Toll样受体激动剂作为佐剂在恶性肿瘤治疗性疫苗中的应用研究进展
Advances in Toll-like Receptor Agonist-based Adjuvants in Therapeutic Vaccines for Malignant Tumors
牟漫 1黄思剑 1李健伦 1操媛 2王丽娟 1卓可星 1李旭 3钟茂华1
作者信息
- 1. 武汉科技大学医学院感染免疫与肿瘤微环境研究所,职业危害识别与控制湖北省重点实验室,武汉 430065
- 2. 武汉科技大学分析测试中心,武汉 430065
- 3. 武汉科技大学附属武汉亚心总医院检验科,武汉 430056
- 折叠
摘要
恶性肿瘤严重威胁人类健康和生命,急需研发新型治疗药物.治疗性肿瘤疫苗通过刺激患者免疫系统产生针对特定肿瘤抗原的T细胞应答,达到肿瘤治疗的目的,成为未来肿瘤免疫治疗最有前景的技术之一.然而,肿瘤抗原免疫原性低下,难以诱导机体产生有效的免疫应答.Toll样受体(Toll-like receptors,TLRs)是一类广泛表达在树突状细胞、巨噬细胞等多种天然免疫细胞的模式识别受体,其配体是一类保守的病原相关分子模式.TLRs激动剂与其受体结合后,可通过刺激天然免疫系统,提高疫苗免疫原性、增强肿瘤特异性T细胞应答,从而提高疫苗保护效率,是一种高效的疫苗佐剂.该文就TLRs激动剂佐剂的分类、分子结构、诱导的信号通路及其在治疗性肿瘤疫苗中的应用研究进展作一综述.
Abstract
Malignant tumors pose a serious threat to health and life of mankind,and there is an urgent need to develop no-vel therapeutic strategies.Therapeutic tumor vaccines have become one of the most promising technologies for future tumor im-munotherapy by stimulating immune system to generate tumor antigen specific T-cell responses in patients for the purpose of tumor treatment.However,the immunogenicity of tumor antigen is low,and it is difficult to induce an effective immune response in the human body.Toll-like receptors(TLRs)are a class of pattern recognition receptors which are widely expressed in dendritic cells,macrophages,and other innate immune cells.The ligands of TLRs are a class of conserved pathogen associated molecular pattern.TLR agonists are highly effective vaccine adjuvants that enhance the efficiency of vaccine protection by stimulating the innate immune system,thereby increasing vaccine immunogenicity and enhancing tumor-specific T-cell responses.This article provides a review on the classification,molecular structure,signaling pathways,and progress of research on TLR agonist-based adjuvants and their application in therapeutic tumor vaccines.
关键词
恶性肿瘤/肿瘤治疗性疫苗/Toll样受体激动剂/佐剂Key words
malignant tumor/tumor therapeutic vaccine/Toll-like receptor agonist/adjuvant引用本文复制引用
基金项目
国家自然科学基金面上项目(81971568)
武汉科技大学湖北省优势特色学科(群)项目(十四五)(2023C0307)
武汉市武昌医院科研创新基金(WCYY2022G01)
出版年
2024
NETL
NSTL
万方数据