The National Cancer Institute (NCI) will deploy an integrating biomedical informatics infrastructure, the cancer Biomedical Informatics Grid (caBIGTM), to expedite the cancer research community's access to key bloinformatics platforms. In partnership with the cancer research community, the NCI is creating a common, extensible informatics platform that integrates diverse data types and supports interoperable analytic tools. This platform will allow research groups to tap into the rich collection of emerging cancer research data while supporting their individual investigations.This Integrative Cancer Research Workspace will provide tools and systems to enable integration and sharing of information among cancer researchers. These tools will facilitate the integration of data not only from different centers, but also data of different types, thereby enabling translational and integrative research. These tools also provide for the integration of clinical and basic research data. The Workspace is tasked to develop a well-documented and validated toolset for use throughout the cancer research community. Workspace activities will include platforms and standards to facilitate the sharing of datasets and repositories, and those appropriate for testing the caBIGTM infrastructure are being asked to participate. A major goal of this workspace will be a demonstration of how a shared informatics platform can allow a comprehensive, federated grid of information to be made available to the cancer research community.The main goal of the Integrative Cancer Research Workspace is to assemble data, tools, and infrastructure that facilitate the cross silo use of cancer biology information to promote integrated cancer research. Working towards this goal, the NCICB is developing an integrative application framework, known as calntegrator, designed to facilitate cross data analysis in support of ongoing cancer research.One study supported here is I-SPY. The I-SPY trial is a national study to identify biomarkers predictive of response to therapy throughout the treatment cycle for women with Stage 3 breast cancer. The I-SPY study is sponsored by the Specialized Programs of Research Excellence (SPORES). The study involves the use of a contrast-enhanced breast MRI for the evaluation of locally-advanced breast cancer patients undergoing neoadjuvant treatment. The I-SPY study aims to correlate MRI results with molecular markers to identify the right surrogate marker for early response. The I-SPY informatics effort involves providing informatics support for the I-SPY trial. This involves the integration, and analysis of diverse data types including clinical, MRI imaging, gene expression, Comparative Genome Hybridization (CGH), immunohistochemistry (IHC), Fluorescent In Situ Hybridization (FISH), and cell lysates throughout the breast cancer treatment cycle. By providing an integrative platform designed to correlate molecular data with MRI patterns, study researchers will be able to more effectively identify surrogate markers for early response which will ultimately result in more effective therapies for breast cancer patients. To facilitate the integration and analysis of diverse data types, the NCICB caIntegrator data warehousing solution in conjunction with existing NCICB datamarts will be leveraged. caIntegrator data mining query and reporting tools will be accessible via a customized I-SPY application portal. The application portal will illustrate how research data is shared across study organizations and integrated in support of translational research. The main research objectives of the I-SPY trial are to:Identify biomarkers that predict response to therapy throughout the course of the cancer treatment cycle. Identify surrogate markers of response to preoperative chemotherapy that are predictive of survival and pathologic remission in Stage 3 breast cancer. Determine whether molecular markers alone or in conjunction with MRI will predict 3 year disease free survival and whether these markers are good predictors of residual disease at the time of surgery. Classify groups of participants with statistically different disease free survival. Determine the correlation between molecular markers and MRI imaging patterns.
