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. 2012 Sep;49(9):601-8.
doi: 10.1136/jmedgenet-2011-100716.

Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status

Anika Hüsing  1 , Federico Canzian, Lars Beckmann, Montserrat Garcia-Closas, W Ryan Diver, Michael J Thun, Christine D Berg, Robert N Hoover, Regina G Ziegler, Jonine D Figueroa, Claudine Isaacs, Anja Olsen, Vivian Viallon, Heiner Boeing, Giovanna Masala, Dimitrios Trichopoulos, Petra H M Peeters, Eiliv Lund, Eva Ardanaz, Kay-Tee Khaw, Per Lenner, Laurence N Kolonel, Daniel O Stram, Loïc Le Marchand, Catherine A McCarty, Julie E Buring, I-Min Lee, Shumin Zhang, Sara Lindström, Susan E Hankinson, Elio Riboli, David J Hunter, Brian E Henderson, Stephen J Chanock, Christopher A Haiman, Peter Kraft, Rudolf Kaaks; BPC3
Affiliations

Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status

Anika Hüsing et al. J Med Genet. 2012 Sep.

Abstract

Objective: There is increasing interest in adding common genetic variants identified through genome wide association studies (GWAS) to breast cancer risk prediction models. First results from such models showed modest benefits in terms of risk discrimination. Heterogeneity of breast cancer as defined by hormone-receptor status has not been considered in this context. In this study we investigated the predictive capacity of 32 GWAS-detected common variants for breast cancer risk, alone and in combination with classical risk factors, and for tumours with different hormone receptor status.

Material and methods: Within the Breast and Prostate Cancer Cohort Consortium, we analysed 6009 invasive breast cancer cases and 7827 matched controls of European ancestry, with data on classical breast cancer risk factors and 32 common gene variants identified through GWAS. Discriminatory ability with respect to breast cancer of specific hormone receptor-status was assessed with the age adjusted and cohort-adjusted concordance statistic (AUROC(a)). Absolute risk scores were calculated with external reference data. Integrated discrimination improvement was used to measure improvements in risk prediction.

Results: We found a small but steady increase in discriminatory ability with increasing numbers of genetic variants included in the model (difference in AUROC(a) going from 2.7% to 4%). Discriminatory ability for all models varied strongly by hormone receptor status.

Discussion and conclusions: Adding information on common polymorphisms provides small but statistically significant improvements in the quality of breast cancer risk prediction models. We consistently observed better performance for receptor-positive cases, but the gain in discriminatory quality is not sufficient for clinical application.

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