An open-source platform for pediatric cancer data exploration: a report from Data for the Common Good.

Objective: The Pediatric Cancer Data Commons (PCDC)-a project of Data for the Common Good-houses clinical pediatric oncology data and utilizes the open-source Gen3 platform. To meet the needs of end users, the PCDC development team expanded the out-of-box functionality and developed additional custom features that should be useful to any group developing similar data commons. Materials and Methods: Modifications of the PCDC data portal software were implemented to facilitate desired functionality. Results: Newly developed functionality includes updates to authorization methods, expansion of filtering capabilities, and addition of data analysis functions. Discussion: We describe the process by which custom functionalities were developed. Features are open source and available to be implemented and adapted to suit needs of data portals that utilize the Gen3 platform. Conclusion: Data portals are indispensable tools for facilitating data sharing. Open-source infrastructure facilitates a modular and collaborative approach for meeting needs of end users and stakeholders.

Automated Matching of Patients to Clinical Trials: A Patient-Centric Natural Language Processing Approach for Pediatric Leukemia.

PURPOSE: Matching patients to clinical trials is cumbersome and costly. Attempts have been made to automate the matching process; however, most have used a trial-centric approach, which focuses on a single trial. In this study, we developed a patient-centric matching tool that matches patient-specific demographic and clinical information with free-text clinical trial inclusion and exclusion criteria extracted using natural language processing to return a list of relevant clinical trials ordered by the patient's likelihood of eligibility. MATERIALS AND METHODS: Records from pediatric leukemia clinical trials were downloaded from ClinicalTrials.gov. Regular expressions were used to discretize and extract individual trial criteria. A multilabel support vector machine (SVM) was trained to classify sentence embeddings of criteria into relevant clinical categories. Labeled criteria were parsed using regular expressions to extract numbers, comparators, and relationships. In the validation phase, a patient-trial match score was generated for each trial and returned in the form of a ranked list for each patient. RESULTS: In total, 5,251 discretized criteria were extracted from 216 protocols. The most frequent criterion was previous chemotherapy/biologics (17%). The multilabel SVM demonstrated a pooled accuracy of 75%. The text processing pipeline was able to automatically extract 68% of eligibility criteria rules, as compared with 80% in a manual version of the tool. Automated matching was accomplished in approximately 4 seconds, as compared with several hours using manual derivation. CONCLUSION: To our knowledge, this project represents the first open-source attempt to generate a patient-centric clinical trial matching tool. The tool demonstrated acceptable performance when compared with a manual version, and it has potential to save time and money when matching patients to trials.

Pediatric Cancer Data Commons: Federating and Democratizing Data for Childhood Cancer Research.

The international pediatric oncology community has a long history of research collaboration. In the United States, the 2019 launch of the Children's Cancer Data Initiative puts the focus on developing a rich and robust data ecosystem for pediatric oncology. In this spirit, we present here our experience in constructing the Pediatric Cancer Data Commons (PCDC) to highlight the significance of this effort in fighting pediatric cancer and improving outcomes and to provide essential information to those creating resources in other disease areas. The University of Chicago's PCDC team has worked with the international research community since 2015 to build data commons for children's cancers. We identified six critical features of successful data commons design and implementation: (1) establish the need for a data commons, (2) develop and deploy the technical infrastructure, (3) establish and implement governance, (4) make the data commons platform easy and intuitive for researchers, (5) socialize the data commons and create working knowledge and expertise in the research community, and (6) plan for longevity and sustainability. Data commons are critical to conducting research on large patient cohorts that will ultimately lead to improved outcomes for children with cancer. There is value in connecting high-quality clinical and phenotype data to external sources of data such as genomic, proteomics, and imaging data. Next steps for the PCDC include creating an informed and invested data-sharing culture, developing sustainable methods of data collection and sharing, standardizing genetic biomarker reporting, incorporating radiologic and molecular analysis data, and building models for electronic patient consent. The methods and processes described here can be extended to any clinical area and provide a blueprint for others wishing to develop similar resources.