Evidence map›Paper›PMID 42675475›Full record

ReviewJournal of biomedical science2026

Artificial intelligence for translational personalized neoantigen cancer vaccine development.

Chun-Yu Wei, Hsuan-Chao Lin, Chang-Jiun Wu, Chun-Nan Kuo, Che-Mai Chang, Wan-Hsuan Chou, Sheng-Po Chou, Sheng-Hsiang Feng, Wan-Chen Huang, Shisong Jiang and 3 more

Abstract readReview
In one paragraph

Review in Journal of biomedical science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

Chun-Yu WeiCore Laboratory of Neoantigen Analysis for Personalized Cancer Vaccine, Office of R&D, Taipei Medical University, Taipei, 110, Taiwan.
Hsuan-Chao LinDepartment of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, 110, Taiwan.
Chang-Jiun WuDepartment of Pharmaceutical Sciences, School of Pharmacy, Taipei Medical University, Taipei, 110, Taiwan.
Chun-Nan KuoDepartment of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, 110, Taiwan.
Che-Mai ChangDepartment of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, 110, Taiwan.
Wan-Hsuan ChouDepartment of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, 110, Taiwan.
Sheng-Po ChouDepartment of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, 110, Taiwan.
Sheng-Hsiang FengDepartment of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei, 110, Taiwan.
Wan-Chen HuangSingle-Molecule Biology Core Lab, Institute of Cellular and Organismic Biology, Academia Sinica, Taipei, Taiwan.
Shisong JiangDepartment of Oncology, University of Oxford, Oxford, UK.
Benjamin P FairfaxDepartment of Oncology, University of Oxford, Oxford, UK.
Kang-Yun LeeMaster Program in Clinical Genomics and Proteomics, School of Pharmacy, Taipei Medical University, Taipei, 110, Taiwan. leekangyun@tmu.edu.tw.
Wei-Chiao ChangCore Laboratory of Neoantigen Analysis for Personalized Cancer Vaccine, Office of R&D, Taipei Medical University, Taipei, 110, Taiwan. wcc@tmu.edu.tw.

Funding

National Science and Technology Council 114-2320-B-038 -016National Science and Technology Council NSTC112-2320-B-038-026-MY3Taipei Medical University 112-5812-004-400Taipei Medical University 113-3437-001-111
6 · The paper itself

Abstract

Personalized neoantigen cancer vaccine is a promising strategy for precision immunotherapy by targeting patient-specific and mutation-derived tumor antigens. Early clinical studies have demonstrated the feasibility, safety, and immunogenicity of these vaccines across multiple solid tumors, with encouraging outcomes particularly when combined with immune checkpoint blockade. However, broader clinical translation remains limited by sequential bottlenecks across the vaccine development pipeline, including false-positive neoantigen selection,  imperfect modeling of antigen processing and HLA presentation, limited prediction of T-cell receptor recognition, and challenges in formulation, delivery, and manufacturing. Artificial intelligence and advanced computational workflows are increasingly integrated into this pipeline to improve candidate prioritization and support more reproducible decision-making. In this review, we summarize clinical progress and key translational barriers in personalized neoantigen vaccination, and discuss how AI-enabled approaches may contribute across four major stages: multi-omics integration for neoantigen discovery, processing-aware HLA presentation prediction, structure-aware and TCR-informed immunogenicity modeling, and data-driven formulation optimization, particularly for lipid nanoparticle-based delivery systems. These approaches are able to help narrow biological and chemical search spaces, improve prioritization, and provide mechanistic insights into antigen presentation and immune recognition rather than replacing experimental validation. This articlefurther addresses future implementation challenges, including dataset diversity, model interpretability, prospective benchmarking, manufacturing traceability, and evolving regulatory frameworks for individualized mRNA cancer immunotherapies. Integrating computational innovation with rigorous immunological validation, scalable manufacturing, and regulatory oversight will be essential for advancing personalized neoantigen vaccines toward broader clinical implementation.

Indexed as

Antigens, NeoplasmArtificial IntelligenceCancer VaccinesImmunotherapyNeoplasmsPrecision MedicineVaccine DevelopmentHumansAntigens, NeoplasmCancer VaccinesArtificial intelligenceHuman leukocyte antigenLipid nanoparticleNeoantigen cancer vaccinePersonalized immunotherapyT-cell receptorTranslational oncology

Identifiers

PMID42675475
PMCPMC13528037

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.