Evidence map›Paper›PMID 33921301›Full record

ReviewCells2021

Depleting Tumor Cells Expressing Immune Checkpoint Ligands-A New Approach to Combat Cancer.

Fabrizio Marcucci, Cristiano Rumio

Abstract readReview
In one paragraph

Review in Cells, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. 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.

Fabrizio MarcucciDepartment of Pharmacological and Biomolecular Sciences, University of Milan, Via Trentacoste 2, 20134 Milan, Italy.
Cristiano RumioDepartment of Pharmacological and Biomolecular Sciences, University of Milan, Via Trentacoste 2, 20134 Milan, Italy.ORCID 0000-0002-2593-7620

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Antibodies against inhibitory immune checkpoint molecules (ICPMs), referred to as immune checkpoint inhibitors (ICIs), have gained a prominent place in cancer therapy. Several ICIs in clinical use have been engineered to be devoid of effector functions because of the fear that ICIs with preserved effector functions could deplete immune cells, thereby curtailing antitumor immune responses. ICPM ligands (ICPMLs), however, are often overexpressed on a sizeable fraction of tumor cells of many tumor types and these tumor cells display an aggressive phenotype with changes typical of tumor cells undergoing an epithelial-mesenchymal transition. Moreover, immune cells expressing ICPMLs are often endowed with immunosuppressive or immune-deviated functionalities. Taken together, these observations suggest that compounds with the potential of depleting cells expressing ICPMLs may become useful tools for tumor therapy. In this article, we summarize the current state of the art of these compounds, including avelumab, which is the only ICI targeting an ICPML with preserved effector functions that has gained approval so far. We also discuss approaches allowing to obtain compounds with enhanced tumor cell-depleting potential compared to native antibodies. Eventually, we propose treatment protocols that may be applied in order to optimize the therapeutic efficacy of compounds that deplete cells expressing ICPMLs.

Indexed as

Antibodies, NeoplasmHumansImmune Checkpoint InhibitorsImmune Checkpoint ProteinsLigandsNeoplasmsTreatment OutcomeAntibodies, NeoplasmImmune Checkpoint InhibitorsImmune Checkpoint ProteinsLigandsADCbispecificCAR T cellscombination therapyeffector functionsepithelial-mesenchymal transitionimmune checkpointoncolytic virusoverexpression

Identifiers

PMID33921301
PMCPMC8069236

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Registered trials

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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.