Abstract Recent advances in multi-omics profiling have accelerated the discovery of molecular targets in pediatric cancers. However, clinical interpretation remains constrained by evolving diagnostic standards and limited representation of rare subtypes. To address this, we developed the Cancer Classifications for Kids (CC4K) - a harmonized, molecular classification-driven framework aligned with WHO tumor classification and recent publication standards for pediatric tumors. Using pathogenic variant data hosted on St. Jude Cloud PeCan Knowledge Base (https://pecan.stjude.cloud), we classified 230 subtypes for hematological malignancies (n=70), solid tumors (n=97), and brain tumors (n=63). Most recently, the pathogenic point mutations, CNVs, and gene fusions from ∼1,511 paired tumor-normal samples, profiled by the ongoing NCI’s Childhood Cancer Data Initiative (CCDI), were integrated into PeCan, extending the subtype repertoire by ∼53% (80 new subtypes). Importantly, classification of additional subtypes required aligning molecular data with clinical features, which revealed 16 evidence categories, including “biomarker-confirmed” (n=440) and “rescued” (n=353). Furthermore, the integration of additional multi-modal approaches provided clarity on existing classifications with ambiguous or conflicting data. For example, integrating the data from the Molecular Characterization Initiative (MCI) improved our definition of several previously ambiguous cases including a small round blue cell tumor redefined as Ewing sarcoma following identification of a novel EWSR1::FUS reciprocal fusion event, reclassification of an ependymoma as intracranial mesenchymal tumor, FET::CREB-fusion positive, and validation of an atypical NRAS-positive alveolar rhabdomyosarcoma. Additionally, the integration of CCDI data fine-tuned our knowledgebase on the therapy-relevant molecular drivers such as activation of the Hedgehog signaling pathway in embryonal rhabdomyosarcoma and activation of the PI-3K pathway by recurrent AKT hotspot mutations in multiple cancer types. Distribution of tumor mutation burdens from each cancer subtype revealed hypermutators with distinct etiologies as identified through subsequent mutational signature analyses. Collectively, these results demonstrate the importance of a harmonized framework for systematic cross-cohort integration that advances diagnostic precision through molecular-pathologic consensus, helps unravel the complex landscape of rare pediatric tumor subtypes, and lays the groundwork for future therapeutic and classification refinements across the pediatric oncology ecosystem. Citation Format: Stephanie Sandor, Delaram Rahbarinia, Yuan Feng, Ramzi Alsallaq, Van L. Nguyen, Daniel K. Putnam, David Finkelstein, Jinman Park, Bo Wang, Jobin Sunny, Jian Wang, Sue Qiu, Michael Edmonson, Robert Greenhalgh, Meghann Kirk, Ira Baranova, Stephen Rice, Abbas Shirinifard, Hoaran Chen, Ali F. Pour, Clay McLeod, Lu Wang, Jeffery Klco, Brent Orr, Michael Dyer, Xiang Chen, Xiaotu Ma, Michael Rusch, Jinghui Zhang. Advancing pediatric tumor subtype classification on the pediatric cancer (PeCan) knowledge base by integrating molecular and morphology data abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 3488.
Sandor et al. (Fri,) studied this question.