Melanoma is currently divided on a genetic level according to mutational status.,However, this classification does not optimally predict prognosis.,In prior studies, we have defined gene expression phenotypes (high-immune, pigmentation, proliferative and normal-like), which are predictive of survival outcome as well as informative of biology.,Herein, we employed a population-based metastatic melanoma cohort and external cohorts to determine the prognostic and predictive significance of the gene expression phenotypes.,We performed expression profiling on 214 cutaneous melanoma tumors and found an increased risk of developing distant metastases in the pigmentation (HR, 1.9; 95% CI, 1.05-3.28; P=0.03) and proliferative (HR, 2.8; 95% CI, 1.43-5.57; P=0.003) groups as compared to the high-immune response group.,Further genetic characterization of melanomas using targeted deep-sequencing revealed similar mutational patterns across these phenotypes.,We also used publicly available expression profiling data from melanoma patients treated with targeted or vaccine therapy in order to determine if our signatures predicted therapeutic response.,In patients receiving targeted therapy, melanomas resistant to targeted therapy were enriched in the MITF-low proliferative subtype as compared to pre-treatment biopsies (P=0.02).,In summary, the melanoma gene expression phenotypes are highly predictive of survival outcome and can further help to discriminate patients responding to targeted therapy.
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.