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mRNA vaccines in oncology: personalized cancer immunization and neoantigen targeting.

Molecular & cellular oncology 2026 Vol.13(1) p. 2652613

Parganiha M, Rathored J, Sai Painkra D

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Precision oncology is evolving with personalized mRNA neoantigen vaccines, although long-term clinical responses vary.

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APA Parganiha M, Rathored J, Sai Painkra D (2026). mRNA vaccines in oncology: personalized cancer immunization and neoantigen targeting.. Molecular & cellular oncology, 13(1), 2652613. https://doi.org/10.1080/23723556.2026.2652613
MLA Parganiha M, et al.. "mRNA vaccines in oncology: personalized cancer immunization and neoantigen targeting.." Molecular & cellular oncology, vol. 13, no. 1, 2026, pp. 2652613.
PMID 41971684

Abstract

Precision oncology is evolving with personalized mRNA neoantigen vaccines, although long-term clinical responses vary. These mRNA-based vaccines have facilitated the development of patient-specific neoantigens. Clinical success is dependent not only on immunogenicity but also on tumor neoantigen clonality, expression, and presentation by the vaccines. Evidence from trials conducted from 2020 to 2025 shows that indicators like minimal residual disease are crucial. The mRNA-4157 (V940) combined with pembrolizumab demonstrated improved recurrence-free survival in resected high-risk melanoma patients (18-month RFS 79% vs 62%; HR 0.56). Similarly, the autogene cevumeran triggered significant neoantigen-specific T cell responses in 8 out of 16 patients with resected pancreatic ductal adenocarcinoma, leading to a delayed recurrence for immune responders (not reached vs 13.4 months; HR 0.08). This highlights a translational model focusing on tumor clonality, antigen quality, and immune accessibility. The review also addresses (i) clonality aware neoantigen selection; (ii) AI-based predictions of antigen presentation and immunogenicity, including issues of false positives; (iii) alternative delivery systems beyond lipid nanoparticles; and (iv) real-world challenges such as turnaround time, batch variability, regulatory frameworks, and operational costs that impact implementation. A structured model for crucial events and validation plans is proposed to bridge the gap between predictions and actual clinical benefits, utilizing techniques like immunopeptidomics and functional T cell assays.