Evidence map›Paper›PMID 33776765›Full record

ArticleFrontiers in pharmacology2021

BAY 60-6583 Enhances the Antitumor Function of Chimeric Antigen Receptor-Modified T Cells Independent of the Adenosine A2b Receptor.

Jiaxing Tang, Yan Zou, Long Li, Fengping Lu, Hongtao Xu, Pengxuan Ren, Fang Bai, Gabriele Niedermann, Xuekai Zhu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in pharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
0.9field-weighted citation impact, top 23% of its field
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

12 citing papers in PubMed, 12 citations in OpenAlex.

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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

9 authors at 3 institutions in 2 countries.

Jiaxing TangShanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai, China.
Yan ZouShanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai, China.
Long LiShanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai, China.
Fengping LuShanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai, China.
Hongtao XuShanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai, China.
Pengxuan RenShanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai, China.
Fang BaiShanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai, China.
Gabriele NiedermannDepartment of Radiation Oncology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Xuekai ZhuShanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai, China.
ShanghaiTech University · CNCenter for Excellence in Molecular Cell Science · CNHeidelberg University · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chimeric antigen receptor (CAR) T cells are powerful in eradicating hematological malignancies, but their efficacy is limited in treating solid tumors. One of the barriers is the immunosuppressive response induced by immunomodulatory signaling pathways. Pharmacological targeting of these immunosuppressive pathways may be a simple way to improve the efficacy of CAR T cells. In this study, anti-CD133 and anti-HER2 CAR T cells were generated from healthy donors, and combination therapy using CAR T cells and small molecules targeting adenosine receptors was performed

Indexed as

BAY 60-6583chimeric antigen receptorimmunosuppressionimmunotherapysolid tumor

Identifiers

PMID33776765
PMCPMC7994267
OpenAlexW3139033888

What OpenQuestion holds

Textmetadata
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.