Evidence map›Paper›PMID 29541007›Full record

ArticleClinical proteomics2018

Proteomics-based insights into mitogen-activated protein kinase inhibitor resistance of cerebral melanoma metastases.

Nina Zila, Andrea Bileck, Besnik Muqaku, Lukas Janker, Ossia M Eichhoff, Phil F Cheng, Reinhard Dummer, Mitchell P Levesque, Christopher Gerner, Verena Paulitschke

Open access · goldAbstract read
In one paragraph

Article in Clinical proteomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.9field-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

7 citing papers in PubMed, 19 citations in OpenAlex.

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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

10 authors at 4 institutions in 2 countries.

Nina Zila1Department of Dermatology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria.
Andrea Bileck2Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria.
Besnik Muqaku2Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria.
Lukas Janker2Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria.
Ossia M EichhoffDepartment of Dermatology, University of Zurich, University Hospital Zurich, Zurich, Switzerland.
Phil F ChengDepartment of Dermatology, University of Zurich, University Hospital Zurich, Zurich, Switzerland.
Reinhard DummerDepartment of Dermatology, University of Zurich, University Hospital Zurich, Zurich, Switzerland.
Mitchell P LevesqueDepartment of Dermatology, University of Zurich, University Hospital Zurich, Zurich, Switzerland.
Christopher Gerner2Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria.
Verena Paulitschke1Department of Dermatology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria.
University of Vienna · ATUniversity of Zurich · CHUniversity Hospital of Zurich · CHMedical University of Vienna · AT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMAP kinase inhibitor (MAPKi) therapy for BRAF mutated melanoma is characterized by high response rates but development of drug resistance within a median progression-free survival (PFS) of 9-12 months. Understanding mechanisms of resistance and identifying effective therapeutic alternatives is one of the most important scientific challenges in melanoma. Using proteomics, we want to specifically gain insight into the pathophysiological process of cerebral metastases.

methodsCerebral metastases from melanoma patients were initially analyzed by a LC-MS shotgun approach performed on a QExactive HF hybrid quadrupole-orbitrap mass spectrometer. For further validation steps after bioinformatics analysis, a targeted LC-QQQ-MS approach, as well as Western blot, immunohistochemistry and immunocytochemistry was performed.

resultsIn this pilot study, we were able to identify 5977 proteins by LC-MS analysis (data are available via ProteomeXchange with identifier PXD007592). Based on PFS, samples were classified into good responders (PFS ≥ 6 months) and poor responders (PFS [Formula: see text] 3 months). By evaluating these proteomic profiles according to gene ontology (GO) terms, KEGG pathways and gene set enrichment analysis (GSEA), we could characterize differences between the two distinct groups. We detected an EMT feature (up-regulation of N-cadherin) as classifier between the two groups, V-type proton ATPases, cell adhesion proteins and several transporter and exchanger proteins to be significantly up-regulated in poor responding patients, whereas good responders showed an immune activation, among other features. We identified class-discriminating proteins based on nearest shrunken centroids, validated and quantified this signature by a targeted approach and could correlate parts of this signature with resistance using the CPL/MUW proteome database and survival of patients by TCGA analysis. We further validated an EMT-like signature as a major discriminator between good and poor responders on primary melanoma cells derived from cerebral metastases. Higher immune activity is demonstrated in patients with good response to MAPKi by immunohistochemical staining of biopsy samples of cerebral melanoma metastases.

conclusionsEmploying proteomic analysis, we confirmed known extra-cerebral resistance mechanisms in the cerebral metastases and further discovered possible brain specific mechanisms of drug efflux, which might serve as treatment targets or as predictive markers for these kinds of metastasis.

Indexed as

BRAF mutationCerebral melanoma metastasesDrug resistanceMAP kinase inhibitorMelanomaProteomics

Identifiers

PMID29541007
PMCPMC5844114
OpenAlexW2793263608

What OpenQuestion holds

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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.