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

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.

Keywords: breast cancer; non-genetic risk factors; polygenic risk score (PRS); risk prediction tools; systematic review.

Figure 1

PRISMA flowchart of exclusion criteria.

Figure 2

Discriminative performance of individual risk models, including only a PRS. Each dot represents a measure of discrimination for different versions (represented by the letters when applicable) of risk models as described in Supplementary Table S2. The horizontal segment represents the 95% CIs when provided. Blue, green and yellow dots indicate that the AUC, c-statistic and c-index were the measure of performance, respectively.

Figure 3

Discriminative performance of individual risk tools for models including a PRS and genetic and non-genetic risk factors. Each dot represents a measure of discrimination for different versions (represented by the letters when applicable) of risk models as described in Supplementary Table S2. The horizontal segment represents the 95% CIs when provided. Blue, green and yellow dots indicate that the AUC, c-statistic and c-index were the measure of performance, respectively.

Figure 4

Summary of the assessment of risk of bias.