Integrative bioinformatics and functional characterization of oncogenic driver aberrations in cancer

项目摘要 大规模的国家和国际癌症测序计划正在产生一个肿瘤概要- 相关的基因组改变，以确定最有希望的药物开发治疗目标的优先顺序。 这些努力揭示了癌症基因组复杂性的惊人水平。尽管我们知道的很多 关于众所周知的癌症基因中反复出现的异常的功能和临床影响，我们知之甚少。 更丰富、更低频率的突变是如何促进肿瘤进展的。有效 将肿瘤基因组数据转化为癌症治疗将需要新的实验系统来 靶点在包括肿瘤间和瘤内在内的相关生物学背景下的功能活性 异质性。为了满足这些需求，我们提出了一个CTD2中心，该中心将为研究社区提供 描述和验证致病“驱动因素”的高通量信息和实验方法 突变和融合基因，以及识别有意义地预测反应或 抗癌治疗的抵抗力。我们将追求以下具体目标：在目标1中，我们将实施 用于识别驱动程序突变的算法框架，具有高灵敏度和特异度。我们将专注于我们的 预测致癌、功能增益突变驱动因素的算法开发、培训和测试工作 多形性胶质母细胞瘤(GBM)、胰腺导管癌(PDAC)和卵巢上皮癌 (平机会)。这些计算方法将适用于所有癌症类型的分析。接下来，我们将 将~1,500个精选突变和~400个融合基因与 个性化的、患者定义的编码突变。在目标2中，我们将把突变等位基因和融合基因输入到 GBM、PDAC和EoC上下文特定的在体功能屏幕，考虑到 遗传背景、肿瘤微环境和选择单一和组合驱动因素的异质性 关于肿瘤发生的研究。在目标3中，我们将确定肿瘤内异质性对肿瘤的影响。 使用DNA条形码的人患者来源的异种移植模型对治疗药物的敏感性和耐药性 这概括了癌症的异质性。我们将确定单一靶向制剂和 它们的合理组合改变了肿瘤的种群动态。我们还将利用AIM 1信息学和 目标2和目标4中的功能表征以表征“持久者”群体以识别异常 与抗药性有关。在目标4中，我们将使用高通量功能蛋白质组学、创新蛋白质组学- 蛋白质相互作用分析和信息药物文库筛选研究以阐明潜在的机制和 由合格司机产生的治疗性责任。我们在CTD2中实施的基础平台 中心将提供一条经过验证的管道，用于快速表征功能增益像差 跨肿瘤谱系的工业化，以指导癌症患者的临床管理。

Project Summary Large-scale national and international cancer sequencing programs are generating a compendium of tumor- associated genomic alterations to prioritize the most promising therapeutic targets for drug development. These efforts have uncovered a staggering level of genome complexity in cancer. Although much is known about the function and clinical impact of recurrent aberrations in well-known cancer genes, less is known about which and how the more abundant, low-frequency mutations contribute to tumor progression. Effective translation of tumor genomic datasets into cancer therapeutics will require new experimental systems to inform the functional activity of targets in the relevant biological context encompassing inter- and intra-tumoral heterogeneity. To address these needs, we propose a CTD2 Center that will provide the research community high-throughput informatic and experimental approaches to characterize and validate pathogenic “driver” mutations and fusion genes as well as identify molecular markers that meaningfully predict responses or resistance to anticancer therapies. We will pursue the following Specific Aims: In Aim 1 we will implement an algorithmic framework for identifying driver mutations with high sensitivity and specificity. We will focus our algorithm development, training and testing efforts on predicting oncogenic, gain-of-function mutation drivers of glioblastoma multiforme (GBM), pancreatic ductal adenocarcinoma (PDAC) and epithelial ovarian cancer (EOC). These computational approaches will be amenable to the analysis of all cancer types. We will next engineer ~1,500 selected mutations and ~400 fusion genes into expression vectors along with cohorts of personalized, patient-defined coding mutations. In Aim 2 we will enter mutant alleles and fusion genes into GBM, PDAC and EOC context-specific, in vivo functional screens that take into account the importance of genetic context, tumor microenvironment and heterogeneity in the selection of single and combinatorial drivers of tumorigenesis. In Aim 3 we will determine the consequences of intra-tumoral heterogeneity on tumor sensitivity and resistance to therapeutic agents using DNA-barcoded, human patient-derived xenograft models that recapitulate the heterogeneity of cancer. We will determine the extent to which single targeted agents and their rational combinations alter tumor population dynamics. We will also leverage Aim 1 informatics and functional characterizations in Aim 2 and 4 to characterize “persistor” populations to identify aberrations associated with drug resistance. In Aim 4 we will use high-throughput functional proteomics, innovative protein- protein interaction assays and informer drug library screening studies to elucidate underlying mechanisms and therapeutic liabilities engendered by validated drivers. The foundational platform implemented in our CTD2 Center will provide a validated pipeline for the rapid characterization of gain-of-function aberrations that can be industrialized across tumor lineages to guide clinical management of cancer patients.