Estrogen metabolism and exposure in a genotypic-phenotypic model for breast cancer risk prediction.

Current models of breast cancer risk prediction do not directly reflect mammary estrogen metabolism or genetic variability in exposure to carcinogenic estrogen metabolites. 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-OHE(2)), expressed as area under the curve metric (4-OHE(2)-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-OHE(2). 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). In the GENICA study, premenopausal women at the 90th percentile of 4-OHE(2)-AUC among control subjects had a risk of breast cancer that was 2.30 times that of women at the 10th control 4-OHE(2)-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. The model combines genotypic and phenotypic factors involved in carcinogenic estrogen metabolite production and cumulative estrogen exposure to predict breast cancer risk. The estrogen carcinogenesis-based model has the potential to provide personalized risk estimates.