A Computational Assessment of the Robustness of Cancer Treatments with Respect to Immune Response Strength, Tumor Size and Resistance

The emergence of bioengineering has paved the way for the in vitro design of immune cells that can detect and destroy tumor cells of low antigenicity. However, the results of clinical trials involving cancer treatments have not matched the success in the lab. A reason for treatment failure is the presence of patient-specific genetic biomarkers that affect long-term effectiveness. The cross-talk between multiple signaling pathways involved in tumor cell survival, the existence of redundant pathways with similar functions, and the intrinsic genetic instability of tumor cells also contribute to treatment failure. With the advent of novel cancer treatments, a need has arisen to undertake a computational approach to identify treatment combinations that maximize long-term effectiveness while minimizing the risk of serious side effects. In the present work, mathematical modeling was used to track the time-varying concentrations of pro- and anti-tumor cells and cytokines after a cancer treatment is administered. The simulations demonstrated the importance of treatment timing and frequency to achieve synergy. A combination therapy based on sunitinib and fresolimumab was found to be robust in reducing tumor size with respect to the strength of a patient’s anti-tumor immune response, the size of the tumor at the start of treatment, and with respect to mutations that can make cancer cells become refractory to the first-line treatment. The robustness of the identified sunitinib + fresolimumab combination therapy confers it with the capability to eliminate heterogeneous tumors made up of sensitive and resistant cells, in a patient whose anti-tumor immune response has become suppressed due to advanced age, chronic inflammation or a prior medical treatment. The model simulations highlight the superiority of combination therapy over monotherapy, and provide guidance to identify protocols that have the greatest potential to eliminate a tumor.