Timp1 Promotes Cell Survival by Activating the PDK1 Signaling Pathway in Melanoma

High TIMP1 expression is associated with poor prognosis in melanoma, where it can bind to CD63 and beta 1 integrin, inducing PI3-kinase pathway and cell survival. Phosphatidylinositol (3,4,5)-trisphosphate (PIP3), generated under phosphatidylinositol-3-kinase (PI3K) activation, enables the recruitment and activation of protein kinase B (PKB/AKT) and phosphoinositide-dependent kinase 1 (PDK1) at the membrane, resulting in the phosphorylation of a host of other proteins. Using a melanoma progression model, we evaluated the impact of Timp1 and AKT silencing, as well as PI3K, PDK1, and protein kinase C (PKC) inhibitors on aggressiveness characteristics. Timp1 downregulation resulted in decreased anoikis resistance, clonogenicity, dacarbazine resistance, and in vivo tumor growth and lung colonization. In metastatic cells, pAKT(Thr308) is highly expressed, contributing to anoikis resistance. We showed that PDK1(Ser241) and PKC beta IISer660 are activated by Timp1 in different stages of melanoma progression, contributing to colony formation and anoikis resistance. Moreover, simultaneous inhibition of Timp1 and AKT in metastatic cells resulted in more effective anoikis inhibition. Our findings demonstrate that Timp1 promotes cell survival with the participation of PDK1 and PKC in melanoma. In addition, Timp1 and AKT act synergistically to confer anoikis resistance in advanced tumor stages. This study brings new insights about the mechanisms by which Timp1 promotes cell survival in melanoma, and points to novel perspectives for therapeutic approaches.Fundacao de Amparo a Pesquisa do Estado de Sao PauloConselho Nacional de Desenvolvimento Cientifico e TecnologicoUniv Fed Sao Paulo, Dept Pharmacol, BR-04039032 Sao Paulo, BrazilUniv Sao Paulo, Sch Med, Canc Inst Sao Paulo, Ctr Translat Invest Oncol LIM 24, BR-01246000 Sao Paulo, BrazilFac Med Santa Casa Sao Paulo, BR-01221020 Sao Paulo, BrazilUniv Fed Sao Paulo, Dept Pharmacol, BR-04039032 Sao Paulo, Brazil|FAPESP: 2010/18715-8FAPESP: 2011/12306-1FAPESP: 2014/13663-0CNPq: 470681/2012-8Web of Scienc

Identification of a gene signature for discriminating metastatic from primary melanoma using a molecular interaction network approach

Understanding the biological factors that are characteristic of metastasis in melanoma remains a key approach to improving treatment. In this study, we seek to identify a gene signature of metastatic melanoma. We configured a new network-based computational pipeline, combined with a machine learning method, to mine publicly available transcriptomic data from melanoma patient samples. Our method is unbiased and scans a genome-wide protein-protein interaction network using a novel formulation for network scoring. Using this, we identify the most influential, differentially expressed nodes in metastatic as compared to primary melanoma. We evaluated the shortlisted genes by a machine learning method to rank them by their discriminatory capacities. From this, we identified a panel of 6 genes, ALDH1A1, HSP90AB1, KIT, KRT16, SPRR3 and TMEM45B whose expression values discriminated metastatic from primary melanoma (87% classification accuracy). In an independent transcriptomic data set derived from 703 primary melanomas, we showed that all six genes were significant in predicting melanoma specific survival (MSS) in a univariate analysis, which was also consistent with AJCC staging. Further, 3 of these genes, HSP90AB1, SPRR3 and KRT16 remained significant predictors of MSS in a joint analysis (HR = 2.3, P = 0.03) although, HSP90AB1 (HR = 1.9, P = 2 × 10−4) alone remained predictive after adjusting for clinical predictors