Biological annotation of TCGA data

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盟（ICGC）将生成所有癌症相关基因组改变的完整概要，目的是识别和优先考虑最有前途的治疗靶点和诊断生物标志物。在过去的两年里，这些大规模努力的成果正在从根本上改变癌症科学的研究方式。与此同时，这些努力揭示了癌症基因组复杂性的惊人水平，这清楚地表明，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，而且更重要的是，需要实验系统来告知相关生物背景下靶点的功能活性。癌症基因发现和药物开发的集体经验告诉该领域，仅对功能性进行注释不足以在癌症药物开发中做出明智的决定。相反，一个富有成效的药物开发工作需要对靶点的癌症相关活性、其运作的特定生物学和基因型背景以及测试最终假设的临床背景（即临床试验的合理设计）的机械理解。鉴于基因组研究获得了成千上万的潜在候选人，因此必须建立一个有效的优先级排序管道，以筛选和优先考虑下游研究。在这里，我们提出了一个CTD 2中心，将带来CTD 2网络的生物学重要性的多层次的功能和药理学评估，在基于细胞和在体内的设置，通过TCGA确定的体细胞突变。这样的“地面实况”将被反复纳入开发和完善的计算模型中，以识别具有更高特异性和灵敏度的“驱动突变”。除了这些功能和药理学数据和预测算法外，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，这将支持网络中其他中心和一般癌症研究社区的活动。具体而言，我们将追求以下目标：（1）通过整合和迭代分析基因组、功能和药理学响应数据，开发用于识别驱动事件的算法框架;（2）实现高通量平台，用于在通过TCGA数据识别的候选基因中工程化体细胞突变，用于下游功能研究;（3）在药理学上评估由基于细胞的活力测定中的候选驱动事件赋予的治疗结果;（4）通过体内致瘤性的上下文特异性筛选功能性地鉴定致癌驱动事件。

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.