The GENIE Data Model: A Solid Tumor, Person-Centric Framework for Scalable, Harmonized Precision Oncology Data Collection
Year: 2026
Type: article
Abstract: Adequately powered analyses in precision oncology often require combining cohorts across institutions. However, integration is constrained by the least granular source and may become infeasible when data elements are too heterogeneous to harmonize and map to a common data model. This challenge is acute in multi-institutional precision oncology research, in which real-world evidence requires harmonized clinico-omic data integration. Existing models often lack sufficient treatment patterns, outcomes, and genomic data, limiting interoperability and scalability. To address these gaps, American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange (GENIE) developed the GENIE Data Model (GDM), a comprehensive, open-source oncology data model for scalable, consistent, and interoperable data collection across solid tumors, designed to effectively capture the journey of patients with cancer. Through iterative consensus-building, four working groups comprising 13 subject matter experts defined data elements across multiple clinical domains: patient characteristics, imaging, diagnosis, surgery, histopathology, biomarkers, systemic therapy, radiation, clinical trial history, disease response and outcomes, and social determinants of health. Elements were defined using standardized terminologies and permissible values to support mapping to Health Level 7 Fast Healthcare Interoperability Resources, Observational Medical Outcomes Partnership, and other existing oncology standards. The model architecture distinguishes manually abstracted elements from computationally collected elements, enabling parallel workflows. The GDM provides an extensible framework that addresses critical gaps and enables scalable, harmonized data collection essential for precision oncology and real-world evidence generation. SIGNIFICANCE: The GDM is an open-source, ready-to-implement, solid tumor framework that standardizes clinical and genomic data capture across institutions to effectively capture the journey of patients with cancer. It enables interoperable mapping to existing standards, accelerates multisite study start-up, and supports scalable real-world evidence generation in precision oncology.
Source: Cancer Research Communications
Authors: Jennifer N. Hoppe, Jocelyn Lee, Tomi F. Akinyemiju, Danielle S. Bitterman, Michael Brudno, Michelle F. Green, Vojtech Huser, Jafi A. Lipson, Sanjay Mishra, Daniel P. Nussbaum, Jai N. Patel, Valentina I. Petkov, Kelli M. Rasmussen, Sahussapont Joseph Sirintrapun, Patricia A. Spears, Sebastiaan Van Sandijk, Anne-Marie Meyer, Umit Topaloglu, Jeremy L. Warner
Institutions: American Association For Cancer Research, Bristol-Myers Squibb (United States), Duke University, Duke Medical Center, Duke Cancer Institute, AstraZeneca (United States), University of Toronto, Sickkids Research Institute, LabCorp (United States), Stanford Medicine, Stanford University, Brown University, Rhode Island Hospital, Atrium Health Wake Forest Baptist, Levine Cancer Institute, Wake Forest Baptist Comprehensive Cancer Center, Wake Forest University, National Cancer Institute, Mass General Brigham, University of North Carolina at Chapel Hill, UNC Lineberger Comprehensive Cancer Center
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,550
OpenAlex ID: W7204453988
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