Oncolytic viral therapy is a targeted therapy in which natural or genetically modified viruses are used specifically to target cancer cells and not harm healthy cells. Despite some promising results in in vitro and in vivo studies of oncolytic viruses, many questions about treatment regimens and outcomes remain unanswered. Mathematical modelling can be helpful to shed light on understanding cancer cell dynamics and treatment outcomes. Firstly, we propose a set of ordinary differential equations that describes the interactions between cancer cells and free virus during oncolytic viral therapy. Then, using stability and sensitivity analyses, we seek to understand possible treatment outcomes. Then, by identifying thresholds for infection-related parameters such as the virulence level of the virus, the viral time scale and the infection transmission rate, we identify the type of virus that can lead to optimal treatment outcome. Some research suggests that a virus-specific immune response, such as one that becomes activated to prevent infection spread, may burden the success of oncolytic viral therapy. Extending our model, we propose models which include interactions between cancer cells, viruses, and antibody molecules/cytotoxic T cells during oncolytic viral therapy. We identify conditions under which each of the mentioned immune responses can be established by focusing on infection-related parameters. Our result shows virus-specific immune responses are not always detrimental: they can also be neutral or beneficial. Then by focusing on the virulence level of the free virus, we identify the extent to which the effect of a virus-specific immune response is detrimental and beneficial and show how the negative effect can be reduced or how beneficial results can be enhanced. Due to properties such as self-renewal and long-lasting quiescence, cancer stem cells are responsible for tumour recurrence and the failure of many conventional therapies. Here, we assess the efficacy of targeting cancer stem cells with oncolytic viruses. We show that targeting cancer stem cells does not always enhance the treatment efficacy, and optimal stem cell specificity depends on the rate of mitosis of infected cells. When infected cells are mitotic, the optimal result is obtained by perfect stem cell targeting.
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