Evidence map›Paper›PMID 35874685›Full record

ReviewFrontiers in immunology2022

CAR T-Cell Therapy Predictive Response Markers in Diffuse Large B-Cell Lymphoma and Therapeutic Options After CART19 Failure.

Ana Carolina Caballero, Laura Escribà-Garcia, Carmen Alvarez-Fernández, Javier Briones

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
2.5field-weighted citation impact, top 9% 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

18 citing papers in PubMed, 27 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

4 authors at 2 institutions in 1 country.

Ana Carolina CaballeroHematology Service, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain.
Laura Escribà-GarciaHematology Service, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain.
Carmen Alvarez-FernándezHematology Service, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain.
Javier BrionesHematology Service, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain.
Josep Carreras Leukaemia Research Institute · ESHospital de Sant Pau · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immunotherapy with T cells genetically modified with chimeric antigen receptors (CARs) has shown significant clinical efficacy in patients with relapsed/refractory B-cell lymphoma. Nevertheless, more than 50% of treated patients do not benefit from such therapy due to either absence of response or further relapse. Elucidation of clinical and biological features that would predict clinical response to CART19 therapy is of paramount importance and eventually may allow for selection of those patients with greater chances of response. In the last 5 years, significant clinical experience has been obtained in the treatment of diffuse large B-cell lymphoma (DLBCL) patients with CAR19 T cells, and major advances have been made on the understanding of CART19 efficacy mechanisms. In this review, we discuss clinical and tumor features associated with response to CART19 in DLBCL patients as well as the impact of biological features of the infusion CART19 product on the clinical response. Prognosis of DLBCL patients that fail CART19 is poor and therapeutic approaches with new drugs are also discussed.

Indexed as

Immunotherapy, AdoptiveLymphoma, Large B-Cell, DiffuseReceptors, Chimeric AntigenHumansNeoplasm Recurrence, LocalT-LymphocytesTreatment FailureReceptors, Chimeric AntigenantibodiesbispecificCAR-TDLBCLlymphoma

Identifiers

PMID35874685
PMCPMC9299440
OpenAlexW4286632583

What OpenQuestion holds

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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.