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的纲要- 相关的基因组改变，以优先考虑最有前途的药物开发的治疗目标。 这些努力揭示了癌症基因组复杂性的惊人水平。虽然我们知道 关于众所周知的癌症基因中复发性畸变的功能和临床影响， 更丰富的低频率突变如何促进肿瘤进展。有效 将肿瘤基因组数据集转化为癌症治疗需要新的实验系统来提供信息， 靶点在相关生物学背景下的功能活性，包括肿瘤间和肿瘤内 异质性为了满足这些需求，我们提出了一个CTD 2中心，将提供研究界 表征和验证致病性“驱动因子”高通量信息学和实验方法 突变和融合基因，以及鉴定有意义地预测反应的分子标记物， 对抗癌疗法的抵抗。我们将追求以下具体目标：在目标1中，我们将实施 用于以高灵敏度和特异性鉴定驱动突变的算法框架。呢我们一起来 算法开发，培训和测试工作，预测致癌，功能获得性突变驱动因素， 多形性胶质母细胞瘤（GBM）、胰腺导管腺癌（PDAC）和上皮性卵巢癌 （平机会）。这些计算方法将适用于所有癌症类型的分析。我们接下来将 将约1，500个选定的突变和约400个融合基因工程化到表达载体中，沿着的还有 个性化的、患者定义的编码突变。在目标2中，我们将输入突变等位基因和融合基因， GBM、PDAC和EOC环境特异性体内功能筛选，考虑到以下因素的重要性： 遗传背景、肿瘤微环境和单一和组合驱动因子选择中的异质性 肿瘤的发生。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条形码化的人患者来源的异种移植物模型对治疗剂的敏感性和抗性 这概括了癌症的异质性。我们将确定在何种程度上单一的目标代理人和 它们的合理组合改变了肿瘤群体动态。我们还将利用Aim 1信息学， 目标2和4中的功能表征，以表征“持久性”群体，从而识别畸变 与耐药性有关。在目标4中，我们将使用高通量功能蛋白质组学，创新的蛋白质， 蛋白质相互作用测定和告密者药物库筛选研究，以阐明潜在的机制， 由有效驱动因素产生的治疗责任。我们的CTD 2中实施的基础平台 中心将提供一个经过验证的管道，用于快速表征功能获得性畸变， 跨肿瘤谱系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.