The authors report on the development of the Cancer Tissue Information Extraction System (caTIES)--an application that supports collaborative tissue banking and text mining by leveraging existing natural language processing methods and algorithms, grid communication and security frameworks, and query visualization methods. The system fills an important need for text-derived clinical data in translational research such as tissue-banking and clinical trials. The design of caTIES addresses three critical issues for informatics support of translational research: (1) federation of research data sources derived from clinical systems; (2) expressive graphical interfaces for concept-based text mining; and (3) regulatory and security model for supporting multi-center collaborative research. Implementation of the system at several Cancer Centers across the country is creating a potential network of caTIES repositories that could provide millions of de-identified clinical reports to users. The system provides an end-to-end application of medical natural language processing to support multi-institutional translational research programs.

The Cancer Biomedical Informatics Grid (caBIG^TM) is a new project initiated by the National Cancer Institute to create a computational network connecting scientists and institutions to enable the sharing of data and the use of common analytical tools. The emergence of genomics and proteomics high-throughput technologies are creating a paradigm shift in biomedical research from small independent labs to large teams of researchers exploring entire genomes and proteomes and how they relate to disease. caBIG^TMis developing new software and modifying existing software within Clinical Trials Management Systems, Tissue Banks and Pathology Tools and Integrated Cancer Research tools to manage the huge volume of data being generated and to facilitate collaboration across the broad spectrum of cancer research.