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纲要，目的是确定和优先考虑最有希望的治疗靶点和诊断生物标志物。过去两年这些大规模努力的成果正在彻底改变癌症科学的运作方式。与此同时，这些努力正在揭示癌症中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，更重要的是，需要实验系统来告知相关生物学背景下靶点的功能活动。癌症基因发现和药物开发的集体经验告诉该领域，仅对功能的注释不足以在癌症药物开发中做出明智的决定。相反，有效的药物开发工作需要对目标癌症相关活性的机制理解，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。鉴于通过基因组努力获得的成千上万的潜在候选物，必须建立一个有效的优先级管道来过滤和优先考虑下游研究。在这里，我们提出了一个CTD2中心，它将为CTD2网络带来多层次的生物学重要性的功能和药理学评估，在基于细胞和体内的环境中，通过TCGA识别体细胞突变。这样的“基本事实”将被迭代地纳入计算模型，开发和改进以识别“驱动突变”，其特异性和敏感性越来越高。除了这些功能和药理学数据和预测算法之外，该中心还开发了新的方法来快速有效地在各种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界的活动。具体而言，我们将追求以下目标：(1)通过基因组，功能和药理学反应数据的整合和迭代分析，开发用于识别驱动事件的算法框架；(2)建立高通量平台，对TCGA数据鉴定的候选基因进行体细胞突变工程，用于下游功能研究；(3)在基于细胞活力的实验中，药理学评估候选驱动事件所带来的治疗效果；(4)通过体内情境特异性的致瘤性筛选，从功能上识别致癌驱动事件。

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.