Evidence map›Paper›PMID 36761417›Full record

ReviewFrontiers in oncology2022

Targeting the cMET pathway to enhance immunotherapeutic approaches for mUM patients.

Devayani Machiraju, Jessica C Hassel

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in oncology, 2022. 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
1.7field-weighted citation impact, top 18% 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

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
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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.

Devayani MachirajuDepartment of Dermatology and National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
Jessica C HasselDepartment of Dermatology and National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
Heidelberg University · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The liver is the most preferential initial site of metastasis for uveal melanoma (mUM), and this preference is associated with rapid mortality in mUM patients. Despite the significant clinical benefits of Immune checkpoint inhibitors (ICIs) in metastatic cutaneous melanoma patients, ICIs have shown little to no benefit in mUM patients. A potential reason for this inefficiency of ICI could be partly devoted to the involvement of the liver itself, thanks to its rich source of growth factors and immunosuppressive microenvironment. Uveal melanoma cells show increased expression of a transmembrane protein called cMET, which is known as the sole receptor for the Hepatocyte growth factor (HGF). Hyperactivation of cMET by HGF contributes to mUM development, and the liver, being the major source of HGF, may partially explain the metastasis of uveal melanoma cells to the liver. In addition, cMET/HGF signaling has also been shown to mediate resistance to ICI treatment, directly and indirectly, involving tumor and immune cell populations. Therefore, targeting the cMET/HGF interaction may enhance the efficacy of immunotherapeutic regimes for mUM patients. Hence in this minireview, we will discuss the rationale for combining cMET inhibitors/antibodies with leading immune checkpoint inhibitors for treating mUM. We will also briefly highlight the challenges and opportunities in targeting cMET in mUM.

Indexed as

cMET signalingcombination (combined) therapyimmunotherapyLAG3: Lymphocyte-activation gene 3liver metastasisPD1 (programmed cell death protein 1)resistance mechanismuveal melanoma

Identifiers

PMID36761417
PMCPMC9902905
OpenAlexW4317821416

What OpenQuestion holds

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LicenceCC BY
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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.