Cancer Vaccine Development: Toward Artificial Intelligence-Assisted Personalized Cell Membrane Nanovaccine

Cancer vaccines are an attractive immunotherapeutic approach, and numerous clinical trials are ongoing. However, most candidates have not achieved statistically significant improvements in patient survival. To address these limitations, nanotechnology-enabled cancer nanovaccine platforms have attracted increasing attention. Relative to conventional vaccine formats, cancer nanovaccines can improve antigen delivery efficiency and promote robust antitumor immune responses. Among these approaches, cell membrane nanoparticle (CNP)-based vaccines have been widely investigated and show strong biocompatibility and immunogenicity. Notably, preservation of major histocompatibility complex (MHC)-associated peptide repertoires on CNPs can help leverage endogenous antigen presentation and may reduce reliance on in silico tumor antigen prediction alone, a longstanding bottleneck in cancer vaccine development. In parallel, artificial intelligence (AI) is driving rapid advances across biomedical engineering and is applied to cancer nanovaccine research, including antigen identification, nanoparticle design optimization, and immune response modeling. This review comprehensively summarizes recent progress in anticancer nanovaccine research, with a particular focus on the development of CNP-based cancer nanovaccines. It also reviews the emerging AI-enabled strategies relevant to this field, including antigen prediction, formulation and design optimization, and computational modeling of immune responses. Finally, an outlook on future research directions and remaining challenges in this rapidly evolving field is provided.