Visual Programming Tool for Integrated Gene/Protein Networks in Cancer Research

描述（由申请人提供）：NIH路线图认识到需要进行系统级的多学科研究，以成功地对抗癌症等疾病。然而，收集，整合和分析异构数据集代表了一项重大的任务，无论是技术还是管理，这需要大多数调查人员无法获得的资源。随着该领域继续朝着需要有效整合各种知识基础和技术的方向发展，将越来越需要一个强大，复杂和商业实力的生物信息学框架，使研究人员能够融合他们独特的专业知识，并最有效地应用他们的贡献对生物现象的全面理解。 在第一阶段项目期间，项目团队对现有技术和信息资源进行了严格评估，并开发了一个概念验证可视化编程软件，以整合蛋白质质谱（MS）和基因表达微阵列（MA）数据。在拟议的第二阶段工作中，该软件将得到扩展，使多学科研究人员团队能够有意义地整合有关基因和蛋白质的知识，以系统地了解癌症生物学。拟议的技术将建立在现有的INCOGEN生物信息学工具VIBE的基础上，并将利用合作机构（马约诊所、约翰霍普金斯大学、东弗吉尼亚医学院、西北大学和Biosystemix）的广泛互补专业知识以及可用的社区资源。为了确保生物信息学工具是强大的，可扩展的，对癌症研究有用的，软件开发将与肾细胞癌（RCC或肾癌）的多学科，综合生物学研究平行进行。坚实的实验设计，复杂的生物信息学和使用最先进的生物学方法验证结果的能力的罕见组合将使我们不仅能够为社区提供强大的生物信息学工具，而且还为RCC的系统理解做出了重大贡献。通过这个多学科项目发现潜在的生物标志物和治疗策略，提供了可以授权给制药公司的临床相关知识产权。反过来，这些临床相关的发现将有助于验证该项目期间开发的方法和工具，从而推动软件的商业成功。 项目目标包括：1）识别统计学/诊断学上显著的MA和MS特征，2）合并MA和MS数据集并生成关系矩阵，3）创建共涨落网络并将现有生物学知识并入网络，4）开发网络可视化工具，以及5）在统计学和生物学背景下优化网络并对假设进行生物学验证。 公共卫生相关性：对于复杂疾病如癌症缺乏有效的全身治疗，部分原因是对导致细胞转化、致癌和疾病进展的分子事件缺乏基本的了解。该项目团队建议开发一种软件工具，使多学科研究人员团队能够有意义地整合有关基因和蛋白质的知识，以系统地了解癌症生物学，从而实现与早期诊断和早期适当治疗相关的重大健康益处。

DESCRIPTION (provided by applicant): The NIH Roadmap recognizes the need for system-level, multidisciplinary research to successfully fight diseases such as cancer. However, collecting, integrating, and analyzing heterogeneous sets of data represent a substantial undertaking, both technical and managerial, that requires resources not available to most investigators. As the field continues to move into a direction that requires effective integration of diverse knowledge bases and technologies, there will be an increasing need for a robust, sophisticated, and commercial-strength bioinformatics framework that will allow researchers to meld their distinct expertise and most efficiently apply their contributions toward a comprehensive understanding of biological phenomena. During the Phase I project, the project team performed a critical evaluation of available technologies and information resources and developed a proof-of-concept visual programming software to integrate protein mass spectrometry (MS) and gene expression microarray (MA) data. During the proposed Phase II work, the software will be extended to allow multidisciplinary teams of researchers to meaningfully integrate knowledge about genes and proteins toward the systematic understanding of cancer biology. The proposed technology will be built upon an existing INCOGEN bioinformatics tool, VIBE, and will leverage extensive complementary expertise at the collaborating institutions (Mayo Clinic, Johns Hopkins University, Eastern Virginia Medical School, Northwestern University, and Biosystemix), as well as available community resources. To ensure a bioinformatics tool that is robust, extensible, and useful for cancer research, the software development will be performed in parallel with a multidisciplinary, integrative biological study of renal cell carcinoma (RCC, or kidney cancer). The rare combination of solid experimental design, sophisticated bioinformatics, and ability to verify the results using state-of-the-art biological methods will enable us to not only deliver a powerful bioinformatics tool to the community, but also provide significant contributions toward the systems understanding of RCC. The discovery of potential biomarkers and therapeutic strategies through this multidisciplinary project offers clinically-relevant IP that can be licensed to pharmaceutical companies. In turn, these clinically relevant discoveries will serve to validate the approach and tools developed during this project, thereby driving the commercial success of the software. The project objectives include: 1) Identification of statistically/diagnostically significant MA and MS features, 2) Merging of MA and MS data sets and generation of relation matrix, 3) Creation of cofluctuation networks and incorporation of existing biological knowledge into the networks, 4) Development of network visualization tools, and 5) Refinement of networks and biological verification of hypotheses in statistical and biological contexts. PUBLIC HEALTH RELEVANCE: The lack of effective systemic therapy for complex diseases such as cancer is, in part, due to a fundamental lack of understanding of the molecular events that result in cellular transformation, carcinogenesis, and disease progression. The project team proposes to develop a software tool that will allow multidisciplinary teams of researchers to meaningfully integrate knowledge about genes and proteins toward the systematic understanding of cancer biology; thereby leading to significant health benefits associated with early diagnosis and early and appropriate treatment.