Discovery, Biology and Risk of Inherited Variants in Breast Cancer

乳腺癌遗传变异的发现、生物学和风险

• 批准号: 7933407
• 负责人: David John HUNTER
• 金额: $ 15.4万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-04-01 至 2014-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7933407
• 关键词: 3-Dimensional; Address; African American; Age; Algorithms; Alleles; Asians; Benefits and Risks; Binding Sites; Biological; Biological Assay; Biology; Breast; Breast Cancer Genetics; Breast Cancer Prevention; Breast Cancer Risk Factor; Charities; Chemoprevention; Clinical; Clinical assessments; Collaborations; Computational Technique; Computer Simulation; Data; Data Analyses; Decision Making; Development; Distant; Early Diagnosis; Enhancers; Epithelial; Epithelial Cells; Estrogen receptor negative; Family history of; Foundations; Funding; Genes; Genetic; Genetic Research; Genetic Transcription; Genetic Variation; Genie; Goals; High Risk Woman; In Vitro; Inherited; Intercistronic Region; Investigation; Lead; Link; Linkage Disequilibrium; Malignant Neoplasms; Malignant neoplasm of prostate; Mammary Neoplasms; Maps; Measures; Modeling; Modification; Molecular Conformation; Morphogenesis; Normal tissue morphology; Pattern; Phase; Phenotype; Primary Prevention; Prospective Studies; Public Health; RNA; Research; Research Personnel; Resources; Risk; Risk Factors; Sample Size; Scanning; Scientist; Series; Stromal Cells; Tamoxifen; Testing; Transcript; Translating; Translations; Tumor Tissue; Variant; Woman; Women' s Health; base; breast cancer diagnosis; breast cancer family registry; cancer risk; clinical decision-making; clinical practice; cohort; computer based statistical methods; data modeling; data sharing; design; early onset; evidence base; gain of function; gene discovery; gene environment interaction; gene function; gene interaction; genetic risk factor; genetic variant; genome wide association study; genome-wide; high risk; malignant breast neoplasm; non-genetic; protective effect; three-dimensional modeling; tool; transcription factor; tumorigenesis

We propose an integrated multiple-PI Project to systematically discover and replicate additional common genetic variants associated with breast cancer, assess their biological significance, and develop evidence based assessments of the clinical validity of prediction algorithms using these variants, and their suitability for translation into clinical practice. In sub-Project 1 we will combine the resources of (a) major GWAS for breast cancer amounting to >15,000 cases and (b) three pre-existing Consortia with over 48,000 additional cases to provide the large sample size needs necessary in the replication phase of GWAS. We will fine map the associated loci in collaboration with the major Consortia conducting GWAS for breast cancer in Asian and African-American women. In sub-Project 2 we will conduct a series of investigations to (a) assign a gene function to each replicated risk variant by measuring expression of 24,000 RNA transcripts in breast tumor tissue and normal tissue, from women for whom we also have an lllumina 540 GWAS available; by identifying networks of genes in which alterations of expression can be linked to specific germline risk variants; and by using Chromosomal Conformation Capture assays to examine whether associated intergenic regions fold physically in a way that brings them into contact with distant genie regions. We will also (b) examine whether loss or gain of function of the genes implicated in (a) in breast epithelial cells or stromal cells alter phenotypes in vitro in a 3-D model of breast morphogenesis and oncogenesis. In sub-Project 3, we will develop breast cancer prediction models that can be used to stratify women according to breast cancer risk. We will attempt to discover gene-gene interactions by reanalyzing the GWAS data, and we will systematically examine the genome-wide significant gene variants for effect modification by established breast cancer risk factors, using the largest set of prospective studies available. We will develop and refine risk models that incorporate both the germline risk factors and the established non-genetic risk factors, and also assess these in a cohort of women with higher familial risk of breast cancer (to specifically address the clinical needs of women at high risk due to a strong family history of breast cancer). Finally, we will analyze data from the major trials of primary prevention of breast cancer to address the question of whether the protective effect of tamoxifen is altered by risk status for our prediction models, data with a direct bearing on clinical decision-making with respect to chemoprevention for women at known high risk.

我们提出了一个综合的多PI项目，系统地发现和复制与乳腺癌相关的其他常见遗传变异，评估其生物学意义，并开发基于证据的评估，评估使用这些变异的预测算法的临床有效性，以及它们是否适合转化为临床实践。在子项目1中，我们将结合联合收割机的资源：（a）用于乳腺癌的主要GWAS，总计> 15，000例病例;（B）三个预先存在的联盟，额外病例超过48，000例，以提供GWAS复制阶段所需的大样本量。我们将与进行GWAS治疗亚洲和非洲裔美国妇女乳腺癌的主要财团合作，对相关基因座进行精细定位。在子项目2中，我们将进行一系列研究，以（a）通过测量乳腺癌组织中24，000个RNA转录物的表达， 肿瘤组织和正常组织，从妇女，我们也有一个Illumina 540 GWAS可用;通过鉴定基因网络，其中表达的改变可以连接到特定的生殖系风险变异;并通过使用染色体构象捕获分析，以检查相关的基因间区域是否折叠的方式，使他们与遥远的基因区域接触的物理。我们还将（B）在乳腺形态发生和肿瘤发生的3-D模型中，检查（a）中涉及的乳腺上皮细胞或基质细胞中基因功能的丧失或获得是否改变体外表型。在子项目3中，我们将开发乳腺癌预测模型，可用于根据乳腺癌风险对女性进行分层。我们将尝试通过重新分析GWAS数据来发现基因-基因相互作用，并使用现有最大的前瞻性研究集，系统地检查全基因组显著基因变异对已确定的乳腺癌风险因素的影响。我们将开发和完善风险模型，将生殖系风险因素和已建立的非遗传风险结合起来 这些因素，并在具有较高乳腺癌家族风险的女性队列中评估这些因素（以专门解决由于乳腺癌家族史而处于高风险的女性的临床需求）。最后，我们将分析来自乳腺癌一级预防的主要试验的数据，以解决他莫昔芬的保护作用是否会因我们的预测模型的风险状态而改变的问题，这些数据与已知高风险女性的化学预防的临床决策直接相关。