Development of Breast Cancer Risk Model Based on Estrogen Metabolomics

描述(申请人提供)：基于雌激素代谢组学的乳腺癌风险模型的发展。抗雌激素为预防乳腺癌提供了一种有效的策略，但符合条件的女性通常会因为不利的益处/风险比而拒绝治疗。来自预防研究的数据表明，50名妇女需要接受这些药物为期5年的治疗才能预防一例乳腺癌。为了提高受益与风险的比率，迫切需要一种更有效的方法来识别罹患乳腺癌风险极高的妇女。对疾病风险的预测最好基于其发病机制中涉及的因素。我们提出了一个关于致癌过程的综合假说，该过程既涉及雌激素受体α(ER？)依赖的作用，也涉及ER？非依赖性的作用。雌二醇(E_2)通过ER？刺激细胞增殖，产生复制突变，促进细胞生长 藏匿着这些突变。雌激素代谢产物不依赖内质网，形成不稳定的DNA加合物，并通过氧化还原循环产生氧自由基，从而引发突变。几种受基因调控的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乳腺癌的创新假说将所有这些过程整合到一个致癌模型中，并将整个雌激素代谢组与乳腺癌的发生联系起来。这些概念表明，雌激素代谢组学的测量应该提供一种强大的、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调节代谢过程的酶的SNPs。最近，几个重要因素结合在一起，使我们能够测试这一概念。一种新的、最先进的质谱仪与超高效液相色谱系统相结合，首次使测定少量血清中的所有雌激素代谢物成为可能。我们可以测量SNPs，它涉及调节雌激素新陈代谢的酶，并已被证明与乳腺癌风险相关。为了建立一个模型，我们将利用3项队列研究(纽约大学、线索I和线索II以及兰乔·贝尔纳多)的血清样本和危险因素数据，这些研究在5-20年前采集了女性的血液，然后前瞻性地跟踪了她们患乳腺癌的情况。这些技术、样本和风险因素数据的可获得性使嵌套病例对照研究能够开发出新的、更强大的风险预测模型。然后，我们将在一个完全独立的数据集中验证这个模型，该数据集涉及这位法语教师的研究。我们预计，使用他莫昔芬可以将预防一例乳腺癌所需治疗的女性人数从50人减少到13人，使用芳香酶抑制剂则可以减少到5人。 公共卫生相关性：乳腺癌是一种常见疾病，预防是最好的治疗策略。该项目将开发一种风险预测模型，以便在未来10年内识别出被诊断为乳腺癌的高概率女性。这些女性可能会接受芳香酶抑制剂作为预防乳腺癌的一种手段，对公众健康的影响将是巨大的。

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. PUBLIC HEALTH RELEVANCE: Breast Cancer is a common disease and prevention is the best therapeutic strategy. This project will develop a risk prediction model which will allow identification of women with a high probability of a being diagnosed with breast cancer over the next 10 years. These women will be likely to accept aromatase inhibitors as a means to prevent breast cancer and the public health impact will be substantial.