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

描述（由申请人提供）：NIH路线图认识到需要系统级的多学科研究来成功地对抗癌症等疾病。然而，收集、集成和分析异构数据集是一项实质性的工作，无论是技术上还是管理上，都需要大多数研究人员无法获得的资源。随着该领域继续向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的、复杂的、商业强度的生物信息学框架，这将使研究人员能够融合他们独特的专业知识，并最有效地将他们的贡献应用于对生物现象的全面理解。

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.