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

Abstract

Abstract 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 currently 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 of 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.