A Systematic Review and Critical Assessment of Breast Cancer Risk Prediction Tools Incorporating a Polygenic Risk Score for the General Population

Simple Summary Several risk prediction tools have been developed to better stratify women according to their risk of developing breast cancer (BC) and inform prevention and early detection strategies. Many recent versions of these tools now incorporate a polygenic risk score (PRS) that uses the aggregated effect of common genetic variants, also known as single nucleotide polymorphisms (SNP), as a reliable predictor to estimate BC risk. However, the characteristics of each tool in terms of PRS development, population, and risk factors included vary considerably, which may affect their predictive performance and limit their use in public health practices. Thus, this systematic review characterizes BC risk prediction tools incorporating a PRS and explores the factors that can influence their ability to predict a woman's risk of developing BC during her lifetime.Abstract Single nucleotide polymorphisms (SNPs) in the form of a polygenic risk score (PRS) have emerged as a promising factor that could improve the predictive performance of breast cancer (BC) risk prediction tools. This study aims to appraise and critically assess the current evidence on these tools. Studies were identified using Medline, EMBASE and the Cochrane Library up to November 2022 and were included if they described the development and/ or validation of a BC risk prediction model using a PRS for women of the general population and if they reported a measure of predictive performance. We identified 37 articles, of which 29 combined genetic and non-genetic risk factors using seven different risk prediction tools. Most models (55.0%) were developed on populations from European ancestry and performed better than those developed on populations from other ancestry groups. Regardless of the number of SNPs in each PRS, models combining a PRS with genetic and non-genetic risk factors generally had better discriminatory accuracy (AUC from 0.52 to 0.77) than those using a PRS alone (AUC from 0.48 to 0.68). The overall risk of bias was considered low in most studies. BC risk prediction tools combining a PRS with genetic and non-genetic risk factors provided better discriminative accuracy than either used alone. Further studies are needed to cross-compare their clinical utility and readiness for implementation in public health practices.