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Review
. 2015 Dec 15;75(24):5194-201.
doi: 10.1158/0008-5472.CAN-15-1973.

A Federated Network for Translational Cancer Research Using Clinical Data and Biospecimens

Affiliations
Review

A Federated Network for Translational Cancer Research Using Clinical Data and Biospecimens

Rebecca S Jacobson et al. Cancer Res. .

Abstract

Advances in cancer research and personalized medicine will require significant new bridging infrastructures, including more robust biorepositories that link human tissue to clinical phenotypes and outcomes. In order to meet that challenge, four cancer centers formed the Text Information Extraction System (TIES) Cancer Research Network, a federated network that facilitates data and biospecimen sharing among member institutions. Member sites can access pathology data that are de-identified and processed with the TIES natural language processing system, which creates a repository of rich phenotype data linked to clinical biospecimens. TIES incorporates multiple security and privacy best practices that, combined with legal agreements, network policies, and procedures, enable regulatory compliance. The TIES Cancer Research Network now provides integrated access to investigators at all member institutions, where multiple investigator-driven pilot projects are underway. Examples of federated search across the network illustrate the potential impact on translational research, particularly for studies involving rare cancers, rare phenotypes, and specific biologic behaviors. The network satisfies several key desiderata including local control of data and credentialing, inclusion of rich phenotype information, and applicability to diverse research objectives. The TIES Cancer Research Network presents a model for a national data and biospecimen network.

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Conflict of interest statement

The authors disclose no potential conflicts of interest.

Figures

Figure 1
Figure 1
Architecture of the TIES Cancer Research Network.
Figure 2
Figure 2
Researcher portal showing query for cases of Adenoid Cystic Carcinoma (2A) with aggregate case counts by TCRN site and gender (2B) and a specific de-identified, annotated document (2C).

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