Biological annotation of TCGA data

TCGA数据的生物学注释

• 批准号: 8464684
• 负责人: LYNDA CHIN
• 金额: $ 85.37万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8464684
• 关键词: Address; Algorithms; Antineoplastic Agents; Behavior; Biological; Biological Assay; Biological Markers; Breast; Cancer Patient; Cancer Science; Candidate Disease Gene; Categories; Cell Line; Cell model; Cells; Clinical; Clinical Trials Design; Cloning; Communities; Complement; Computational algorithm; Computer Simulation; DNA Sequence Rearrangement; DNA purification; Data; Databases; Dependence; Development; Diagnostic; Educational process of instructing; Engineering; Ensure; Event; Exhibits; Face; Flowcharts; Future; Genetic; Genome; Genomics; Goals; Human; IL3 gene; In Vitro; International; Knowledge; Libraries; Literature; Malignant Neoplasms; Mutation; Nature; Oncogenes; Oncogenic; Open Reading Frames; Output; Patient Care; RNA Splicing; Reaction; Reagent; Resistance; Sensitivity and Specificity; Signal Transduction; Site; Site-Directed Mutagenesis; Somatic Mutation; Statistical Models; System; Testing; The Cancer Genome Atlas; Therapeutic; Time; Translations; Tube; Tumorigenicity; Variant; anticancer research; base; cancer genome; cellular engineering; drug development; experience; expression vector; falls; flexibility; functional genomics; gene discovery; in vitro activity; in vivo; mutant; next generation sequencing; novel strategies; response; therapeutic target; tumor; tumorigenesis; tumorigenic; vector

DESCRIPTION (provided by applicant): The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) will generate a complete compendium of all cancer-associated genomic alterations with the goal of identifying and prioritizing the most promising therapeutic targets and diagnostic biomarkers. The output from these large-scale efforts in the last 2 years is radically transforming the way cancer science is conducted. At the same time, these efforts are uncovering a staggering level of genome complexity in cancer, making it clear that the effective translation of our new-found genomic knowledge into cancer therapeutics and diagnostics will require not only sophisticated computational analyses but, importantly, experimental systems to inform the functional activity of targets in the relevant biological context. The collective experience in cancer gene discovery and drug development has taught the field that an annotation of functionality alone is not sufficient to make informed decisions in cancer drug development. Rather, a productive drug development effort requires mechanistic understanding of a target's cancer-relevant activity, the specific biological and genotypic context in which it operates, and the clinical context in which to test the ultimate hypothesis, i.e. rational design of clinical trials. Given the hundreds and thousands of potential candidates from obtained by genomic efforts, it is imperative that an efficient prioritization pipeline is in place to filter and prioritize for downstream studies. Here we propose a CTD2 Center that will bring to the CTD2 Network multi-level functional and pharmacological assessments of biological importance, in both cell-based and in vivo settings, for somatic mutations identified by TCGA. Such "ground-truth" will be incorporated iteratively into computational models developed and refined to identify "driver mutations" with increasing specificity and sensitivity. In addition to these functional and pharmacological data and prediction algorithms, this Center has also developed novel approaches to rapidly and efficiently engineer somatic mutations in diverse vector systems which will support the activities of other centers in the Network and in the general cancer research community. Specific, we will pursue the following Aims: (1) Develop an algorithmic framework for identification of driver events through integrative and iterative analyses of genomic, functional and pharmacological response data; (2) Implement a high throughput platform for engineering somatic mutations in candidate genes identified by TCGA data for downstream functional studies; (3) Pharmacologically assess the therapeutic consequences conferred by candidate driver events in cell- based viability assays; (4) Functionally identify oncogenic driver events through in vivo Context-Specific screen for tumorigenicity.

描述(由申请人提供)：癌症基因组图谱(TCGA)和国际癌症基因组联合会(ICGC)将生成所有与癌症相关的基因组变化的完整概要，目的是确定并优先考虑最有希望的治疗靶点和诊断生物标记物。过去两年这些大规模努力的成果正在从根本上改变癌症科学的开展方式。与此同时，这些努力正在揭示癌症中令人震惊的基因组复杂性水平，清楚地表明，将我们新发现的基因组知识有效地转化为癌症治疗和诊断不仅需要复杂的计算分析，而且重要的是，需要实验系统来告知相关生物学背景下靶标的功能活动。癌症基因发现和药物开发方面的集体经验告诉该领域，仅靠功能注释不足以在癌症药物开发中做出明智的决定。相反，富有成效的药物开发工作需要对目标的癌症相关活动、其运作的特定生物学和基因背景以及检验最终假设的临床背景--即临床试验的合理设计--的机械理解。考虑到通过基因组努力获得的成千上万的潜在候选者，必须有一个有效的优先排序管道来筛选和确定下游研究的优先顺序。在这里，我们建议建立一个CTD2中心，它将为CTD2网络带来对TCGA确定的体细胞突变的生物学重要性的多层次功能和药理学评估，无论是基于细胞还是在体内的环境。这样的“地面事实”将被迭代地纳入开发和改进的计算模型中，以提高特异性和敏感性来识别“驱动因素突变”。除了这些功能和药理数据和预测算法，该中心还开发了新的方法来快速有效地在不同的载体系统中设计体细胞突变，这将支持网络中和一般癌症研究社区的其他中心的活动。具体而言，我们将追求下列目标：(1)通过对基因组、功能和药理学反应数据的整合和迭代分析建立确定驱动事件的算法框架；(2)实现一个高通量平台，用于对由TCGA数据确定的候选基因进行体细胞突变工程，以便进行下游功能研究；(3)在细胞活性检测中对候选驱动事件进行药理学评估；(4)通过体内特定背景的致瘤筛选，从功能上确定致癌驱动事件。