Personalized neoantigen cancer vaccines: Why clinical benefit remains inconsistent

Personalized cancer vaccines have re-emerged as a promising strategy in precision immunotherapy, driven by advances in tumor sequencing, neoantigen identification, and vaccine delivery platforms. Early-phase clinical trials have consistently demonstrated feasibility, safety, and induction of neoantigen-specific immune responses, with clinical benefit observed in a subset of patients. Despite these advances, translation into consistent and durable clinical outcomes remains limited across tumor types. This review critically synthesizes recent clinical and translational evidence to examine the structural barriers constraining the efficacy of personalized neoantigen cancer vaccines. Key challenges include limited predictive accuracy of neoantigen selection pipelines, prolonged manufacturing timelines that permit tumor evolution and immunoediting, immunosuppressive tumor microenvironments that restrict effector T-cell function, and the absence of validated biomarkers for prospective patient stratification. We further evaluate the role of artificial intelligence as an incremental optimizer of existing workflows rather than a transformative solution and discuss emerging antigen sources beyond canonical point mutations. Together, these factors help explain the persistent gap between vaccine-induced immunogenicity and reproducible clinical benefit. Recognition of these translational constraints is essential for guiding clinical trial design, informing combination and sequencing strategies, and establishing realistic expectations for the current and near-term clinical utility of personalized neoantigen cancer vaccines.