Elucidation of the genetic mechanisms driving prostate tumorigenesis through integrative computational and functional approaches

通过综合计算和功能方法阐明驱动前列腺肿瘤发生的遗传机制

基本信息

批准号：
10576263
负责人：
MATTHEW L FREEDMAN
金额：
$ 66.14万
依托单位：
DANA-FARBER CANCER INST
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-03-02 至 2026-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10576263
关键词：
3-Dimensional Address Affect Automobile Driving Big Data Biological Assay Cancer cell line Cell Line Chromatin Clinic Collaborations Computing Methodologies Consumption Data Data Set Down-Regulation Epigenetic Process Etiology Foundations Funding Gene Expression Gene Expression Regulation Genes Genetic Genetic Diseases Genetic Variation Genetic study Genome Genomics Genotype-Tissue Expression Project Germ-Line Mutation Goals Histones Individual Inherited Joints LNCaP Linkage Disequilibrium Malignant Neoplasms Malignant neoplasm of prostate Maps Mediating Methods Modeling Modification Molecular Conformation Pathogenesis Population Prostate Regulation Regulatory Element Resolution Risk Site Solid Statistical Algorithm Susceptibility Gene The Cancer Genome Atlas Time Tissues Transcript Translating Validation Variant causal variant cell type cohort cost disorder risk epigenetic variation epigenome editing epigenomics experience functional group genetic association genome editing genome wide association study genome-wide analysis improved innovation men multiple data types new therapeutic target next generation novel overexpression personalized medicine prostate cancer model prostate cancer risk prostate carcinogenesis risk variant success targeted treatment three dimensional structure trait transcriptome transcriptomics whole genome

3-Dimensional Address Affect Automobile Driving Big Data Biological Assay Cancer cell line Cell Line Chromatin Clinic Collaborations Computing Methodologies Consumption Data Data Set Down-Regulation Epigenetic Process Etiology Foundations Funding Gene Expression Gene Expression Regulation Genes Genetic Genetic Diseases Genetic Variation Genetic study Genome Genomics Genotype-Tissue Expression Project Germ-Line Mutation Goals Histones Individual Inherited Joints LNCaP Linkage Disequilibrium Malignant Neoplasms Malignant neoplasm of prostate Maps Mediating Methods Modeling Modification Molecular Conformation Pathogenesis Population Prostate Regulation Regulatory Element Resolution Risk Site Solid Statistical Algorithm Susceptibility Gene The Cancer Genome Atlas Time Tissues Transcript Translating Validation Variant causal variant cell type cohort cost disorder risk epigenetic variation epigenome editing epigenomics experience functional group genetic association genome editing genome wide association study genome-wide analysis improved innovation men multiple data types new therapeutic target next generation novel overexpression personalized medicine prostate cancer model prostate cancer risk prostate carcinogenesis risk variant success targeted treatment three dimensional structure trait transcriptome transcriptomics whole genome

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项目摘要

PROJECT SUMMARY/ABSTRACT Although genome-wide association studies (GWAS) have been extremely successful in identifying numerous germline variants associated to risk for prostate cancer, the causal mechanism between genetic variation and disease risk remains largely unknown at the vast majority of these loci. This prohibits the full realization of novel drug targets and/or personalized treatments. In the quest to address this gap, post-GWAS studies are experiencing a “big data” revolution driven by the exponentially decreasing costs of high-throughput genomic assays. Multiple layers of data (genetic variation, transcriptome levels, epigenetic modifications, localization of tissue-specific regulatory sites, 3D interactions, etc.) are routinely collected in increasingly large cohorts of individuals. This raises the need for rigorous computational and experimental frameworks that integrate various types of data to identify and validate causal genes and variants in prostate cancer. Here we propose a rigorous framework aimed at loci where risk is mediated through alteration in gene expression levels. We deliberately and exhaustively propose to examine all risk loci for prostate cancer to prioritize causal variants and genes and to functionally validate them in prostate cancer tissue and cell lines.

项目摘要/摘要尽管全基因组关联研究（GWAS）在识别众多与前列腺癌风险相关的种系变异，遗传变异与在这些地区的绝大多数情况下，疾病风险在很大程度上仍然未知。这禁止完全实现新颖的药物靶标和/或个性化治疗方法。为了解决这一差距，GWAS后的研究是由于高通量基因组的成本降低成本，经历了“大数据”革命测定。多层数据（遗传变异，转录组水平，表观遗传修饰，定位组织特异性的调节位点，3D相互作用等）通常在越来越大的同类群体中收集个人。这增加了对整合各种的严格计算和实验框架的需求识别和验证前列腺癌因果基因和变异的数据类型。在这里，我们提出了一个严格的框架是针对局部通过基因表达水平改变风险的局部框架。我们故意并详尽地建议检查前列腺癌的所有风险基因座，以确定因果变异和基因的优先级和在功能上验证它们在前列腺癌组织和细胞系中。