Evidence map›Paper›PMID 39776913›Full record

ArticleFrontiers in immunology2024

Generation of effective and specific human TCRs against tumor/testis antigen NY-ESO-1 in mice with humanized T cell recognition system.

Xiaojing Tina Chen, Matthias Leisegang, Ioannis Gavvovidis, Seth M Pollack, Felix K M Lorenz, Ton N Schumacher, Oliver Daumke, Thomas Blankenstein

Abstract read
In one paragraph

Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
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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

8 authors.

Xiaojing Tina ChenMolecular Immunology and Gene Therapy, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany.
Matthias LeisegangDavid and Etta Jonas Center for Cellular Therapy, the University of Chicago, Chicago, IL, United States.
Ioannis GavvovidisMolecular Immunology and Gene Therapy, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany.
Seth M PollackFred Hutchinson Cancer Research Center, University of Washington, Seattle, WA, United States.
Felix K M LorenzMax Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany.
Ton N SchumacherDivision of Molecular Oncology & Immunology, Oncode Institute, The Netherlands Cancer Institute, Amsterdam, Netherlands.
Oliver DaumkeStructural Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.
Thomas BlankensteinMolecular Immunology and Gene Therapy, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Generation of high avidity T cell receptors (TCRs) reactive to tumor-associated antigens (TAA) is impaired by tolerance mechanisms, which is an obstacle to effective T cell therapies for cancer treatment. NY-ESO-1, a human cancer-testis antigen, represents an attractive target for such therapies due to its broad expression in different cancer types and the restricted expression in normal tissues. Utilizing transgenic mice with a diverse human TCR repertoire, we isolated effective TCRs against NY-ESO-1

Indexed as

Antigens, NeoplasmMembrane ProteinsMice, TransgenicReceptors, Antigen, T-CellAnimalsCell Line, TumorHLA-A2 AntigenHumansImmunotherapy, AdoptiveMaleMiceT-LymphocytesAntigens, NeoplasmCTAG1B protein, humanHLA-A*02:01 antigenHLA-A2 AntigenMembrane ProteinsReceptors, Antigen, T-Celladoptive T cell therapyHLA-A*0201humanized miceNY-ESO-1TCR engineering

Identifiers

PMID39776913
PMCPMC11703889

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Registered trials

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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.