Polygenic risk scores and breast cancer risk prediction

Abstract

Polygenic Risk Scores (PRS) are a major component of accurate breast cancer risk prediction and have the potential to improve screening and prevention strategies. PRS combine the risk from Single nucleotide polymorphisms (SNPs) associated with breast cancer in Genome Wide Association Studies (GWAS) and explain over 30% of breast cancer heritability. When incorporated into risk models, the more personalised risk assessment derived from PRS, help identify women at higher risk of breast cancer development and enables the implementation of stratified screening and prevention approaches. This review describes the role of PRS in breast cancer risk prediction including the development of PRS and their clinical application. We have also examined the role of PRS within more well-established risk prediction models which incorporate known classic risk factors and discuss the interaction of PRS with these factors and their capacity to predict breast cancer subtypes. Before PRS can be implemented on a population-wide scale, there are several challenges that must be addressed. Perhaps the most pressing of these is the use of PRS in women of non-White European origin, where PRS have been shown to have attenuated risk prediction both in discrimination and calibration. We discuss progress in developing and applying PRS in non-white European populations. PRS represent a significant advance in breast cancer risk prediction and their further development will undoubtedly enhance personalisation.

Keywords: Breast; Cancer; Polygenic; Prediction; Prevention; Risk; Scores; screening.

Fig. 1

5-Year net survival (%) of women diagnosed with BC by the stage at which the BC was diagnosed. All data: adults diagnosed 2013–2017, followed up to 2018. (CRUK 2018.) [2].

Fig. 2

The role of allele variants and the size of the effect they can have on disease penetrance. Generally, rare pathogenic variants have a large impact on disease development and common variants have a smaller impact on disease development [25].