Stochastic modeling suggests that noise reduces differentiation efficiency by inducing a heterogeneous drug response in glioma differentiation therapy.

Xiaoqiang Sun,Tianshou Zhou,Wenbo Zhu,Xing Chen,Qi Zhao,Guangmei Yan,Jiajun Zhang

doi:10.1186/s12918-016-0316-x

Xiaoqiang Sun, Tianshou Zhou + Show 5 more

Open Access

https://doi.org/10.1186/s12918-016-0316-x

Copy DOI

Abstract

BackgroundGlioma differentiation therapy is a novel strategy that has been used to induce glioma cells to differentiate into glia-like cells. Although some advances in experimental methods for exploring the molecular mechanisms involved in differentiation therapy have been made, a model-based comprehensive analysis is still needed to understand these differentiation mechanisms and improve the effects of anti-cancer therapeutics. This type of analysis becomes necessary in stochastic cases for two main reasons: stochastic noise inherently exists in signal transduction and phenotypic regulation during targeted therapy and chemotherapy, and the relationship between this noise and drug efficacy in differentiation therapy is largely unknown.ResultsIn this study, we developed both an additive noise model and a Chemical-Langenvin-Equation model for the signaling pathways involved in glioma differentiation therapy to investigate the functional role of noise in the drug response. Our model analysis revealed an ultrasensitive mechanism of cyclin D1 degradation that controls the glioma differentiation induced by the cAMP inducer cholera toxin (CT). The role of cyclin D1 degradation in human glioblastoma cell differentiation was then experimentally verified. Our stochastic simulation demonstrated that noise not only renders some glioma cells insensitive to cyclin D1 degradation during drug treatment but also induce heterogeneous differentiation responses among individual glioma cells by modulating the ultrasensitive response of cyclin D1. As such, the noise can reduce the differentiation efficiency in drug-treated glioma cells, which was verified by the decreased evolution of differentiation potential, which quantified the impact of noise on the dynamics of the drug-treated glioma cell population.ConclusionOur results demonstrated that targeting the noise-induced dynamics of cyclin D1 during glioma differentiation therapy can increase anti-glioma effects, implying that noise is a considerable factor in assessing and optimizing anti-cancer drug interventions.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0316-x) contains supplementary material, which is available to authorized users.

Highlights

Glioma differentiation therapy is a novel strategy that has been used to induce glioma cells to differentiate into glia-like cells
It has been shown that the elevation of cyclic adenosine monophosphate (cAMP) levels by cholera toxin (CT) can induce glioma cell differentiation, which is mediated by cAMPresponse element binding protein (CREB) phosphorylation at Ser-133 in a protein kinase A (PKA) dependent manner [2]. cAMP/PKA signaling can inhibit the phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) pathway, leading to the activation of the downstream molecule glycogen synthase kinase 3 beta (GSK-3β) and subsequent degradation of cyclin D1 [3]
We adopted glioma differentiation therapy as a realistic case for investigating how the noise that inevitably exists in signaling networks influences drug efficacy and contributes to drug resistance, focusing on the functional role of this noise in the drug response of glioma cancer cells. We developed both an additive noise model (ANM) and a Chemical-LangenvinEquation (CLE) model to simulate the stochastic dynamics of the signaling network during glioma differentiation therapy

Summary

Introduction

Glioma differentiation therapy is a novel strategy that has been used to induce glioma cells to differentiate into glia-like cells. Some advances in experimental methods for exploring the molecular mechanisms involved in differentiation therapy have been made, a model-based comprehensive analysis is still needed to understand these differentiation mechanisms and improve the effects of anti-cancer therapeutics. This type of analysis becomes necessary in stochastic cases for two main reasons: stochastic noise inherently exists in signal transduction and phenotypic regulation during targeted therapy and chemotherapy, and the relationship between this noise and drug efficacy in differentiation therapy is largely unknown. Glial fibrillary acidic protein (GFAP) is applied as a reliable marker for evaluating the differentiation of glioma cells

Methods

Results

Discussion

Conclusion