Nanovesicles loaded with a TGF-β receptor 1 inhibitor overcome immune resistance to potentiate cancer immunotherapy
The immune-excluded tumors (IETs) show limited response to current immunotherapy due to intrinsic and adaptive immune resistance. In this study, it is identified that inhibition of transforming growth factor-β (TGF-β) receptor 1 can relieve tumor fibrosis, thus facilitating the recruitment of tumor-...
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Veröffentlicht in: | Nature communications 2023-06, Vol.14 (1), p.3593-3593, Article 3593 |
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Sprache: | eng |
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Zusammenfassung: | The immune-excluded tumors (IETs) show limited response to current immunotherapy due to intrinsic and adaptive immune resistance. In this study, it is identified that inhibition of transforming growth factor-β (TGF-β) receptor 1 can relieve tumor fibrosis, thus facilitating the recruitment of tumor-infiltrating T lymphocytes. Subsequently, a nanovesicle is constructed for tumor-specific co-delivery of a TGF-β inhibitor (LY2157299, LY) and the photosensitizer pyropheophorbide a (PPa). The LY-loaded nanovesicles suppress tumor fibrosis to promote intratumoral infiltration of T lymphocytes. Furthermore, PPa chelated with gadolinium ion is capable of fluorescence, photoacoustic and magnetic resonance triple-modal imaging-guided photodynamic therapy, to induce immunogenic death of tumor cells and elicit antitumor immunity in preclinical cancer models in female mice. These nanovesicles are further armored with a lipophilic prodrug of the bromodomain-containing protein 4 inhibitor (i.e., JQ1) to abolish programmed death ligand 1 expression of tumor cells and overcome adaptive immune resistance. This study may pave the way for nanomedicine-based immunotherapy of the IETs.
Targeting the TGF-β signaling pathway has been exploited to relieve immunosuppression in the tumor microenvironment. Here the authors describe the design of a nanoplatform integrating the TGF-β receptor 1 inhibitor LY2157299 and the ROS-responsive JQ1 pro-drug, promoting anti-tumor immune responses in preclinical cancer models. |
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ISSN: | 2041-1723 2041-1723 |
DOI: | 10.1038/s41467-023-39035-x |