AbstractEstrogen is a well-known risk factor for breast cancer. Current models of breast cancer risk prediction are based on cumulative estrogen exposure but do not directly reflect mammary estrogen metabolism or address genetic variability between women in exposure to carcinogenic estrogen metabolites. We are proposing a mathematical model that forecasts breast cancer risk for a woman based on three factors: (1) estimated estrogen exposure, (2) kinetic analysis of the oxidative estrogen metabolism pathway in the breast, and (3) enzyme genotypes responsible for inherited differences in the production of carcinogenic metabolites. The model incorporates the main components of mammary estrogen metabolism, i.e. the con- version of 17β-estradiol (E2) by the phase I and II enzymes cytochrome P450 (CYP) 1A1 and 1B1, catechol-O-methyltransferase (COMT), and glutathione S-transferase P1 (GSTP1) into reactive metabolites, including catechol estrogens and estrogen quinones, such as E2-3,4-Q which can damage DNA. Each of the four genes is genotyped and the SNP data used to derive the haplotype configuration for each subject. The model then utilizes the kinetic and genotypic data to calculate the amount of E2-3,4-Q carcinogen as ultimate risk factor for each woman. The proposed model extends existing models by combining the traditional “phenotypic” measures of estrogen exposure with genotypic data associated with the metabolic fate of E2 as determined by critical phase I and II enzymes. Instead of providing a general risk estimate our model would predict the risk for each individual woman based on her age, reproductive experiences as well as her genotypic profile.

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