Identification and modeling of driver genetic modules in glioblastoma

描述（申请人提供）：胶质母细胞瘤（GBM）是最常见的恶性脑肿瘤，也是最具挑战性的癌症治疗形式之一。GBM基因组的特征是许多不确定的致病意义的遗传畸变。更具体地说，大量的乘客突变和大范围的拷贝数改变使GBM中驱动突变的定义变得复杂。为了研究GBM基因组，需要能够从旁观者基因组噪声中区分因果遗传改变的策略，以及识别一致确定不同癌症表型和患者预后组的病变组。为了解决这一挑战并发现人类GBM的新驱动基因，我们开发了一个计算平台，该平台集成了来自大型全外显子组和转录组数据集的拷贝数变化、体细胞突变和框架内基因融合的分析。该提案将在特定肿瘤的自然遗传和细胞环境中推断出人类GBM遗传网络的变化，并确定特定GBM亚群依赖的生长，生存和进展的关键驱动模块。有了这些信息，我们就可以用特定的药物来针对关键的改变，这些药物通常已经可以用于其他类型的疾病。通过专注于GBM，我们能够在过去两年中沿着这条路线取得令人难以置信的进展。我们最近的工作发现了GBM中高致癌性和复发性基因融合的第一个例子，针对它们在特定肿瘤亚型中的依赖性，并观察到显著的抗肿瘤作用。这种整合的肿瘤基因组学方法是系统和全面的，并且已经确定了驱动人类疾病不同亚群的两个相互排斥的遗传模块，我们准备在体外和体内进行功能实验。从新模块的识别中出现的范例表明，当整合到我们的提案中时，计算和实验计划具有使GBM的整个遗传肿瘤模块功能化的能力。因此，该提案的完成将为脑肿瘤社区提供第一个计算和实验验证的管道，用于无偏重建任何形式的人类癌症的独特驱动模块。

DESCRIPTION (provided by applicant): Glioblastoma (GBM), the most common malignant brain tumor, remains one of the most challenging forms of cancer to treat. The GBM genome is characterized by numerous genetic aberrations of uncertain pathogenetic significance. More specifically, the abundance of passenger mutations and large regions of copy number alterations has complicated the definition of the landscape of driver mutations in GBM. To study the GBM genome, strategies enabling the distinction of causal genetic alterations from bystander genomic noise are needed as well as the identification of sets of lesions that concur to determine distinct cancer phenotypes and prognostic groups of patients. To address this challenge and uncover new driver genes in human GBM, we developed a computational platform that integrates the analysis of copy number variations, somatic mutations and in-frame gene fusions from a large whole-exome and transcriptome dataset. This proposal will contextualize alterations in genetic networks inferred from human GBM in the natural genetic and cellular environment of a specific tumor and identify the key driving modules on which specific GBM subgroups rely for growth, survival and progression. With this information in hand, we can target the critical alterations with specific drugs, often already available for other typesof diseases. By focusing on GBM, we have been able to make incredible progress along this line in the last two years. Our recent work identified the first example of highly oncogenic and recurrent gene fusions in GBM, target their dependency in a particular tumor subtype, and observe dramatic anti- tumor effects. This integrative oncogenomic approach is systematic and comprehensive and has already identified two mutually exclusive genetic modules that drive different subgroups of the human disease and that we are ready to explore with functional experiments in vitro and in vivo. The paradigm that emerges from the identification of the new modules establishes that, when integrated in our proposal, the computational and experimental plans have the power to functionalize the entire set of genetic tumor modules of GBM. Thus, completion of the proposal will deliver to the brain tumor community the first computational and experimentally validated pipeline for the unbiased reconstruction of distinctive driver modules of any form of human cancer.