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

• 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
• 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

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)已经非常成功地识别了 生殖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 在这些基因座中的绝大多数，疾病风险在很大程度上仍然未知。这妨碍了充分实现 新的药物靶点和/或个性化治疗。在寻求解决这一差距的过程中，后Gwas的研究是 经历一场由高通量基因组成本成倍下降推动的“大数据”革命 化验。多层数据(遗传变异、转录组水平、表观遗传修饰、定位 特定于组织的调控部位、3D交互作用等)它们通常被收集在越来越大的 个人。这增加了对严格的计算和实验框架的需求，这些框架集成了各种 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 针对风险通过基因表达水平的改变进行调节的基因座框架。我们故意 并详尽地建议检查前列腺癌的所有风险基因，以确定因果变异和基因的优先顺序 在前列腺癌组织和细胞系中对它们进行功能验证。