Proteogenomics Reveals Perturbed Signaling Networks in Malignant Melanoma Cells Resistant to BRAF Inhibition.


Journal

Molecular & cellular proteomics : MCP
ISSN: 1535-9484
Titre abrégé: Mol Cell Proteomics
Pays: United States
ID NLM: 101125647

Informations de publication

Date de publication:
2021
Historique:
received: 30 04 2021
revised: 04 08 2021
accepted: 12 10 2021
pubmed: 22 10 2021
medline: 25 3 2022
entrez: 21 10 2021
Statut: ppublish

Résumé

Analysis of nucleotide variants is a cornerstone of cancer medicine. Although only 2% of the genomic sequence is protein coding, mutations occurring in these regions have the potential to influence protein structure or modification status and may have severe impact on disease aetiology. Proteogenomics enables the analysis of sample-specific nonsynonymous nucleotide variants with regard to their effect at the proteome and phosphoproteome levels. Here, we developed a proof-of-concept proteogenomics workflow and applied it to the malignant melanoma cell line A375. Initially, we studied the resistance to serine/threonine-protein kinase B-raf (BRAF) inhibitor (BRAFi) vemurafenib in A375 cells. This allowed identification of several oncogenic nonsynonymous nucleotide variants, including a gain-of-function variant on aurora kinase A (AURKA) at F31I. We also detected significant changes in abundance among (phospho)proteins, which led to reactivation of the MAPK signaling pathway in BRAFi-resistant A375 cells. Upon reconstruction of the multiomic integrated signaling networks, we predicted drug therapies with the potential to disrupt BRAFi resistance mechanism in A375 cells. Notably, we showed that AURKA inhibition is effective and specific against BRAFi-resistant A375 cells. Subsequently, we investigated amino acid variants that interfere with protein posttranslational modification (PTM) status and potentially influence A375 cell signaling irrespective of BRAFi resistance. Mass spectrometry (MS) measurements confirmed variant-driven PTM changes in 12 proteins. Among them was the runt-related transcription factor 1 (RUNX1) displaying a variant on a known phosphorylation site S(Ph)276L. We confirmed the loss of phosphorylation site by MS and demonstrated the impact of this variant on RUNX1 interactome.

Identifiants

pubmed: 34673281
pii: S1535-9476(21)00135-3
doi: 10.1016/j.mcpro.2021.100163
pmc: PMC8603206
pii:
doi:

Substances chimiques

Core Binding Factor Alpha 2 Subunit 0
Protein Kinase Inhibitors 0
RUNX1 protein, human 0
Vemurafenib 207SMY3FQT
BRAF protein, human EC 2.7.11.1
Proto-Oncogene Proteins B-raf EC 2.7.11.1

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

100163

Informations de copyright

Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Conflict of interest The authors declare no competing interests.

Auteurs

Marisa Schmitt (M)

Quantitative Proteomics, University of Tuebingen, Tuebingen, Germany.

Tobias Sinnberg (T)

Division of Dermatooncology, University of Tuebingen, Tuebingen, Germany; Cluster of Excellence iFIT (EXC 2180), Image-Guided and Functionally Instructed Tumor Therapies, University of Tuebingen, Tuebingen, Germany.

Katrin Bratl (K)

Quantitative Proteomics, University of Tuebingen, Tuebingen, Germany.

Katharina Zittlau (K)

Quantitative Proteomics, University of Tuebingen, Tuebingen, Germany.

Claus Garbe (C)

Division of Dermatooncology, University of Tuebingen, Tuebingen, Germany; Cluster of Excellence iFIT (EXC 2180), Image-Guided and Functionally Instructed Tumor Therapies, University of Tuebingen, Tuebingen, Germany.

Boris Macek (B)

Quantitative Proteomics, University of Tuebingen, Tuebingen, Germany; Cluster of Excellence iFIT (EXC 2180), Image-Guided and Functionally Instructed Tumor Therapies, University of Tuebingen, Tuebingen, Germany. Electronic address: boris.macek@uni-tuebingen.de.

Nicolas C Nalpas (NC)

Quantitative Proteomics, University of Tuebingen, Tuebingen, Germany. Electronic address: nicolas.nalpas@ifiz.uni-tuebingen.de.

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Classifications MeSH