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. 2018 Dec 28;37(1):326.
doi: 10.1186/s13046-018-0982-1.

TNF-alpha and metalloproteases as key players in melanoma cells aggressiveness

Stefania Rossi  1 , Martina Cordella  1 , Claudio Tabolacci  1 , Giovanni Nassa  2 , Daniela D'Arcangelo  3 , Cinzia Senatore  1 , Paolo Pagnotto  1 , Roberta Magliozzi  4 , Annamaria Salvati  2 , Alessandro Weisz  2   5 , Antonio Facchiano  6 , Francesco Facchiano  7
Affiliations

TNF-alpha and metalloproteases as key players in melanoma cells aggressiveness

Stefania Rossi et al. J Exp Clin Cancer Res. .

Abstract

Background: Melanoma aggressiveness determines its growth and metastatic potential. This study aimed at identifying new molecular pathways controlling melanoma cell malignancy.

Methods: Ten metastatic melanoma cell lines were characterized by their proliferation, migration and invasion capabilities. The most representative cells were also characterized by spheroid formation assay, gene- and protein- expression profiling as well as cytokines secretion and the most relevant pathways identified through bioinformatic analysis were tested by in silico transcriptomic validation on datasets generated from biopsies specimens of melanoma patients. Further, matrix metalloproteases (MMPs) activity was tested by zymography assays and TNF-alpha role was validated by anti-TNF cell-treatment.

Results: An aggressiveness score (here named Melanoma AGgressiveness Score: MAGS) was calculated by measuring proliferation, migration, invasion and cell-doubling time in10human melanoma cell lines which were clustered in two distinct groups, according to the corresponding MAGS. SK-MEL-28 and A375 cell lines were selected as representative models for the less and the most aggressive phenotype, respectively. Gene-expression and protein expression data were collected for SK-MEL-28 and A375 cells by Illumina-, multiplex x-MAP-and mass-spectrometry technology. The collected data were subjected to an integrated Ingenuity Pathway Analysis, which highlighted that cytokine/chemokine secretion, as well as Cell-To-Cell Signaling and Interaction functions as well as matrix metalloproteases activity were significantly different in these two cell types. The key role of these pathways was then confirmed by functional validation. TNF role was confirmed by exposing cells to the anti-TNF Infliximab antibody. Upon such treatment melanoma cells aggressiveness was strongly reduced. Metalloproteases activity was assayed, and their role was confirmed by comparing transcriptomic data from cutaneous melanoma patients (n = 45) and benign nevi (n = 18).

Conclusions: Inflammatory signals such as TNF and MMP-2 activity are key intrinsic players to determine melanoma cells aggressiveness suggesting new venue sin the identification of novel molecular targets with potential therapeutic relevance.

Keywords: Cancer; Cutaneous melanoma; Cytokines; Inflammation; Malignancy; Metalloproteases; Proteomics; TNF; Uveal melanoma.

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Conflict of interest statement

Ethics approval and consent to participate

The study does not contain experiments with animal models.

No human tissues have been used. Data regarding human datasets of melanoma patients are from a dataset available online at the GEO database (https://www.ncbi.nlm.nih.gov/sites/GDSbrowser).

Consent for publication

All Authors have approved the manuscript and agree with submission to Journal of Experimental and Clinical Cancer Research.

Competing interests

The Authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Characterization of melanoma cell aggressiveness: all the cell lines were analysed simultaneously using the same experimental procedures. Evaluations and quantifications were assessed by two different operators in blind. a Growth rate after 24 and 48 h of serum starvation. After serum deprivation, cells were incubated for 24 and 48 h and then they are harvested and counted. Cell counts for each cell line was: 61729 for SK-MEL-110, 57,250 for Mel 397, 46,171 for SK-MEL-30, 57,000 for Preyer, 61,333 for A375M, 60,212 for A375, 61,143 for SK-MEL-28, 60,600 for Mewo, 60,500 for Mel 501, 60,500 for Me 665. The data represent the mean ± SD of three experiments carried out in triplicate (statistical significance versus control: **P < 0.001; § P < 0.0001). b Migration ability of melanoma cell lines. The scratch test on confluent cells were performed for 24 h. c Invasion analysis of melanoma cells for 24 h. The invasion capability is expressed as number of cells per mm2 of filter. d Aggressiveness index (MAGS index calculated as reported in Methods), to cluster melanoma cell lines accordingly to their malignancy: a combination of growth, invasion and migration rates was used to get such aggressiveness index
Fig. 2
Fig. 2
Characterization of A375 and SK-MEL-28, respectively, the most and the less aggressive model of human melanoma cell lines. a Cell proliferation after 24 h of serum deprivation. A375, under an extreme growing condition, showed a significantly (**P < 0.001) higher growth rate than SK-MEL-28. Data are expressed as percent of 61,143 cells for SK-MEL-28 and 60,212 cells for A375 and represent the mean ± SD of three experiments carried out in triplicate. b Invasion ability of melanoma cell lines by Boyden chamber assay. A375 were significant able to invade in respect to SK-MEL-28 (**p < 0.001); in particular, a mean of 42 and 5 cell per field were counted respectively). c Microphotographs showing forming spheroid capability. d The panel indicates the quantification of total cell number forming A375 and SK-MEL-28 spheroids (§ p < 0.0001)
Fig. 3
Fig. 3
Aggressiveness driven de-regulation of transcriptome in melanoma cells behaving and functional analyses of the results. a Heat map of differentially expressed transcripts in A375 vsSK-MEL-28 human melanoma cells according to log2 AVG signals (left) and fold-change (right). b Graph showing most significantly enriched molecular functions identified by the IPA analysis. Each histogram reports the –log of the p-value (Fisher’s exact test) for each molecular function. The straight orange lines mark the significance p-value threshold (0.05)
Fig. 4
Fig. 4
a Weak, not significant, MMP2 mRNA up-regulation in A375 compared to SK-MEL-28. b Highly significant (§§p < 0.00001; §p < 0.0001) down-regulation of Tissue Inhibitor of Metalloproteinases (TIMP) c The MMP2 activity evaluated by a gelatin zymography confirming the higher aggressiveness of A375 cells compared to SK-MEL-28
Fig. 5
Fig. 5
Proteomic Analysis a Workflow for the proteomic analysis: proteins analyzed were extracted from cells cultured under serum deprivation conditions. b Veen diagram summarizing protein specifically identified in A375 and SK-MEL-28. c Functional classification of proteins extracted from cultured cells and identified by proteomic analysis
Fig. 6
Fig. 6
An anti-TNF drug (IFX) affects melanoma cell proliferation rate (**p < 0.001; § p < 0.0001). Panel a-d show the anti-proliferative effects of IFX on four different cell lines, while panel E compare the IFX-effects on the MAG scores calculated for the same cells
Fig. 7
Fig. 7
Cartoon depicting molecular mechanisms potentially underlying the TNF/MMPs activity in determining melanoma cell aggressive phenotype
See this image and copyright information in PMC

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References

    1. Matthews NH, Li W-Q, Qureshi AA, Weinstock MA, Cho E. Epidemiology of Melanoma. Cutan Melanoma Etiol Ther. 2017; Section 1, Chapter 1, p.3–22.
    1. Eggermont AM, Spatz A, Robert C. Cutaneous melanoma. Lancet. 2014;383:816–827. doi: 10.1016/S0140-6736(13)60802-8. - DOI - PubMed
    1. Hendrix MJC, Seftor EA, Margaryan N V, Seftor REB. Heterogeneity and Plasticity of Melanoma: Challenges of Current Therapies. Cutan. Melanoma Etiol. Ther. 2017. Section 1, Chapter 4, p.57–66. - PubMed
    1. Testa U, Castelli G, Pelosi E. Melanoma: genetic abnormalities, tumor progression, clonal evolution and tumor initiating cells. Med Sci. 2017;5:28. - PMC - PubMed
    1. Jang S, Atkins MB. Which drug, and when, for patients with BRAF-mutant melanoma? Lancet Oncol. 2013;14:e60–e69. doi: 10.1016/S1470-2045(12)70539-9. - DOI - PubMed