Treating cancerous cells with viruses

insights from aminimal model for oncolytic virotherapy

  • Adrianne L. Jenner School of Mathematics and Statistics, University of Sydney, Sydney, Australia
  • Adelle C. F. Coster bSchool of Mathematics and Statistics, University of New South Wales, Sydney, Australia
  • Peter S. Kim School of Mathematics and Statistics, University of Sydney, Sydney, Australia
  • Federico Frascoli Faculty of Science, Engineering and Technology, Department of Mathematics
Keywords: Oncolytic virotherapy, bifurcation analysis, oscillations

Abstract

In recent years, interest in the capability of virus particles as a treatment for cancer has increased. In this work, we present a mathematical model embodying the interaction between tumour cells and virus particles engineered to infect and destroy cancerous tissue. To quantify the effectiveness of oncolytic virotherapy, we conduct a local stability analysis and bifurcation analysis of our model. In the absence of tumour growth or viral decay, the model predicts that oncolytic virotherapy will successfully eliminate the tumour cell population for a large proportion of initial conditions. In comparison, for growing tumours and decaying viral particles there are no stable equilibria in the model; however, oscillations emerge for certain regions in our parameter space. We investigate how the period and amplitude of oscillations depend on tumour growth and viral decay. We find that higher tumour replication rates result in longer periods between oscillations and lower amplitudes for uninfected tumour cells. From our analysis, we conclude that oncolytic viruses can reduce growing tumours into a stable oscillatory state, but are insufficient to completely eradicate them. We propose that it is only with the addition of other anti-cancer agents that tumour eradication may be achieved by oncolytic virus.

Published
2018-06-30
How to Cite
Jenner, Adrianne, Adelle Coster, Peter Kim, and Federico Frascoli. 2018. “Treating Cancerous Cells With Viruses”. Letters in Biomathematics 5 (2), S117–S136. https://doi.org/10.1080/23737867.2018.1440977.
Section
Research

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