The contributions of breast density and common genetic variation to breast cancer risk.

Vachon CM1, Pankratz VS2, Scott CG2, Haeberle L2, Ziv E2, Jensen MR2, Brandt KR2, Whaley DH2, Olson JE2, Heusinger K2, Hack CC2, Jud SM2, Beckmann MW2, Schulz-Wendtland R2, Tice JA2, Norman AD2, Cunningham JM2, Purrington KS2, Easton DF2, Sellers TA2, Kerlikowske K2, Fasching PA2, Couch FJ2.

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Abstract

We evaluated whether a 76-locus polygenic risk score (PRS) and Breast Imaging Reporting and Data System (BI-RADS) breast density were independent risk factors within three studies (1643 case patients, 2397 control patients) using logistic regression models. We incorporated the PRS odds ratio (OR) into the Breast Cancer Surveillance Consortium (BCSC) risk-prediction model while accounting for its attributable risk and compared five-year absolute risk predictions between models using area under the curve (AUC) statistics. All statistical tests were two-sided. BI-RADS density and PRS were independent risk factors across all three studies (P interaction = .23). Relative to those with scattered fibroglandular densities and average PRS (2(nd) quartile), women with extreme density and highest quartile PRS had 2.7-fold (95% confidence interval [CI] = 1.74 to 4.12) increased risk, while those with low density and PRS had reduced risk (OR = 0.30, 95% CI = 0.18 to 0.51). PRS added independent information (P < .001) to the BCSC model and improved discriminatory accuracy from AUC = 0.66 to AUC = 0.69. Although the BCSC-PRS model was well calibrated in case-control data, independent cohort data are needed to test calibration in the general population.