RBIO-16. Mapping the Spatiotemporal Evolution of Diffuse Midline Glioma Under Radiotherapy

Abstract Diffuse midline glioma (DMG) is an incurable pediatric brain tumor, with radiotherapy offering only transient benefit before inevitable recurrence. While recent studies have revealed the cellular complexity of DMG, the relationship between intratumoral heterogeneity and disease progression — particularly under therapeutic pressure — remains poorly understood. In this study, we integrate longitudinal single-cell- and spatial transcriptomics with single-cell chromatin accessibility profiling and radiomics to investigate how distinct DMG cell states and microenvironmental components evolve in response to treatment, contributing to resistance and the emergence of therapy-persistent niches. We applied this multi-omic approach to a unique cohort of 10 matched diagnostic and autopsy DMG samples obtained from children enrolled in the PNOC023 clinical trial. Spatial transcriptomic profiling revealed that post-treatment autopsy tissues showed consistent enrichment of oligodendrocyte progenitor cell-like malignant populations, suggesting that this stem-like subpopulation is selectively retained or expanded following radiotherapy in DMG. Moreover, integration of single-cell RNA and ATAC sequencing with cerebrospinal fluid microRNA analyses further revealed molecular signatures of resistance and uncovered minimally invasive biomarkers of treatment response. To complement our molecular analyses, we applied radiomic profiling of longitudinal MRI scans to non-invasively monitor changes in tumor heterogeneity over the course of treatment in this patient cohort. In parallel, we developed a data-driven mathematical model using patient-derived DMG xenografts to simulate tumor cell responses to radiotherapy and generate quantitative predictions of preclinical treatment outcomes. We employed this model to explore alternative radiation fractionation schemes and identify optimal dosing strategies that most effectively deplete therapy-resistant tumor cell populations in DMG. Together, these integrated spatial and molecular datasets provide a high-resolution framework for understanding how radiotherapy remodels the DMG tumor ecosystem. By identifying persistent cellular populations and spatial niches that survive treatment, this work lays the groundwork for future precision therapies aimed at targeting the cellular reservoirs that drive DMG recurrence.