The caIntegrator knowledge framework provides researchers with the ability to perform ad hoc querying and reporting across multiple domains. This application framework comprises an n-tier service oriented architecture that allows pluggable web-based graphical user interfaces, a business object layer, server components that process the queries and result sets, a data access layer and a robust data warehouse. At the heart of caIntegrator is the Clinical Genomics Object Model (CGOM) that provides standardized programmatic access to the integrated biomedical data collected in the caIntegrator data system. Design of the CGOM is driven by usecases from two critical NCI-sponsored studies, a brain tumor trail called GMDI (Glioma Molecular Diagnostic Initiative) and a breast cancer study called I-SPY TRIAL (Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging And moLecular analysis). The model represents data from clinical trials, microarray-based gene expression, SNP genotyping and copy number experiments, and Immunohistochemistry-based protein assays. Clinical domain objects in CGOM allow access to Clinical trial protocol, treatment arms, patient information, sample histology, clinical observations and assessments. Genomic domain objects allow access to biospecimen information, raw experimental data, in-silico transformation and analyses performed on the raw experimental datasets and biomarker findings. The clinical and genomic findings domain objects have relationships to the FindingsOntology object, as the findings can be complex concepts which, in turn, can be generically represented as items occurring in an ontology (for example, WHO histopathological classification for brain tumor histology findings).

CaIntegrator is envisioned to be the foundation for a number of translational applications. One such reference implementation at NCICB is called Rembrandt (Repository of Molecular BRAin Neoplasia DaTa) – https://caintegrator.nci.nih.gov/rembrandt. This knowledge framework offers a paradigm for rapid sharing of information and accelerates the process of analyzing results from various biomedical studies with the ultimate goal to rapidly change routine patient care.

Figure 1 – Integrate gene expression data with clinical outcome	Figure 2 – Perform Principal component analysis and correlate with clinical data
Figure 3: Plot copy number data from patient DNA samples against physical genome location