Childhood Cancer Data Initiative Annual Symposium (Abstract Registration): Submission #72

Abstract: The Childhood Cancer Data Initiative (CCDI) strives to accelerate pediatric cancer research by uniting clinical records with emerging spatial omics data but lacks a unified framework for joint analysis. Although CCDI and public repositories provide rich patient records and high-resolution spatial profiles, no unified data standards and framework exist to extract, harmonize, and process these multimodal datasets for interpretable discovery.

We developed the GAIPO (Graph Artificial Intelligence for Pediatric Oncology) clinicospatial data standard and platform, enabling graph artificial intelligence (AI) on the CCDI ecosystem. The GAIPO clinicospatial standard unifies spatial assay formats, spatial resolutions, and registration of spatial data across modalities. To promote interoperability and reproducibility, we define a spatial data model that encompasses data standards, formats, and interfaces to integrate imaging and omics data through spatial graph representation, thereby enabling the implementation of explainable graph AI models. The GAIPO platform provides: flexible interfaces to CCDI APIs and public portals for retrieval of clinical metadata, spatial omics data, and associated histopathology images; a preprocessing pipeline for omics and imaging data; a graph AI module that integrates multiple modalities for downstream analysis; and an interpretation module to identify important features and cells and reveal critical enrichment pathways and cell-cell interactions. We demonstrated GAIPO’s functionalities by using xSiGra as the explainable graph AI model, graph Grad-CAM as the interpretability module, ScPCA as the CCDI-participating resource, and Wilms tumor as the pediatric cancer.

The GAIPO standards and platform facilitate graph AI on clinicospatial data in precision oncology for pediatric cancers.