Gene Interactions, Estrogen, and Risk of Breast Cancer

描述(申请人提供)：调节雌激素合成、生物利用度和新陈代谢的基因的多态变异可能导致个人患乳腺癌的风险。此前很少有研究有统计能力来检验这些基因之间的复杂相互作用。此外，以前的大多数研究(大多为零)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。在这项应用中，我们建议使用在美国进行的大型人群病例对照研究--协作性乳腺癌研究中收集的现有数据和材料，研究雌激素基因变异对乳腺癌的贡献。在4年的时间里(1997-2001年)，4400多名最近被诊断为乳腺癌的妇女和3800名以人群为基础的对照组完成了关于乳腺癌风险因素的电话访谈，并为遗传学研究提供了口腔粘膜DNA。对于这种类型的研究，病例组(72%)和对照组(63%)的应答率很高，平均DNA产量足以对数百个变种进行分型。为了这项计划中的研究，所有匿名样本都已被提取用于DNA，等分并储存在-70倍的温度下。我们现在建议测试目前的假设，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。这些基因涉及雌激素合成的关键分支点(STAR、Cyp11a1、HSD 3B、CyP17、CyP19、HSD17B、TNF、IL6、PPAR G、STS)、类固醇信号转导(ESR1、ESR2、PGR、SHBG、AIB1)和雌激素代谢(CYP1A1、CYP1A2、CYPIB1、CYP3A4)和失活(ults、UGTS、COMT、NQOS、GSTs)。利用现有的最佳信息，我们建议既研究已知的功能变异，也研究基因其他非连锁区域的单核苷酸多态，以增加检测关联的机会。其中有几个基因对于这一应用是新的，我们建议使用CEPH家系进行详细的遗传分析，以确定该群体的单倍型结构。变异之间的相互作用将使用旨在检测高阶遗传相互作用的统计程序进行测试。这是用于乳腺癌研究的DNA和流行病学数据的最大集合资源之一。这项申请中提出的工作将提供有关乳腺癌遗传途径的及时和经济有效的新信息，这些信息可能被证明与筛查、检测和更有针对性的治疗策略相关。

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.