Messenger RNA (mRNA) vaccines represent a versatile and scalable platform for cancer immunotherapy; however, their clinical efficacy depends critically on precise vaccine design capable of eliciting robust, selective, and durable antitumor immune responses. Recent advances in bioinformatics and artificial intelligence (AI) have substantially improved the rational design, evaluation, and optimization of mRNA-based cancer vaccines. In particular, personalized vaccine strategies targeting patient-specific tumor neoantigens have demonstrated significant promise, although challenges remain in accurately identifying immunogenic targets within highly heterogeneous tumors and overcoming immune evasion mechanisms. Machine learning and deep learning approaches enhance neoantigen prediction by integrating peptide–major histocompatibility complex (MHC) binding, antigen processing, and T cell receptor recognition, thereby improving immunogenicity assessment beyond conventional pipelines. AI-driven mRNA sequence optimization including codon usage refinement and untranslated region (UTR) engineering further enhances protein expression, translation efficiency, and mRNA stability. In parallel, AI-guided modeling of mRNA secondary structures and lipid nanoparticle (LNP) formulations supports efficient intracellular delivery, improved stability, and controlled immune activation. This review provides a structured overview of AI-enabled computational frameworks for mRNA cancer vaccine development and offers practical guidance for integrating in silico predictions with experimental validation. By addressing tumor heterogeneity, antigen processing constraints, and patient-specific immune landscapes, bioinformatics-driven strategies enable more rational and translatable mRNA vaccine design. Collectively, these advances establish a robust foundation for the development of personalized mRNA-based cancer immunotherapies with improved immunogenicity and therapeutic efficacy.
Ruby Srivastava (Tue,) studied this question.