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）：癌症基因组地图集（TCGA）和国际癌症基因组联盟（ICGC）将对所有与癌症相关的基因组变量进行完整的汇编，以识别和优先考虑最有希望的治疗靶标和诊断生物标志物。在过去两年中，这些大规模努力的产出从根本上改变了癌症科学的进行方式。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，我们对新发现的基因组知识对癌症治疗剂和诊断的有效翻译不仅需要精致的计算分析，而且还需要实验性系统来为目标在相关生物学环境中的功能活性提供信息。癌症基因发现和药物开发方面的集体经验告诉该领域，仅功能的注释就不足以在癌症药物开发中做出明智的决定。相反，生产性药物开发工作需要对目标与癌症相关的活性，其运作的特定生物学和基因型环境以及检验最终假设的临床环境，即临床试验的合理设计。考虑到基因组努力获得的成千上万个潜在的候选者，必须有一个有效的优先级管道过滤并优先考虑下游研究。在这里，我们提出了一个CTD2中心，该中心将在基于细胞和体内的CTD2网络多级功能和药理学评估中，用于TCGA确定的体体突变。这种“地面真相”将迭代地纳入开发和精制的计算模型中，以识别特异性和灵敏度的“驱动器突变”。除了这些功能和药理数据和预测算法外，该中心还开发了新颖的方法来快速有效地设计各种媒介系统中的体细胞突变，这些方法将支持网络中其他中心和一般癌症研究社区的活动。具体来说，我们将追求以下目的：（1）通过基因组，功能和药理响应数据的综合和迭代分析来开发算法框架，以识别驾驶员事件；（2）在通过TCGA数据识别的候选基因中实施高吞吐量平台，用于下游功能研究；（3）在基于细胞的生存力测定中，候选驾驶员事件带来的药理评估治疗后果；（4）通过体内特定于特定于肿瘤性的体内上下文筛选在功能上识别致癌驱动器事件。