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; 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.

项目摘要/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 在整个博士后奖学金完成期间的职业发展以及向独立的过渡 终身教授。该计划的关键要素是： 候选人：我在开发和应用计算模型方面具有丰富的研究经验 了解复杂的生物系统。该提案的培训部分将着重于获取 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，我将接受实验室管理，网络和 合作和授予提交。这个全面的培训计划将加速我成为一个目标 独立的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 与临床和基础生物医学科学领域的世界一流研究人员。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尊敬的专家一起，以确保我 该培训计划的成功，并指导我成功地获得了任期教师 位置。其中包括我的导师Simon Gayther博士和四位顾问Benjamin Berman和Shelly博士 Lu来自Cedars-Sinai和Bogdan Pasaniuc博士，以及来自洛杉矶分校的Paul Boutros博士。 研究：人类遗传学的基本目标是破译基因型与 表型。癌症是一种疾病，包括一种可赋予癌症易感性的可遗传成分 获得的（体细胞）成分在疾病发育过程中发生遗传改变的积累。 基于人群的全基因组关联研究（GWAS）和整个基因组测序（WGS）分析 已经确定了卵巢癌的成千上万种种系风险变异和躯体非编码突变 发展。通常，蛋白质编码的癌症驱动基因具有有害种系风险变体，并且 体细胞突变。该提议假设非编码癌症驱动因素也是如此。与 大量的表观基因组学和监管数据集，目标是确定存在的基因组区域 种系和躯体变体之间的相互作用。具体目的是：（1）确定功能调节 非编码种系和体卵巢癌变体共定位的元素； （2）识别非编码 通过机器学习模型，通过多词的监管证据通过卵巢癌驱动器。提议 研究将建立系统和定量模型，以识别卵巢癌非编码驱动因素，并 提高我们对疾病病因的理解。