Evidence map›Paper›PMID 41971684›Full record

ReviewMolecular & cellular oncology2026

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

Mridula Parganiha, Jaishriram Rathored, Deepika Sai Painkra

Abstract readReview
In one paragraph

Review in Molecular & cellular oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Personalized neoantigen mRNA vaccines for pancreatic cancer: the role of lipid nanoparticle delivery systems.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2026
    Review
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Mridula ParganihaDepartment of Central Research Laboratory and Molecular Diagnostics, Datta Meghe Institute of Higher Education & Research, Sawangi Meghe, Wardha, Maharashtra, India.
Jaishriram RathoredDepartment of Central Research Laboratory and Molecular Diagnostics, Datta Meghe Institute of Higher Education & Research, Sawangi Meghe, Wardha, Maharashtra, India.ORCID https://orcid.org/0000-0001-6075-7505
Deepika Sai PainkraDepartment of Central Research Laboratory and Molecular Diagnostics, Datta Meghe Institute of Higher Education & Research, Sawangi Meghe, Wardha, Maharashtra, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AI in oncologyclinical trialslipid nanoparticlesmRNA cancer vaccinesneoantigen targetingpersonalized immunotherapyprecision medicinetumor microenvironment

Identifiers

PMID41971684
PMCPMC13064569

What OpenQuestion holds

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Registered trials

None linked

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.