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

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.

项目概要/摘要尽管全基因组关联研究（GWAS）在识别大量种系变异与前列腺癌风险相关，遗传变异与前列腺癌之间的因果机制绝大多数这些位点的疾病风险仍然很大程度上未知，这阻碍了全面实现。为了弥补这一差距，开展了 GWAS 后研究。经历一场由高通量基因组成本呈指数下降驱动的“大数据”革命多层数据（遗传变异、转录组水平、表观遗传修饰、定位）组织特异性调节位点、3D相互作用等）通常被收集在越来越大的群体中这提出了对整合各种严格计算和实验框架的需求。在此，我们提出了一种严格的数据类型来识别和验证前列腺癌的致病基因和变异。该框架针对通过基因表达水平的改变介导风险的位点。并详尽地建议检查前列腺癌的所有风险位点，以优先考虑因果变异和基因，在前列腺癌组织和细胞系中对其进行功能验证。