Unveiling Transcriptional Heterogeneity and Discovering Footprints in Pediatric Medulloblastoma via Spatial RNA-Seq and Machine Learning

Accurate characterization and classification of these subtypes are critical for prognosis and personalized treatment. In this study, we integrate spatial transcriptomics, machine learning, and explainable artificial intelligence to comprehensively analyze the spatial and transcriptional heterogeneity of the most prevalent and aggressive medulloblastoma subtypes (SHH, Group 3, and Group 4) in Mexican pediatric patients.

By implementing a multiscale framework, we uncover extensive intra- and intertumoral variability that challenges traditional molecular classification. Our approach reveals subtype-specific gene expression signatures, identifies a core set of robust biomarkers and highlights distinct spatial patterns of tumor organization, including invasive and metabolically active niches. Notably, we demonstrate that Group 3 tumors, despite being the most aggressive, exhibit the lowest transcriptional heterogeneity, suggesting a streamlined malignant program centered on invasion and plasticity.