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Area of Science:

  • Bioinformatics
  • Data Management
  • Cancer Research

Background:

  • Data sharing in precision cancer medicine requires mapping between diverse classification systems.
  • The Genomics Evidence Neoplasia Information Exchange (GENIE) and Genomic Data Commons (GDC) partnership highlighted challenges in data element mapping.
  • Iterative mapping between dynamic classification systems necessitates a robust solution.

Purpose of the Study:

  • To develop a generic, robust, and shareable architecture for mapping data elements between multiple classification systems.
  • To enhance the efficiency and transparency of the data mapping process.
  • To maintain data integrity during cross-system mapping.

Main Methods:

  • Developed the Linked Entity Attribute Pair (LEAP) database framework.
  • Utilized LEAP to store and manage term mappings between GENIE and GDC.
  • Applied the framework to address data submission challenges.

Main Results:

  • Identified and remediated 195 mappings between GENIE and GDC.
  • Achieved a 28% reduction in effort for resolving mapping issues.
  • Reduced mapping time between OncoTree and ICD-O, 3rd Edition from months to under a week.

Conclusions:

  • The LEAP framework offers a streamlined and reusable solution for mapping data elements across classification systems.
  • The framework facilitates straightforward creation and adjustment of mappings.
  • LEAP's change-tracking capability enhances mapping across dynamic systems.