Preclinical study reveals that scFv-dependent restraint of tonic signaling enhances CAR-T persistence and antitumor efficacy in diffuse midline glioma, highlighting a framework for CAR engineering.
BACKGROUND: Diffuse middle glioma (DMG or DIPG) is a fatal pediatric brain tumor. Although chimeric antigen receptor (CAR) T-cell therapy shows promise, clinical outcomes remain inconsistent due to premature exhaustion, underscoring a critical need to improve CAR-T persistence. A major barrier to CAR-T efficacy is antigen-independent tonic signaling, yet the extent to which tonic signaling shapes CAR-T durability and clinical outcomes, particularly in DMG, remains incompletely defined. METHODS: Using a clinically investigated B7-H3 MGA271-based CAR as a reference platform, we generated alternative B7-H3 CARs incorporating either a human codon-optimized 376.96 (B7H3.BC) or Hu8H9 scFv antigen binding domain to systematically assess scFv-dependent effects on tonic signaling and therapeutic efficacy. CAR-T cells were evaluated using integrated in vitro and in vivo functional assays, alongside multi-omics profiling and computational modeling. We further derived a tonic signaling-associated gene signature and evaluated its predictive performance across independent clinical datasets. RESULTS: B7H3.BC CAR-T cells exhibit markedly restrained tonic signaling compared with MGA271- and Hu8H9-based counterparts, accompanied by superior antitumor activity and enhanced persistence across patient-derived DMG cells. Integrated multi-omics and single-cell profiling further identified a tonic signaling-associated gene signature that outperforms conventional T-cell exhaustion signatures in predicting therapeutic efficacy across multiple clinical trials, including DMG and other tumors. CONCLUSIONS: Our findings establish that scFv-dependent modulation of tonic signaling critically governs CAR-T persistence and antitumor efficacy in DMG. By linking CAR design to transcriptional and epigenetic programs, our study provides a principle-based and predictive framework to inform rational CAR engineering and improve therapeutic outcomes.
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Deng et al. (2026) studied this question.
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