In recent years, cancer therapy research has rapidly advanced, particularly in bio-based engineered therapeutics. One such promising approach is oncolytic virotherapy, which uses viruses to selectively infect and kill cancer cells while stimulating immune responses. However, the complex interaction between the virus, the immune system, and the tumour remains poorly understood, which limits the predictability of therapeutic outcomes. To address this gap, I turned to computational modelling, particularly agent-based spatio-temporal models, which provide a powerful way to simulate the complex virus-tumour-immune interactions.

Modelling the spatial dynamics of oncolytic virotherapy

During my PhD in the lab of tumor virologist Prof. Toos Daemen, I initiated a collaboration with theoretical biologist Prof. Franjo Weissing and scientific programmer Dr. Thijs Janzen. This interdisciplinary effort combined insights from virology and evolutionary biology to shed light on mechanisms underlying the outcomes of oncolytic virotherapy. We first developed a simple base model to investigate how virus-cell interactions in the tumour influence the success of virotherapy¹. This model effectively demonstrated that therapeutic outcomes are highly stochastic, with success or failure both occurring in similar (replicate) scenarios. However, for each setting of the parameters, the model made clear predictions on the probability of the therapeutic success. Such predictions allowed us to study how therapy success is influenced by physiologically relevant factors such as the virus infection rate, the spatial configuration of the tumour, or the timing of therapy.

Adding immune responses to the model

Building on these initial insights, the current study expands on the model by incorporating virus-induced immune responses which play a pivotal role in determining therapeutic outcomes². For example, virus-induced immunogenic signals stimulate immune cells like T cells to attack the tumour and promote long term protection against cancer. Whereas, on the other hand, the activated immune cells can also instead attack the virus and block its therapeutic effects.  Therefore, our goal was to systematically investigate how immune cells are activated by viral signals, how these signals spread through the tumour microenvironment, and how different factors—such as tumour density, immune cell migration, and virus delivery—impact therapeutic success. In the end, this would help us optimize virus-induced immunogenic signals for improved therapeutic success.

Key Insights – timing, anticancer T-cells, diffusion of immune signals play complex roles.

Our study revealed several interesting insights. First, we found that the outcome of oncolytic virotherapy remains highly stochastic in the presence of immune response as well. This means that even with strong immune responses working against the tumour, therapeutic success is not always guaranteed. In some cases, an overly strong immune response against the tumour inhibits viral spread, reducing the treatment's effectiveness. This highlights the need for balance in immune activation to avoid hindering the virus’s ability to target tumour cells.

Additionally, we found that the method of virus delivery—whether local or systemic—can significantly influence the therapy's success. While local delivery allows for precise targeting of tumours, systemic delivery can result in better tumour eradication by infecting multiple tumour sites. Furthermore, we discovered that the timing of virus introduction plays a critical role. Virus administration at later stages of tumour progression, when immune responses are more pronounced, can enhance therapeutic outcomes.

Another critical finding was the importance of the diffusion rate of immunogenic signals. The rate at which these signals diffuse across the tumour can determine how effectively immune cells are recruited to attack cancer cells. If the diffusion rate is too fast or slow, immune activation is impaired, leading to suboptimal results.

Perspectives

In conclusion, our work underscores the need for a more comprehensive approach to oncolytic virotherapy, one that accounts for the complex interplay between the virus, tumour, and immune system. Our new modelling approach, thus, provides a foundation for insights into the dynamics of oncolytic virotherapy and opportunities to optimise treatment strategies.

References

1. Bhatt, D. K., Janzen, T., Daemen, T. & Weissing, F. J. Modelling the spatial dynamics of oncolytic virotherapy in the presence of virus-resistant tumour cells. PLoS Comput Biol 18, e1010076 (2022).
2. Bhatt, D. K., Janzen, T., Daemen, T. & Weissing, F. J. Effects of virus-induced immunogenic cues on oncolytic virotherapy. Sci Rep 14, 28861 (2024).