Canonical residual physics-informed neural networks and algorithmic perturbation landscapes map cell-fate transitions in longitudinal H3K27M glioma

Abstract

Pediatric high-grade glioma is a dynamically heterogeneous disease in which therapeutic selection pressures, microenvironmental selection and developmental plasticity can reorganize cellular-state structure over time. Here we investigated these adaptive behaviors in glioma dynamics from single-cell ecologies by merging physics-driven artificial intelligence and complex systems theory. Specifically, this study explores canonical residual-based physics-informed neural networks (PINNs), dynamical systems modelling comparators, explainable artificial intelligence (XAI), and Block Decomposition Method (BDM) perturbation analysis to understand glioma cell fate decisions. The primary analyses distinguished longitudinal initial from recurrent H3K27M cells and, within the recurrent specimen set, stem/progenitor-like from differentiated/glial-like annotation-derived states. Across five-fold cross-validation, RD achieved AUC $0.995 \pm 0.005$ and macro-F1 $0.973 \pm 0.016$ for initial--recurrence discrimination, and AUC $0.698 \pm 0.012$ and macro-F1 $0.629 \pm 0.014$ for recurrent-state subtype discrimination. FP achieved AUC $0.931 \pm 0.018$ and $0.665 \pm 0.012$, respectively. Cross-model XAI repeatedly prioritized PTGDS, CHI3L1, IGFBP7, SPARCL1 and NRXN3 in longitudinal progression. BDM perturbation rankings emphasized heat-shock/stress-response and immune-associated network signatures. These findings are candidate computational network biomarkers rather than experimentally established causal signatures. The study provides a reproducible bridge between physics-constrained latent dynamics, interpretable gene attribution, and data-driven algorithmic perturbation landscapes for in silico pattern generation about glioma cell fate plasticity and early recurrence detection.