纳米技术
癌症
纳米医学
癌症免疫疗法
癌症治疗
堆积
材料科学
肿瘤微环境
免疫系统
免疫原性细胞死亡
金属有机骨架
癌症治疗
化学
免疫疗法
癌细胞
共价键
免疫检查点
肿瘤缺氧
合理设计
计算机科学
计算生物学
纳米器件
设计要素和原则
癌症研究
作者
Yutao Zou,Min Chen,Jiayi Chen,Weiqi Wang,Xin Zheng
标识
DOI:10.3389/fimmu.2025.1719005
摘要
Covalent organic frameworks (COFs) have emerged as promising candidates in cancer immunotherapy, owing to their tunable pore structures, versatile functionality, and favorable biocompatibility. This review systematically highlights recent advances in COF-based materials that enhance immunotherapeutic efficacy through multiple strategies. Particular emphasis is placed on functionalized COFs for remodeling the immunosuppressive tumor microenvironment, by alleviating hypoxia and depleting glutathione, and their role as core sensitizers in various therapeutic modalities, including photodynamic, sonodynamic, radiotherapy, and chemodynamic therapy, to efficiently trigger immunogenic cell death (ICD). In addition, we comprehensively summarize how strategic structural engineering enhances phototherapeutic efficacy. This includes modulating the metal ions incorporated into the COF, controlling COF stacking modes, and adjusting the planarity or conformational twist of the building units to precisely tune bandgap energy and light absorption properties, thereby promoting stronger ICD induction. Furthermore, COFs serve as intelligent delivery platforms capable of controlled release of immune adjuvants and checkpoint inhibitors. The discussion also extends to cutting-edge applications, such as imaging-guided therapy, induction of tertiary lymphoid structure (TLS) formation, and activation of abscopal effects. These developments discussed in this review underscore the immense potential of COFs as multifunctional nanoplatforms in advancing effective and precise combination cancer immunotherapy. The insights provided in this review offer valuable reference for the biomedical applications of COFs, particularly in the integrated development of multimodal therapies and immunotherapy.
科研通智能强力驱动
Strongly Powered by AbleSci AI