Evidence map›Paper›PMID 39220130›Full record

ReviewOncology research2024

Influencing factors and solution strategies of chimeric antigen receptor T-cell therapy (CAR-T) cell immunotherapy.

Zhengyi Wang, Liang Zhou, Xiaoying Wu

Abstract readReview
In one paragraph

Review in Oncology research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Zhengyi WangDepartment of Institute of Laboratory Animal Sciences, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Liang ZhouDepartment of Institute of Laboratory Animal Sciences, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Xiaoying WuMinistry of Education and Training, Chengdu Second People's Hospital, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chimeric antigen receptor T-cesll therapy (CAR-T) has achieved groundbreaking advancements in clinical application, ushering in a new era for innovative cancer treatment. However, the challenges associated with implementing this novel targeted cell therapy are increasingly significant. Particularly in the clinical management of solid tumors, obstacles such as the immunosuppressive effects of the tumor microenvironment, limited local tumor infiltration capability of CAR-T cells, heterogeneity of tumor targeting antigens, uncertainties surrounding CAR-T quality, control, and clinical adverse reactions have contributed to increased drug resistance and decreased compliance in tumor therapy. These factors have significantly impeded the widespread adoption and utilization of this therapeutic approach. In this paper, we comprehensively analyze recent preclinical and clinical reports on CAR-T therapy while summarizing crucial factors influencing its efficacy. Furthermore, we aim to identify existing solution strategies and explore their current research status. Through this review article, our objective is to broaden perspectives for further exploration into CAR-T therapy strategies and their clinical applications.

Indexed as

Immunotherapy, AdoptiveNeoplasmsReceptors, Chimeric AntigenTumor MicroenvironmentAnimalsAntigens, NeoplasmHumansReceptors, Antigen, T-CellT-LymphocytesAntigens, NeoplasmReceptors, Antigen, T-CellReceptors, Chimeric AntigenChimeric antigen receptor T-cell therapy (CAR–T)Influencing factorSolution strategiesTumor targeting therapy

Identifiers

PMID39220130
PMCPMC11361912

What OpenQuestion holds

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LicenceCC BY
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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.