Evidence map›Paper›PMID 32416056›Full record

ReviewStem cells translational medicine2020

Killers at the crossroads: The use of innate immune cells in adoptive cellular therapy of cancer.

May Sabry, Mark W Lowdell

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Stem cells translational medicine, 2020. 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
1.2field-weighted citation impact, top 19% 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, 24 citations in OpenAlex.

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  15. KIR2DS2 Expression Identifies NK Cells With Enhanced Anticancer Activity.Journal of immunology (Baltimore, Md. : 1950) · 2022
    Article
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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

2 authors at 1 institution in 1 country.

May SabryDepartment of Haematology, University College London, London, UK.ORCID https://orcid.org/0000-0003-2466-4066
Mark W LowdellDepartment of Haematology, University College London, London, UK.
University College London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Adoptive cell therapy (ACT) is an approach to cancer treatment that involves the use of antitumor immune cells to target residual disease in patients after completion of chemo/radiotherapy. ACT has several advantages compared with other approaches in cancer immunotherapy, including the ability to specifically expand effector cells in vitro before selection for adoptive transfer, as well as the opportunity for host manipulation in order to enhance the ability of transferred cells to recognize and kill established tumors. One of the main challenges to the success of ACT in cancer clinical trials is the identification and generation of antitumor effector cells with high avidity for tumor recognition. Natural killer (NK) cells, cytokine-induced killers and natural killer T cells are key innate or innate-like effector cells in cancer immunosurveillance that act at the interface between innate and adaptive immunity, to have a greater influence over immune responses to cancer. In this review, we discuss recent studies that highlight their potential in cancer therapy and summarize clinical trials using these effector immune cells in adoptive cellular therapy for the treatment of cancer.

Indexed as

Adoptive TransferImmunity, InnateAnimalsClinical Trials as TopicGenetic TechniquesHumansKiller Cells, NaturalNeoplasmsadoptive cell therapycancercytokine-induced killer cellsimmunotherapynatural killer cellsnatural killer T cells

Identifiers

PMID32416056
PMCPMC7445022
OpenAlexW3024706376

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.