Objective: As clinical trials evolve in complexity, clinical trial data models that can capture relevant trial data in meaningful, structured annotations and computable forms are needed to support accrual.
Material and methods: We have developed a clinical trial information model, curation information system, and a standard operating procedure for consistent and accurate annotation of cancer clinical trials. Clinical trial documents are pulled into the curation system from publicly available sources. Using a web-based interface, a curator creates structured assertions related to disease-biomarker eligibility criteria, therapeutic context, and treatment cohorts by leveraging our data model features. These structured assertions are published on the My Cancer Genome (MCG) website.
Results: To date, over 5000 oncology trials have been manually curated. All trial assertion data are available for public view on the MCG website. Querying our structured knowledge base, we performed a landscape analysis to assess the top diseases, biomarker alterations, and drugs featured across all cancer trials.
Discussion: Beyond curating commonly captured elements, such as disease and biomarker eligibility criteria, we have expanded our model to support the curation of trial interventions and therapeutic context (ie, neoadjuvant, metastatic, etc.), and the respective biomarker-disease treatment cohorts. To the best of our knowledge, this is the first effort to capture these fields in a structured format.
Conclusion: This paper makes a significant contribution to the field of biomedical informatics and knowledge dissemination for precision oncology via the MCG website.
Key words: knowledge representation, My Cancer Genome, precision oncology, knowledge curation, cancer informatics, clinical trial data model.
© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association.