A cancer vaccine-mediated postoperative immunotherapy for recurrent and metastatic tumors

Vaccines to induce effective and sustained antitumor immunity have great potential for postoperative cancer therapy. However, a robust cancer vaccine simultaneously eliciting tumor-specific immunity and abolishing immune resistance continues to be a challenge. Here we present a personalized cancer v...

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Veröffentlicht in:Nature communications 2018-04, Vol.9 (1), p.1532-12, Article 1532
Hauptverfasser: Wang, Tingting, Wang, Dangge, Yu, Haijun, Feng, Bing, Zhou, Fangyuan, Zhang, Hanwu, Zhou, Lei, Jiao, Shi, Li, Yaping
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Sprache:eng
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Zusammenfassung:Vaccines to induce effective and sustained antitumor immunity have great potential for postoperative cancer therapy. However, a robust cancer vaccine simultaneously eliciting tumor-specific immunity and abolishing immune resistance continues to be a challenge. Here we present a personalized cancer vaccine (PVAX) for postsurgical immunotherapy. PVAX is developed by encapsulating JQ1 (a BRD4 inhibitor) and indocyanine green (ICG) co-loaded tumor cells with a hydrogel matrix. Activation of PVAX by 808 nm NIR laser irradiation significantly inhibits the tumor relapse by promoting the maturation of dendritic cells and eliciting tumor infiltration of cytotoxic T lymphocytes. A mechanical study reveals that NIR light-triggered antigen release and JQ1-mediated PD-L1 checkpoint blockade cumulatively contribute to the satisfied therapeutic effect. Furthermore, PVAX prepared from the autologous tumor cells induces patient-specific memory immune response to prevent tumor recurrence and metastasis. The PVAX model might provide novel insights for postoperative immunotherapy. Cancer vaccines represent a promising personalized therapeutic approach to treating cancer. Here, the authors report the efficacy in a metastatic model of a cancer vaccine-mediated postoperative immunotherapy, based on the coencapsulation of the JQ1 and a photosensitizer ICG together with inactivated tumor cells into a hydrogel matrix.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-018-03915-4