Prediction of PD‐l1 expression in neuroblastoma via computational modeling

Salvo Danilo Lombardo, Mario Presti, Katia Mangano, Maria Cristina Petralia, Maria Sofia Basile, Massimo Libra, Saverio Candido, Paolo Fagone, Emanuela Mazzon, Ferdinando Nicoletti, Alessia Bramanti

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Abstract

Immunotherapy is a promising new therapeutic approach for neuroblastoma (NBM): An anti-GD2 vaccine combined with orally administered soluble beta-glucan is undergoing a phase II clinical trial and nivolumab and ipilimumab are being tested in recurrent and refractory tumors. Unfortunately, predictive biomarkers of response to immunotherapy are currently not available for NBM patients. The aim of this study was to create a computational network model simulating the different intracellular pathways involved in NBM, in order to predict how the tumor phenotype may be influenced to increase the sensitivity to anti-programmed cell death-ligand-1 (PD-L1)/programmed cell death-1 (PD-1) immunotherapy. The model runs on COPASI software. In order to determine the influence of intracellular signaling pathways on the expression of PD-L1 in NBM, we first developed an integrated network of protein kinase cascades. Michaelis-Menten kinetics were associated to each reaction in order to tailor the different enzymes kinetics, creating a system of ordinary differential equations (ODEs). The data of this study offers a first tool to be considered in the therapeutic management of the NBM patient undergoing immunotherapeutic treatment.

Original languageEnglish
Article number221
JournalBrain Sciences
Volume9
Issue number9
DOIs
Publication statusPublished - Sep 2019

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Keywords

  • Computational modelling
  • COPASI
  • Immunotherapy
  • Neuroblastoma
  • PD-L1

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

Lombardo, S. D., Presti, M., Mangano, K., Petralia, M. C., Basile, M. S., Libra, M., Candido, S., Fagone, P., Mazzon, E., Nicoletti, F., & Bramanti, A. (2019). Prediction of PD‐l1 expression in neuroblastoma via computational modeling. Brain Sciences, 9(9), [221]. https://doi.org/10.3390/brainsci9090221