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Estrogens, Enzyme Variants, and Breast Cancer: A Risk Model

Departments of ¹ Mathematics, ² Molecular Physiology and Biophysics (Center for Human Genetics Research), ³ Pharmacology, and ⁴ Pathology, Vanderbilt University, Nashville, Tennessee

Requests for reprints: Fritz F. Parl, Department of Pathology, TVC 4918, Vanderbilt University, Nashville, TN 37232. Phone: 615-343-9117; Fax: 615-343-9563. E-mail: fritz.parl{at}vanderbilt.edu

Oxidative metabolites of estrogens have been implicated in the development of breast cancer, yet relatively little is known about the metabolism of estrogens in the normal breast. We developed a mathematical model of mammary estrogen metabolism based on the conversion of 17ß-estradiol (E₂) by the enzymes cytochrome P450 (CYP) 1A1 and CYP1B1, catechol-O-methyltransferase (COMT), and glutathione S-transferase P1 into eight metabolites [i.e., two catechol estrogens, 2-hydroxyestradiol (2-OHE₂) and 4-hydroxyestradiol (4-OHE₂); three methoxyestrogens, 2-methoxyestradiol, 2-hydroxy-3-methoxyestradiol, and 4-methoxyestradiol; and three glutathione (SG)-estrogen conjugates, 2-OHE₂-1-SG, 2-OHE₂-4-SG, and 4-OHE₂-2-SG]. When used with experimentally determined rate constants with purified enzymes, the model provides for a kinetic analysis of the entire metabolic pathway. The predicted concentration of each metabolite during a 30-minute reaction agreed well with the experimentally derived results. The model also enables simulation for the transient quinones, E₂-2,3-quinone (E₂-2,3-Q) and E₂-3,4-quinone (E₂-3,4-Q), which are not amenable to direct quantitation. Using experimentally derived rate constants for genetic variants of CYP1A1, CYP1B1, and COMT, we used the model to simulate the kinetic effect of enzyme polymorphisms on the pathway and identified those haplotypes generating the largest amounts of catechols and quinones. Application of the model to a breast cancer case-control population identified a subset of women with an increased risk of breast cancer based on their enzyme haplotypes and consequent E₂-3,4-Q production. This in silico model integrates both kinetic and genomic data to yield a comprehensive view of estrogen metabolomics in the breast. The model offers the opportunity to combine metabolic, genetic, and lifetime exposure data in assessing estrogens as a breast cancer risk factor. (Cancer Epidemiol Biomarkers Prev 2006;15(9):1620–9)

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