Systemic network analysis identifies XIAP and IκBα as potential drug targets in TRAIL resistant BRAF mutated melanoma. (December 2018)

• Record Type: Journal Article
• Authors: Mistro, Greta; Lucarelli, Philippe; Müller, Ines; Landtsheer, Sébastien; Zinoveva, Anna; Hutt, Meike; Siegemund, Martin; Kontermann, Roland; Beissert, Stefan; Sauter, Thomas; Kulms, Dagmar
• Is Part Of: Npj systems biology and applications. Volume 4(2018)
• Page Start: 1
• Page End: 12
• Publication Date: 2018-12
• Subjects: Systems biology -- Periodicals; 570.113
• DOI: 10.1038/s41540-018-0075-y
• Languages: English
• ISSNs: 2056-7189

Abstract

Abstract Metastatic melanoma remains a life-threatening disease because most tumors develop resistance to targeted kinase inhibitors thereby regaining tumorigenic capacity. We show the 2nd generation hexavalent TRAIL receptor-targeted agonist IZI1551 to induce pronounced apoptotic cell death inmut BRAF melanoma cells. Aiming to identify molecular changes that may confer IZI1551 resistance we combined Dynamic Bayesian Network modelling with a sophisticated regularization strategy resulting in sparse and context-sensitive networks and show the performance of this strategy in the detection of cell line-specific deregulations of a signalling network. Comparing IZI1551-sensitive to IZI1551-resistant melanoma cells the model accurately and correctly predicted activation of NFκB in concert with upregulation of the anti-apoptotic protein XIAP as the key mediator of IZI1551 resistance. Thus, the incorporation of multiple regularization functions in logical network optimization may provide a promising avenue to assess the effects of drug combinations and to identify responders to selected combination therapies. Systems Biology and Cancer: Network analysis for TRAIL resistance in Melanoma Even though targeted kinase inhibitors have been invented to mutation-specifically fight melanoma, most patients present with initial or acquired resistance and suffer from tumor relapse. A multidisciplinary team lead by Dagmar Kulms at the Technical University of Dresden, has developed a predictive Dynamic Bayesian Network model that is able to predict alternative targets for melanoma treatment. Comparing melanoma cells being sensitive or resistant to death receptor induced apoptosis by a second-generation TRAIL agonist the model predicted molecular signatures of pro-survival/anti-apoptotic responses in a dynamic manner. They show that TRAIL holds a great potential of success for targeted cancer therapy in the near future, and that the incorporation of multiple regularization functions in logical networks may provide a promising avenue to assess the effects of drug combinations for personalized medicine.