Development of a Mathematical Model to Understand, Design & Improve Oncolytic Virus Therapies

Abstract

Oncolytic viruses (OVs) are emerging as a potent therapeutic platform for the treatment of malignant disease. The tumor cells inability to induce antiviral defences in response to a small cytokine known as interferon (IFN) is a common defect exploited by OVs. Heterogeneity in IFN signalling across tumors is therefore a pillar element of resistance to these therapies. I have generated a mathematical model and simulation platform to study the impact of IFN on OV dynamics in normal and cancerous tissues. In the first part of my thesis, I used this model to identify novel OV engineering strategies which could be implemented to overcome IFN based resistance in tumor tissues. From these simulations, it appears that a positive feedback loop, established by virus-mediated expression of an interferon-binding decoy receptor, could increase tumor cytotoxicity without compromising normal cells. The predictions set forth by this model have been validated both qualitatively and quantitatively in in-vitro and in-vivo models using two independent OV strains. This model has subsequently been used to investigate OV attenuation mechanisms, the impact of tumor cell heterogeneity, as well as drug-OV interactions. Following these results, it became apparent that selectivity should equally be observed when overwhelming the cell with a non replicating virus. While normal tissues will clear this pseudo-infection rapidly, owing to their high baseline in antiviral products at the onset of infection, tumor cells with defective anti-viral pathways should not have readily available biomachinery required to degrade this pro-apoptotic signal. Recapitulated by the mathematical model, non-replicating virus-derived particles generated by means of UV irradiation selectively kill tumor cells in cultured cell lines and patient samples, leading to long term cures in murine models. Taken together, this thesis uses a novel mathematical model and simulation platform to understand, design & improve oncolytic virus-based therapeutics.