Evidence map›Paper›PMID 42023226›Full record

ReviewFrontiers in immunology2026

Reprogramming CAR T cells across genetic, epigenetic, metabolic and microenvironmental axes to improve efficacy and safety in cancer and autoimmune disease.

Marc Scherlinger

Abstract readReview
In one paragraph

Review in Frontiers in immunology, 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

1 author.

Marc ScherlingerRheumatology Department, National Reference Center for Systemic Autoimmune Diseases (CNR RESO), Strasbourg University Hospital, Strasbourg, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

CAR T-cell therapy has delivered durable remissions in several hematologic cancers, yet activity in solid tumors and extension to immune-mediated diseases remain constrained by recurring failure modes: imperfect antigen specificity, inadequate trafficking, progressive dysfunction under chronic stimulation, and toxic inflammatory syndromes. Early reports of CAR-based immune "resets" in refractory autoimmune disease amplify both promise and stakes, because acceptable risk is lower than in cancer and "on-target" effects may still be clinically unacceptable if they create long-term immunodeficiency. This review treats CAR T optimization as multi-layer reprogramming across genetic circuitry, epigenetic state, metabolism, and the tissue microenvironment. We argue that many celebrated single-layer upgrades (stronger signaling, checkpoint deletion, constitutive cytokine armoring) often trade one failure mode for another. Instead, the most credible path to simultaneously improving efficacy and safety is disciplined, failure-mode-driven design: (i) programmable antigen logic and titratable activation to reduce off-tissue damage; (ii) epigenetic programming that preserves renewable functional states without removing essential restraints; (iii) metabolic rewiring evaluated under physiologic stress conditions; and (iv) microenvironment-aware strategies that prioritize access and local control over brute-force potency.

Indexed as

Autoimmune DiseasesCellular ReprogrammingEpigenesis, GeneticImmunotherapy, AdoptiveNeoplasmsReceptors, Chimeric AntigenT-LymphocytesAnimalsHumansMetabolic ReprogrammingTumor MicroenvironmentReceptors, Chimeric AntigenautoimmunityCAR T cellsCAR-Tregexhaustiongenome editingimmunometabolismsafety switchestumor microenvironment

Identifiers

PMID42023226
PMCPMC13096093

What OpenQuestion holds

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LicenceCC BY
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.