Biodata Management of Genomics Data from Cancer Cell Lines and Tumors

描述(由申请人提供)：现在很清楚，癌症靶向治疗的反应取决于单个患者肿瘤的分子特征。该行业越来越多地寻求基于基因组学的分析，以将药物反应表型与潜在的基因突变和多重分子签名联系起来。这样的分析受到三个根本挑战的阻碍。首先，癌症由可能数百种不同的分子疾病组成的多样性集合，大多数临床前实验没有适当的规模来代表这种多样性。其次，大多数分析局限于它们的基因组关联范围，只关注几种可能的特征中的一种(例如突变、基因表达、DNA拷贝数)。最后，大多数分析忽略了最后一个关键步骤，即将药物反应的多个遗传生物标记物映射到临床肿瘤群体。Compendia Bioscience致力于解决这些根本挑战，并提供癌基因组药物概况解决方案，该解决方案包括：评估一系列癌细胞系对特定化合物的反应；确定敏感和耐药细胞系群体；对基因表达、DNA拷贝数和突变进行详细的基因组学关联分析；开发和完善多重反应遗传生物标记物(S)；以及分析近30,000个高度精选的临床癌症标本的反应生物标记物(S)，以确定可能有反应的患者群体和亚群。该解决方案建立在Oncomine之前建立的功能和资源之上，Oncomine是一个全面的基因表达数据库，收集、标准化和分析公开可用的基因表达数据。这将通过扩大Oncomine的灵活性来获取现有研究的新元数据来实现：克服与纳入新数据类型相关的技术障碍；以合理的方式利用所有可用的数据来支持药物开发工作流程。这一阶段提案的具体目标是：1.对300多个癌细胞系小组公开获得的基因突变数据进行编目和注释。2.处理和集成300多个癌细胞系面板的DNA拷贝数数据。3.开发了从DNA拷贝数数据中调用扩增和缺失并与突变数据集成的方法。总而言之，这项建议的目的显著有助于证明整体方法的可行性：通过为大型细胞系面板收集关键数据元素，然后直接比较细胞系数据和肿瘤数据之间的一致性。与公共健康相关：尽管在旨在改善药物开发渠道的基因组学技术上投入了大量资金，但癌症仍然是美国人死亡的主要原因。这项建议寻求通过最大限度地发挥实验上强大的、特征良好的细胞系的价值，并利用这些研究的结果来为患者在新的治疗化合物的临床试验中进行选择提供信息，从而推动临床前药物开发工作。这将使药物开发公司更容易识别可能从新化合物中受益的患者群体，并通过临床试验成功地将这些化合物推向市场，从而改善公众健康。

DESCRIPTION (provided by applicant): It is now clear that response to targeted therapies in cancer is dictated by the molecular characteristics of an individual patient's tumor. The industry is increasingly looking to genomics-based analyses to link drug response phenotypes to underlying genetic mutations and multiplex molecular signatures. Such analyses are hindered by three fundamental challenges. First, cancer comprises a diverse collection of potentially hundreds of distinct molecular diseases and most preclinical experiments are not of the appropriate scale to represent this diversity. Second, most analyses are limited in their scope of genomic associations, focusing on only one out of several possible characterizations (e.g. mutations, gene expression, DNA copy number). Finally, most analyses are missing the last crucial step, which is to map multiplex genetic biomarkers of drug response to clinical tumor populations. Compendia Bioscience seeks to address these fundamental challenges and provide an oncogenomics drug profiling solution that: assesses a broad panel of cancer cell lines for response to a given compound; defines sensitive and refractory cell line populations; performs detailed genomics correlation analysis spanning gene expression DNA copy number and mutations; develops and refines multiplex genetic biomarker(s) of response; and analyzes response biomarker(s) across nearly 30,000 highly curated clinical cancer specimens to identify patient populations and subpopulations likely to respond. The solution builds upon the functionality and resources previously established in Oncomine, a comprehensive gene expression database that collects, standardizes, and analyzes publicly available gene expression data. This will be accomplished by extending the flexibility of Oncomine to capture new metadata on existing studies: by overcoming technical obstacles associated with incorporating new data types; and by utilizing the full range of available data in logical ways to support drug development workflows. The Specific Aims of this Phase I proposal are to: 1. Catalog and annotate publicly available gene mutation data for 300+ cancer cell line panel. 2. Process and integrate DNA copy number data for 300+ cancer cell line panel. 3. Develop method to call amplifications and deletions from DNA copy number data and integrate with mutation data. Altogether the aims of this proposal significantly contribute to proving the feasibility of the overall approach: by assembling critical data elements for a large cell line panel and then directly comparing concordance between cell line data and tumor data. PUBLIC HEALTH RELEVANCE: Despite enormous investments in genomics technology aimed at improving the drug development pipeline, cancer remains a leading cause of mortality in the United States. This proposal seeks to advance preclinical drug development efforts by maximizing the value of experimentally robust, well-characterized cell lines, and using the results of those studies to inform patient selection in clinical trials of novel therapeutic compounds. This will improve public health by making it easier for drug development companies to identify patient populations likely to benefit from novel compounds, and to successfully advance those compounds through clinical trials and to the marketplace.