Bioinformatic Tools in Cancer Research

肿瘤基因组图谱计划(TCGA)等大规模肿瘤基因组变化研究所产生的多维癌症基因组数据的综合视图有望极大地促进我们对癌症病因学的理解。为了应对这一努力带来的分析挑战，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(http://cgwb.nci.nih.gov)，)，这是一个门户网站，集成并展示了由TCGA生成的体细胞突变、拷贝数变异、基因表达和甲基化数据的全基因组集合。这种多平台、高分辨率基因组数据的关键发现，如胶质母细胞瘤的反复突变和拷贝数变化，可以在基因组视图、热图视图、蛋白质视图、三维结构视图和序列跟踪视图中可视化。我们目前正在努力支持由下一代测序技术产生的数据。CGWB的长期计划是通过整合多个癌症研究项目的数据，使其成为最全面的癌症变更数据资源。我们的团队已经使用CGWB工具来识别TCGA数据中的假定突变，这些突变随后得到验证，并为基因组测序中心生成的数据提供质量保证。使用这些工具，我们的团队首次发现NF1是胶质母细胞瘤中最常见的突变基因之一，这一结果发表在《自然》杂志上发表的TCGA网络论文上。TCGA网络成员还使用CGWB来确定涉及大区域管理的核心途径。TCGA项目的突变分析是一个持续的过程，我们最近向TCGA指导委员会提交了第二阶段TCGA基因列表中高度突变的基因。除了TCGA项目外，我们团队还负责分析NCI的治疗应用研究的突变，以产生有效的治疗(TARGET)项目。我们最近在所有预后不佳的患者中发现并验证了新的复发性体细胞突变。突变激活了受体酪氨酸激酶通路，现有的突变基因抑制剂的可用性表明，这一发现可以转化为对预后不佳的患者的治疗。我们小组还一直在分析用于癌症研究的300个细胞系的体细胞拷贝数变化。这将为在这些常用的癌细胞系中观察到不同的药物反应提供洞察力。三种互补的方法被用来创建路径模型：1)统计建模、2)逻辑建模和3)计算建模。被称为通径分析的统计方法正被用来对基因表达数据进行建模。这些努力将扩展到包括从癌症(和正常组织)数据集衍生的癌症研究感兴趣的途径模型的集合。该实验室还与NCICB和CGAP合作开发通路数据的逻辑模型。这项工作将利用基于KEGG和BioCarta途径数据的人类和小鼠生物分子相互作用数据库。实验室正在探索的最后一种策略是计算建模。途径中的每个元件都有一组传入和传出的连接，这些连接将基因或复合体连接到系统中的其他节点。将节点的状态设置为“开”或“关”会触发通过该节点的依赖连接在整个系统中传播更改的影响。目前正在使用表情数据评估这种方法的实用性。认识到没有单一的最佳方法来创建生物途径等复杂过程的模型，正在使用和评估这三种互补的方法。将路径实例化为代码是开发更复杂的计算模型的第一步。

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.