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

项目总结/摘要 尽管全基因组关联研究（GWAS）在识别许多 生殖系变异与前列腺癌风险相关，遗传变异与 在这些基因座中的绝大多数，疾病风险在很大程度上仍然是未知的。这就阻碍了全面实现 新的药物靶点和/或个性化治疗。在寻求解决这一差距，后GWAS研究是 正在经历一场“大数据”革命，这场革命是由高通量基因组测序的成本呈指数级下降推动的。 测定。多层数据（遗传变异、转录组水平、表观遗传修饰、 组织特异性调控位点、3D相互作用等）在越来越大的队列中定期收集， 个体这就提出了对严格的计算和实验框架的需要，这些框架整合了各种 数据类型来识别和验证前列腺癌的致病基因和变异。在这里，我们提出了一个严格的 该框架旨在通过基因表达水平的改变介导风险的位点。我们故意 并详尽地提出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， 以在前列腺癌组织和细胞系中验证它们的功能。

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.