Evidence map›Paper›PMID 32426136›Full record

ReviewBiomarker research2020

Biomarkers in individualized management of chimeric antigen receptor T cell therapy.

Mengyi Du, Parameswaran Hari, Yu Hu, Heng Mei

Abstract readReview
In one paragraph

Review in Biomarker research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

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  9. Multiple myeloma: a paradigm for blending community and academic care.Hematology. American Society of Hematology. Education Program · 2023
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  20. Neurological Immunotoxicity from Cancer Treatment.International journal of molecular sciences · 2021
    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

4 authors.

Mengyi Du1Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China.
Parameswaran Hari2Division of Hematology/Oncology, Medical College of Wisconsin (MCW), Milwaukee, WI USA.
Yu Hu1Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China.
Heng Mei1Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The development of chimeric antigen receptor (CAR) T cell immunotherapy has achieved promising results, both in clinical studies and in commercial products for patients with hematologic malignancies. Despite high remission rates of CAR-T cell therapy in previously untreatable, refractory and/or relapsed patients, several challenges in CAR-T therapy remain to be overcome, especially in integrating such therapies into personalized disease management approaches. Given the unique characteristics of CAR-T therapy, it is particularly urgent to identify biomarkers to maximize their clinical benefits. This systematic review summarizes clinically relevant biomarkers that may help individualized disease management in patients receiving CAR-T cell therapy in terms of toxicity warning, efficacy prediction and relapse monitoring. We summarize data from 18 clinical trials, including traditional indicators like cytokines, biochemical proteins, tumor burden, as well as potential novel indicators such as CAR-T cell expansion and persistency. The establishment of a biomarker-based system aimed at individualized management is recommended to guide better clinical application of CAR-T products.

Indexed as

BiomarkerCAR-T therapyEfficacyPrognosisSafety

Identifiers

PMID32426136
PMCPMC7216329

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.