Precision Oncology Core Data Model to Support Clinical Genomics Decision Making

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.