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Development of Breast Cancer Risk Model Based on Estrogen Metabolomics

基于雌激素代谢组学的乳腺癌风险模型的开发

基本信息

批准号：
8550778
负责人：
Ian Alexander Blair
金额：
$ 33.58万
依托单位：
UNIVERSITY OF VIRGINIA
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-09-25 至 2014-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8550778
关键词：
Adverse effects American Androgens Aromatase Inhibitors Biological Assay Biostatistical Methods Blood Clinical Data Cohort Studies Coupled DNA Adducts DNA Damage Data Data Set Development Diagnosis Drug Metabolic Detoxication Enzymes Estradiol Estrogen Antagonists Estrogen Metabolism Estrogen Receptor alpha Estrogens Estrone Gene Mutation Generations Genes High Risk Woman Induced Mutation Liquid Chromatography Mass Spectrum Analysis Measurement Measures Mediating Metabolism Methods Modeling Mutation Neoplastic Cell Transformation Nested Case-Control Study Oxidation-Reduction Pathogenesis Pathway interactions Perception Performance Postmenopause Prevention Probability Process Public Health Regulation Risk Risk Factors Role Sampling Serum Steroids System Tamoxifen Techniques Testing Therapeutic Time Toxic effect Woman base cancer risk carcinogenesis cell growth cohort disorder prevention disorder risk follow-up free radical oxygen high risk improved innovation malignant breast neoplasm mass spectrometer metabolomics prevent prospective repaired risk benefit ratio teacher

项目摘要

DESCRIPTION (provided by applicant): Development of breast cancer risk model based on estrogen metabolomics. Anti-estrogens provide an effective strategy to prevent breast cancer but eligible women generally decline therapy because of unfavorable benefit/risk ratios. Data from prevention studies indicate that fifty women need to be treated with these agents for five years to prevent one breast cancer. To improve the ratio of benefit to risk, a more powerful method of identifying women at very high risk of developing breast cancer is urgently needed. Prediction of disease risk is best grounded on factors involved in its pathogenesis. We propose an integrative hypothesis regarding the carcinogenic process which involves both estrogen receptor alpha (ER¿) dependent as well as ER¿ independent actions. Through ER¿, estradiol (E2) stimulates proliferation with resultant replicative mutations and promotes the growth of cells harboring those mutations. Independent of ER¿, estrogen metabolites both form unstable DNA adducts and generate oxygen free radicals thorough redox cycling to initiate mutations. Several genetically regulated enzymes modulate estrogen metabolism and the process of repair of estrogen induced mutations. Our innovative hypothesis regarding estrogen induced breast cancer integrates all of these processes into a model of carcinogenesis and implicates the entire estrogen metabolome in the genesis of breast cancer. These concepts suggest that measurement of estrogen metabolomics should provide a powerful, mechanism-based method of predicting who will develop breast cancer. Metabolomic assessment entails quantitative measurement of aromatizable androgens, estrogens, and their metabolites and SNPs from enzymes regulating the metabolic process. Several important factors have recently come together to enable us to test this concept. A new, state of the art, mass spectrometer coupled with an ultra-performance liquid chromatography system makes it possible for the first time to measure all estrogen metabolites in small amounts of serum. We can measure SNPs which involve enzymes regulating estrogen metabolism and have been shown to correlate with breast cancer risk. To develop a model, we will utilize serum samples and risk factor data from 3 cohort studies (NYU, Clue I and II, and Rancho Bernardo) which had collected blood from women 5-20 years ago and then followed them prospectively for development of breast cancer. Availability of these techniques, samples and risk factor data allows performance of a nested case-control study to develop a new, more powerful risk prediction model. We will then validate this model in a completely independent data set involving the French Teacher's Study. We anticipate reducing the number of women needed to be treated to prevent one breast cancer from 50 to 13 with tamoxifen and to 5.with aromatase inhibitors.

描述（由申请人提供）：基于雌激素代谢组学的乳腺癌风险模型的开发。抗雌激素提供了预防乳腺癌的有效策略，但符合条件的女性通常会因为不利的获益/风险比而拒绝治疗。来自预防研究的数据表明，50名妇女需要用这些药物治疗5年才能预防1例乳腺癌。为了提高获益风险比，迫切需要一种更有效的方法来识别患乳腺癌风险极高的妇女。疾病风险的预测最好是基于其发病机制所涉及的因素。我们提出了一个综合性的假设，关于致癌过程，涉及雌激素受体α（ER <$）依赖以及ER <$独立的行动。通过雌激素受体，雌二醇（E2）刺激增殖，产生复制突变，促进细胞生长隐藏着这些变异独立于ER？雌激素代谢产物既形成不稳定的DNA加合物，又通过氧化还原循环产生氧自由基，引发突变。几种遗传调节酶调节雌激素代谢和雌激素诱导突变的修复过程。我们关于雌激素诱导的乳腺癌的创新假设将所有这些过程整合到一个致癌模型中，并暗示整个雌激素代谢组在乳腺癌的发生中。这些概念表明，雌激素代谢组学的测量应该提供一个强大的，基于机制的方法来预测谁会患乳腺癌。代谢组学评估需要定量测量可芳香化的雄激素、雌激素及其代谢物和来自调节代谢过程的酶的SNP。最近有几个重要因素使我们能够检验这一概念。一种新的，最先进的，质谱仪加上超高效液相色谱系统，使之有可能首次测量少量血清中的所有雌激素代谢物。我们可以测量涉及调节雌激素代谢的酶的SNP，并已被证明与乳腺癌风险相关。为了开发模型，我们将利用来自3项队列研究（NYU，Clue I和II以及Rancho Bernardo）的血清样本和风险因素数据，这些研究在5-20年前收集了女性的血液，然后前瞻性地跟踪她们的乳腺癌发展。这些技术、样本和风险因素数据的可用性允许进行嵌套病例对照研究，以开发新的、更强大的风险预测模型。然后，我们将验证这个模型在一个完全独立的数据集，涉及法国教师的研究。我们预计，使用他莫昔芬和芳香化酶抑制剂预防乳腺癌所需治疗的妇女人数将分别从50人减少到13人和5.5人。