Zhang Zhongkun, Yao Siyu, Wang Yufei, Luo Kaiwen, Amiji Mansoor, Anderson Kenneth C
Department of Pharmacology, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China; Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, 02115, USA.
Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, China.
Biomaterials. 2026 Feb;325:123615. doi: 10.1016/j.biomaterials.2025.123615. Epub 2025 Aug 7.
Cancer vaccines stimulate antitumor immunity by delivering tumor antigens and, in recent years, have emerged as a promising therapeutic strategy against cancer. The recent success of mRNA-based cancer vaccines from Moderna in treating patients with late-stage melanoma demonstrates the feasibility of clinical translation of antigen-based cancer vaccines. However, the success of cancer vaccine development not only relies on the screening of effective tumor antigens but also on the treatment strategy and delivery system. This review elaborates on the methods applied to each step of cancer vaccine development including tumor antigen screening, vaccine development, and clinical treatment strategies. Artificial intelligence-assisted models have recently applied to facilitate tumor antigens screening, and novel delivery platforms have been designed to enhance payload delivery and antitumor immune responses. By illustrating these novel strategies in recent years, we hope that this review provides insights into future cancer vaccine development and clinical translation globally.
癌症疫苗通过递送肿瘤抗原刺激抗肿瘤免疫，近年来已成为一种有前景的癌症治疗策略。莫德纳公司基于信使核糖核酸的癌症疫苗在治疗晚期黑色素瘤患者方面最近取得的成功证明了基于抗原的癌症疫苗临床转化的可行性。然而，癌症疫苗开发的成功不仅依赖于有效肿瘤抗原的筛选，还依赖于治疗策略和递送系统。本综述阐述了应用于癌症疫苗开发各个步骤的方法，包括肿瘤抗原筛选、疫苗开发和临床治疗策略。人工智能辅助模型最近已应用于促进肿瘤抗原筛选，并且已设计出新型递送平台以增强有效载荷递送和抗肿瘤免疫反应。通过阐述近年来的这些新策略，我们希望本综述能为全球未来的癌症疫苗开发和临床转化提供见解。
