Several single nucleotide polymorphisms (SNPs) are associated with an increased risk of breast cancer. The clinical utility of genotyping individuals at these loci is not known. Subjects were 519 unaffected women without BRCA mutations. Gail, Claus, and IBIS models were used to estimate absolute breast cancer risks. Subjects were then genotyped at 15 independent risk loci. Published per-allele and genotype-specific odds ratios were used to calculate the composite cumulative genomic risk (CGR) for each subject. Affected age- and ethnicity-matched BRCA mutation-negative women were also genotyped as a comparison group for the calculation of discriminatory accuracy. The CGR was used to adjust absolute breast cancer risks calculated by Gail, Claus and IBIS models to determine the proportion of subjects whose recommendations for chemoprevention or MRI screening might be altered (reclassified) by such adjustment. Mean lifetime breast cancer risks calculated using the Gail, Claus, and IBIS models were 19.4, 13.0, and 17.7%, respectively. CGR did not correlate with breast cancer risk as calculated using any model. CGR was significantly higher in affected women (mean 3.35 vs. 3.12, P = 0.009). The discriminatory accuracy of the CGR alone was 0.55 (SE 0.019; P = 0.006). CGR adjustment of model-derived absolute risk estimates would have altered clinical recommendations for chemoprevention in 11–19% of subjects and for MRI screening in 8–32%. CGR has limited discriminatory accuracy. However, the use of a genomic risk term to adjust model-derived estimates has the potential to alter individual recommendations. These observations warrant investigation to evaluate the calibration of adjusted risk estimates.
Keywords: Breast cancer risk, Single nucleotide polymorphism, Risk prediction model