Neoantigen vaccines: advancing personalized cancer immunotherapy

Neoantigen vaccines are a promising strategy in cancer immunotherapy that leverage tumor-specific mutations to elicit targeted immune responses. Although they have considerable potential, development challenges related to antigen prediction accuracy, manufacturing complexity, and scalability remain key obstacles to their widespread clinical use. This literature review was conducted using PubMed, Scopus, Web of Science, and Google Scholar databases to identify relevant studies. Keywords included “neoantigen vaccines,” “personalized cancer immunotherapy,” “tumor heterogeneity,” “bioinformatics pipelines,” and “prediction algorithms”. Clinical trial data were sourced from ClinicalTrials.gov, Trialtrove, and other publicly available registries. Eligible studies included peer-reviewed research articles, systematic reviews, and clinical trials focusing on neoantigen vaccine development, bioinformatic strategies, and immunotherapy. Tumor heterogeneity and clonal evolution significantly impact vaccine efficacy, necessitating multi-epitope targeting and adaptive vaccine design. Current neoantigen prediction algorithms suffer from high false-positive and false-negative rates, requiring further integration with multi-omics data and machine learning to enhance accuracy. Manufacturing remains complex, time-intensive, and costly, necessitating advancements in standardization and automation. Combination therapies, such as immune checkpoint inhibitors and adoptive cell therapies, counteract the immunosuppressive tumor microenvironment, improving treatment outcomes. Neoantigen vaccines hold great potential for personalized cancer therapy but require advancements in bioinformatics, manufacturing scalability, and immunomodulatory strategies to enhance clinical efficacy. Continued research and interdisciplinary collaboration are essential for refining clinical applications.