer-Lemeshow p Due to matching and","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75972247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}