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.
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