Identification and characterization of genomic features affecting survival duration in cancer

 描述(由申请人提供)：基因组技术的进步推动了对人类癌症前所未有的数据量的收集。关键挑战包括从这些数据中提取生物学见解，然后将这些发现转化为改善患者护理。为此，我对98项与基因表达相关的癌症存活率研究进行了荟萃分析。在这些研究中，研究人员测量了从患者身上切除的原发肿瘤的基因表达，然后将这些数据与患者治疗和结果的临床信息相关联。虽然公共资料库中有20种不同肿瘤类型的20,000多名患者的数据，但之前还没有全面的泛癌症分析报告。我的分析发现，在许多常见的人类癌症中，数百个基因的上调或下调与患者的生存期高度相关。这组基因提供了对癌症生物学一个鲜为人知的方面的独特见解：尽管区分肿瘤细胞和正常细胞的分子变化已经变得越来越清楚，但高度侵袭性肿瘤和良性、惰性肿瘤之间的差异在很大程度上仍不清楚。我的研究小组将利用这条生物信息学管道作为发现引擎，识别和表征具体导致癌症死亡的基因。在目标1中，我描述了几种单基因分析和靶向筛选，它们将评估其表达与癌症死亡相关的基因的功能。这些检测将确定这些基因中哪些是真正的癌基因，哪些促进肿瘤转移。在目标2中，我描述了评估基因功能的实验，这些基因的表达与癌症患者的延长生存期相关。这些基因可能代表新的肿瘤抑制基因，或者以其他方式抑制肿瘤细胞的扩散。在目标3中，我提出了几个机械实验，以阐明将本研究中确定的基因的表达或抑制与患者生存联系起来的生物学。这些功能研究 将确定这些基因对已知癌症相关途径的影响，包括上皮-间充质转化，这是一种被假设在肿瘤转移中发挥关键作用的发育程序。最后，在目标4中，我描述了一组扩展的与癌症生存相关的分子数据，这些数据将被分析，以创建驱动或抑制肿瘤进展的基因组特征的“死亡图谱”。这一更广泛的分析将用于识别新的癌症相关基因，用于未来的功能研究。总而言之，这些目标将极大地扩展我们对区分致命和非致命人类肿瘤的特征的了解。此外，这项工作将提供丰富的数据，有可能改善癌症患者风险的分层，并将确定药物开发的新靶点，以特别抑制最具侵袭性的癌症的生长。

 DESCRIPTION (provided by applicant): Advances in genomic technology have driven the collection of unprecedented amounts of data from human cancers. Critical challenges include extracting biological insights from this data and then translating these findings into improved patient care. Toward those ends, I performed a meta-analysis on 98 gene expression-linked cancer survival studies. In these studies, investigators measured gene expression in primary tumors surgically excised from patients and then correlated that data with clinical information on the patient's treatment and outcome. While data from over 20,000 patients with 20 distinct tumor types are available in public repositories, no comprehensive pan-cancer analysis has previously been reported. My analysis resulted in the discovery that the up-regulation or down regulation of hundreds of genes is highly correlated with the duration of patient survival across many common human cancers. This gene set provides unique insight into a poorly-understood facet of cancer biology: while the molecular alterations that distinguish tumor cells from normal cells have become increasingly well-characterized, the differences between highly-aggressive tumors and benign, indolent tumors remain largely unknown. My research group will utilize this bioinformatics pipeline as a discovery engine to identify and characterize the genes specifically responsible for cancer mortality. In Aim 1, I describe several single-gene assays and targeted screens that will assess the function of genes whose expression is associated with death from cancer. These assays will determine which of these genes are bona fide oncogenes and which promote tumor metastasis. In Aim 2, I describe experiments to assess the functions of genes whose expression correlates with prolonged survival in cancer patients. These genes could represent novel tumor suppressors or could otherwise inhibit tumor cell dissemination. In Aim 3, I propose several mechanistic experiments to elucidate the biology that connects the expression or repression of the genes identified in this study with patient survival. These functional studies will determine the effects of these genes on known cancer-related pathways, including the epithelial-mesenchymal transition, a developmental program hypothesized to play a crucial role in tumor metastasis. Lastly, in Aim 4, I describe an expanded set of molecular data linked to cancer survival that will be analyzed in order to create a "mortality map" of genomic features that drive or suppress tumor progression. This broader analysis will be used to identify new cancer-associated genes for future functional studies. Collectively, these Aims will greatly expand our knowledge of the features that differentiate fatal and non-fatal human tumors. Additionally, this work will provide an abundance of data with the potential to improve the stratification of cancer patient risk, and will identify novel targets for drug development to specifically inhibit the growth of the most aggressive cancers.