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The TCGA project is producing a comprehensive genomic characterization and analysis of 200 types of cancer and providing this information to the research community. TCIA and the underlying National Biomedical Image Archive (NBIA) manage well-curated, publicly-available collections of medical image data. The linkages between TCGA and TCIA are valuable to researchers who want to study diagnostic images associated with the tissue samples sequenced by TCGA. TCIA currently supports over 40 active research groups including researchers who are exploiting these linkages.

Although TCGA and TCIA comprise a rich, complementary, multi-discipline data set, they are in an infrastructure that provides limited ability to query the data. Researchers want to query both databases at the same time to identify cases based on all available data types. While TCGA and TCIA are DICOM-compliant, digital pathology and co-clinical/small animal model environments do not share the same data standards or do not use them consistently.

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To make data comparable, it must first be collected in a structured fashion. For example, TCGA relies on Common Data Elements, which are the standard elements used to validate TCGA clinical data. Second, data comparisons require common data standards. For example, when a tumor is described in a human or an animal, a data standard would require that the type of tumor match one of a discrete number of options using approved vocabulary, such as "brain".

All three research disciplines need an imaging archive that can be leveraged for integration across multiple data types and sources. TCIA currently supports over 40 active research groups including researchers who are exploiting the existing linkages between TCGA and TCIAThe integrated query system currently in development will serve as an archive of images from multiple imaging disciplines. This requires technical solutions. TCIA has recently released an Application Program Interface (API) that provides a REST API to TCIA metadata and image collections. This API is built using a middleware platform called Bindaas,and this API is being designed to support federation of multiple information repositories using the concepts of a data mashups. This infrastructure can be expanded to include more data types and additional integration, and provide analytic and decision support, which will act as a foundation for a broader set of novel community research projects.

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