Introduction
Breast cancer is the most common cancer diagnosed among women in Western countries. While rare mutations in genes such as BRCA1 and BRCA2 confer high risks of developing breast cancer, these account for only a small proportion of breast cancer cases in the general population. Multiple common breast cancer susceptibility variants discovered through genome-wide association studies (GWASs)1, 2 confer small risk individually, but their combined effect, when summarized as a polygenic risk score (PRS), can be substantial.3, 4, 5 Such genomic profiles can be used to stratify women according to their risk of developing breast cancer.6 This in turn holds the promise of improved breast cancer prevention and survival, by targeting screening or other preventative strategies at those women most likely to benefit.
We previously derived a PRS based on 77 established breast cancer susceptibility single-nucleotide polymorphisms (SNPs) and reported levels of risk stratification achieved by this PRS.7 Based on our findings, several studies have investigated the potential for combining PRSs and other known risk factors for risk stratification and evaluated the impact of risk reduction strategies across risk strata defined by the PRS.8, 9, 10 Preliminary studies investigating the use of the PRS to inform targeted breast cancer screening programs are underway (see CORDIS and GenomeCanada in Web Resources).11, 12 Empirical validation and characterization of the PRS in large-scale epidemiological studies has, however, not been carried out previously. In addition, more informative PRSs would improve the clinical utility of risk prediction. GWASs have now identified ∼170 breast cancer susceptibility loci.1, 2 Moreover, genome-wide heritability estimates indicate that these loci explain only ∼40% of the heritability explained by all common variants on genome-wide SNP arrays. This suggests that the discrimination provided by the PRS could be improved by incorporating variants associated at more liberal significance thresholds. In addition, many variants confer risks that differ by breast cancer subtype (estrogen-receptor [ER]-positive or -negative), suggesting that subtype-specific PRSs might allow better prediction of subtype-specific disease, including the more aggressive ER-negative breast cancer, and enable selection of women for preventative medication.
Here, we used data from 79 studies conducted by the Breast Cancer Association Consortium (BCAC) to optimize PRSs for overall and subtype-specific disease, and we validate their performance in independent datasets.1, 13, 14, 15