Integrative Analysis to Identify Therapeutic Targets for Lung Cancer

总结 分子靶向药物的发展，特别是那些调节一个分子活性的药物。 或参与癌症发病机制的几种蛋白质，是癌症治疗中最令人兴奋的领域 因为靶向抗癌药物具有提供显著临床益处而毒性小的潜力。在 为了开发新的分子靶向药物治疗肺癌，世界上主要的癌症原因，我们 已经收集了大量的数据，包括遗传/表观遗传（突变，拷贝数变异， 甲基化）、mRNA表达、蛋白质表达和基因组范围的RNAi功能筛选数据。 非小细胞肺癌（NSCLC）细胞系。将这些大规模和互补的数据集从 不同的来源将为发现肺癌新的分子机制提供巨大的机会。在Aim中 1，我们将开发一个强大的计算模型来整合多个基因组，蛋白质组和 功能数据集来识别新的肺癌驱动基因。 只有一小部分肿瘤驱动基因是传统上“可药物化”的靶点。在本研究的目标2中，我们 将使用数据驱动和公正的方法来发现和评估潜在的新治疗靶点， 肺癌本研究将提出一种新的逆向工程方法来构建肺癌特异性基因 网络 在本研究的目标3中，我们将开发一个公开可用的综合性肺癌数据库， 用户友好的界面和强大的分析引擎。该数据库将包括所有基因组，蛋白质组和 功能数据以及本研究中使用的去识别临床数据。通过使用最先进的 资讯科技方面，我们会把这些数据集与分析算法和方便用户的界面结合起来， 一个公开的数据库，使世界各地的研究人员可以利用和测试的数据和计算 从这项研究中产生的工具。

Summary The development of molecularly targeted drugs, specifically those which modulate the activities of one or several proteins involved in the pathogenesis of a cancer, is the most exciting field for cancer treatment because targeted anticancer drugs have the potential to provide dramatic clinical benefits with little toxicity. In order to develop new molecularly targeted drugs for lung cancer, the leading cause of cancer in the world, we have collected a large amount of data, including genetic/epigenetic (mutations, copy number variation, and methylation), mRNA expression, protein expression and genome-wide RNAi functional screening data on 108 non-small cell lung cancer (NSCLC) cell lines. Integrating these large-scale and complementary datasets from different sources will provide great opportunities to discover new molecular mechanisms of lung cancer. In Aim 1 of this study, we will develop a powerful computational model to integrate multiple genomic, proteomic and functional datasets to identify new lung cancer driver genes. Only a small subset of tumor driver genes is traditionally "druggable" targets. In Aim 2 of this study, we will use a data-driven and unbiased approach to discover and evaluate potential new therapeutic targets in lung cancer. A novel reverse engineering approach will be proposed to construct a lung-cancer-specific gene network. In Aim 3 of this study, we will develop a publicly available comprehensive lung cancer database with a user-friendly interface and powerful analysis engine. This database will include all genomic, proteomic and functional data together with the de-identified clinical data used in this study. By using the state-of-the-art information technology, we will integrate these datasets with analytic algorithms and a user-friendly interface in a publicly available database so that researchers worldwide can utilize and test the data and computational tools generated from this study.