Implementing a data infrastructure for precision oncology projects leveraging REDCap

ObjectivesTo facilitate clinical implementation and research in precision oncology, notably the pairing of patients, variants and treatments to identify candidates for clinical trials, we have built a data infrastructure to 1) capture and store data, 2) reduce manual tasks for clinical and genomic data collection and management, 3) combine data for quality controls, reporting and findability. InfrastructureThe infrastructure uses REDCap repositories to capture and store data. The structure of these repositories is customized for each project. Additionally, a cross-project web platform was developed using software development best practices and state-of-the-art web technologies to circumvent REDCaps limitations and integrate other third-party resources. Using REDCaps application programming interfaces, this platform allowed validation of data across multiple repositories, easy import of data from external sources, generation of overviews of included patients and available data, combination of genomic and clinical data to generate tumour board reports and the findability of data. Its design was driven by data stewardship best practices. UsageAcross four precision medicine projects, the infrastructure has been used to collect data for 1921 patients, including 453 genomic data files. The custom-built web platform made it possible to import, validate, and present data in a comprehensive manner. This included building tumour board reports for clinicians, combining clinical and genomic data, and search functionalities for researchers. DiscussionREDCap allowed us to capitalize on the numerous data capture and management features developed in this solution. Designing a cross-project platform guarantees long-term relevance where developments can be mutualised across projects and allowed us to make the overall solution more compliant with the FAIR (Findable, Accessible, Interoperable, Reusable) data principles. Further developments should be considered, notably automatic retrieval of data from electronic health records to limit the number of manual tasks. ConclusionThe proposed infrastructure allowed our precision oncology projects to gain efficiency in data collection and increase data quality by reducing manual work, and it gave a straightforward and customized access to data for researchers and clinicians.