Evidence map›Paper›PMID 42677342›Full record

ReviewJournal of immunology research2026

Integrating AI, RNA Vaccines, and CAR-T Cells for Personalized Treatment.

Diala Haykal, Brigitte Dréno

Abstract readReview
In one paragraph

Review in Journal of immunology research, 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

2 authors.

Diala HaykalCentre Médical Laser Palaiseau, Palaiseau, France.ORCID https://orcid.org/0000-0001-7528-5088
Brigitte DrénoImmunology and New Concepts in ImmunoTherapy, Nantes Université, CNRS, INCIT, INSERM, UMR 1302/EMR6001, Nantes, France, inserm.fr.ORCID https://orcid.org/0000-0001-5574-5825

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Oncology is undergoing a profound transformation driven by the convergence of artificial intelligence (AI), RNA-based vaccines, and chimeric antigen receptor T-cell (CAR-T) therapies. Individually, these technologies have advanced cancer diagnosis, treatment, and patient stratification. AI-driven approaches enhance drug discovery, optimize clinical trial design, and enable personalized therapeutic decision-making. RNA vaccines provide a flexible platform for encoding tumor-specific neoantigens, while CAR-T therapies enable targeted immune-mediated tumor cell elimination. Early-phase clinical trials, particularly those combining RNA vaccines with immune checkpoint inhibitors, have demonstrated promising improvements in recurrence-free survival (RFS) and immunogenicity. However, evidence supporting direct combinations of RNA vaccines and CAR-T therapies remains largely preclinical or limited to early-phase investigation and should therefore be interpreted with caution. This review explores how AI facilitates neoantigen discovery, RNA vaccine optimization, and CAR-T cell engineering, and examines the emerging interplay between these modalities. While their integration represents a compelling framework for personalized oncology, significant challenges remain, including clinical validation, scalability, regulatory oversight, and equitable access.

Indexed as

Artificial IntelligenceCancer VaccinesImmunotherapy, AdoptivemRNA VaccinesNeoplasmsPrecision MedicineReceptors, Chimeric AntigenAnimalsAntigens, NeoplasmHumansAntigens, NeoplasmCancer VaccinesmRNA VaccinesReceptors, Chimeric Antigen

Identifiers

PMID42677342
PMCPMC13531379

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.