Abstract 2704: NCI’s childhood cancer data initiative: Advancing pediatric cancer research.

Abstract The Childhood Cancer Data Initiative (CCDI) is a transformative effort to accelerate research and improve outcomes for children, adolescents, and young adults (AYAs) with cancer. Data are the cornerstone of cancer research, and CCDI is building a FAIR-aligned data ecosystem that functions as a multi-stakeholder interoperability platform, integrating diverse tools and services to advance pediatric cancer research. All CCDI tools and resources are accessible through the CCDI Hub, the central entry point to the ecosystem. The foundation begins with the CCDI data model, which establishes harmonized data standards to ensure consistency and interoperability across data sets, offering a framework for discovery and analysis. Building Cohorts: The CCDI Childhood Cancer Data Catalog serves as a comprehensive inventory of pediatric and AYA cancer data resources, including registries, repositories, knowledgebases, and catalogs. It provides study-level summaries and helps users identify where specific data types and studies are located. Once users identify studies of interest, they can move to the CCDI Hub Explore Dashboard to search CCDI data sets. The dashboard indexes data at the file level to facilitate cohort building. Users can build cohorts within the Childhood Cancer Data Commons (C3DC) using clinical data, or within the National Childhood Cancer Registry (NCCR) using real-world data. Cohorts built in C3DC can be exported as manifests containing participant identifiers, which can then be uploaded to the CCDI Hub to locate associated genomic, imaging, or other data. Similarly, users can export cohorts to NCCR to access related real-world data. For cross-institutional discovery, users can query pediatric data sets across participating repositories using the CCDI Federation API, as if querying a single virtual database.All of these resources are interconnected through the CCDI Participant Index, which maps participant identifiers across studies and repositories and allows researchers to connect data from the same participant, supporting data integration and cohort expansion. Analysis and Visualization: After cohorts are built, researchers can leverage integrated analysis and visualization platforms to further explore, interpret, and derive insights from the data. The Cancer Genomics Cloud provides a cloud-based computational environment where researchers can analyze data using custom workflows and advanced analytical tools. To make data accessible to clinicians and non-bioinformaticians, CCDI offers intuitive visualization platforms like the CCDI cBioPortal Cancer Data Explorer. Together, CCDI’s many tools and resources, integrating data from thousands of participants, enable new insights that would not be possible when studied in isolation. Citation Format: Subhashini Jagu, Jaime Guidry Auvil, Mark Cunningham, Bahar Sayoldin, Patrick Dunn, Sean Burke, Catherine Bullen, Hayley Dingerdissen, Rebecca Steck, Martin Ferguson, Johanna Lucinda Goderre, Gregory Reaman, Warren A. Kibbe, . NCI’s childhood cancer data initiative: Advancing pediatric cancer research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2704.

Authors

Institutions

Publication Details

Journal
Cancer Research
Published
2026-04-03
DOI
https://doi.org/10.1158/1538-7445.am2026-2704
Primary Topic
Acute Lymphoblastic Leukemia research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract 2704: NCI’s childhood cancer data initiative: Advancing pediatric cancer research.

Jaime Guidry Auvil, Gregory Reaman, Catherine Bullen, Warren A. Kibbe et al.
Cancer Research
Acute Lymphoblastic Leukemia research
article

Abstract 2704: NCI’s childhood cancer data initiative: Advancing pediatric cancer research.

Jaime Guidry Auvil, Gregory Reaman, Catherine Bullen, Warren A. Kibbe, Bahar Sayoldin, Martin L. Ferguson, Subhashini Jagu, Hayley Dingerdissen, Mark W. Cunningham, Rebecca Steck, Sean Burke, Patrick Dunn, Johanna Lucinda Goderre
article en

Abstract

Abstract The Childhood Cancer Data Initiative (CCDI) is a transformative effort to accelerate research and improve outcomes for children, adolescents, and young adults (AYAs) with cancer. Data are the cornerstone of cancer research, and CCDI is building a FAIR-aligned data ecosystem that functions as a multi-stakeholder interoperability platform, integrating diverse tools and services to advance pediatric cancer research. All CCDI tools and resources are accessible through the CCDI Hub, the central entry point to the ecosystem. The foundation begins with the CCDI data model, which establishes harmonized data standards to ensure consistency and interoperability across data sets, offering a framework for discovery and analysis. Building Cohorts: The CCDI Childhood Cancer Data Catalog serves as a comprehensive inventory of pediatric and AYA cancer data resources, including registries, repositories, knowledgebases, and catalogs. It provides study-level summaries and helps users identify where specific data types and studies are located. Once users identify studies of interest, they can move to the CCDI Hub Explore Dashboard to search CCDI data sets. The dashboard indexes data at the file level to facilitate cohort building. Users can build cohorts within the Childhood Cancer Data Commons (C3DC) using clinical data, or within the National Childhood Cancer Registry (NCCR) using real-world data. Cohorts built in C3DC can be exported as manifests containing participant identifiers, which can then be uploaded to the CCDI Hub to locate associated genomic, imaging, or other data. Similarly, users can export cohorts to NCCR to access related real-world data. For cross-institutional discovery, users can query pediatric data sets across participating repositories using the CCDI Federation API, as if querying a single virtual database.All of these resources are interconnected through the CCDI Participant Index, which maps participant identifiers across studies and repositories and allows researchers to connect data from the same participant, supporting data integration and cohort expansion. Analysis and Visualization: After cohorts are built, researchers can leverage integrated analysis and visualization platforms to further explore, interpret, and derive insights from the data. The Cancer Genomics Cloud provides a cloud-based computational environment where researchers can analyze data using custom workflows and advanced analytical tools. To make data accessible to clinicians and non-bioinformaticians, CCDI offers intuitive visualization platforms like the CCDI cBioPortal Cancer Data Explorer. Together, CCDI’s many tools and resources, integrating data from thousands of participants, enable new insights that would not be possible when studied in isolation. Citation Format: Subhashini Jagu, Jaime Guidry Auvil, Mark Cunningham, Bahar Sayoldin, Patrick Dunn, Sean Burke, Catherine Bullen, Hayley Dingerdissen, Rebecca Steck, Martin Ferguson, Johanna Lucinda Goderre, Gregory Reaman, Warren A. Kibbe, . NCI’s childhood cancer data initiative: Advancing pediatric cancer research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2704.

Cancer ResearchVol. 86(7_Supplement)
National Cancer Institute (US)
Openalex Percentile: Top 28%
Acute Lymphoblastic Leukemia research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.