Evidence map›Paper›PMID 35372786›Full record

ReviewAntibody therapeutics2022

Chimeric antigen receptor T-cell therapy: challenges and opportunities in lung cancer.

Caili Xu, Dianwen Ju, Xuyao Zhang

Open access · goldAbstract readReview
In one paragraph

Review in Antibody therapeutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.2field-weighted citation impact, top 21% 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

8 citing papers in PubMed, 12 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Innate Immunity in Cancer Biology and Therapy.International journal of molecular sciences · 2023
    Review
  5. Article
  6. Review
  7. Review
  8. 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

3 authors at 1 institution in 1 country.

Caili XuDepartment of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, School of Pharmacy, Fudan University, Shanghai 201203, China.
Dianwen JuDepartment of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, School of Pharmacy, Fudan University, Shanghai 201203, China.
Xuyao ZhangDepartment of Biological Medicines & Shanghai Engineering Research Center of Immunotherapeutics, School of Pharmacy, Fudan University, Shanghai 201203, China.ORCID https://orcid.org/0000-0003-1654-5550
Fudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chimeric antigen receptor (CAR) T-cell therapy has revolutionized the paradigm in hematological malignancies treatment, driving an ever-expanding number of basic research and clinical trials of genetically engineering T cells to treat solid tumors. CAR T-cell therapies based on the antibodies targeting Mesothelin, CEA, EGFR, EGFR, MUC1, DLL3, and emerging novel targets provide promising efficacy for lung cancer patients. However, clinical application of CAR T-cell therapy against lung cancer remains limited on account of physical and immune barriers, antigen escape and heterogeneity, on-target off-tumor toxicity, and many other reasons. Understanding the evolution of CAR structure and the generalizable requirements for manufacturing CAR T cells as well as the interplay between lung tumor immunology and CAR T cells will improve clinical translation of this therapeutic modality in lung cancer. In this review, we systematically summarize the latest advances in CAR T-cell therapy in lung cancer, focusing on the CAR structure, target antigens, challenges, and corresponding new strategies.

Indexed as

CAR structureCAR T-cell therapychallengeslung cancernew strategies

Identifiers

PMID35372786
PMCPMC8972219
OpenAlexW4214898962

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.