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 tumors with different hormone receptor
status.
Material and Methods
Within the Breast and Prostate Cancer Cohort Consortium (BPC3), we
analyzed 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- and cohort-adjusted concordance statistic
(AUROCa). Absolute risk scores were
calculated with external reference data. Integrated discrimination
improvement (IDI) 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
AUROCa going from 2.7 to 4%). Discriminatory ability
for all models varied strongly by hormone receptor status
Discussion and Conclusion
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.