Estrogen Metabolism and Exposure in a Genotypic-Phenotypic Model for Breast Cancer Risk Prediction
Estrogen Metabolism and Exposure in a Genotypic-Phenotypic Model for Breast Cancer Risk Prediction
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DOI：
10.1158/1055-9965.epi-11-0060
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发表时间：
2011-07-01
影响因子：
3.8
通讯作者：
Parl, Fritz F.
中科院分区：
文献类型：
--
作者：
Crooke, Philip S.;Justenhoven, Christina;Parl, Fritz F.
背景资料：目前的乳腺癌风险预测模型不能直接反映乳腺雌激素代谢或暴露于致癌雌激素代谢物的遗传变异性。我们开发了一个模型，模拟酶CYP 1A 1、CYP 1B 1和COMT的遗传变异体对主要致癌雌激素代谢产物4-羟基雌二醇（4-OHE 2）产生的动力学效应，表示为曲线下面积度量（4-OHE 2-AUC）。该模型还纳入了表型因素（年龄，体重指数，激素替代疗法，口服避孕药和家族史），这些因素可能会影响雌激素代谢和4-OHE 2的产生。我们将该模型应用于两个独立的、基于人群的乳腺癌病例对照组，德国GENICA研究（967例，971例对照）和纳什维尔乳腺队列研究（NBC; 465例，885例对照）。在GENICA研究中，绝经前女性4-OHE 2-对照组受试者的AUC发生乳腺癌的风险是对照组4-OHE 2-AUC第10百分位数女性的2.30倍（95% CI：1.7-3.2，P = 2.9 x 10（-7））。绝经后妇女的相对危险度为1.89（95% CI：1.5-2.4，P = 2.2 x 10（-8））。在NBC中，绝经后妇女的相对危险度为1.81（95% CI：1.3-2.6，P = 7.6 x 10（-4）），增加至1.83（95%CI：1.4-2.3，P = 9.5 x 10（-7））。结论：该模型结合了基因型和表型因素参与致癌雌激素代谢产物的生产和累积雌激素暴露，以预测乳腺癌risk.Impact：雌激素致癌为基础的模型有可能提供个性化的风险估计。癌症流行病学生物标志物Prev; 20（7）; 1502-15。(C)2011年AACR。
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-OHE2), expressed as area under the curve metric (4-OHE2-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-OHE2. 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-OHE2-AUC among control subjects had a risk of breast cancer that was 2.30 times that of women at the 10th control 4-OHE2-AUC percentile (95% CI: 1.7-3.2, P = 2.9 x 10(-7)). This relative risk was 1.89 (95% CI: 1.5-2.4, P = 2.2 x 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 x 10(-4)), which increased to 1.83 (95% CI: 1.4-2.3, P = 9.5 x 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. Cancer Epidemiol Biomarkers Prev; 20(7); 1502-15. (C) 2011 AACR.