Hidden-driver inference reveals synergistic brain-penetrant therapies for medulloblastoma

ABSTRACT Effective therapies for high-risk medulloblastoma (MB), particularly MYC-driven Group 3 (G3) MB, remain elusive due to limited druggable mutations, poor blood–brain barrier (BBB) penetration, and rapid resistance. We developed SINBA (Synergy Inference by Data-driven Network-Based Bayesian Analysis), a systems biology framework that predicts synergistic, BBB-permeable drug combinations by identifying hidden drivers that sustain oncogenic programs without detectable genetic or transcriptional alterations. Integrating MB-specific gene networks, transcriptomic data, and drug–gene interactions, SINBA prioritized 32 candidate combinations, of which 19 were experimentally validated as synergistic. The MEK inhibitor mirdametinib and p38 inhibitor regorafenib emerged as the top brain-penetrant pair, suppressing G3 MB progression and extending survival in human xenograft and immunocompetent mouse models, with efficacy enhanced by low-dose radiation. Single-cell analysis revealed selective targeting of the developmental origins of G3 MB, accompanied by immune microenvironment reprogramming. These findings establish hidden-driver inference as a generalizable framework for rational drug combination discovery. SIGNIFICANCE The SINBA platform enables rapid identification of brain-penetrant, synergistic drug combinations by targeting hidden bottleneck drivers in high-risk tumors. SINBA-guided screening revealed that MEK inhibitors combined with regorafenib selectively eliminate the developmental cell-of-origin in G3 medulloblastoma and reprogram the tumor microenvironment, providing a clinically actionable precision oncology strategy. Competing Interest Statement The authors have declared no competing interest. Footnotes Author name - middle name was added for Abigail Wang