ReviewFrontiers in oncology2026
AI-powered mapping of tumor immunity for optimized mRNA vaccine engineering.
Review in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- mRNA Therapeutics Beyond Infectious Diseases: Expanding Therapeutic Applications and Future Perspectives.Immunity, inflammation and disease · 2026Review
- Review
- Review
- Advances in linear epitope-based subunit vaccines powered by artificial intelligence: current status and challenges.Frontiers in immunology · 2026Review
- Therapeutic melanoma vaccines: Platforms, neoantigen strategies, and emerging combination immunotherapies.Therapeutic advances in vaccines and immunotherapy · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.