Building Pediatric Cancer Cohorts and Accessing Data Using Childhood Cancer Data Initiative Tools.

PURPOSE: Data sharing is necessary to advance understanding of the etiology and biology of cancer in children, adolescents, and young adults; drive therapeutic discoveries; and improve treatment outcomes. To meet this critical need, the National Cancer Institute's Childhood Cancer Data Initiative (CCDI) provides innovative, user-friendly tools and resources that enable researchers and pediatric oncologists to access and analyze the large volume of diverse childhood cancer data (over 1 million files) that has been collected and harmonized from multiple studies, including CCDI's Molecular Characterization Initiative, Pediatric MATCH, Childhood Cancer Survivor Study, etc. This article outlines how to find, request, access, download, and analyze data indexed in the CCDI Hub Explore Dashboard and Childhood Cancer Clinical Data Commons (C3DC), key components of the CCDI Data Ecosystem, to accelerate progress in pediatric cancer research. METHODS: Both CCDI resources support cohort-based analysis and use data models that include study, participant, sample, diagnosis, and treatment data. These models are updated in collaboration with field experts. Additionally, CCDI drafted a Pediatric Cancer Core common data elements list, which serves as a standard reference for researchers. RESULTS: The CCDI Hub is the primary access point for finding data, tools, and applications managed by CCDI. The C3DC provides harmonized, participant-level clinical data and the CCDI Hub Explore Dashboard catalogs data at the file level. These resources enable users to search for and download manifests of harmonized, de-identified participant data and build cohorts. CONCLUSION: CCDI prioritizes data accessibility and interoperability and, with its resources and data, continues to aid in pediatric cancer research discovery, data-driven insights, and collaboration across the pediatric cancer community.

Duke Scholars

Author Warren Alden Kibbe Biostatistics & Bioinformatics, Division of Translational Bi ...