Estrogen Metabolism and Exposure in a Genotypic–Phenotypic Model for Breast Cancer Risk Prediction

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Abstract
Background: Current models of breast cancer risk prediction do not directly reflect mammary estrogen metabolism or genetic variability in exposure to carcinogenic estrogen metabolites. Methods: We developed a model that simulates the kinetic effect of genetic variants of the enzymes CYP1A1, CYP1B1, and COMT on the production of the main carcinogenic estrogen metabolite, 4-hydroxyestradiol (4-OHE2), expressed as area under the curve metric (4-OHE2-AUC). The model also incorporates phenotypic factors (age, body mass index, hormone replacement therapy, oral contraceptives, and family history), which plausibly influence estrogen metabolism and the production of 4-OHE2. We applied the model to two independent, population-based breast cancer case–control groups, the German GENICA study (967 cases, 971 controls) and the Nashville Breast Cohort (NBC; 465 cases, 885 controls). Results: In the GENICA study, premenopausal women at the 90th percentile of 4-OHE2-AUC among control subjects had a risk of breast cancer that was 2.30 times that of women at the 10th control 4-OHE2-AUC percentile (95% CI: 1.7–3.2, P = 2.9 × 10−7). This relative risk was 1.89 (95% CI: 1.5–2.4, P = 2.2 × 10−8) in postmenopausal women. In the NBC, this relative risk in postmenopausal women was 1.81 (95% CI: 1.3–2.6, P = 7.6 × 10−4), which increased to 1.83 (95% CI: 1.4–2.3, P = 9.5 × 10−7) when a history of proliferative breast disease was included in the model. Conclusions: The model combines genotypic and phenotypic factors involved in carcinogenic estrogen metabolite production and cumulative estrogen exposure to predict breast cancer risk. Impact: The estrogen carcinogenesis–based model has the potential to provide personalized risk estimates. Cancer Epidemiol Biomarkers Prev; 20(7); 1502–15. ©2011 AACR.