Vascular phenotype identification and anti-angiogenic treatment recommendation: A pseudo-multiscale mathematical

L G Hutchinson1, E A Gaffney1, P K Maini1

  • 1Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford OX2 6GG, UK.

Insights

Mathematical modeling reveals that vascular phenotype changes can predict anti-angiogenic therapy response. This approach offers insights into tumor angiogenesis and potential synergistic drug combinations for cancer treatment.

Area of Science:

  • Oncology
  • Mathematical Biology
  • Biomedical Engineering

Background:

  • Anti-angiogenic drug development for cancer faces challenges due to disappointing results and a lack of predictive biomarkers.
  • The complex mechanisms of tumor angiogenesis are not fully understood, hindering effective therapeutic interventions.

Purpose of the Study:

  • To develop a mathematical model to understand tumor angiogenesis and predict responses to anti-angiogenic therapies.
  • To identify potential biomarkers for anti-angiogenic treatment efficacy.

Main Methods:

  • Formulated a spatially averaged multiscale mathematical model coupling key angiogenic factors (VEGF, Ang1, Ang2, PDGF) with endothelial and pericyte cell dynamics.
  • Simulated treatment responses to anti-Ang2, anti-VEGF, and combination therapies.
  • Analyzed steady-state behaviors to define angiogenic and non-angiogenic vascular phenotypes.

Main Results:

  • The model qualitatively reproduced experimental findings on pericyte coverage after antibody treatments.
  • Identified distinct angiogenic and non-angiogenic vascular phenotypes based on model dynamics.
  • Predicted a synergistic effect of combined anti-VEGF and anti-Ang2 therapies on immature vascular phenotypes, but not on normalized ones.

Conclusions:

  • Changes in vascular phenotype are predicted to serve as a valuable experimental biomarker for anti-angiogenic treatment response.
  • Non-spatial mathematical models can generate tractable predictions for anti-angiogenic therapy strategies.
  • Understanding pre-treatment vascular phenotype is crucial for optimizing combination anti-angiogenic treatments.