Complexity-informed causal modeling of neurodevelopmental trajectories in pediatric high-grade gliomas: divergences from neural stem cell signatures

Abstract Pediatric high-grade gliomas (pHGGs) are lethal evolutionary disorders with stalled developmental pathways and disturbed differentiation hierarchies. We integrate transcriptional and algorithmic network complexity-based perturbation analysis to elucidate gene expression patterns and molecular divergence between Diffuse Midline Gliomas (DMG; previously DIPG) and glioblastoma (GBM), revealing shared developmental programs steering cell fate decision-making. Our complex systems approach supports the emerging paradigm that pHGGs are neurodevelopmental disorders with hybrid lineage identities and disrupted patterning. We identify dysregulated neurodevelopmental and morphogenetic signatures, alongside bioelectric and neurotransmitter signaling programs that alter synaptic organization, neuronal fate commitment, and phenotypic plasticity, regulating glioma phenotypic switching (cell state-transitions). In silico candidate transition-associated network signatures and regulators of plasticity were predicted as possible predictive biomarkers and therapeutic targets, reinforcing the view that pediatric gliomas are disorders of cell fate decisions and collective cellular identity. Neuronal and synaptic enrichment is interpreted as a candidate neurodevelopmental network program emerging from disrupted cell fate decision-making. Across validation cohorts, these predictive signatures localized to progenitor-like, NPC/OPC-like, hybrid/ambiguous, immune, and extracellular matrix-associated compartments, supporting recurrence-associated plasticity dynamics. Decoded cell fate transition-associated signatures indicate recurrence-associated remodeling of neural progenitor, OPC-like, neurosynaptic interface, immune, and matrisome-associated programs, as computationally inferred network features. Using graph network-based complexity signatures, our algorithms identified candidate network-associated plasticity markers and interpretable regulatory drivers underlying latent glioma cell fate dynamics. These computational findings may help advance ‘differentiation therapy’ as a systems medicine strategy, discovering plasticity regulators to reprogram malignant fates toward stable lineages, offering complex systems-based targets for cancer reversion and predictive, preventive, and precision oncology.