Bioinformatic Tools in Cancer Research

癌症研究中的生物信息工具

• 批准号: 7733041
• 负责人: Kenneth H Buetow
• 金额: $ 36.39万
• 依托单位: DIVISION OF BASIC SCIENCES - NCI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7733041
• 关键词: Atlases; Bioinformatics; Cancer Etiology; Cancer Research Project; Cancer cell line; Cell Line; Code; Collection; Communities; Complex; Computer Simulation; Copy Number Polymorphism; Data; Data Set; Databases; Development; Gene Expression; Genes; Genome; Genomics; Glioblastoma; Human; Indium; Internet; Investigation; Laboratories; Link; Malignant Neoplasms; Methodology; Methylation; Modeling; Mus; Mutate; Mutation; Mutation Analysis; Nature; Normal tissue morphology; Numbers; Outcome; Paper; Pathway interactions; Patients; Pharmaceutical Preparations; Phase; Process; Proteins; Publishing; Receptor Protein-Tyrosine Kinases; Recurrence; Reporting; Research; Resolution; Resources; Somatic Mutation; Statistical Models; System; Technology; Translating; Work; anticancer research; base; cancer genome; cancer genomics; genome sequencing; inhibitor/antagonist; insight; interest; member; next generation; novel; response; three dimensional structure; tool; tumor

Integrated view of multi-dimensional cancer genomic data generated by large-scale investigation of tumor genomic alterations such as the The Cancer Genome Atlas Project (TCGA) is expected to greatly facilitate our understanding of cancer etiology. To meet the analytical challenges presented by this effort and to disseminate the results to the cancer research community, we developed Cancer Genome Workbench (CGWB) (http://cgwb.nci.nih.gov), a web portal that integrates and displays the genome-wide collection of somatic mutation, copy number variation, gene expression and methylation data generated by TCGA. Key discoveries of this multiple-platform, high-resolution genomic data such as recurrent mutations and copy number changes in glioblastomas can be visualized in genomic view, heatmap view, protein view, 3D structure view and sequence trace view. We are currently work on supporting data generated by the Next-Generation sequencing technology. The long-term plan for CGWB is to make it the most comprehensive cancer alteration data resource by integrating data across multiple cancer research projects. CGWB tools have been used by our group to identify putative mutations in TCGA data that are subsequently validated and to provide QA for data generated by Genome Sequencing Centers. Using these tools our group was the first to identify NF1 as one of the most frequently mutated genes in glioblastomas and the result was reported in the TCGA network paper published in Nature. CGWB was also used by the TCGA network members in identifying core pathways involved in GBM. Mutation analysis for TCGA project is an ongoing process and we recently have presented the highly mutated genes among the phase II TCGA gene list to the TCGA steering committee. In addition to TCGA project, our group is responsible for analyzing mutations for NCI's Therapeutically Applicable Research to Generate Effective Treatments (TARGET) project. We have recently identified and validated novel recurrent somatic mutations in ALL patients who had poor outcome. The mutation activates the receptor tyrosine kinase pathway and the availability of an existing inhibitor of the mutated gene suggests that this finding can be translated into therapy for poor outcome patients. Our group has also been analyzing the somatic copy number changes in 300 cell lines used for cancer research. This will provide insight into different drug response observed in these commonly used cancer cell lines. Three complementary approaches are being utilized to create pathway models: 1) statistical modeling, 2) logical modeling, and 3) computational modeling. The statistical methodology known as path analysis is being used to model gene expression data. These efforts will be extended to include a collection of pathway models of interest to cancer research derived from cancer (and normal tissue) data sets. The laboratory is also collaborating with the NCICB and CGAP to develop Logical Models of pathway data. This effort will utilize databases of biomolecular interactions in human and mouse based on KEGG and BIOCARTA pathway data. The last strategy being explored within the laboratory is computational modeling. Each element in the pathway is annotated with a set of incoming and outgoing connections, which link the gene or complex to other nodes in the system. Setting the state of a node to "on" or "off" triggers the propagation of the effects of the change throughout the system via the node's dependent connections. The utility of this approach is currently being assessed using expression data. Recognizing that there is no single best way to create a model of such complex processes as biologic pathways, these three complementary approaches are being employed and evaluated. The instantiation of pathways as code represents the first step in development of more complex computational models.

通过大规模研究肿瘤基因组改变（例如癌症基因组图书馆项目（TCGA））产生的多维癌症基因组数据的综合视图，预计将极大地促进我们对癌症病因的理解。为了满足这项工作提出的分析挑战并将结果传播给癌症研究界，我们开发了癌症基因组工作台（CGWB）（http://cgwb.nci.nih.gov），该网站是一个集成并通过基因组及基因组的体现突变，基因数量变异的基因组收集，复制，用甲基化数据和甲基化数据来显示。可以在基因组视图，热图视图，蛋白质视图，3D结构视图和序列跟踪视图的情况下可视化这种多平台高分辨率基因组数据的关键发现，例如复发突变和副本数量变化。我们目前正在支持下一代测序技术生成的数据。 CGWB的长期计划是通过整合多个癌症研究项目的数据来使其成为最全面的癌症改变数据资源。我们的小组已使用CGWB工具来识别TCGA数据中的推定突变，这些突变随后验证并为基因组测序中心生成的数据提供质量检查。使用这些工具，我们的小组是第一个将NF1识别为胶质母细胞瘤中最常见的基因之一，结果在自然界发表的TCGA网络论文中得到了报道。 TCGA网络成员还使用了CGWB来识别GBM中涉及的核心途径。 TCGA项目的突变分析是一个持续的过程，我们最近向TCGA指导委员会介绍了II期TCGA基因列表中的高度突变基因。除TCGA项目外，我们的小组还负责分析NCI治疗适用研究的突变，以生成有效的治疗（目标）项目。我们最近在所有预后差的患者中鉴定了并验证了新型的复发性体细胞突变。该突变激活受体酪氨酸激酶途径，并且突变基因的现有抑制剂的可用性表明，该发现可以转化为治疗效果较差的患者。我们的小组还在分析用于癌症研究的300个细胞系中的体细胞拷贝数变化。这将提供对在这些常用的癌细胞系中观察到的不同药物反应的见解。正在利用三种互补方法来创建途径模型：1）统计建模，2）逻辑建模和3）计算建模。被称为路径分析的统计方法用于建模基因表达数据。这些努力将扩展到包括癌症（和正常组织）数据集的癌症研究途径模型的集合。该实验室还与NCICB和CGAP合作开发了途径数据的逻辑模型。这项工作将根据KEGG和Biocarta途径数据利用人和小鼠中生物分子相互作用的数据库。实验室内探索的最后一个策略是计算建模。路径中的每个元素都用一组传入和传出连接注释，这些连接将基因或复杂性与系统中的其他节点联系起来。将节点的状态设置为“ ON”或“ OFF”，从而通过节点的依赖连接触发了整个系统中变化的影响的传播。目前使用表达数据评估了这种方法的实用性。认识到没有一种最佳方法来创建像生物学途径这样的复杂过程的模型，因此正在采用和评估这三种互补方法。路径的实例化作为代码代表了开发更复杂的计算模型的第一步。