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Computational modeling demonstrates that glioblastoma cells can survive spatial environmental challenges through exploratory adaptation.

ABSTRACT: Glioblastoma (GBM) is an aggressive type of brain cancer with remarkable cell migration and adaptation capabilities. Exploratory adaptation-utilization of random changes in gene regulation for adaptive benefits-was recently proposed as the process enabling organisms to survive unforeseen conditions. We investigate whether exploratory adaption explains how GBM cells from different anatomic regions of the tumor cope with micro-environmental pressures. We introduce new notions of phenotype and phenotype distance, and determine probable spatial-phenotypic trajectories based on patient data. While some cell phenotypes are inherently plastic, others are intrinsically rigid with respect to phenotypic transitions. We demonstrate that stochastic exploration of the regulatory network structure confers benefits through enhanced adaptive capacity in new environments. Interestingly, even with exploratory capacity, phenotypic paths are constrained to pass through specific, spatial-phenotypic ranges. This work has important implications for understanding how such adaptation contributes to the recurrence dynamics of GBM and other solid tumors.

SUBMITTER: Celiku O

PROVIDER: S-EPMC6911112 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Computational modeling demonstrates that glioblastoma cells can survive spatial environmental challenges through exploratory adaptation.

Celiku Orieta O Gilbert Mark R MR Lavi Orit O

Nature communications 20191213 1

Glioblastoma (GBM) is an aggressive type of brain cancer with remarkable cell migration and adaptation capabilities. Exploratory adaptation-utilization of random changes in gene regulation for adaptive benefits-was recently proposed as the process enabling organisms to survive unforeseen conditions. We investigate whether exploratory adaption explains how GBM cells from different anatomic regions of the tumor cope with micro-environmental pressures. We introduce new notions of phenotype and phen ...[more]

PMID: 31836713

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Project description:Oncogenic protein tyrosine phosphatases have long been viewed as drug targets of interest, and recently developed allosteric inhibitors of SH2 domain-containing phosphatase-2 (SHP2) have entered clinical trials. However, the ability of phosphatases to regulate many targets directly or indirectly and to both promote and antagonize oncogenic signaling may make the efficacy of phosphatase inhibition challenging to predict. Here we explore the consequences of antagonizing SHP2 in glioblastoma, a recalcitrant cancer where SHP2 has been proposed as a useful drug target. Measuring protein phosphorylation and expression in glioblastoma cells across 40 signaling pathway nodes in response to different drugs and for different oxygen tensions revealed that SHP2 antagonism has network-level, context-dependent signaling consequences that affect cell phenotypes (e.g., cell death) in unanticipated ways. To map specific signaling consequences of SHP2 antagonism to phenotypes of interest, a data-driven computational model was constructed based on the paired signaling and phenotype data. Model predictions aided in identifying three signaling processes with implications for treating glioblastoma with SHP2 inhibitors. These included PTEN-dependent DNA damage repair in response to SHP2 inhibition, AKT-mediated bypass resistance in response to chronic SHP2 inhibition, and SHP2 control of hypoxia-inducible factor expression through multiple MAPKs. Model-generated hypotheses were validated in multiple glioblastoma cell lines, in mouse tumor xenografts, and through analysis of The Cancer Genome Atlas data. Collectively, these results suggest that in glioblastoma, SHP2 inhibitors antagonize some signaling processes more effectively than existing kinase inhibitors but can also limit the efficacy of other drugs when used in combination. SIGNIFICANCE: These findings demonstrate that allosteric SHP2 inhibitors have multivariate and context-dependent effects in glioblastoma that may make them useful components of some combination therapies, but not others.

Project description:BackgroundIn the context of environmentally influenced communicable diseases, proximity to environmental sources results in spatial heterogeneity of risk, which is sometimes difficult to measure in the field. Most prevention trials use randomization to achieve comparability between groups, thus failing to account for heterogeneity. This study aimed to determine under what conditions spatial heterogeneity biases the results of randomized prevention trials, and to compare different approaches to modeling this heterogeneity.MethodsUsing the example of a malaria prevention trial, simulations were performed to quantify the impact of spatial heterogeneity and to compare different models. Simulated scenarios combined variation in baseline risk, a continuous protective factor (age), a non-related factor (sex), and a binary protective factor (preventive treatment). Simulated spatial heterogeneity scenarios combined variation in breeding site density and effect, location, and population density. The performances of the following five statistical models were assessed: a non-spatial Cox Proportional Hazard (Cox-PH) model and four models accounting for spatial heterogeneity-i.e., a Data-Generating Model, a Generalized Additive Model (GAM), and two Stochastic Partial Differential Equation (SPDE) models, one modeling survival time and the other the number of events. Using a Bayesian approach, we estimated the SPDE models with an Integrated Nested Laplace Approximation algorithm. For each factor (age, sex, treatment), model performances were assessed by quantifying parameter estimation biases, mean square errors, confidence interval coverage rates (CRs), and significance rates. The four models were applied to data from a malaria transmission blocking vaccine candidate.ResultsThe level of baseline risk did not affect our estimates. However, with a high breeding site density and a strong breeding site effect, the Cox-PH and GAM models underestimated the age and treatment effects (but not the sex effect) with a low CR. When population density was low, the Cox-SPDE model slightly overestimated the effect of related factors (age, treatment). The two SPDE models corrected the impact of spatial heterogeneity, thus providing the best estimates.ConclusionOur results show that when spatial heterogeneity is important but not measured, randomization alone cannot achieve comparability between groups. In such cases, prevention trials should model spatial heterogeneity with an adapted method.Trial registrationThe dataset used for the application example was extracted from Vaccine Trial #NCT02334462 ( ClinicalTrials.gov registry).

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Computational modeling demonstrates that glioblastoma cells can survive spatial environmental challenges through exploratory adaptation.

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Computational modeling demonstrates that glioblastoma cells can survive spatial environmental challenges through exploratory adaptation.

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