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

描述(由申请者提供)：美国国立卫生研究院路线图认识到系统级、多学科研究的必要性，以成功抗击癌症等疾病。然而，收集、整合和分析不同种类的数据集是一项巨大的任务，无论是技术上还是管理上，都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不同知识库和技术的方向发展，将越来越需要一个强大、复杂和商业强度的生物信息学框架，使研究人员能够融合他们独特的专业知识，并最有效地将他们的贡献应用于对生物现象的全面理解。 在第一阶段项目期间，项目组对现有技术和信息资源进行了关键评估，并开发了概念验证可视化编程软件，以整合蛋白质质谱学(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，允许多学科研究团队有意义地将基因和蛋白质知识整合到对癌症生物学的系统理解中。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(梅奥诊所、约翰·霍普金斯大学、东弗吉尼亚医学院、西北大学和Biosystemix)的广泛补充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、可扩展的和对癌症研究有用的，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并行进行。可靠的实验设计、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，而且还将为了解RCC的系统做出重大贡献。通过这个多学科项目发现潜在的生物标记物和治疗策略提供了与临床相关的知识产权，可以授权给制药公司。反过来，这些与临床相关的发现将有助于验证在该项目期间开发的方法和工具，从而推动该软件的商业成功。 项目目标包括：1)确定具有统计/诊断意义的MA和MS特征；2)合并MA和MS数据集并生成关系矩阵；3)创建协同波动网络并将现有生物学知识纳入网络；4)开发网络可视化工具；5)改进网络并对统计和生物学方面的假设进行生物学验证。 公共卫生相关性：对癌症等复杂疾病缺乏有效的系统治疗，部分原因是对导致细胞转化、癌症发生和疾病进展的分子事件缺乏根本了解。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关于基因和蛋白质的知识，从而系统地了解癌症生物学；从而导致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.