Warren Alden Kibbe

Despite vast investments in data collection, generation, and sharing from childhood cancer studies, for example, Gabriella Miller Kids First Research Program, Childhood Cancer Data Initiative, and other efforts, secondary use of data remains a challenge especially for rare diseases. The National Cancer Institute (NCI) Office of Data Sharing (ODS) aimed to promote FAIR (Findable, Accessible, Interoperable, Reusable) data sharing practices to enhance data utility, accelerate discovery, and foster interdisciplinary collaboration. NCI ODS launched its inaugural Data Jamboree focusing on childhood cancer alongside its third Annual Symposium in September 2025. Over 120 participants with diverse backgrounds coalesced into 23 multidisciplinary teams to solve real-world problems in a collaborative setting. The Jamboree built a diverse pool of data users and catalyzed pediatric cancer research, with several project findings being prepared for publication. Moreover, key feedback and recommendations for areas of improvement were provided to NCI in real time. The event highlighted the power of team science, data sharing, use, and reuse to accelerate childhood cancer research and improve diagnostic and therapeutic outcomes.

PURPOSE: To facilitate integrated multimodal data analysis, it is critical to connect data from multiple sources to address multifaceted research questions, better understand disease biology and natural history, develop new therapies, and improve existing treatments. The Childhood Cancer Data Initiative (CCDI) Participant Index, an application programming interface, aims to address this challenge by providing a digital ID mapping and matching service which collects and cross-references all known IDs associated with a participant. METHODS: A variety of retrospective and prospective data collected through the CCDI Data Ecosystem equal or surpass the complexity of patient data systems in large health care organizations. The CCDI Data Ecosystem includes participant data collected under multiple protocols and at multiple sites, which often results in the same participant being associated with multiple IDs depending on the source, time, or other variables. CCDI is exploring ways to integrate diverse data types (such as genomic, proteomic, imaging, transcriptomic, clinical trial, and electronic health record data), collected over time and from different sources at the participant level, while ensuring privacy protection. RESULTS: This mapping allows researchers to access a more complete picture of a participant, even when data are collected at different time points, organizations, protocols, and consents. CONCLUSION: This facilitates the creation of a connected data ecosystem and promotes data reuse, which, in turn, can accelerate research and improve participant outcomes.

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.

The Molecular Characterization Initiative (MCI), a key effort of the National Cancer Institute’s Childhood Cancer Data Initiative (CCDI), was launched in 2022 in collaboration with the Children’s Oncology Group (COG) to bring comprehensive genomic and molecular profiling to children, adolescents, and young adults diagnosed with cancer. The MCI provides paired tumor and germline molecular testing, with results returned to clinicians to inform care. Deidentified data are made available to the research community through the CCDI Data Ecosystem to facilitate the discovery of new treatment strategies. This commentary outlines the MCI’s development, key accomplishments to date, and its role in laying the foundation for standardized clinical diagnostics in pediatric oncology.

Objectives: In response to requests from several Rapid Acceleration of Diagnostics-Underserved Populations (RADx-UP) community-engaged research projects to include Long COVID common data elements (CDEs) in the existing RADx-UP CDEs, the RADx-UP Coordination and Data Collection Center (CDCC) leadership formed the Long COVID CDEs Task Force. Materials and Methods: The Task Force, composed mainly of community partners and RADx-UP project members, participated in various activities to evaluate the Long COVID CDEs fit for purpose from the Researching COVID to Enhance Recovery (RECOVER) program for RADx-UP use. Results and Discussion: The Task Force’s efforts led to a compilation of lessons learned and the creation of a novel set of 28 CDEs that are appropriate for community-engaged research in Long COVID. Conclusion: Utilization of standardized CDEs does not always work for the communities involved in the research, but creation of a community-involved task force can lead to a meaningful, rich set of CDEs.

Objectives: We propose and validate a domain knowledge-driven classification model for diagnosing post-acute sequelae of SARS-CoV-2 infection (PASC), also known as Long COVID, using Electronic Health Records (EHRs) data. Materials and Methods: We developed a robust model that incorporates features strongly indicative of PASC or associated with the severity of COVID-19 symptoms as identified in our literature review. The XGBoost tree-based architecture was chosen for its ability to handle class-imbalanced data and its potential for high interpretability. Using the training data provided by the Long COVID Computation Challenge (L3C), which was a sample of the National COVID Cohort Collaborative (N3C), our models were fine-tuned and calibrated to optimize Area Under the Receiver Operating characteristic curve (AUROC) and the F1 score, following best practices for the class-imbalanced N3C data. Results: Our age-stratified classification model demonstrated strong performance with an average 5-fold cross-validated AUROC of 0.844 and F1 score of 0.539 across the young adult, mid-aged, and older-aged populations in the training data. In an independent testing dataset, which was made available after the challenge was over, we achieved an overall AUROC score of 0.814 and F1 score of 0.545. Discussion: The results demonstrated the utility of knowledge-driven feature engineering in a sparse EHR data and demographic stratification in model development to diagnose a complex and heterogeneously presenting condition like PASC. The model’s architecture, mirroring natural clinician decision-making processes, contributed to its robustness and interpretability, which are crucial for clinical translatability. Further, the model’s generalizability was evaluated over a new cross-sectional data as provided in the later stages of the L3C challenge. Conclusion: The study proposed and validated the effectiveness of age-stratified, tree-based classification models to diagnose PASC. Our approach highlights the potential of machine learning in addressing the diagnostic challenges posed by the heterogeneity of Long-COVID symptoms.

In August 2022, the Cancer Informatics for Cancer Centers brought together cancer informatics leaders for its biannual symposium, Precision Medicine Applications in Radiation Oncology, co-chaired by Quynh-Thu Le, MD (Stanford University), and Walter J. Curran, MD (GenesisCare). Over the course of 3 days, presenters discussed a range of topics relevant to radiation oncology and the cancer informatics community more broadly, including biomarker development, decision support algorithms, novel imaging tools, theranostics, and artificial intelligence (AI) for the radiotherapy workflow. Since the symposium, there has been an impressive shift in the promise and potential for integration of AI in clinical care, accelerated in large part by major advances in generative AI. AI is now poised more than ever to revolutionize cancer care. Radiation oncology is a field that uses and generates a large amount of digital data and is therefore likely to be one of the first fields to be transformed by AI. As experts in the collection, management, and analysis of these data, the informatics community will take a leading role in ensuring that radiation oncology is prepared to take full advantage of these technological advances. In this report, we provide highlights from the symposium, which took place in Santa Barbara, California, from August 29 to 31, 2022. We discuss lessons learned from the symposium for data acquisition, management, representation, and sharing, and put these themes into context to prepare radiation oncology for the successful and safe integration of AI and informatics technologies.

Data-driven basic, translational, and clinical research has resulted in improved outcomes for children, adolescents, and young adults (AYAs) with pediatric cancers. However, challenges in sharing data between institutions, particularly in research, prevent addressing substantial unmet needs in children and AYA patients diagnosed with certain pediatric cancers. Systematically collecting and sharing data from every child and AYA can enable greater understanding of pediatric cancers, improve survivorship, and accelerate development of new and more effective therapies. To accomplish this goal, the Childhood Cancer Data Initiative (CCDI) was launched in 2019 at the National Cancer Institute. CCDI is a collaborative community endeavor supported by a 10-year, $50-million (in US dollars) annual federal investment. CCDI aims to learn from every patient diagnosed with a pediatric cancer by designing and building a data ecosystem that facilitates data collection, sharing, and analysis for researchers, clinicians, and patients across the cancer community. For example, CCDI's Molecular Characterization Initiative provides comprehensive clinical molecular characterization for children and AYAs with newly diagnosed cancers. Through these efforts, the CCDI strives to provide clinical benefit to patients and improvements in diagnosis and care through data-focused research support and to build expandable, sustainable data resources and workflows to advance research well past the planned 10 years of the initiative. Importantly, if CCDI demonstrates the success of this model for pediatric cancers, similar approaches can be applied to adults, transforming both clinical research and treatment to improve outcomes for all patients with cancer.

PURPOSE: Using real-world data (RWD)–based trial simulation approach, we aim to simulate colorectal cancer (CRC) trials and examine both effectiveness and safety end points in different simulation scenarios. METHODS: We identified five phase III trials comparing new treatment regimens with an US Food and Drug Administration–approved first-line treatment in patients with metastatic CRC (ie, fluorouracil, leucovorin, and irinotecan) as the standard-of-care (SOC) control arm. Using Electronic Health Record–derived data from the OneFlorida network, we defined the study populations and outcome measures using the protocols from the original trials. Our design scenarios were (1) simulation of the SOC fluorouracil, leucovorin, and irinotecan arm and (2) comparative effectiveness research (CER) simulation of the control and experimental arms. For each scenario, we adjusted for random assignment, sampling, and dropout. We used overall survival (OS) and severe adverse events (SAEs) to measure effectiveness and safety. RESULTS: We conducted CER simulations for two trials, and SOC simulations for three trials. The effect sizes of our simulated trials were stable across all simulation runs. Compared with the original trials, we observed longer OS and higher mean number of SAEs in both CER and SOC simulation. In the two CER simulations, hazard ratios associated with death from simulations were similar to that reported in the original trials. Consistent with the original trials, we found higher risk ratios of SAEs in the experiment arm, suggesting potentially higher toxicities from the new treatment regimen. We also observed similar SAE rates across all simulations compared with the original trials. CONCLUSION: In this study, we simulated five CRC trials, and tested two simulation scenarios with several different configurations demonstrated that our simulations can robustly generate effectiveness and safety outcomes comparable with the original trials using real-world data.