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

Nat Commun. 2019 Dec 13;10(1):5704. doi: 10.1038/s41467-019-13726-w.

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.

Publication types

  • Research Support, N.I.H., Intramural
  • Video-Audio Media

MeSH terms

  • Adolescent
  • Aged
  • Brain / pathology
  • Brain Neoplasms / genetics*
  • Brain Neoplasms / pathology
  • Cell Movement / genetics
  • Cohort Studies
  • Computational Biology
  • Datasets as Topic
  • Evolution, Molecular
  • Gene Expression Profiling
  • Gene Expression Regulation, Neoplastic*
  • Gene Regulatory Networks
  • Glioblastoma / genetics*
  • Glioblastoma / pathology
  • Humans
  • Middle Aged
  • Models, Genetic*
  • Neoplasm Recurrence, Local / genetics*
  • Neoplasm Recurrence, Local / pathology