Why one-size-fits-all vaso-modulatory interventions fail to control glioma invasion: in silico insights

J C L Alfonso1,2, A Köhn-Luque3,4, T Stylianopoulos5

  • 1Braunschweig Integrated Centre of Systems Biology and Helmholtz Center for Infectious Research, Braunschweig, Germany.

Scientific Reports
|November 24, 2016
PubMed

Insights

Vaso-modulatory therapies for glioma (brain tumors) show limited success. A mathematical model reveals a critical cell proliferation/diffusion ratio dictates treatment response, suggesting personalized approaches are needed for better outcomes.

Area of Science:

  • Neuro-oncology
  • Mathematical Biology
  • Cancer Therapy

Background:

  • Gliomas are aggressive brain tumors with poor prognosis and limited treatment efficacy.
  • Vaso-modulatory therapies, targeting tumor vasculature, are debated for glioma treatment.
  • Current strategies include tumor blood vessel normalization or deterioration, with unclear outcomes.

Purpose of the Study:

  • To investigate the limited success of vaso-modulatory interventions in glioma treatment.
  • To explore the impact of glioma cell migration and proliferation on treatment response.
  • To identify factors influencing the efficacy of vascular-targeting therapies.

Main Methods:

  • Development of a mathematical model based on glioma cell migration and proliferation.
  • Analysis of the dichotomy between cell migration and proliferation dynamics.
  • In silico investigation of glioma response to vaso-modulatory interventions.

Main Results:

  • Identification of a critical cell proliferation/diffusion ratio separating distinct glioma response regimes.
  • Vascular modulation can either decrease invasion speed and increase infiltration width, or vice versa.
  • Tumor response to interventions is highly dependent on this critical ratio.

Conclusions:

  • The proliferation/diffusion ratio is a key determinant of glioma response to vaso-modulatory therapies.
  • Understanding these distinct regimes can guide personalized treatment strategies.
  • In silico findings offer a framework for optimizing glioma treatment success rates.

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