Continuous Petri Nets and microRNA Analysis in Melanoma

Personalized target therapies represent one of the possible treatment strategies to fight the ongoing battle against cancer. New treatment interventions are still needed for an effective and successful cancer therapy. In this scenario, we simulated and analyzed the dynamics of BRAF V600E melanoma patients treated with BRAF inhibitors in order to find potentially interesting targets that may make standard treatments more effective in particularly aggressive tumors that may not respond to selective inhibitor drugs. To this aim, we developed a continuous Petri Net model that simulates fundamental signalling cascades involved in melanoma development, such as MAPK and PI3K/AKT, in order to deeply analyze these complex kinase cascades and predict new crucial nodes involved in melanomagenesis. The model pointed out that some microRNAs, like hsa-mir-132, downregulates expression levels of p120RasGAP: under high concentrations of p120RasGAP, MAPK pathway activation is significantly decreased and consequently also PI3K/PDK1/AKT activation. Furthermore, our analysis carried out through the Genomic Data Commons (GDC) Data Portal shows the evidence that hsa-mir-132 is significantly associated with clinical outcome in melanoma cancer genomic data sets of BRAF-mutated patients. In conclusion, targeting miRNAs through antisense oligonucleotides technology may suggest the way to enhance the action of BRAF-inhibitors.

[1]  De-Shuang Huang,et al.  Normalized Feature Vectors: A Novel Alignment-Free Sequence Comparison Method Based on the Numbers of Adjacent Amino Acids , 2013, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[2]  Jianlin Cheng,et al.  An overview of bioinformatics methods for modeling biological pathways in yeast. , 2016, Briefings in functional genomics.

[3]  Xiao-shi Zhang,et al.  Targeted therapy: resistance and re-sensitization , 2015, Chinese journal of cancer.

[4]  H. Westerhoff,et al.  Recurrent design patterns in the feedback regulation of the mammalian signalling network , 2008, Molecular systems biology.

[5]  E. Martínez-Balibrea,et al.  Resistant mechanisms to BRAF inhibitors in melanoma. , 2016, Annals of translational medicine.

[6]  S. Anand,et al.  Ras pathway inhibition prevents neovascularization by repressing endothelial cell sprouting. , 2013, The Journal of clinical investigation.

[7]  Giulia Russo,et al.  Computational modeling of the expansion of human cord blood CD133+ hematopoietic stem/progenitor cells with different cytokine combinations , 2015, Bioinform..

[8]  Salvatore Cavalieri,et al.  A methodological approach for using high-level Petri Nets to model the immune system response , 2016, BMC Bioinformatics.

[9]  MengChu Zhou,et al.  Liveness Enforcing Supervision of Video Streaming Systems Using Nonsequential Petri Nets , 2009, IEEE Transactions on Multimedia.

[10]  A. Bosserhoff,et al.  The role of microRNAs in melanoma. , 2014, European journal of cell biology.

[11]  Y. Kawamoto,et al.  RAS/RAF/MEK/ERK and PI3K/PTEN/AKT Signaling in Malignant Melanoma Progression and Therapy , 2011, Dermatology research and practice.

[12]  C. Mathers,et al.  Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012 , 2015, International journal of cancer.

[13]  Wolfgang Reisig,et al.  Modeling in Systems Biology, The Petri Net Approach , 2010, Computational Biology.

[14]  J. Beebe-Dimmer,et al.  The cancer burden attributable to biologic agents. , 2015, Annals of epidemiology.

[15]  Francesco Pappalardo,et al.  Computational Modeling of PI3K/AKT and MAPK Signaling Pathways in Melanoma Cancer , 2016, PloS one.

[16]  A. Lallas,et al.  Dabrafenib: a new opportunity for the treatment of BRAF V600-positive melanoma , 2016, OncoTargets and therapy.

[17]  Antonio L Amelio,et al.  Comprehensive Molecular Characterization of Pheochromocytoma and Paraganglioma. , 2017, Cancer cell.

[18]  Martyn Amos,et al.  SimZombie: A Case-Study in Agent-Based Simulation Construction , 2011, KES-AMSTA.

[19]  F. Pappalardo,et al.  Analysis of vaccine's schedules using models. , 2006, Cellular immunology.

[20]  Lin Wang,et al.  Targeting a novel cancer-driving protein (LAPTM4B-35) by a small molecule (ETS) to inhibit cancer growth and metastasis , 2016, Oncotarget.

[21]  E. Van Cutsem,et al.  Cetuximab plus irinotecan, fluorouracil, and leucovorin as first-line treatment for metastatic colorectal cancer: updated analysis of overall survival according to tumor KRAS and BRAF mutation status. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[22]  Grzegorz Rozenberg,et al.  High-level Petri Nets: Theory And Application , 1991 .

[23]  Susumu Goto,et al.  The KEGG resource for deciphering the genome , 2004, Nucleic Acids Res..

[24]  F. McCormick,et al.  ETS-targeted therapy: can it substitute for MEK inhibitors? , 2017, Clinical and Translational Medicine.

[25]  Monika Heiner,et al.  Snoopy - a unifying Petri net framework to investigate biomolecular networks , 2010, Bioinform..

[26]  Sean R. Davis,et al.  NCBI GEO: archive for functional genomics data sets—update , 2012, Nucleic Acids Res..

[27]  Magnus Bosse,et al.  Regulation of Raf-Akt Cross-talk , 2002, The Journal of Biological Chemistry.

[28]  Armando Reyes-Palomares,et al.  First steps in computational systems biology: A practical session in metabolic modeling and simulation , 2009, Biochemistry and molecular biology education : a bimonthly publication of the International Union of Biochemistry and Molecular Biology.

[29]  De-Shuang Huang,et al.  Mining the bladder cancer-associated genes by an integrated strategy for the construction and analysis of differential co-expression networks , 2015, BMC Genomics.

[30]  A. Shaw,et al.  Resisting Resistance: Targeted Therapies in Lung Cancer. , 2016, Trends in cancer.

[31]  Ferdinando Chiacchio,et al.  Mathematical modeling of the immune system recognition to mammary carcinoma antigen , 2012, BMC Bioinformatics.

[32]  Ling Dong,et al.  Clinical Next Generation Sequencing for Precision Medicine in Cancer , 2015, Current genomics.

[33]  De-Shuang Huang,et al.  Predicting Hub Genes Associated with Cervical Cancer through Gene Co-Expression Networks , 2016, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[34]  S. Majid,et al.  MicroRNA‐mediated regulation of melanoma , 2014, The British journal of dermatology.

[35]  C. Bracken,et al.  Experimental strategies for microRNA target identification , 2011, Nucleic acids research.

[36]  Lei Zhang,et al.  Prediction of protein-protein interactions based on protein-protein correlation using least squares regression. , 2014, Current protein & peptide science.

[37]  J. Ferlay,et al.  An assessment of GLOBOCAN methods for deriving national estimates of cancer incidence , 2016, Bulletin of the World Health Organization.