Forecasting the stochastic vicious cycle of cancer progression and immune response

It is accepted that cancer progression is a stochastic process, and there is a bifurcation in cancer cell count, which gets chaotic if not treated at preliminary stages. Therefore, strategies for fighting cancer at early stages are highly desired. However, the interaction of the immune system with cancer cells is not a straightforward process. The stochastic cell interactions lead to uncontrollable dynamics and sometimes to the death of the patient. A stochastic computational framework developed based on principles of the cancer-immune cell interaction is proposed in this article. The results obtained using the framework for breast cancer are close to the experimental findings, confirming that it can be a useful tool for identifying possible control measures. This study concludes that a control strategy based on stochastic modeling is promising and that a deep understanding of the interaction cell rates is essential for timely cancer control measures.