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

Erika L. Spaeth, Gillian S. Dite, John L. Hopper, Richard Allman

Accurate breast cancer risk prediction could improve risk-reduction paradigms if thoughtfully employed 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-SNP versus 313-SNP polygenic risk score integration, we found improved discrimination and risk categorization of both BRISK models compared to one of the most well-known models, the Breast Cancer Risk Assessment Tool (BRCAT). Over a five-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 to the population, which was higher than the 1.413 (95% CI = 1.217 to 1.640) times increased incidence for women classified ≥3% by BCRAT.