Weinberg, Clarice R.

Abstract We evaluated the joint associations between a new 313-variant PRS (PRS313) and questionnaire-based breast cancer risk factors for women of European ancestry, using 72 284 cases and 80 354 controls from the Breast Cancer Association Consortium. Interactions were evaluated using standard logistic regression and a newly developed case-only method for breast cancer risk overall and by estrogen receptor status. After accounting for multiple testing, we did not find evidence that per-standard deviation PRS313 odds ratio differed across strata defined by individual risk factors. Goodness-of-fit tests did not reject the assumption of a multiplicative model between PRS313 and each risk factor. Variation in projected absolute lifetime risk of breast cancer associated with classical risk factors was greater for women with higher genetic risk (PRS313 and family history) and, on average, 17.5% higher in the highest vs lowest deciles of genetic risk. These findings have implications for risk prevention for women at increased risk of breast cancer.

Abstract Germline mutations in several cancer predisposition genes included in hereditary multigene testing panels have been associated with increased breast cancer risk. However, estimates of breast cancer risks for each gene have in large part been derived from studies of high-risk populations enriched for family history of breast and ovarian cancer, young age of breast cancer diagnosis, or founder mutations. The breast cancer risks associated with mutations in many of these genes in the general population remain to be clearly defined. As clinical hereditary cancer genetic testing is already provided to many women at increased risk and may soon be offered to all breast cancer patients, population-based risk estimates associated with predisposition gene mutations will be needed for appropriate clinical management of mutation carriers. The “CAnceR RIsk Estimates Related to Susceptibility” (CARRIERS) consortium of 17 cohort-based case-control, population-based case-control, and family studies focused on estimation of population-based breast cancer risks and penetrance analysis for mutations in known and candidate breast cancer predisposition genes. Germline DNA from 39486 breast cancer cases and 35868 matched controls was subjected to dual bar-coded QIAseq multiplex amplification of 1733 target regions in 37 predisposition genes. Products from sets of 768 samples were pooled and sequenced in each lane of a HiSeq 4000 system. Mutations were called by GATK Haplotype Caller and Verdict. High quality sequence data was obtained for 99.3% of target regions. Here we report on results from 34741 population-based breast cancer cases and 32728 matched unaffected controls. The mean age of breast cancer diagnosis for cases was 61.2 years and mean age for controls was 61.1 years. Overall, 20.9% of cases and 14.3% of controls had a family history of breast cancer. Furthermore, 74.3% of cases and 75.8% of controls were non-Hispanic white, whereas 13.3% of cases and 15.3% of controls were African American. Among cases with tumor information available, 81.7% were ER-positive and 7.7% were triple negative breast cancers (TNBC). We assessed mutation frequencies in 24 genes including 12 established breast cancer predisposition genes (ATM, BARD1, BRCA1, BRCA2, CDH1, CHEK2, NF1, PALB2, PTEN, RAD51C, RAD51D, andTP53). The overall mutation frequency was 6.2% in cases and 2.7% in controls. Case-control association analysis after adjusting for study, age, family history of breast cancer, and race/ethnicity showed that BRCA1(OR=7.45; 95%CI:5.24-10.95) and BRCA2(OR=5.31; 95%CI:4.15-6.88) were associated with high risks of breast cancer in the general population. However, PALB2mutations were only associated with moderate risks (OR=3.63; 95%CI:2.57-5.26). ATMmutations conferred attenuated risks (OR=1.83; 95%CI:1.47-2.28) relative to studies of high-risk families or young onset breast cancers. NBNmutations were not associated with increased breast cancer risk. Mutations in BARD1, RAD51C, RAD51Dand XRCC2were specifically significantly associated with moderate risks of ER-negative breast cancer and TNBC, whereas mutations in ATM, CDH1and CHEK2were specifically significantly associated with ER-positive disease. On the basis of these findings age-related absolute breast cancer risks for commonly mutated genes were estimated. Overall, results from the CARRIERS study establish that mutations in predisposition genes are associated with lower risks of breast cancer in the general population than in enriched populations including high-risk families and young onset, bilateral, or multiple primary cases. We anticipate that the results from this study will inform cancer screening and other risk management strategies for women in the general population with mutations in predisposition genes. Citation Format: Fergus J Couch, Chunling Hu, Steven N Hart, Rohan Gnanaolivu, Kun Y Lee, Jie Na, Chi Gao, Nicholas J Boddicker, Bruce Eckloff, Raed Samara, Josh Klebba, Christine B Ambrosone, Hoda Anton-Culver, Paul Auer, Leslie Bernstein, Mia M Gaudet, Christopher Haiman, Esther M John, Susan Neuhausen, Janet E Olson, Tuya Pal, Julie R Palmer, Dale P Sandler, Jack A Taylor, Amy Trentham-Dietz, Celine M Vachon, Clarice Weinberg, Song Yao, Jeffrey N Weitzel, David E Goldgar, Susan M Domchek, Katherine L Nathanson, Peter Kraft, Eric C Polley. Population-based breast cancer risk estimates for predisposition gene mutations: Results from the CARRIERS study [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr PD3-01.

AbstractPrevious transcriptome‐wide association studies (TWAS) have identified breast cancer risk genes by integrating data from expression quantitative loci and genome‐wide association studies (GWAS), but analyses of breast cancer subtype‐specific associations have been limited. In this study, we conducted a TWAS using gene expression data from GTEx and summary statistics from the hitherto largest GWAS meta‐analysis conducted for breast cancer overall, and by estrogen receptor subtypes (ER+ and ER−). We further compared associations with ER+ and ER− subtypes, using a case‐only TWAS approach. We also conducted multigene conditional analyses in regions with multiple TWAS associations. Two genes, STXBP4 and HIST2H2BA, were specifically associated with ER+ but not with ER– breast cancer. We further identified 30 TWAS‐significant genes associated with overall breast cancer risk, including four that were not identified in previous studies. Conditional analyses identified single independent breast‐cancer gene in three of six regions harboring multiple TWAS‐significant genes. Our study provides new information on breast cancer genetics and biology, particularly about genomic differences between ER+ and ER− breast cancer.

Abstract Background: Prospective validation of breast cancer risk models integrating classical risk factors and genetic variants is required for risk-stratified prevention and screening strategies. The objective of this study was to validate a breast cancer risk model integrating classical risk factors and a 313-variant polygenic risk score (PRS) in multiple prospective cohort studies, and to project five-year risk of breast cancer in six different countries. Methods: The study population included 7,529 cases and 230,103 controls from 14 prospective cohort studies in Australia, Germany, the Netherlands, Sweden, UK, and USA. We used the Individualized Coherent Absolute Risk Estimator (iCARE) tool for risk model building, validation, and risk projection. Expected five-year risk of invasive or in situ breast cancer was compared to observed risk, overall and within deciles of expected risk using goodness of fit statistics. We evaluated calibration of the relative risk through meta-analysis across cohorts, and of the absolute risk within each cohort. Model discrimination was evaluated using the area under the curve (AUC), and percentages of women crossing risk thresholds. Projections of five-year risk distributions were estimated for women of European ancestry aged 50-70 years in the general populations of these six countries. Results: Analysis showed overall good calibration of the integrated iCARE-based model relative risk for both women younger than 50 years (χ2=14.9, P=0.09) and aged 50 years or older (χ2=14.6, P=0.10), with a small overestimation of risk for women in the highest decile of expected risk (RR = 3.5 expected vs 2.3 (95% CI 1.6 to 3.2) observed for women <50 years; and 2.8 expected vs 2.3 (95% CI 2.0 to 2.7) observed for women 50+ years). The age-adjusted AUCs for the integrated model were 63.1 (95% CI 60.9 to 65.3) and 62.9 (95% CI 61.8 to 64.0), for the two age groups respectively. The calibration of absolute risk showed substantial variation across cohorts, particularly for the older group, but had no systematic bias. Model based projections in the general populations showed that compared to the population average, women in the 1st and 99th percentiles of the integrated risk score had relative risks 0.19 and 3.56 respectively. The proportion of women of European ancestry aged 50-70 years with a five-year risk greater than 3% (threshold for consideration of risk-lowering drugs by U.S. Preventive Services Task Force) ranged from 7.1% in Germany to 18.2% in the US, which corresponds to ~5.5 million women in the US. Conclusions: Five-year risk predictions from a model with classical risk factors and PRS are well calibrated and provide substantial risk stratification across multiple cohorts in six different countries. Further studies are needed to evaluate the clinical utility of the validated model for risk stratified screening and prevention of breast cancer. Citation Format: Parichoy Pal Choudhury, Amber Wilcox, Chi Gao, Brian Carter, Anika Husing, Mark Brook, Mikael Eriksson, Kara Martin, Chris Scott, Min Shi, Thomas Ahearn, Michael Jones, Nick Orr, Minouk Schoemaker, Kamila Czene, Jenny Chang-Claude, Jacques Simard, Doug Easton, Marjanka K. Schmidt, Dale Sandler, Clarice R. Weinberg, Celine Vachon, Roger Milne, Per Hall, Anthony Swerdlow, Rudolph Kaaks, Myrto Barrdahl, Mia Gaudet, Antonis Antoniou, Peter Kraft, Montserrat Garcia-Closas, Nilanjan Chatterjee. Validation of breast cancer risk model incorporating classical risk factors and polygenic risk scores in 14 prospective cohort studies in 6 countries [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 962.

AbstractOver the past several years, genome‐wide association studies (GWAS) have succeeded in identifying hundreds of genetic markers associated with common diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. To identify obstacles to future progress in genetic epidemiology research and provide recommendations to NIH for overcoming these barriers, the National Cancer Institute sponsored a workshop entitled “Next Generation Analytic Tools for Large‐Scale Genetic Epidemiology Studies of Complex Diseases” on September 15–16, 2010. The goal of the workshop was to facilitate discussions on (1) statistical strategies and methods to efficiently identify genetic and environmental factors contributing to the risk of complex disease; and (2) how to develop, apply, and evaluate these strategies for the design, analysis, and interpretation of large‐scale complex disease association studies in order to guide NIH in setting the future agenda in this area of research. The workshop was organized as a series of short presentations covering scientific (gene‐gene and gene‐environment interaction, complex phenotypes, and rare variants and next generation sequencing) and methodological (simulation modeling and computational resources and data management) topic areas. Specific needs to advance the field were identified during each session and are summarized.Genet. Epidemiol. 36 : 22–35, 2012. © 2011 Wiley Periodicals, Inc.