NCI Office of Data Sharing (ODS) Data Jamboree (Abstract Submissions): Submission #49

Presenter Information
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First Name: Diana









Middle Initial: {Empty}









Last Name: Thomas









Degree(s): MD, PhD









Position/Title/Career Status: Associate Professor of Pathology, Director of Digital Pathology 









Organization: Nationwide Children's Hospital









Organization Address:
Columbus, Ohio










Email: diana.thomas@nationwidechildrens.org









Other (Please Specify): {Empty}













Abstract Information
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Abstract Category: Building specific disease cohorts, or visualization techniques









Abstract Keywords: pediatric brain tumors, data standardization, interactive data visualization, image analysis and machine learning









Abstract Title: Enabling Discovery in Rare Pediatric Brain Tumors Through Data Integration and Visualization









Abstract Summary:
The increasing availability of robust publicly accessible childhood cancer datasets offers a significant opportunity to advance research and improve outcomes for children with rare, understudied tumors such as pediatric brain cancers. Our project ideas aim to enhance existing data and web-based platforms, including those from the CCDI Molecular Characterization Initiative, by developing tools that make complex data more accessible to the pediatric brain cancer research community. We envision a user-friendly platform featuring interactive visualization tools such as oncoprints, survival plots, and mutation heatmaps that allow researchers without coding or bioinformatics expertise to build and analyze cohorts using clinical, genomic, pathology, treatment, and follow-up data. By enabling intuitive exploration, the tool will support clinicians, pathologists, and researchers in identifying patterns and generating hypotheses, particularly for rare or newly characterized tumor types.

A key component of CNS tumor classification is DNA methylation profiling. Current datasets have been processed with varying classifier versions, resulting in inconsistent tumor class labeling. To address this, we propose re-processing raw data files (.idat) using one or more current classifiers (e.g., NCI Bethesda, IGM v1.0). This approach will harmonize classification across datasets, improve diagnostic precision and support reproducibility and cross-study comparisons, with outputs made available via dbGaP. Additionally, we aim to leverage whole slide pathology images to develop algorithms for histopathologic feature extraction and machine learning. These algorithms could enhance diagnostic capabilities, especially important given the shortage of pediatric neuropathologists nationwide. By integrating clinical, genomic, and image-based data, this project will accelerate discovery in rare tumor types and inform clinical trial design and treatment strategies in pediatric neuro-oncology. We welcome collaboration on any or all aspects of this proposed work.












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