Gaming the cancer-immunity cycle by synchronizing the dose schedules

Area of Science:

• Mathematical oncology
• Systems biology
• Evolutionary game theory

Background:

• Cancer evolution is influenced by immune responses and treatment pressures.
• Chemotherapy and immunotherapy are key cancer treatments with complex interactions.
• Optimizing treatment schedules is crucial for maximizing efficacy and minimizing toxicity.

Purpose of the Study:

• To develop a mathematical model of the cancer-immunity cycle.
• To investigate optimal combination, timing, and dosing of chemotherapy and immunotherapy.
• To explore treatment strategies under competitive and time-dependent selection pressures.

Main Methods:

• Developed a nonlinear dynamical system modeling cancer, healthy, and T cells as a rock-paper-scissors game.
• Integrated chemotherapy and immunotherapy as control functions synchronized with the cancer-immunity cycle's fundamental period.
• Analyzed dosing schedules, durations, and sequencing for optimal therapeutic outcomes.

Main Results:

• Chemotherapy and immunotherapy pulse-dosing schedules are not commutative.
• Optimal chemotherapy duration is one-quarter of the cancer-immunity cycle; immunotherapy is one-half cycle.
• Immunotherapy should precede chemotherapy and last twice as long for improved outcomes.

Conclusions:

• Optimized timing of chemotherapy and immunotherapy dosing can enhance treatment efficacy.
• Strategic scheduling can compensate for lower total drug doses, reducing toxicity.
• Accurate measurement of the cancer-immunity cycle period is vital for clinical application.