Are Adaptive Chemotherapy Schedules Robust? A Three-Strategy Stochastic Evolutionary Game Theory Model

Area of Science:

• Mathematical Biology
• Computational Oncology
• Cancer Research

Background:

• Chemotherapy resistance is a major challenge in cancer treatment.
• Adaptive chemotherapy schedules aim to mitigate resistance by dynamically adjusting treatment strategies.
• Understanding the robustness of these schedules is crucial for clinical application.

Purpose of the Study:

• To investigate the robustness of adaptive chemotherapy schedules over repeated cycles and varying tumor sizes.
• To quantify the variance in treatment response associated with multidrug adaptive schedules.
• To assess the performance of idealized adaptive schedules in finite cell populations.

Main Methods:

• Utilized a non-stationary stochastic three-component fitness-dependent Moran process model.
• Modeled populations of chemosensitive and two chemoresistant cell types.
• Employed adjusted replicator equations (N→∞ limit) to determine adaptive schedules based on evolutionary cycles.

Main Results:

• For finite cell populations, distributions remained approximately multi-Gaussian for the first three cycles.
• Variances increased exponentially with each cycle, indicating reduced predictability.
• Treatment failure occurred with increased cycles due to competitive release of resistant sub-populations, breaking down the multi-Gaussian distribution.

Conclusions:

• Adaptive chemotherapy schedules are sensitive to errors and degrade exponentially over repeated cycles.
• Effective clinical implementation necessitates highly accurate tumor models and precise measurements of cell sub-population frequencies.
• Synergistic drug interactions exacerbate the degradation of schedule effectiveness.