Abstract 2623: Common genetic variants associated with breast cancer risk used in the Athena study to enhance models identifying women for breast cancer chemoprevention

Background: The U.S. Preventive Services Task Force recommends that women with a >3% five-year risk of developing breast cancer consider taking selective estrogen receptor modifiers (SERMs) or aromatase inhibitors (AIs) to reduce their risk. Polygenic risk score (PRS), calculated by adding the individual breast cancer risk association for each common genetic variant (SNP), has been found to predict women at low- to high-risk of breast cancer. We analyze associations between SNP risk alleles and known breast cancer risk factors (ethnicity, family history of breast cancer and number of biopsies); furthermore, we quantify the likely impact on chemoprevention recommendations by adding the PRS to known risk models in a subset of women participating in the University of California 100,000 women Athena Breast Health Network. Methods: Our research cohort included 838 women with no previous diagnosis of breast cancer from the University of California, San Francisco, and was enriched for women determined to be at elevated risk for developing breast cancer by the Gail model. A panel of 75 breast cancer risk SNPs were evaluated on saliva and blood samples (Akesogen Inc; COGS oncochip array). The PRS for each patient was calculated by converting the odds ratio for each SNP into a likelihood ratio (LR) and combining LR's across SNPs. Breast Cancer Surveillance Consortium (BCSC), Gail, BCSC-PRS and Gail-PRS scores (risk models incorporating PRS within a Bayesian framework), were evaluated for each patient. Associations between variables were assessed using t-test or ANOVA. A threshold of p