Characterizing non-coding somatic and germline variant interactions in ovarian cancer

摘要 人类遗传学的一个基本目标是破译基因型和表型之间的关系。 癌症被定义为包含赋予癌症易感性的可遗传遗传基因成分的疾病 和获得性（体细胞）成分，其中疾病由基因突变的积累驱动 导致正常细胞功能的不断增加的失调。全基因组群体 关联研究（GWAS）和全基因组测序（WGS）分析已经确定了数千个 卵巢癌的生殖系风险变异和与卵巢癌相关的体细胞非编码突变 发展确定生殖系和体细胞变异体之间存在相互作用的基因组区域 可以帮助我们识别疾病的关键驱动因素。我们已经建立了一个端到端的管道， 一次有效地评估疾病中数千种遗传变异的功能意义。我们有 还建立了输卵管分泌上皮细胞（卵巢癌前体）的离体模型， 化疗耐药卵巢癌的体外3D模型。在这份提案中，我们计划解决挑衅性的问题#3 “生殖系变异和体细胞突变之间的遗传相互作用是否有助于肿瘤细胞的差异？ 进化还是对治疗的反应“具体目标如下：（1）使用计算方法， 鉴定生殖系和体细胞遗传变异会聚以指示共享靶点的基因组区域 基因和调控网络驱动卵巢癌的发展;（2）使用染色体构象 捕获测定以验证调控靶标与其靶基因之间的相互作用;（3）使用 CRISPR/Cas9筛选以确定生殖细胞-体细胞相互作用区域在人类生殖细胞中的功能意义 卵巢癌的发展

ABSTRACT A fundamental goal of human genetics is to decipher the relationship between genotype and phenotype. Cancer is defined as a disease comprising a heritable genetic component that confers cancer predisposition and an acquired (somatic) component where disease is driven by an accumulation of genetic mutations leading to ever increasing deregulation of normal cellular functions. Population based genome wide association studies (GWAS) and whole genome sequencing (WGS) analyses have identified thousands of germline risk variants for ovarian cancer and somatic non-coding mutations involved in ovarian cancer development. Identifying genomic regions where there are interactions between germline and somatic variants may enable us to identify the critical drivers of disease. We have established an end-to-end pipeline that can efficiently evaluate the functional significance of thousands of genetic variants in disease at once. We have also established ex-vivo models of fallopian tube secretary epithelial cells (precursors of ovarian cancer) and in vitro 3D models of chemoresistant ovarian cancer. In this proposal, we plan to address provocative question #3 “Do genetic interactions between germline variations and somatic mutations contribute to differences in tumor evolution or response to therapy?” with the following specific aims: (1) Use computational approaches, to identify genomic regions where germline and somatic genetic variants converge to indicate shared target genes and regulatory networks driving ovarian cancer development; (2) Use chromosome conformation capture assays to validate interactions between regulatory targets and their target genes; (3) Use CRISPR/Cas9 screens to establish the functional significance of germline-somatic interacting regions in ovarian cancer development.