Identifying new drivers of ovarian cancer from the non-coding genome by converging germline risk variants and somatic mutations

项目摘要/摘要 拟议的研究培训方案的目标是提供量身定制的额外培训，以促进成功 在完成博士后奖学金和过渡到独立的整个职业发展过程中 终身教职教授。该计划的主要内容包括： 应聘者：我在开发和应用计算模型方面有丰富的研究经验 了解复杂的生物系统。该提案的培训部分将侧重于获取 癌症遗传学和基因组学、综合计算方法和下一代的知识 测序技术。此外，我还将接受实验室管理、网络和 合作，以及拨款申请。这个全面的培训计划将加速我成为一名 独立研究人员和开发计算模型，以更好地了解癌症生物学。 环境：锡达斯-西奈医疗中心的培训环境促进了生产力和协作 拥有世界一流的临床和基础生物医学研究人员。我已经组建了一个咨询委员会 与表观基因组学、遗传学、数据科学和癌症生物学领域的受人尊敬的专家一起确保我的 在这个培训计划中取得成功，并指导我成功获得终身教职 位置。这些人包括我的导师西蒙·盖瑟博士和四位顾问本杰明·伯曼博士和谢利博士 来自锡达斯-西奈的Lu博士，来自加州大学洛杉矶分校的Bogdan Pasaniuc博士和Paul Boutros博士。 研究：人类遗传学的一个基本目标是破译基因和基因之间的关系 表型。癌症是一种由一种可遗传成分组成的疾病，它使人有癌症的易感性和一种 后天获得的(体细胞)成分，在疾病发展过程中基因改变的积累。 基于群体的全基因组关联研究和全基因组测序分析 已经确定了数千个与卵巢癌有关的生殖系风险变异和体细胞非编码突变 发展。通常，编码蛋白质的癌症驱动基因既含有有害的生殖系风险变异，也含有 体细胞突变。这项提议假设，非编码癌症驱动因素也是如此。与 丰富的表观基因组学和调控数据集，目标是识别存在 生殖系和体细胞变异之间的相互作用。具体目标是：(1)确定功能性监管 非编码生殖系和体细胞卵巢癌变异共定位的元件；(2)识别非编码 卵巢癌驱动因素通过机器学习模型的多组学调控证据。建议数 研究将建立系统和定量的模型来识别卵巢癌的非编码驱动因素和 提高我们对疾病病因学的理解。

PROJECT SUMMARY/ABSTRACT The goal of the proposed research training program is to provide tailored additional training to facilitate successful career development throughout the completion of postdoctoral fellowship and the transition to independent tenure track professor. The key elements of this plan are: Candidate: I have considerable research experience in developing and applying computational models to understand complex biological systems. The training component of this proposal will focus on acquisition of knowledge in cancer genetics and genomics, integrative computational methodologies, and next-generation sequencing technologies. Additionally, I will receive training in laboratory management, networking and collaborations, and grant submissions. This well-rounded training plan will accelerate my goals of being an independent researcher and developing computational models to better understand cancer biology. Environment: The training environment at Cedars-Sinai Medical Center fosters productivity and collaboration with world class researchers in clinical and basic biomedical science. I have assembled an advisory committee with esteemed experts in the areas of epigenomics, genetics, data science and cancer biology to ensure my success in this training program and to guide me through the successful acquisition of a tenure track faculty position. These include my mentor Dr. Simon Gayther and four advisors, Dr. Benjamin Berman and Dr. Shelly Lu from Cedars-Sinai, and Dr. Bogdan Pasaniuc, and Dr. Paul Boutros from University of California, Los Angeles. Research: A fundamental goal of human genetics is to decipher the relationship between genotype and phenotype. Cancer is a disease comprising a heritable component that confers cancer predisposition and an acquired (somatic) component where accumulation of genetic alterations occurs during disease development. Population based genome-wide association studies (GWAS) and whole genome sequencing (WGS) analyses have identified thousands of germline risk variants and somatic non-coding mutations involved in ovarian cancer development. Often, protein-coding cancer driver genes harbor both deleterious germline risk variants and somatic mutations. This proposal hypothesizes that the same is true for non-coding cancer drivers. With the wealth of epigenomics and regulatory datasets, the goal is to identify genomic regions where there are interactions between germline and somatic variants. The specific aims are: (1) identify functional regulatory elements where non-coding germline and somatic ovarian cancer variants co-localize; (2) identify non-coding ovarian cancer drivers through multi-omics regulatory evidence by machine learning models. The proposed studies will establish systematic and quantitative models to identify ovarian cancer non-coding drivers and improve our understanding of disease etiology.