NCI Data Jamboree (Project Abstract Submission): Submission #27

Abstract Immune checkpoint inhibitors (ICIs) have transformed cancer treatment but can also cause serious immune-related adverse events (irAEs) affecting multiple organ systems. Clinical, laboratory, imaging, pathology, treatment, and outcome data relevant to toxicity risk are routinely collected in clinical care. However, these data are often distributed across multiple systems and represented inconsistently in unstructured data formats. This limits interoperability, data reuse, reproducibility, and the development of future predictive and AI-enabled approaches for toxicity assessment.
This three-day Jamboree project will develop and demonstrate a minimum interoperable data model for immunotherapy toxicity research using ICI-associated myocarditis as a high-information multimodal use case. The objective is not to build a predictive model during the Jamboree, but rather to identify and organize the core data elements needed to support future toxicity-risk prediction, data harmonization, and integration across datasets. The framework is intended for immunotherapy-exposed cancer populations, including patients with and without toxicity.
Expert-adjudicated myocarditis cases will serve as a reference phenotype for examining how baseline risk factors, immune context, treatment exposures, early warning signals, diagnostic testing, attribution assessments, uncertainty, and clinical outcomes can be represented in a computable and reusable framework. ICI-associated myocarditis provides a particularly informative test case because diagnosis often requires integration of multiple forms of evidence, including treatment timing, biomarker changes, multimodality imaging findings including electrocardiograms, echocardiograms, and magnetic resonance imaging, endomyocardial-biopsy histopathology interpretation, overlap syndromes such as myositis or myasthenia, and outcomes including both major adverse cardiovascular events and cancer response.
Project outputs will include a FAIR data dictionary, a minimum data model, standards mappings, SHACL validation rules, synthetic demonstration cases, competency queries, reusable notebooks or workbooks, and documentation of provenance and uncertainty considerations. The project directly supports Jamboree goals related to interoperability, data harmonization, data quality, AI-readiness, and multidisciplinary collaboration across oncology, immunology, pathology, radiology, ontology engineering, and biomedical informatics.