Systems Biology Platform for Integrative Cancer Biology Research

用于综合癌症生物学研究的系统生物学平台

• 批准号: 7481677
• 负责人: Yuri V. Nikolsky
• 金额: $ 20万
• 依托单位: GENEGO, INC.
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-06-01 至 2010-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7481677
• 关键词: Algorithms; Area; Biochemistry; Biological Assay; Biological Markers; Biological Sciences; Biology; Cancer Biology; Cancer Patient; Chemistry; Clinical; Clinical Data; Complex; Comprehension; Computer software; Computers; Correlation Studies; Data; Data Analyses; Data Set; Databases; Descriptor; Development; Disease; Disease Association; Drug Delivery Systems; Funding; Gene Dosage; Gene Expression; Gene Proteins; Generations; Generic Drugs; Genes; Genetic; Genome; Human; Human Biology; Imagery; Individual; Industry; Informatics; Knowledge; Licensing; Link; Malignant Neoplasms; Metabolic; Methods; Methylation; Molecular Biology; Molecular Medicine; Molecular Profiling; Mus; Oceans; Oncologist; Ontology; Outcome; Pathway Analysis; Pathway interactions; Patients; Peer Review; Pharmaceutical Chemistry; Pharmaceutical Preparations; Pharmacologic Substance; Phase; Population; Procedures; Process; Proteins; Proteomics; Public Domains; Public Health; Publishing; Range; Rattus; Reporting; Research; Research Infrastructure; Research Personnel; Research Project Grants; Resources; Retrieval; Review Literature; Sampling; Scheme; Screening Result; Structure; System; Systems Analysis; Systems Biology; Technology; Today; Universities; Update; Vendor; Work; analytical tool; anticancer research; base; cancer type; cohort; data integration; data mining; gene interaction; genetic epidemiology; knowledge base; metabolomics; novel; novel strategies; oncology; prototype; research study; tool; translational medicine; Algorithms; Area; Biochemistry; Biological Assay; Biological Markers; Biological Sciences; Biology; Cancer Biology; Cancer Patient; Chemistry; Clinical; Clinical Data; Complex; Comprehension; Computer software; Computers; Correlation Studies; Data; Data Analyses; Data Set; Databases; Descriptor; Development; Disease; Disease Association; Drug Delivery Systems; Funding; Gene Dosage; Gene Expression; Gene Proteins; Generations; Generic Drugs; Genes; Genetic; Genome; Human; Human Biology; Imagery; Individual; Industry; Informatics; Knowledge; Licensing; Link; Malignant Neoplasms; Metabolic; Methods; Methylation; Molecular Biology; Molecular Medicine; Molecular Profiling; Mus; Oceans; Oncologist; Ontology; Outcome; Pathway Analysis; Pathway interactions; Patients; Peer Review; Pharmaceutical Chemistry; Pharmaceutical Preparations; Pharmacologic Substance; Phase; Population; Procedures; Process; Proteins; Proteomics; Public Domains; Public Health; Publishing; Range; Rattus; Reporting; Research; Research Infrastructure; Research Personnel; Research Project Grants; Resources; Retrieval; Review Literature; Sampling; Scheme; Screening Result; Structure; System; Systems Analysis; Systems Biology; Technology; Today; Universities; Update; Vendor; Work; analytical tool; anticancer research; base; cancer type; cohort; data integration; data mining; gene interaction; genetic epidemiology; knowledge base; metabolomics; novel; novel strategies; oncology; prototype; research study; tool; translational medicine

DESCRIPTION (provided by applicant): Cancer is the most complex and the most comprehensively studied disease area in molecular medicine. A vast ocean of small experiments data and OMICs datasets is being accumulated in oncology research, and none of the currently available life sciences informatics platforms is capable of a comprehensive handling and meaningful functional analysis of these data. Here we propose to build such a system, MetaMiner (Oncology) on the base of our mature human systems biology platform MetaCore/MetaDrug. The system will include a large structured database of cancer domain knowledge, including gene-disease associations, anti-cancer compounds, cancer-specific pathways and perturbed networks manually annotated at GeneGo for over 3 years. MetaMiner will be primarily applied for functional analysis of different types of OMICs data in cancer research multi-parallel sequencing data, genome-wide methylation, SNP and gene copy number assays, gene expression, proteomics and metabolomics, and cross-coreketion of data of different types on pathways and networks. The tools will include an enrichment analysis procedures in 8 functional ontologies, a comprehensive toolkit for network and interactome analyses. MetaMiner will be integrated with the major OMICs hardware vendors, third parties bionformatics software, workflow software packages, translational medicine platforms, public domain resources such as caBig. In the scope of Phase I, we propose to further advance the technology of cancer systems biology in two ways. First, we will develop a module for clustering OMICs samples of the same type (for instance, microaray expression profiles) in large cancer patient cohorts based on functional descriptors. This method will help for selecting clinically distinct patients' sub-populations (clusters) with unique combination of functional biomarkers and pathway-linked drug targets. Second, we will apply our algorithms for topological network analysis for integration of associations between different types of OMICs data for the same patient. This method is already successfully applied in personalized and translational medicine. Here, we propose to implement it at the level of the off-the-shelf product. Finally, we will develop the integrative database schema and interface of MetaMiner. The final deliverable for Phase I will be the functioning prototype. PUBLIC HEALTH RELEVANCE: We propose to develop a comprehensive, systems-level analytical system for integrative data analysis in cancer research. The platform, MetaMiner (Oncology), will include a comprehensive knowledge database on cancer biology and human biology in general, including protein interactions, gene-disease associations, pathways and networks, as well as cancer-relevant medicinal chemistry. MetaMiner will handle any type of cancer OMICs data and enable its comprehensive functional analysis. The system will include novel tools for clustering of cancer patients based on functional descriptors and integration of data of different types based on network topology.

描述（由申请人提供）：癌症是分子医学中最复杂，最全面研究的疾病领域。在肿瘤学研究中积累了大量的小型实验数据和OMIC数据集，目前可用的生命科学信息平台都没有能力对这些数据进行全面的处理和有意义的功能分析。在这里，我们建议在我们成熟的人类系统生物学平台元/元素的基础上构建这样的系统，地铁（肿瘤学）。该系统将包括大型的癌症领域知识的结构化数据库，包括基因 - 疾病关联，抗癌化合物，癌症特异性途径和在Genego手动注释的扰动网络超过3年。在癌症研究中，将主要用于对不同类型的OMIC数据的功能分析多 - 全基因组甲基化，SNP和基因拷贝数测定，基因表达，蛋白质组学和代谢组学以及路径和网络不同类型数据的跨核心。这些工具将包括8个功能本体论的富集分析程序，这是一个用于网络和交互分析的全面工具包。 Metaminer将与主要的OMIC硬件供应商，第三方Bionformatics软件，工作流软件包，转化医学平台，公共领域资源（例如Cabig）集成。在第一阶段的范围内，我们建议通过两种方式进一步推进癌症系统生物学的技术。首先，我们将开发一个模块，用于基于功能描述符的大型癌症患者队列中相同类型的OMICS样本（例如Microaray表达曲线）。此方法将有助于选择具有功能性生物标志物和途径连接药物靶标的独特组合的临床不同患者的子群（集群）。其次，我们将应用算法用于拓扑网络分析，以集成同一患者的不同类型的OMIC数据之间的关联。该方法已经成功地应用于个性化和转化医学。在这里，我们建议在现成产品的级别上实施它。最后，我们将开发Metaminer的集成数据库架构和接口。第一阶段的最终可交付方式将是功能原型。公共卫生相关性：我们建议开发一个综合的，系统级别的分析系统，用于癌症研究中的综合数据分析。该平台Metaminer（肿瘤学）将包括有关癌症生物学和人类生物学的全面知识数据库，包括蛋白质相互作用，基因 - 疾病酶关联，途径和网络，以及与癌症相关的药物化学。 Metaminer将处理任何类型的癌症OMIC数据，并实现其全面的功能分析。该系统将根据功能描述符和基于网络拓扑的不同类型的数据集成癌症患者的新颖工具。