Evidence map›Paper›PMID 38027932›Full record

ReviewHeliyon2023

CAR-T cell therapy for hematological malignancies: History, status and promise.

Chao Wang, Jianpeng Wang, Shusheng Che, Hai Zhao

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing 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

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

  1. Pooled it
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  19. From lab to lifesaver: the rise of CAR T-cell therapy in oncology.Journal of the Egyptian National Cancer Institute · 2025
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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.

Chao WangDepartment of Neurosurgery, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, Shandong, 266005, China.
Jianpeng WangDepartment of Neurosurgery, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, Shandong, 266005, China.
Shusheng CheDepartment of Neurosurgery, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, Shandong, 266005, China.
Hai ZhaoDepartment of Neurosurgery, The Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, Shandong, 266005, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

For many years, the methods of cancer treatment are usually surgery, chemotherapy and radiation therapy. Although these methods help to improve the condition, most tumors still have a poor prognosis. In recent years, immunotherapy has great potential in tumor treatment. Chimeric antigen receptor T-cell immunotherapy (CAR-T) uses the patient's own T cells to express chimeric antigen receptors. Chimeric antigen receptor (CAR) recognizes tumor-associated antigens and kills tumor cells. CAR-T has achieved good results in the treatment of hematological tumors. In 2017, the FDA approved the first CAR-T for the treatment of B-cell acute lymphoblastic leukemia (ALL). In October of the same year, the FDA approved CAR-T to treat B-cell lymphoma. In order to improve and enhance the therapeutic effect, CAR-T has become a research focus in recent years. The structure of CAR, the targets of CAR-T treatment, adverse reactions and improvement measures during the treatment process are summarized. This review is an attempt to highlight recent and possibly forgotten findings of advances in chimeric antigen receptor T cell for treatment of hematological tumors.

Indexed as

Chimeric antigen receptorHematological malignanciesTarget therapyT cell immunotherapy

Identifiers

PMID38027932
PMCPMC10658259

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.