Evidence map›Paper›PMID 40379910›Full record

ReviewNature reviews. Immunology2025

In vivo CAR engineering for immunotherapy.

Yan-Ruide Li, Yichen Zhu, Tyler Halladay, Lili Yang

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
63citing papers in PubMed, 1 pooled it
–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

63 citing papers in PubMed, 1 synthesis or guideline pooled it.

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3 more citing papers are in PubMed but not listed here.

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

4 authors.

Yan-Ruide Li *Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA.ORCID http://orcid.org/0000-0002-9858-300X
Yichen Zhu *Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
Tyler HalladayDepartment of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
Lili YangDepartment of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA. liliyang@ucla.edu.ORCID http://orcid.org/0000-0001-6568-0404

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chimeric antigen receptor (CAR)-engineered immune cell therapy represents an important advance in cancer treatments. However, the complex ex vivo cell manufacturing process and stringent patient selection criteria curtail its widespread use. In vivo CAR engineering is emerging as a promising off-the-shelf therapy, providing advantages such as streamlined production, elimination of patient-specific manufacturing, reduced costs and simplified logistics. A large set of preclinical findings has inspired further investigation into treatments for hard-to-treat diseases such as solid tumours and has facilitated the development of advanced products to enhance in vivo CAR engineering efficacy, the persistence of the cellular therapeutic and safety. In this Review, we summarize current in vivo CAR engineering strategies, including nanoparticle-based and viral delivery systems as well as bioinstructive implantable scaffolds, and discuss their advantages and disadvantages. Additionally, we provide a systematic comparison between in vivo and conventional ex vivo CAR engineering methods and address the challenges and future prospects of in vivo CAR engineering.

Indexed as

ImmunotherapyImmunotherapy, AdoptiveNeoplasmsReceptors, Chimeric AntigenAnimalsCell EngineeringHumansReceptors, Chimeric Antigen

Identifiers

PMID40379910

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.