Quantitative systems pharmacology modeling of class I–III BRAF-mutant NSCLC enables subgroup treatment analysis and combination therapy design

BRAF-mutant non-small cell lung cancer (NSCLC) comprises three functional classes (I–III) with distinct driving mechanisms, yet effective therapies for classes II and III remain limited. To address this, we developed a quantitative systems pharmacology (QSP) model that mechanistically recapitulates signaling networks and drug responses across all three classes, integrating multiscale preclinical-to-clinical datasets. The model enabled high-throughput prediction of clinical efficacy for monotherapies and combination therapies across diverse dosing regimens, and we identified that combining MEK and ERK inhibition represents a novel strategy for class II/III mutants, validated by original in vivo MiniPDX data using NSCLC patient tumors. Simulations further revealed that only triple blockade of EGFR, IGF1R, and FGFR pathway initiation can potently reduce tumor growth and overcome resistance in these classes. Our work is the first multiscale QSP platform mechanistically addressing class I–III BRAF mutations at the systems level for disease-centric translational combination therapy discovery and precision medicine design.