A protein deep sequencing evaluation of metastatic melanoma tissues.

Research output: Contribution to journalArticle

Abstract

Malignant melanoma has the highest increase of incidence of malignancies in the western world. In early stages, front line therapy is surgical excision of the primary tumor. Metastatic disease has very limited possibilities for cure. Recently, several protein kinase inhibitors and immune modifiers have shown promising clinical results but drug resistance in metastasized melanoma remains a major problem. The need for routine clinical biomarkers to follow disease progression and treatment efficacy is high. The aim of the present study was to build a protein sequence database in metastatic melanoma, searching for novel, relevant biomarkers. Ten lymph node metastases (South-Swedish Malignant Melanoma Biobank) were subjected to global protein expression analysis using two proteomics approaches (with/without orthogonal fractionation). Fractionation produced higher numbers of protein identifications (4284). Combining both methods, 5326 unique proteins were identified (2641 proteins overlapping). Deep mining proteomics may contribute to the discovery of novel biomarkers for metastatic melanoma, for example dividing the samples into two metastatic melanoma "genomic subtypes", ("pigmentation" and "high immune") revealed several proteins showing differential levels of expression. In conclusion, the present study provides an initial version of a metastatic melanoma protein sequence database producing a total of more than 5000 unique protein identifications. The raw data have been deposited to the ProteomeXchange with identifiers PXD001724 and PXD001725.

Details

Authors
Organisations
External organisations
  • Warsaw University of Life Sciences
  • National Korányi Institute for Tuberculosis and Pulmonology, Hungary
  • Medical University of Vienna
  • Lund University
  • Tokyo Medical University
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Cancer and Oncology
Original languageEnglish
Article numbere0123661
JournalPLoS ONE
Volume10
Issue number4
Publication statusPublished - 2015
Publication categoryResearch
Peer-reviewedYes

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