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Updated: Apr 14, 2026
A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis
Published on: March 8, 2022
A protein deep sequencing evaluation of metastatic melanoma tissues
Charlotte Welinder1, Krzysztof Pawłowski2, Yutaka Sugihara3
1Oncology and Pathology, Dept. of Clinical Sciences, Lund University, Lund, Sweden; Centre of Excellence in Biological and Medical Mass Spectrometry "CEBMMS", Biomedical Centre D13, Lund University, Lund, Sweden.
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.
Insights
Researchers built a metastatic melanoma protein database to find new biomarkers. This deep proteomics analysis identified over 5000 proteins, aiding in understanding disease subtypes and treatment resistance.
Area of Science:
- Oncology
- Proteomics
- Biomarker Discovery
Background:
- Malignant melanoma incidence is rising, with limited cure options for metastatic disease.
- Drug resistance in metastatic melanoma is a significant clinical challenge.
- There is a critical need for biomarkers to monitor disease progression and treatment effectiveness.
Purpose of the Study:
- To construct a comprehensive protein sequence database for metastatic melanoma.
- To identify novel and relevant protein biomarkers for metastatic melanoma.
- To explore potential protein signatures associated with distinct metastatic melanoma subtypes.
Main Methods:
- Global protein expression analysis of ten lymph node metastases using two proteomics approaches.
- Orthogonal fractionation was employed to enhance protein identification.
- Data analysis involved deep mining of proteomic data to identify differentially expressed proteins.
Main Results:
- A total of 5326 unique proteins were identified, with 2641 proteins overlapping between methods.
- Orthogonal fractionation increased protein identifications to 4284.
- Analysis revealed differentially expressed proteins when samples were categorized into "pigmentation" and "high immune" genomic subtypes.
Conclusions:
- The study provides an initial version of a metastatic melanoma protein sequence database.
- Over 5000 unique protein identifications were achieved, offering a valuable resource.
- Deep mining proteomics holds potential for discovering novel biomarkers for metastatic melanoma management.
