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

通过融合种系风险变异和体细胞突变，从非编码基因组中识别卵巢癌的新驱动因素

• 批准号: 10746897
• 负责人: Pei-Chen Peng
• 金额: $ 24.9万
• 依托单位: CEDARS-SINAI MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10746897
• 关键词: Acceleration; Advisory Committees; Affect; Area; BRCA1 gene; Binding Sites; Biological Assay; Buffers; California; Cancer Biology; Cell physiology; Cessation of life; ChIP-seq; Chromatin; Clinical; Code; Collaborations; Complex; Computer Models; Computing Methodologies; DNA Sequence; DNA Sequence Alteration; Data; Data Science; Data Set; Development; Disease; Elements; Enhancers; Ensure; Environment; Epithelial ovarian cancer; Etiology; Faculty; Fellowship; Fostering; Future; Gene Expression; Genes; Genetic; Genetic Risk; Genetic Variation; Genome; Genomic Segment; Genotype; Germ-Line Mutation; Goals; Grant; Heritability; Human Genetics; Interdisciplinary Study; Intervention; Knowledge; Knowledge acquisition; Laboratories; Learning; Los Angeles; Machine Learning; Malignant Neoplasms; Malignant neoplasm of ovary; Measurement; Medical center; Mentors; Modeling; Molecular Profiling; Mutation; Nature; Noise; Normal tissue morphology; Nucleic Acid Regulatory Sequences; Ovarian; Penetrance; Phenotype; Positioning Attribute; Postdoctoral Fellow; Prevention approach; Process; Productivity; Prognosis; Proteins; Regulatory Element; Research; Research Personnel; Research Proposals; Research Training; Science; Series; Somatic Mutation; Susceptibility Gene; TP53 gene; Techniques; Technology; Training; Training Programs; Transcriptional Regulation; Tumor Tissue; Universities; Untranslated RNA; Variant; cancer genetics; cancer genomics; cancer initiation; cancer predisposition; cancer type; career development; cell type; clinical translation; cohort; complex biological systems; epigenetic regulation; epigenomics; experience; genetic information; genetic variant; genome sequencing; genome wide association study; genome-wide; genome-wide analysis; genomic data; histone modification; improved; insight; machine learning model; mortality; multiple omics; next generation sequencing; novel; ovarian neoplasm; population based; precursor cell; professor; promoter; risk variant; success; tenure track; transcription factor; tumor; tumor progression; tumorigenesis; whole genome

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.

项目总结/摘要 拟议的研究培训计划的目标是提供量身定制的额外培训，以促进成功的 整个职业发展博士后奖学金的完成和过渡到独立 终身教授该计划的主要内容是： 候选人：我在开发和应用计算模型方面有相当丰富的研究经验， 了解复杂的生物系统。本提议的培训部分将侧重于购置 癌症遗传学和基因组学知识，综合计算方法学和下一代 测序技术。此外，我将接受实验室管理，网络和 合作和赠款提交。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：Cedars-Sinai医疗中心的培训环境促进了生产力和协作 在临床和基础生物医学科学领域拥有世界一流的研究人员。我召集了一个顾问委员会 与表观基因组学、遗传学、数据科学和癌症生物学领域的知名专家合作， 成功地在这个培训计划，并指导我通过一个终身教职员工的成功收购 位置其中包括我的导师西蒙·盖瑟博士和四位顾问本杰明·伯曼博士和雪莉博士 来自西达斯-西奈的卢博士，来自洛杉矶的加州大学的波格丹·帕萨纽克博士和保罗·布特罗斯博士。 研究：人类遗传学的一个基本目标是破译基因型与基因型之间的关系。 表型癌症是一种疾病，其包含赋予癌症易感性的可遗传组分和 获得性（体细胞）成分，其中在疾病发展期间发生遗传改变的积累。 基于人群的全基因组关联研究（GWAS）和全基因组测序（WGS）分析 已经确定了数千种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含有有害的生殖系风险变体和 体细胞突变该提案假设非编码癌症驱动程序也是如此。与 丰富的表观基因组学和监管数据集，目标是确定基因组区域， 生殖系和体细胞变异之间的相互作用。具体目标是：（1）识别功能性监管 其中非编码生殖系和体细胞卵巢癌变体共定位的元件;（2）鉴定非编码 通过机器学习模型的多组学监管证据来驱动卵巢癌。拟议 研究将建立系统和定量模型，以确定卵巢癌非编码驱动因素， 提高我们对疾病病因的认识。