CELL INTELLIGENCE · 肿瘤细胞治疗研究

BACKGROUND: Immune checkpoint inhibitors (ICIs) are a frontline treatment for advanced non-small cell lung cancer (NSCLC), yet 80% of the patients exhibit resistance, creating an urgent need for novel therapeutic strategies. In this study, we investigated the synergistic efficacy of a triple-combination therapy comprising a PD-1 antibody, adoptive NK (natural killer) cells, and an oncolytic adenovirus Ad-anti-TGF- RII (encoding a TGF- inhibitor) in NSCLC xenograft mouse models. METHODS: We investigated the combined effect of Ad-anti-TGF- RII and NK cells on PD-1 antibody monotherapy using both in vitro cell experiments and the mouse model of NSCLC. Cytotoxicity assays, quantitative real-time PCR, and western blot analysis demonstrated that Ad-anti-TGF- RII exhibited stronger tumor-killing activity compared to the control oncolytic adenovirus Ad-null. Furthermore, cytotoxicity assays and flow cytometry were employed to explore how the combination of Ad-anti-TGF- RII and NK cells with PD-1 antibody promotes NK cell proliferation and activation, as well as the potent tumor-killing effect of the combination therapy. In the mouse model of NSCLC, the anti-tumor efficacy of the combination therapy was evaluated by monitoring tumor volume changes, hematoxylin and eosin (H&E) staining. The underlying mechanisms were further investigated using immunofluorescence, immunohistochemistry, quantitative real-time PCR, western blot, and flow cytometry. RESULTS: The triple-combination therapy markedly inhibited tumor growth, augmented NK cell cytotoxicity and elevated the expression levels of perforin, granzyme B, and IFN- . Furthermore, it significantly increased lymphocyte recruitment and infiltration into tumor tissue. Comprehensive analysis demonstrated the favorable safety profile of this therapeutic regimen. CONCLUSIONS: Our findings suggest that the combination of a PD-1 antibody, NK cells, and the oncolytic adenovirus Ad-anti-TGF- RII represents a promising therapeutic strategy for NSCLC by remodeling the tumor microenvironment (TME) to overcome ICI resistance.