Estrogen Exposure, Metabolism, and Enzyme Variants in a Model for Breast Cancer Risk Prediction

Fritz F Parl,Philip S Crooke,Chun Li,Kathleen M Egan

doi:10.4137/cin.s2262

Fritz F Parl, Philip S Crooke + Show 2 more

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https://doi.org/10.4137/cin.s2262

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Abstract

Estrogen 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 conversion of 17beta-estradiol (E(2)) 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 E(2)-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 E(2)-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 E(2) 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.

Highlights

Estrogens have long been recognized as the primary risk factor for the development of breast cancer.[1,2] Epidemiologic studies have indicated that breast cancer risk is higher in women with early menarche and late menopause, who have longer exposure to estrogens.[3]
The questions are: (1) How do estrogens cause breast cancer? and (2) Since all women are exposed to estrogens, how do we better delineate risk? To close these gaps in our knowledge we need to explain mechanisms of estrogen carcinogenesis and inter-individual risk variation and our approach is to examine the dynamics of a pathway for estrogen metabolism and use its prediction of the level of DNA corrupting compounds as a predictor of breast cancer risk
Among the 10 women with the highest E2-3,4-Q values in the entire study population, there were nine cases and one control (p-value = 0.01). These results suggest for the first time the possibility that breast cancer risk prediction may be enhanced by incorporation of inherited differences in estrogen metabolism

Summary

Introduction

Estrogens have long been recognized as the primary risk factor for the development of breast cancer.[1,2] Epidemiologic studies have indicated that breast cancer risk is higher in women with early menarche and late menopause, who have longer exposure to estrogens.[3]. We have 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 E2 is metabolized.[41] We employed both gas and liquid chromatography with mass spectrometry (GC/MS and LC/MS) to measure the parent hormone E2 as well as eight metabolites, i.e. the catechol estrogens, methoxyestrogens, and estrogen-GSH conjugates With this important experimental data, an in silico model of the metabolic pathway has been developed.[42]

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