Breast cancer risk stratification using genetic and non-genetic risk assessment tools for 246,142 women in the UK Biobank

Purpose: The benefit of using individual risk prediction tools to identify high-risk individuals for breast cancer (BC) screening is uncertain, despite the personalized approach of risk-based screening. Methods: We studied the overlap of predicted high-risk individuals among 246,142 women enrolled in the UK Biobank. Risk predictors assessed include the Gail model (Gail), BC family history (FH, binary), BC polygenic risk score (PRS), and presence of loss-of-function (LoF) variants in BC predisposition genes. Youden J-index was used to select optimal thresholds for defining high-risk. Results: In total, 147,399 were considered at high risk for developing BC within the next 2 years by at least 1 of the 4 risk prediction tools examined (Gail2-year > 0.5%: 47%, PRS2-yea r > 0.7%: 30%, FH: 6%, and LoF: 1%); 92,851 (38%) were flagged by only 1 risk predictor. The overlap between individuals flagged as high-risk because of genetic (PRS) and Gail model risk factors was 30%. The best-performing combinatorial model comprises a union of high-risk women identified by PRS, FH, and, LoF (AUC2-year [95% CI]: 62.2 [60.8 to 63.6]). Assigning individual weights to each risk prediction tool increased discriminatory ability. Conclusion: Risk-based BC screening may require a multipronged approach that includes PRS, predisposition genes, FH, and other recognized risk factors. & COPY; 2023 The Authors. Published by Elsevier Inc. on behalf of American College of Medical Genetics and Genomics. This is an open access article under the CC BY-NC-ND license