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Area of Science:

  • Biomedical Informatics
  • Genomic Medicine
  • Health Data Standards

Background:

  • Precision oncology relies on standardized common data models (CDMs) for data analysis and clinical decision-making.
  • Molecular Tumor Boards (MTBs) are crucial for processing clinical-genomic data to match patients with targeted therapies.

Purpose of the Study:

  • To develop a precision oncology core data model (Precision-DM) for capturing key clinical-genomic data elements.
  • To use the Johns Hopkins University MTB as a use case for developing and validating the Precision-DM.

Main Methods:

  • Leveraged existing CDMs, building upon the Minimal Common Oncology Data Elements (mCODE) model.
  • Defined Precision-DM as a set of profiles focusing on next-generation sequencing and variant annotations, mapped to terminologies and Fast Healthcare Interoperability Resources (FHIR).
  • Compared Precision-DM with existing CDMs: NCI GDC, mCODE, OSIRIS, cGDM, and gCDM.

Main Results:

  • Precision-DM includes 16 profiles and 355 data elements, with 61% mapped to FHIR.
  • Significant overlap (50.7%) with mCODE, but expanded profiles for genomic annotations.
  • Limited overlap with other CDMs (OSIRIS, NCI GDC, cGDM, gCDM), indicating unique contributions.

Conclusions:

  • Precision-DM facilitates clinical-genomic data standardization for the MTB use case.
  • Potential for harmonized data retrieval across diverse healthcare settings.
  • Supports advancement of precision medicine through standardized data representation.