Evidence map›Paper›PMID 34268421›Full record

ReviewAnnals of translational medicine2021

A narrative review of chimeric antigen receptor-T (CAR-T) cell therapy for lung cancer.

Yujia Liu, Yayi He

Open access · diamondAbstract readReview
In one paragraph

Review in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.7field-weighted citation impact, top 27% 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

6 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. The role of CD4Translational cancer research · 2024
    Article
  3. Article
  4. Article
  5. Article
  6. Article
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

2 authors at 2 institutions in 1 country.

Yujia LiuTongji University, Shanghai, China.
Yayi HeTongji University, Shanghai, China.
Shanghai Pulmonary Hospital · CNTongji University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer represents one of the most common and deadliest cancers in the world. Chimeric antigen receptor-T cell (CAR-T) therapy which can recognize antigens in a major histocompatibility complex (MHC)-independent manner provides a new approach for tumor treatment. However, lung cancer, as a solid tumor, faces several formidable barriers to adoptive cell transfer, which includes inhibition of T-cell localization and suppression of T-cell function. Therefore, lung cancer fails to respond significantly to infusions of CAR-T cells in most trials until now. PubMed was researched using the terms "CAR-T" and "lung cancer" only in English from 2000 through June 2020. We also included results presented in international conferences, such as the American Society of Clinical Oncology (ASCO) and the European Society for Medical Oncology (ESMO). Besides, we found new progress in CAR-T therapy for solid tumors as a supplement. To enhance the efficacy and conquer the limitations, we collected some applications in lung cancer. In recent years, there have been some improvements in selecting the proper target and reducing toxicity. CAR-T technology provides an excellent way for tumor treatment, which does not depend on MHC molecules and provides a new method for the utilization of tumor targets. Targeting different antigens and overcoming the solid barrier, there are some improvements in responding significantly and reducing toxicity. CAR-T technology will play a decisive role in the treatment of lung cancer.

Indexed as

cancer immunityChimeric antigen receptor-T (CAR-T)lung cancer

Identifiers

PMID34268421
PMCPMC8246176
OpenAlexW3152531484

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.