Data commons can change how the world does research.

A collaborative data-sharing approach has the potential to change the landscape of healthcare research. As we build the PCDC and other data commons, our mission includes sharing our methods and lessons learned with the scientific community and assessing further opportunities to advance the state of the art in this field.

The work below includes publications and presentations about the development of our tools and consortia, discussions of the current landscape of data sharing, and thought leadership from D4CG team members.

Publications

Advancing Monogenic Diabetes Research and Clinical Care by Creating a Data Commons: The Precision Diabetes Consortium (PREDICT)
McCullough ME, Letourneau-Freiberg LR, Naylor RN, et al. J Diabetes Sci Technol. 2025 Jan. doi: 10.1177/19322968241310896

This paper discusses how PREDICT, following the Data for the Common Good model, has successfully established a multicenter data commons for monogenic diabetes, as well as a consensus data dictionary that will be utilized to address critical gaps in understanding of these rare types of diabetes. Read more

Rethinking Human Abstraction as the Gold Standard
Wyatt KD, Furner BT, Volchenboum SL. JCO Clin Cancer Inform. 2024 Nov. doi: 10.1200/CCI-24-00218

Highlighting the limitations of human manual data extraction and entry, this editorial explores how automated tools can improve data accuracy, speed, and cost-efficiency. Though there are challenges to implementation, a new “gold standard” has the potential to transform clinical research. Read more

Making sense of artificial intelligence and large language models—including ChatGPT—in pediatric hematology/oncology
Wyatt KD, Alexander N, Hills GD, et al. Pediatr Blood Cancer. 2024 Jun. doi: 10.1002/pbc.31143

This commentary discusses some of the capabilities, limitations, and applications of currently available AI tools and provides an evaluation and implementation framework that can be used by pediatric hematologist/oncologists considering the use of AI in clinical practice. Read more

GDPR and data sharing: the Pediatric Cancer Data Commons experience
Wyatt KD, Minard-Colin V, Schleiermacher G, Willi M, Volchenboum SL. Lancet Oncol. 2024 Jun;25(6):e227. doi: 10.1016/S1470-2045(24)00250-X

This letter shares our experience navigating the challenges of the General Data Protection Regulation (GDPR) to successfully execute international data sharing agreements for the Pediatric Cancer Data Commons. Read more

Extracting Electronic Health Record Neuroblastoma Treatment Data With High Fidelity Using the REDCap Clinical Data Interoperability Services Module
Furner B, Cheng A, Desai AV, et al. JCO Clin Cancer Inform. 2024 May. doi: 10.1200/CCI.24.00009

Learn more about this work in our Science Spotlight!

This paper explores a method for extracting detailed treatment data from electronic health records (EHRs) of children with neuroblastoma using the REDCap Clinical Data Interoperability Services (CDIS) module. The results demonstrate the feasibility of extracting EHR treatment data with high fidelity via this method, unlocking a potential pathway to enriching data commons with real-world treatment data. Read more

Big Data in Pediatric Oncology: Hope, Hype, Reality
Wyatt KD, Volchenboum SL. Adv Oncol. 2024 May;4(1):91-99. doi: 10.1016/j.yao.2024.02.005

We discuss how big data and artificial intelligence can advance pediatric cancer research, current progress and challenges, and how we can separate hype from realistic expectations as this field is transformed by new technology. Read more

Targeted Enrollment in Pediatric Oncology Trials: A Vision for Just-in-Time Matching
Wyatt KD, Volchenboum SL. JCO Oncol Prac. 2024 Feb. doi: 10.1200/OP.23.00826

Standards-powered tools and innovative methods for trials administration and deployment can transform the clinical trials landscape, resulting in improved enrollment and, ultimately, better outcomes and improved care for children with cancer. Read more

Accelerating pediatric hodgkin lymphoma research: the hodgkin lymphoma data collaboration (NODAL)
Wyatt KD, Birz S, Castellino SM, et al. J Natl Cancer Inst. 2024. doi: 10.1093/jnci/djae013

We discuss the development of the Hodgkin Lymphoma Data Collaboration (NODAL) and foundational goals to advance pediatric Hodgkin lymphoma research. Read more

Using A Standardized Nomenclature to Semantically Map Oncology-Related Concepts from Common Data Models to a Pediatric Cancer Data Model
Carlson B, Watkins M, Li M, Furner B, Cohen E, Volchenboum SL. AMIA Annu Symp Proc. 2023;2023:874-883. PMID: 38222364

We describe an effort to utilize SSSOM, an emerging specification for semantically-rich data mappings, to provide a “hub and spoke” model of mappings from several common data models (CDMs) to the PCDC data model. Read more

Sociome Data Commons: A scalable and sustainable platform for investigating the full social context and determinants of health
Tilmon S, Nyenhuis S, Solomonides A, et al. J Clin Transl Sci. 2023;7(1):e255. doi:10.1017/cts.2023.670

Non-clinical aspects of life, such as social, environmental, behavioral, psychological, and economic factors, what we call the sociome, play significant roles in shaping patient health and health outcomes. This paper introduces the Sociome Data Commons, a new research platform that enables large-scale data analysis for investigating such factors. Read more

Data in pediatric oncology: Something old, something new
Wyatt KD. Pediatr Blood Cancer. Epub Nov 2023. doi:10.1002/pbc.30769

D4CG Senior Clinical Advisor Kirk Wyatt discusses the vital role of leveraging data to improve outcomes in pediatric cancer and the investments needed from the pediatric oncology community for these efforts to succeed. Read more

Automated Matching of Patients to Clinical Trials: A Patient-Centric Natural Language Processing Approach for Pediatric Leukemia
Kaskovich S, Wyatt KD, Oliwa T, et al. JCO Clin Cancer Inform. 2023;7:e2300009. doi: 10.1200/CCI.23.00009

We discuss the development of an automated tool for processing free-text clinical trial inclusion and exclusion criteria and matching patients to relevant clinical trials. Read more

Creating a data commons: The INternational Soft Tissue SaRcoma ConsorTium (INSTRuCT)
Wyatt KD, Birz S, Hawkins DS, et al. Pediatr Blood Cancer. 2022;69(11):e29924. doi: 10.1002/pbc.29924

We discuss the genesis, evolution, and progress of INSTRuCT, including challenges and research priorities, the development of the consortium, and how INSTRuCT aims to address key research priorities. Read more

Mapping Pediatric Oncology Clinical Trial Collaborative Groups on the Global Stage
Major A, Palese M, Ermis E, et al. JCO Glob Oncol. 2022;8:e2100266. doi: 10.1200/GO.21.00266

We describe pediatric cancer clinical trial groups on the international stage, with the goal of identifying the structure and function of these consortia, as well as the clinical data sources they collect, to reveal opportunities for collaborative efforts within these regions. Read more

Pediatric Cancer Data Commons: Federating and Democratizing Data for Childhood Cancer Research
Plana A, Furner B, Palese M, et al. JCO Clin Cancer Inform. 2021;5:1034-1043. doi: 10.1200/CCI.21.00075

We present our experience constructing the Pediatric Cancer Data Commons to highlight the significance of developing a rich and robust data ecosystem for pediatric oncology and to provide essential information to those creating resources in other disease areas. Read more

Using big data in pediatric oncology: Current applications and future directions
Major A, Cox SM, Volchenboum SL. Sem Oncol. 2020;47(1):56-64. doi: 10.1053/j.seminoncol.2020.02.006

We discuss the uses of big data in pediatric cancer, existing pediatric cancer registry initiatives and research, the challenges in harmonizing data to improve accessibility for study, and the future opportunities we see for innovation in this area. Read more

Data Commons to Support Pediatric Cancer Research
Volchenboum SL, Cox SM, Heath A, Resnick A, Cohn SL, Grossman R. Am Soc Clin Oncol Educ Book. 2017;37:746–752. doi: 10.1200/EDBK_175029

We describe current data commons and how they operate in the oncology landscape, and offer a practical paradigm for developing new commons. By centralizing data, processing power, and tools, there is a valuable opportunity to share resources and thus increase the efficiency, power, and impact of research. Read more

Tailoring Therapy for Children With Neuroblastoma on the Basis of Risk Group Classification: Past, Present, and Future
Liang WH, Federico SM, London WB, et al. JCO Clin Cancer Inform. 2020;4:895-905. doi: 10.1200/CCI.20.00074

In this review, the authors discuss the history of neuroblastoma risk classification in North America and Europe and highlight efforts by the International Neuroblastoma Risk Group (INRG) Task Force to develop a consensus approach for pretreatment stratification using seven risk criteria including an image-based staging system—the INRG Staging System. Read more

Presentations

  • Carlson B, Watkins M, Furner B, Li M, Cohen E, Volchenboum S. Using A Standardized Nomenclature to Semantically Map Oncology-Related Concepts from Common Data Models to a Pediatric Cancer Data Model. Presented at American Medical Informatics Association Annual Symposium; November 2023; New Orleans, LA.

  • Wyatt KD, Birz S, Palacios R, Graglia L, Furner B, Volchenboum S. Assessment of safeguards to discourage p-hacking on the Pediatric Cancer Data Commons Data Portal. Presented at the 55th Congress of the International Society of Pediatric Oncology; October 2023; Ottawa, Canada.

  • Tilmon S, Nyenhuis S, Solomonides A, et al. Sociome Data Commons: A Scalable and Sustainable Platform for Investigating the Full Social Context and Determinants of Health. Presented at International Society of Exposure Science Annual Meeting; August 2023; Chicago, IL.

  • Furner B, Cheng A, Desai AV, et al. Building a REDCap on FHIR Tool to Abstract Neuroblastoma Data from Electronic Health Records (EHRs): A Proof-of-Concept Study. Presented at Advances in Neuroblastoma Research; May 2023; Amsterdam, Netherlands.

  • Watkins M, Furner B, Li M, et al. Harmonizing Genetic Data Element Modeling Across Cancer Trials. Presented at the 54th Congress of the International Society of Paediatric Oncology; October 2022; Barcelona, Spain.

  • Furner B, Graglia L, Sathar S, et al. Genomic Eligibility Algorithm At Relapse For Better Outcomes (GEARBOx): A decision support tool for matching children with relapsed acute myeloid leukemia to clinical trials. Presented at the 53rd Congress of the International Society of Paediatric Oncology; October 2021.

  • Graglia L, Sathar S, Palese M, Furner B, Volchenboum S. The Pediatric Cancer Data Commons: A Demonstration of a Novel Implementation and Extension of the Gen3 Infrastructure for Cohort Discovery and Data Sharing. Presented at AMIA 2021 Virtual Informatics Summit; March 2021.

  • Volchenboum S, Cohn S, Furner B, et al. INRG visualization and analytics platform. Presented at Advances in Neuroblastoma Research; January 2021.

  • Plana A, Palese M, Furner B, et al. The Pediatric Cancer Data Commons: A centralized system for aggregating and sharing pediatric cancer data. Presented at the 52nd Congress of the International Society of Pediatric Oncology; October 2020.

  • Plana A, Furner B, Palese M, Kolb EA, Nichols G, Volchenboum S. Building International Pediatric Cancer Data Commons: The Pediatric Acute Leukemia (PedAL) Initiative. Presented at the 51st Congress of the International Society of Pediatric Oncology; October 2019; Lyon, France.

  • Furner B, Oliwa T, Graglia L, et al. Linking clinical trials data with images via the Pediatric Cancer Data Commons and the National Biomedical Imaging Archive (NBIA). Presented at Childhood Cancer Data Initiative Symposium; July 2019; Washington, DC.

  • Furner B, Plana A, Palese M, Nichols G, Kolb EA, Volchenboum S. The Pediatric Acute Leukemia (PedAL) Initiative – an innovative platform for real-time matching of children with relapsed AML to early-phase clinical trials. Presented at Childhood Cancer Data Initiative Symposium; July 2019; Washington, DC.

  • Plana A, Furner B, Birz S, Palese M, Volchenboum S. A Synthesized Common Data Model and Data Standards for the University of Chicago’s Pediatric Cancer Data Commons. Presented at Childhood Cancer Data Initiative Symposium; July 2019; Washington, DC.

  • Plana A, Furner B, Palese M, Birz S, Hawkins DS, Volchenboum S. Rapid consensus building and development of the International Soft Tissue Sarcoma Consortium (INSTRuCT) data commons. Presented at Childhood Cancer Data Initiative Symposium; July 2019; Washington, DC.

View publications from our disease-specific consortia here.

Transforming the Way Researchers Share Data
Presented by Sam Volchenboum, MD, PhD
April 2022