Journal
JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE
Volume 107, Issue 5, Pages -Publisher
OXFORD UNIV PRESS INC
DOI: 10.1093/jnci/dju397
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Funding
- National Cancer Institute [R01 CA128931, R01 CA128978, R01 CA97396, P50 CA116201, R01 CA240386, K24 CA169004, R21 CA179442, P01 CA154292, R01 CA140286]
- National Cancer Institute (Cancer Center Support Grant) [CA15083]
- Breast Cancer Research Foundation
- Erlanger Leistungsbezogene Anschubsfinanzierung und Nachwuchsfoerderung program of the Medical Faculty, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg
- EU
- Cancer Research UK
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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 (2nd 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.
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