Gao, Chi

Abstract Introduction Incorporation of mammographic density to breast cancer risk models could improve risk stratification to tailor screening and prevention strategies according to risk. Robust evaluation of the value of adding mammographic density to models with comprehensive information on questionnaire-based risk factors and polygenic risk score is needed to determine its effectiveness in improving risk stratification of such models. Methods We used the Individualized Coherent Absolute Risk Estimator (iCARE) tool for risk model building and validation to incorporate density to a previously validated literature-based model with questionnaire-based risk factors and a 313-variant polygenic risk score (PRS). The model was evaluated for calibration and discrimination in three prospective cohorts of European-ancestry women (1,468 cases, 19,104 controls): US-based Nurses’ Health Study (NHS I and II) and Mayo Mammography Health Study (MMHS); and Sweden-based Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) study. Analyses were done separately for women younger (NHS II, KARMA) and older than 50 years (NHS I, MMHS, KARMA). Improvements in terms of risk stratification and reclassification proportions were assessed among European-ancestry women aged 50–70 years in US and Sweden. Results For women younger and older than 50 years, the model with questionnaire-based risk factors, PRS and density was generally well calibrated across risk with some evidence of miscalibration at the extremes of the risk distribution. Incorporation of density led to modest improvements risk discrimination beyond the model with questionnaire-based risk factors and PRS: the area under the curve (AUC) among younger women was 67.0% (95% CI: 63.5–70.6%) vs. 65.6% (95% CI: 61.9–69.3%) for models with and without density; and 66.1% (95% CI 64.4–67.8%) vs. 65.5% (95% CI: 63.8–67.2%) among older women. The model with density identified 18.4% of US women 50–70 years old ≥ 3% 5-year predicted risk (threshold used for recommending risk-reducing medication in the US), with 42.4% of future cases expected to occur in this group. At this threshold, 7.9% of US women were reclassified by adding density to the model, resulting in the identification of 2.8% of additional future cases. The model with density identified 10.3% of Swedish women ≥ 3% 5-year predicted risk, with 29.4% of future cases expected to occur in this group. At this threshold, 5.3% of women were reclassified with the addition of density, leading to the identification of an additional 4.4% of future cases. Conclusion Integrating density with questionnaire-based risk factors and PRS could potentially identify more women of European-ancestry with elevated risk of breast cancer in the United States and Sweden. Further investigations of the integrated model in non-European ancestry populations are needed prior to considering clinical applications.

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.

Abstract Clinical germline genetic testing of cancer predisposition gene panels is used to identify women at increased risk for breast cancer. Pathogenic mutations in cancer predisposition genes are associated with established risks of breast cancer and are actively used for risk management of breast cancer for women qualifying for genetic testing because of a family history of breast and/or ovarian cancer or young age of diagnosis. However, the risks of breast cancer associated with mutations in these genes have likely been overestimated for many women in the general population. The goal of the “CAnceR RIsk Estimates Related to Susceptibility” (CARRIERS) study was to estimate breast cancer risks associated with mutations in hereditary cancer panel genes. Germline DNA from 32,298 breast cancer patients and 31,869 matched unaffected controls enrolled in cohorts and population-based case-control studies were sequenced to identify pathogenic mutations in 37 cancer predisposition genes.The average age at diagnosis was 61.2 years and the average age of last follow-up was 62.1 years. DNA was subjected to dual bar-coded QIAseq multiplex PCR-based amplification of 1733 target regions in 37 predisposition genes. Products from 768 samples were pooled and sequenced in a single lane of a HiSeq 4000 system. Mutations were called by GATK Haplotype Caller and Vardict.High quality sequence data was obtained for 99.3% of target regions. Pathogenic mutations in 12 known breast cancer predisposition genes were identified in 6.2% of all breast cancer cases and 2.1% of controls; and in 6.7% of African American breast cancer cases and 1.8% of controls. In case-control association analyses adjusted for age at diagnosis or last follow up and family history of breast and ovarian cancer, mutations in BRCA1, BRCA2, and PALB2 were associated with high risks of breast cancer (odds ratio (OR)>4.0). Mutations in CHEK2 and BARD1 were associated with moderate risks of breast cancer (OR>2.0), whereas mutations in ATM had lower clinical relevance (OR=1.6). Stratification by ER, PR, and HER2 status of tumors found that BRCA1, BRCA2, PALB2 and TP53 were associated with high risks of studies of triple negative breast cancer (TNBC), and that BARD1, BRIP1, FANCM, and RAD51C were associated with moderate risks of TNBC. 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 high-risk families. The age-related estimates of breast cancer risk for each of the hereditary cancer panel genes in this study may inform on the use of 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, Jenna Lilyquist, Kun Y. Lee, Chi Gao, Bruce Eckloff, Hoda Anton-Culver, Paul Auer, Leslie Bernstein, Mia Gaudet, Christopher Haiman, Julie Palmer, Amy Tentham-Dietz, Song Yao, Susan Domchek, Jeffrey N. Weitzel, David E. Goldgar, Katherine L. Nathanson, Peter Kraft, Eric C. Polley. Population-based breast cancer risk estimates associated with cancer predisposition gene mutations from 32,298 breast cancer patients and 31,869 matched unaffected controls from the CARRIERS study [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 4169.