Supported by NIH (1R01CA/ES83752, U54CA113007) and the Vanderbilt Integrative Cancer Biology Center.
Estrogen Metabolism and Breast Cancer
A Risk Model
Article first published online: 26 FEB 2009
© 2009 New York Academy of Sciences
Annals of the New York Academy of Sciences
Volume 1155, Steroid Enzymes and Cancer pages 68–75, February 2009
How to Cite
Parl, F. F., Dawling, S., Roodi, N. and Crooke, P. S. (2009), Estrogen Metabolism and Breast Cancer. Annals of the New York Academy of Sciences, 1155: 68–75. doi: 10.1111/j.1749-6632.2008.03676.x
- Issue published online: 26 FEB 2009
- Article first published online: 26 FEB 2009
- breast cancer;
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 an experimental in vitro model of mammary estrogen metabolism in which we combined purified, recombinant phase I enzymes CYP1A1 and CYP1B1 with the phase II enzymes COMT and GSTP1 to determine how 17β-estradiol (E2) is metabolized. We employed both gas and liquid chromatography with mass spectrometry to measure the parent hormone E2 as well as eight metabolites, that is, the catechol estrogens, methoxyestrogens, and estrogen−GSH conjugates. We used these experimental data to develop an in silico model, which allowed the kinetic simulation of converting E2 into eight metabolites. The simulations showed excellent agreement with experimental results and provided a quantitative assessment of the metabolic interactions. Using rate constants of genetic variants of CYP1A1, CYP1B1, and COMT, the model further allowed examination of the kinetic impact of enzyme polymorphisms on the entire metabolic pathway, including the identification of those haplotypes producing the largest amounts of catechols and quinones. Application of the model to a breast cancer case-control population defined the estrogen quinone E2-3,4-Q as a potential risk factor and identified a subset of women with an increased risk of breast cancer based on their enzyme haplotypes and consequent E2-3,4-Q production. Our in silico model integrates diverse types of data and offers the exciting opportunity for researchers to combine metabolic and genetic data in assessing estrogenic exposure in relation to breast cancer risk.