Integrative Analysis to Identify Therapeutic Targets for Lung Cancer

综合分析确定肺癌治疗靶点

• 批准号: 8743190
• 负责人: Guanghua Xiao
• 金额: $ 32万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-26 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8743190
• 关键词: Advisory Committees; Algorithms; Antineoplastic Agents; Basic Science; Bayesian Modeling; Biochemistry; Biological Models; Cancer Center; Cancer Etiology; Cancer Patient; Cancer cell line; Cause of Death; Clinical; Clinical Data; Collaborations; Computer Simulation; Copy Number Polymorphism; Data; Data Set; Databases; Development; Drug Targeting; Engineering; Ensure; Epidermal Growth Factor Receptor; Epigenetic Process; Gene Mutation; Genes; Genetic; Genomics; Goals; Histones; Information Technology; Institutes; Interdisciplinary Study; KRAS2 gene; Lead; Malignant Neoplasms; Malignant neoplasm of lung; Methods; Methylation; Modeling; Molecular; Molecular Biology; Molecular Profiling; Mutation; Network-based; Non-Small-Cell Lung Carcinoma; Outcome; PTEN gene; Pathogenesis; Pathology; Patients; Pharmacology; Proteins; Proteomics; RNA Interference; Regulator Genes; Research; Research Personnel; Sampling; Scientist; Source; Specificity; Statistical Models; Survival Rate; Systems Biology; Testing; The Cancer Genome Atlas; Toxic effect; Translational Research; United States; Universities; Validation; Woman; anticancer research; base; cancer initiation; cancer therapy; cohort; college; computerized tools; drug discovery; functional genomics; genome-wide; mRNA Expression; men; molecular phenotype; new therapeutic target; novel; novel therapeutics; protein expression; public health relevance; screening; software development; statistics; therapeutic target; translational medicine; tumor; tumor progression; usability; user-friendly

DESCRIPTION (provided by applicant): 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-drive 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 technolog, 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.

描述（由申请人提供）：分子靶向药物的开发，特别是那些调节与癌症发病机制有关的一种或多种蛋白质活性的药物，是癌症治疗中最令人兴奋的领域，因为靶向抗癌药物有可能提供显着的临床益处且毒性很小。为了开发针对全球主要癌症的肺癌的新型分子靶向药物，我们收集了大量数据，包括108个非小细胞肺癌（NSCLC）细胞系的遗传/表观遗传（突变、拷贝数变异和甲基化）、mRNA表达、蛋白表达和全基因组RNAi功能筛选数据。整合这些来自不同来源的大规模且互补的数据集将为发现肺癌的新分子机制提供良好的机会。在本研究的目标 1 中，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，以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“可用药”的靶标。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肺癌的潜在新治疗靶点。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的综合肺癌数据库，具有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化临床数据。通过使用最先进的信息技术，我们将把这些数据集与分析算法和用户友好的界面集成到一个公开的数据库中，以便全世界的研究人员可以利用和测试本研究生成的数据和计算工具。