Gene Interactions, Estrogen, and Risk of Breast Cancer

描述（申请人提供）：调节雌激素合成、生物利用度和代谢的基因多态性变异可能导致个体乳腺癌风险。以前的研究很少有统计能力来研究这些基因之间的复杂相互作用。此外，大多数以前的研究（大部分是空的）只考虑了候选基因中的单个遗传标记，可能没有检测到重要的关系。在本申请中，我们建议研究雌激素基因变异对乳腺癌的影响，使用在美国进行的大型基于人群的病例对照研究协作乳腺癌研究中收集的现有数据和材料。在4年期间（1997-2001年），4,400多名最近诊断为乳腺癌的妇女和3,800名基于人口的对照组完成了关于乳腺癌危险因素的电话采访，并提供了颊粘膜DNA用于遗传研究。对于这种类型的研究，病例（72%）和对照（63%）的应答率很高，DNA的平均产量足以分型数百个变体。所有匿名样本均已提取DNA，等分并储存在-70 x下，用于本计划研究。我们现在正准备检验目前的假说，将雌激素生物合成和代谢中的关键基因与乳腺癌的风险联系起来。本提案中包含的基因涉及雌激素合成（星星、CYP 11 A1、HSD 3B、CYP 17、CYP 19、HSD 17 B、TNF、IL 6、PPAR G、STS）、类固醇信号传导（ESR 1、ESR 2、PGR、SHBG、AIB 1）以及雌激素代谢（CYP 1A 1、CYP 1A 2、CYP 1B 1、CYP 3A 4）和失活（SULT、UGT、COMT、NQO、GST）的关键分支点。利用现有的最佳信息，我们建议研究已知的功能变体，以及其他非连锁基因区域的单核苷酸多态性，以增加检测关联的机会。几个基因是新的这种应用，我们提出了详细的遗传分析，使用CEPH家系，以确定在这个人口中的单倍型结构。将使用设计用于检测高阶遗传相互作用的统计程序检测变体之间的相互作用。这是乳腺癌研究中最大的DNA和流行病学数据集合资源之一。本申请中提出的工作将提供有关乳腺癌遗传途径的及时和具有成本效益的新信息，这些信息可能与筛查，检测和更有针对性的治疗策略有关。

DESCRIPTION (provided by applicant): Polymorphic variation in genes regulating estrogen synthesis, bioavailability and metabolism may contribute to the individual risk for breast cancer. Few previous studies had statistical power to examine complex interactions among these genes. Moreover, the majority of previous (mostly null) studies considered only single genetic markers in candidate genes with the possibility that important relationships were not detected. In this application we are proposing to study the contribution of variation in the estrogen genes to breast cancer, using existing data and materials collected in the Collaborative Breast Cancer Study, a large, population-based case-control study conducted in the US. Over a 4-year period (1997-2001), more than 4,400 women with a recent diagnosis of breast cancer and 3,800 population-based controls completed a telephone interview on breast cancer risk factors and provided buccal mucosal DNA for genetic research. Response rates in cases (72%) and controls (63%) were high for this type of research, and the average yield of DNA was adequate for typing several hundred variants. All of the anonymized samples have been extracted for DNA, aliquoted and stored at -70 x for this planned research. We are now proposing to test current hypotheses linking critical genes in estrogen biosynthesis and metabolism to the risk for breast cancer. Genes included in this proposal are involved at key branch points in estrogen synthesis (STAR, CYP11A1, HSD 3B, CYP17, CYP19, HSD17B , TNF, IL6, PPAR G, STS), in steroid signaling (ESR1, ESR2, PGR, SHBG, AIB1), and in estrogen metabolism (CYP1A1, CYP1A2, CYPIB1, CYP3A4) and inactivation (SULTs, UGTs, COMT, NQOs, GSTs). Using the best available information, we propose to study both known functional variants, and also single nucleotide polymorphisms in other unlinked regions of genes to increase the chance of detecting associations. Several of the genes are novel to this application, and we propose detailed genetic analyses using CEPH pedigrees to determine haplotype structures in this population. Interactions among variants will be tested using statistical procedures designed to detect high-order genetic interaction. This is one of the largest assembled resources of DNA and epidemiologic data for research in breast cancer. The work proposed in this application will provide timely and cost-effective new information on the genetic pathways to breast cancer that may prove relevant to screening, detection, and more targeted treatment strategies.