Genomic and Transcriptomic Analysis of Breast and Ovarian Cancers

摘要 卵巢癌和乳腺癌有着共同的遗传和生活方式/环境因素。GWAS已经确定 超过100个基因组区域包含与这些癌症风险相关的常见变异， 其中一些会导致两种癌症的风险。这些侵袭性亚型的分子特征 癌症-高级别浆液性卵巢癌（HGSOC）和雌激素受体（ER）阴性乳腺癌- 也非常相似，这表明共同的遗传和生物学机制驱动疾病的发展。 在目前的建议中，我们的目标是关注卵巢癌易感性的多效性机制， 乳腺癌，通过组织特异性转录范围内的基因表达分析与常见的 GWAS鉴定的卵巢癌和乳腺癌的变异风险等位基因。我们将应用一种新的基于基因的 关联方法，PrediXcan，以测试遗传变异影响的分子机制 卵巢癌和乳腺癌的发展。建议的生殖系遗传数据与注释的整合， 相关组织类型中的全基因组转录有效地减少了 GWAS通过在基因水平上将多个风险基因座分组在一起，并进一步简化了额外的 表征所涉及的途径。我们假设，生物相关性的预测提供的 PrediXcan将使我们能够克服肿瘤的异质性，并确定卵巢癌的新基因/途径， 乳腺癌然后，我们建议在乳腺和卵巢的实验模型中进行功能分析， 癌症来验证我们使用PrediXcan识别的基因和途径。 该提案采用了整合多个基因组和转录组数据来源的新方法 确定遗传调节基因表达特征在卵巢和乳腺癌发病机制中的作用， 癌的我们将重点关注的这些癌症的特定亚型的死亡率最高，因为 这些癌症亚型的发病机制尚不清楚。基因型和 来自GAME-ON、OCAC和BCAC联盟的表型数据，沿着公开可用的数据集， TCGA、METABRIC和GTEx，代表了在大型医疗器械中应用PrediXcan方法的独特机会， 量表，用于定义明确的病例组和对照组，具有高质量的流行病学数据资源。

ABSTRACT Ovarian and breast cancers share common genetic and lifestyle/environmental factors. GWAS have identified more than a hundred genomic regions containing common variants associated with risks of these cancers, several of which confer risks to both cancers. The molecular features of the aggressive subtypes of these cancers--high grade serous ovarian cancer (HGSOC) and estrogen receptor (ER) negative breast cancer—are also remarkably similar, suggesting common genetic and biological mechanisms driving disease development. In the current proposal, we aim to focus on pleiotropic mechanisms underlying susceptibility to ovarian and breast cancer, through tissue-specific transcription-wide analysis of gene expression associated with common variant risk alleles for ovarian and breast cancer identified by GWAS. We will apply a new gene-based association method, PrediXcan, to test the molecular mechanisms through which genetic variation affects ovarian and breast cancer development. The proposed integration of germline genetic data with annotation of whole genome transcription in the relevant tissue types effectively reduces the multiple testing burden faced by GWAS by grouping together multiple risk loci at the gene-level, and further simplifies additional characterization of implicated pathways. We hypothesize that the biological relevance of predictors provided by PrediXcan will allow us to overcome tumor heterogeneity and identify novel genes/pathways for ovarian and breast cancer. We then propose performing functional analyses in experimental models of breast and ovarian cancer to validate the genes and pathways we identify using PrediXcan. This proposal incorporates novel methodology integrating multiple sources of genomic and transcriptomic data to identify the role of genetically regulated gene expression traits in the pathogenesis of ovarian and breast cancers. Mortality is highest for the specific subtypes of these cancers that we will focus on, since the mechanisms underlying the pathogenesis of these cancer subtypes are poorly understood. Genotype and phenotype data from the GAME-ON, OCAC and BCAC consortia, along with the publicly available datasets, TCGA, METABRIC and GTEx, represent a unique opportunity to apply the PrediXcan approach in a large- scale for a well-defined group of cases and controls with high quality epidemiologic data resources .