Evidence map›Paper›PMID 31163679›Full record

ReviewCancers2019

Natural Killer Cells as Allogeneic Effectors in Adoptive Cancer Immunotherapy.

Kyle B Lupo, Sandro Matosevic

Open access · goldAbstract readReview
In one paragraph

Review in Cancers, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 131 papers.

0numbers the graph read from it
0cells of the map it votes in
131citing papers in PubMed
8.8field-weighted citation impact, top 2% 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

131 citing papers in PubMed, 199 citations in OpenAlex.

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71 more citing papers are in PubMed but not listed here.

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.

Kyle B LupoDepartment of Industrial and Physical Pharmacy, Purdue University, West Lafayette, IN 47907, USA. klupoo@purdue.edu.
Sandro MatosevicDepartment of Industrial and Physical Pharmacy, Purdue University, West Lafayette, IN 47907, USA. sandro@purdue.edu.ORCID 0000-0001-5118-2455
Purdue University West Lafayette · US

Funding

Indiana Clinical and Translational Sciences Institute UL1TR001108
6 · The paper itself

Abstract

Natural killer (NK) cells are attractive within adoptive transfer settings in cancer immunotherapy due to their potential for allogeneic use; their alloreactivity is enhanced under conditions of killer immunoglobulin-like receptor (KIR) mismatch with human leukocyte antigen (HLA) ligands on cancer cells. In addition to this, NK cells are platforms for genetic modification, and proliferate in vivo for a shorter time relative to T cells, limiting off-target activation. Current clinical studies have demonstrated the safety and efficacy of allogeneic NK cell adoptive transfer therapies as a means for treatment of hematologic malignancies and, to a lesser extent, solid tumors. However, challenges associated with sourcing allogeneic NK cells have given rise to controversy over the contribution of NK cells to graft-versus-host disease (GvHD). Specifically, blood-derived NK cell infusions contain contaminating T cells, whose activation with NK-stimulating cytokines has been known to lead to heightened release of proinflammatory cytokines and trigger the onset of GvHD in vivo. NK cells sourced from cell lines and stem cells lack contaminating T cells, but can also lack many phenotypic characteristics of mature NK cells. Here, we discuss the available published evidence for the varying roles of NK cells in GvHD and, more broadly, their use in allogeneic adoptive transfer settings to treat various cancers.

Indexed as

allogeneic immunotherapygraft-versus-host diseaseHLA mismatchnatural killer cells

Identifiers

PMID31163679
PMCPMC6628161
OpenAlexW2948485623

What Socratic holds

Textmetadata
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 Socratic graph.