The GENIE Data Model: A Solid Tumor, Person-Centric Framework for Scalable, Harmonized Precision Oncology Data Collection

Adequately powered analyses in precision oncology often require combining cohorts across institutions. Yet 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, where 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, AACR Project GENIE™ (Genomics Evidence Neoplasia Information Exchange) 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 patient's journey 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 HL7 FHIR, OMOP, 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.

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Publication Details

Journal
Cancer Research Communications
Published
2026-08-27
DOI
https://doi.org/10.1158/2767-9764.crc-26-0148
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
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article

The GENIE Data Model: A Solid Tumor, Person-Centric Framework for Scalable, Harmonized Precision Oncology Data Collection

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Cancer Research Communications
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The GENIE Data Model: A Solid Tumor, Person-Centric Framework for Scalable, Harmonized Precision Oncology Data Collection

Jafi A. Lipson, Daniel P. Nussbaum, Michael Brudno, Jai N. Patel, Jocelyn Lee, Ümit Topaloĝlu, Valentina I. Petkov, Patricia A. Spears, Danielle S. Bitterman, Jeremy L. Warner, Vojtech Huser, Kelli M. Rasmussen, Anne‐Marie Meyer, Sanjay Mishra, Tomi Akinyemiju, S. Joseph Sirintrapun, Michelle Green, Sebastiaan Van Sandijk, Jennifer N. Hoppe
article en

Abstract

Adequately powered analyses in precision oncology often require combining cohorts across institutions. Yet 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, where 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, AACR Project GENIE™ (Genomics Evidence Neoplasia Information Exchange) 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 patient's journey 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 HL7 FHIR, OMOP, 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.

Cancer Research Communications
University of North Carolina at Chapel Hill (US), Brigham and Women's Hospital (US), Duke University (US), University of Toronto (CA), Brown University (US), Rhode Island Hospital (US), American Association For Cancer Research (US), European Association of Urology (NL), LabCorp (United States) (US), Levine Cancer Institute (US), Southeast Clinical Oncology Research Consortium (US), Bristol-Myers Squibb (Sweden) (SE), National Cancer Institute (MY), National Cancer Institute (US), Duke University Hospital (US), Mass General Brigham (US), Epic Systems (United States) (US), Stanford University (US)
American Association for Cancer Research, Amgen, Brown University, National Institutes of Health, Genentech
Partnerships for the goals
Openalex Percentile: Top 14%
Cancer Genomics and Diagnostics
DOIOpen Access PDFOpenAlex
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