Proteomic analysis in melanoma cell lines with different genotypes

In the context of personalized medicine, an important role will be played by enabling "omics" technologies for molecular profiling, such as genomic profiling (using approaches of next-generation sequencing, NGS), transcriptomic (using microarrays or quantitative PCR), proteomics (using mass spectrometry methods) and metabolomic (by NMR assays). The combination of these methodologies on both in vitro tumor models and, particularly, in vivo tissue samples will allow to identify biomarkers for diagnosis (associated with various clinical and pathological aspects of the disease), prediction of response and/or resistance to therapies, and prognosis. On this regard, our group has set itself to achieve the characterization of genomic (genetic mutations and polymorphisms) and proteomic profiles in different melanoma cell lines from excised lesions in patients at different stages of the disease (primary tumor; skin, lymph node, or visceral metastases). The main goal is the identification of differentially expressed proteins derived from the comparison with different genetic patterns in melanoma cells in order to identify the biochemical and cellular processes of initiation and progression of the disease and elucidate potential mechanisms involved in the development of the different melanoma genotypes. For the mutation analysis, genomic DNA was isolated from melanoma cell lines, using standard methods. Specimens were analyzed for mutations in most common genes involved in melanoma pathogenesis with a specific Panel on the Ion Torrent platform (PGM sequencer). All variants detected by NGS were confirmed through PCR-based Sanger sequencing For the proteomic analysis, the melanoma cell lines have been analyzed by MudPIT approach. Data obtained from the MudPIT analysis in the different melanoma cell lines at various stages of the disease have been investigated by using specific identification software (SEQUEST Cluster and Bioworks). All samples were analyzed in duplicate, therefore, having available a substantial number of listings protein, it was possible to compare most of them. The comparison was performed using the MAProMA software which allows the preliminary assessment of differentially expressed proteins through specific algorithms (DAVE - Differential Average- and -Differential Confidence Index-DCI). The Dave parameter is indicative of the difference in relative amounts of the same proteins found in the two samples being analyzed; the DCI parameter provides the absolute value of the differential expression confidence. These two parameters are thus used to identify proteins with a different level of abundance, which is present in the two samples in a manner significantly different. From a preliminary analysis of the data - we have just generated, a significant number of differentially expressed proteins was found to play an essential role in cancer tumorigenesis, including those involved in proliferation control, cellular motility, and resistance to apoptosis of melanoma cells. More accurate correlations and association analyses between genetic and proteomic data are ongoing.