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

Rebecca S Jacobson1, Michael J Becich2, Roni J Bollag3

  • 1University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania. rebeccaj@pitt.edu.

Cancer Research
|December 17, 2015
PubMed
Summary

A new federated network called the Text Information Extraction System (TIES) Cancer Research Network enables secure sharing of de-identified cancer data and biospecimens. This infrastructure supports personalized medicine and translational research, especially for rare cancer studies.

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Area of Science:

  • Oncology
  • Bioinformatics
  • Translational Research

Background:

  • Advancing cancer research and personalized medicine necessitates improved infrastructure for linking human tissue data with clinical outcomes.
  • Existing systems often lack robust mechanisms for comprehensive data and biospecimen sharing among institutions.

Purpose of the Study:

  • To establish a federated network (TIES Cancer Research Network) facilitating secure data and biospecimen sharing among four cancer centers.
  • To create a centralized repository of rich phenotype data linked to clinical biospecimens using natural language processing.

Main Methods:

  • Formation of a federated network connecting four cancer centers.
  • Implementation of the Text Information Extraction System (TIES) for de-identification and processing of pathology data.
  • Integration of security and privacy best practices, legal agreements, and network policies for regulatory compliance.

Main Results:

  • The TIES Cancer Research Network provides integrated access to de-identified pathology data and biospecimens for investigators.
  • Multiple investigator-driven pilot projects are currently active within the network.
  • Federated search capabilities demonstrate potential for translational research, particularly for rare cancers and phenotypes.

Conclusions:

  • The TIES Cancer Research Network offers a model for a national data and biospecimen network.
  • The network successfully integrates rich phenotype information with local data control and credentialing.
  • This infrastructure enhances the potential for diverse research objectives and accelerates translational studies.