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

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.

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.