Modelling the spatial dynamics of oncolytic virotherapy in the presence of virus-resistant tumour cells

Abstract

Oncolytic virotherapy is a promising form of cancer treatment that uses native or genetically engineered viruses to target, infect and kill cancer cells. Unfortunately, this form of therapy is not effective in a substantial proportion of cancer patients, partly due to the occurrence of infection-resistant tumour cells. To shed new light on the mechanisms underlying therapeutic failure and to discover strategies that improve therapeutic efficacy we designed a cell-based model of viral infection. The model allows us to investigate the dynamics of infection-sensitive and infection-resistant cells in tumour tissue in presence of the virus. To reflect the importance of the spatial configuration of the tumour on the efficacy of virotherapy, we compare three variants of the model: two 2D models of a monolayer of tumour cells and a 3D model. In all model variants, we systematically investigate how the therapeutic outcome is affected by the properties of the virus (e.g. the rate of viral spread), the tumour (e.g. production rate of resistant cells, cost of resistance), the healthy stromal cells (e.g. degree of resistance to the virus) and the timing of treatment. We find that various therapeutic outcomes are possible when resistant cancer cells arise at low frequency in the tumour. These outcomes depend in an intricate but predictable way on the death rate of infected cells, where faster death leads to rapid virus clearance and cancer persistence. Our simulations reveal three different causes of therapy failure: rapid clearance of the virus, rapid selection of resistant cancer cells, and a low rate of viral spread due to the presence of infection-resistant healthy cells. Our models suggest that improved therapeutic efficacy can be achieved by sensitizing healthy stromal cells to infection, although this remedy has to be weighed against the toxicity induced in the healthy tissue.

Author summary

Oncolytic virotherapy is a promising form of cancer treatment that uses viruses to target, infect and kill cancer cells. Unfortunately, this form of therapy is often not effective, due to the occurrence of virus-resistant tumor cells. As it is challenging to assess the emergence and spread of resistance experimentally or in (pre)clinical studies, we designed a model that allows to study the spatial dynamics of virus-sensitive and virus-resistant tumor cells in various scenarios, and to predict the efficacy of virotherapy. By analysing the model systematically, we demonstrate the importance of 2D and 3D spatial interactions, the effects of viral properties (such as replication rate and range of infection), the properties of virus-resistant cancer cells (such as the cost of resistance), and the sensitivity of healthy (non-tumor) cells towards viral infection. Our goal is to provide a sound conceptual understanding of the mechanisms underlying therapeutic failure, which eventually may lead to the discovery of strategies that improve therapeutic efficacy. We therefore provide the reader with a graphical and a terminal interface of our model (executable on a local computer), allowing practitioners to reflect on their intuition regarding the complex yet fascinating dynamics of oncolytic virotherapy.