Biological annotation of TCGA data

TCGA数据的生物学注释

基本信息

批准号：
8657939
负责人：
LYNDA CHIN
金额：
$ 109.78万
依托单位：
UNIVERSITY OF TX MD ANDERSON CAN CTR
依托单位国家：
美国
项目类别：
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/8657939
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 MCF10A cells 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

项目摘要

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盟 (ICGC) 将生成所有癌症相关基因组改变的完整概要，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从根本上改变癌症科学的开展方式。与此同时，这些努力揭示了癌症中令人震惊的基因组复杂性，这清楚地表明，将我们新发现的基因组知识有效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，还需要实验系统告知相关生物背景下目标的功能活动。癌症基因发现和药物开发的集体经验告诉该领域，仅功能注释不足以在癌症药物开发中做出明智的决策。相反，富有成效的药物开发工作需要对目标的癌症相关活性、其发挥作用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）有机械的理解。鉴于通过基因组工作获得了成百上千的潜在候选者，必须建立有效的优先级管道来过滤和优先考虑下游研究。在这里，我们提议建立一个 CTD2 中心，该中心将为 CTD2 网络带来对 TCGA 识别的体细胞突变在细胞和体内环境中的生物学重要性的多层次功能和药理学评估。这种“基本事实”将被迭代地纳入开发和完善的计算模型中，以识别具有不断增加的特异性和敏感性的“驱动突变”。除了这些功能和药理学数据以及预测算法之外，该中心还开发了新的方法来快速有效地设计不同载体系统中的体细胞突变，这将支持网络中其他中心和一般癌症研究界的活动。具体来说，我们将追求以下目标：（1）通过对基因组、功能和药理学反应数据的综合和迭代分析，开发识别驾驶员事件的算法框架； (2) 建立一个高通量平台，对TCGA数据确定的候选基因进行体细胞突变工程，用于下游功能研究； (3) 药理学评估基于细胞的活力测定中候选驱动事件所带来的治疗效果； (4) 通过体内特定背景的致瘤性筛选，从功能上识别致癌驱动事件。