Proteomics encompasses large-scale analyses involving the structure or function of proteins. The contribution of proteomics in various fields of science is well-acknowledged. Since the amount, type and activity of proteins synthesized inside a cell is constantly governed by external stimuli, proteomic analyses can provide vital cellular information at any point of time. Here, we have summarized the major findings in melanoma in the past five years that can be attributed to proteomics.

Melanoma is a deadly disease that accounts for a majority (~ 75%) of skin-cancer related deaths. The incidence of melanoma has almost doubled since 1973, and it keeps increasing every year. The advanced stage of the disease is associated with poor prognosis and very low survival rate; stage IV melanoma typically has a 5 year survival rate of approximately 15-20%. Despite of the increasing incidence of melanoma cases, the survival rate of melanoma patients has improved over the years due to improved diagnosis, and availability of better treatment options. The field of proteomics has contributed significantly in the development of effective diagnostic and prognostic tools available to clinicians currently.

A major role played by proteomics in melanoma has been identification of potential biomarkers for diagnostic purposes. Efficient, sensitive and specific biomarkers are key to early diagnosis and initiation of effective treatment and tremendous progress in the field of proteomics in the past few years have resulted in the discovery of several putative biomarkers through comprehensive proteomic analysis of cell lines, tissues and serum [1]. Proteomics is an alternative tool for biomarker discovery compared to genomics, since it does not call for constant access to fresh tissue unlike genomic profiling [2]. Although it is possible to perform quantitative gene profiling using formalin-fixedparaffin- embedded (FFPE) tissues, but the RNA derived from such archived tissues are often degraded and fragmented which sometimes results in “FFPE bias” [3]. Recent work by Kawahara et al. [4], showed the potential use of discovery-based proteomics data obtained from the secretion of melanoma cell lines along with other human cell lines. They used a MS-based approach followed by clustering techniques and bioinformaticsto determine proteins that were differentially expressed in the melanoma cell lines (A2058 and SK-MEL-28) compared to noncancerous cell lines (HaCaT and HEK293). The differentially identified proteins were later validated by other approaches like immunoblotting and tissue microarrays. Using this approach they were able to identify 271 potential biomarkers for melanoma.

Over the years, proteomics has emerged as a vital tool for the identification of active molecular pathways and unveiling mechanisms of pathogenesis as well as drug action and resistance. Byrum et al. [5] used quantitative proteomics on formalin-fixed paraffin-embedded human melanoma tissues to identify molecular pathways that were aberrant in melanoma. They were able to identify 171 proteins that were differentially expressed in the three types of tissues - benign nevi, primary melanoma, and metastatic melanoma, many of which constitute molecular pathways associated with apoptosis, tumor cell proliferation and cell motility. These provided mechanistic insights into pathogenesis associated with advanced melanoma. In 2014, Rebecca et al. [6] used liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM) to study the molecular mechanisms of responses of melanoma cells to MEK and HSP90 inhibitors. Another study used quantitative protein profiling by tandem mass spectrometry for comprehensive proteomic analysis of responders and non-responders to Dacarbazine (DTIC) or temozolomide (TMZ) chemotherapy [7]. In this study, the group was able to detect S100A13 as the protein responsible for resistance to chemotherapy in the nonresponders. Singh et al. [8] utilized a gel free quantitative proteomics approach to identify targets of the histone deacetylase SIRT1, which is upregulated in melanoma. Upon treatment with a SIRT1 inhibitor, they identified 1091 proteins of which 20 were differentially expressed in the treatment group, including the BUB family proteins (BUB3, BUB1 and BUBR1). Using proteomics approaches, they were able to conclude that BUB family proteins are downstream targets of SIRT1. Cholewa et al. [9] used label-free comparative proteomics analysis with nano- LC-MS/MS technology to determine the cause of failure of Polo-like kinase 1 (Plk1) inhibitors as cancer therapeutics. When they performed a large-scale comprehensive analysis of proteins in BRAF mutant melanoma cells treated with a Plk1-specific inhibitor, they detected down-regulation of several proteins including metabolic proteins and multiple proteosomal subunits and up-regulation of proteins like hnRNPC all of which provided mechanistic insights into the function of Plk-1 in cancer. In 2015, Lai et al. [10] used a temporal quantitative proteomics approach, iTRAQ 2D-LC-MS/MS, to reveal insight into the mechanism of cytotoxicity of the drug panduratin A (PA) in A375 melanoma cells. They found that proteins associated with mitochondrial oxidative phosphorylation, ER stress pathway, and apoptosis were down-regulated in cells treated with PA indicating prolonged ER stress as the primary cause of apoptosis. Hence, it can be concluded that the tool of proteomics coupled with the recent advancements in the field of bioinformatics have proved to be tremendously useful in understanding the complexities of drug action and resistance, which contributes significantly to designing effective treatment regimens.

Often more than one proteomic approach is used to solve a particular problem. In 2013, Gholami et al. [11] investigated protein and kinase expression in the model system NCI-60 cell line using a combination of three proteomic approaches - proteomic profiling, kinomic profiling and deep proteomics. Overall, 10,350 proteins (including 375 protein kinases and a core cancer proteome of 5,578 proteins) were quantified across all nine tissue types (including melanoma cell lines). Bioinformatic evaluation identified hundreds of potential biomarkers along with potential protein markers for drug sensitivity and resistance. Another such study utilizing more than one proteomic approaches was performed recently by Paulitschke et al. [12] who used shotgun analysis, pressure cycling technology, and selected reaction monitoring to investigate the mechanism of resistance against BRAF inhibitors in melanoma patients. Using these techniques they showed that BRAFi resistance is caused chiefly by epithelial-mesenchymal transformation that occurs when melanoma cells turn invasive as a result of treatment using BRAFi. Some of the major contributions of proteomics in the field of melanoma are summarized in Table 1.

Table 1: Major contributions of proteomics in melanoma.

Although complex analyses involved in large-scale proteomics poses challenges, the tool of proteomics has proved to be indispensable in various fields of cancer including melanoma for identification of novel and effective diagnostic and prognostic markers. Furthermore, the growing field of proteomics is increasingly being used for effective drug design, in addition to understanding drug action and mechanism of resistance. It can be expected that with the recent progress in proteomics, it will soon be possible to customize medications personalized for each individual to guarantee maximum effectiveness with least toxicity.

This was supported by the National Institutes of Health (R01GM106024, R33CA173264, UL1TR000039, P20GM103625, S10OD018445 and P20GM103429).