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

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-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).

Results: 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.

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.