Validation of an Abridged Breast Cancer Risk Prediction Model for the General Population

Accurate breast cancer risk prediction could improve risk -reduction paradigms if thoughtfully used in clinical practice. Identification of at-risk women is the first step in tailoring risk screening and risk-reduction protocols to women's needs. Using the UK Biobank, we validated a simple risk model to predict breast cancer risk in the general population. Our simple breast cancer risk (BRISK) model integrates a combination of impactful breast cancer-associated risk factors including extended family history and polygenic risk allowing for the removal of moderate factors cur-rently found in comprehensive traditional models. Using two versions of BRISK, differing by 77-single-nucleotide polymorphisms (SNP) versus 313-SNP polygenic risk score integration, we found improved discrimination and risk categorization of both BRISK models compared with one the most well-known models, the Breast Cancer Risk Assessment Tool (BRCAT). Over a 5-year period, at-risk women classified >= 3% 5-year risk by BRISK had a 1.829 (95% CI = 1.710-1.956) times increased incidence of breast cancer compared with the population, which was higher than the 1.413 (95% CI = 1.217-1.640) times increased incidence for women classified >= 3% by BCRAT. Prevention Relevance: In this prospective population -based cohort study, we show the improved performance of a new risk assessment model compared with a gold-standard model (BCRAT). The classification of at-risk women using this new model highlights the opportunity to improve risk stratification and implement existing clinical risk-reduction interventions.