Evidence map›Paper›PMID 22178904›Full record

ReviewAdvanced drug delivery reviews2012

Genetic engineering with T cell receptors.

Ling Zhang, Richard A Morgan

Abstract readReview
In one paragraph

Review in Advanced drug delivery reviews, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
0.8field-weighted citation impact, top 26% 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

19 citing papers in PubMed, 27 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Review
  6. Review
  7. T-Cell-Mimicking Nanoparticles Can Neutralize HIV Infectivity.Advanced materials (Deerfield Beach, Fla.) · 2018
    Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Cellular immunotherapy for malignant gliomas.Expert opinion on biological therapy · 2016
    Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Genetically engineered T cells for cancer immunotherapy.Signal transduction and targeted therapy
    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

2 authors at 1 institution in 1 country.

Ling ZhangNational Institutes of Health, National Cancer Institute, Surgery Branch, Bethesda, MD 20892, USA.
Richard A Morgan
National Cancer Institute · US

Funding

Intramural NIH HHS Z99 CA999999NCI NIH HHS Y99 CA999999
6 · The paper itself

Abstract

In the past two decades, human gene transfer research has been translated from a laboratory technology to clinical evaluation. The success of adoptive transfer of tumor-reactive lymphocytes to treat the patients with metastatic melanoma has led to new strategies to redirect normal T cells to recognize tumor antigens by genetic engineering with tumor antigen-specific T cell receptor (TCR) genes. This new strategy can generate large numbers of defined antigen-specific cells for therapeutic application. Much progress has been made to TCR gene transfer systems by optimizing gene expression and gene transfer protocols. Vector and protein modifications have enabled excellent expression of introduced TCR chains in human lymphocytes with reduced mis-pairing between the introduced and endogenous TCR chains. Initial clinical studies have demonstrated that TCR gene-engineered T cells could mediate tumor regression in vivo. In this review, we discuss the progress and prospects of TCR gene-engineered T cells as a therapeutic strategy for treating patients with melanoma and other cancers.

Indexed as

Genetic TherapyGene Transfer TechniquesAnimalsHumansNeoplasmsReceptors, Antigen, T-CellReceptors, Antigen, T-Cell

Identifiers

PMID22178904
PMCPMC3404458
OpenAlexW2038566729

What OpenQuestion holds

Textmetadata
LicenceTDM
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.