Preventing evolutionary rescue in cancer

Area of Science:

• Oncology
• Mathematical Biology
• Evolutionary Biology

Background:

• First-line cancer treatments often fail due to emerging therapeutic resistance in rare cancer cells.
• Clinical challenge lies in optimizing the scheduling of subsequent treatments to achieve complete tumor eradication.
• Understanding tumor dynamics and evolutionary principles is crucial for developing effective treatment strategies.

Purpose of the Study:

• To provide a theoretical framework for scheduling sequential cancer treatments to maximize tumor eradication.
• To identify the optimal timing for switching treatments based on tumor size and evolutionary dynamics.
• To explore the influence of treatment parameters and tumor characteristics on clinical outcomes.

Main Methods:

• Utilized mathematical analysis and extensive stochastic simulations.
• Applied the framework of evolutionary rescue theory to model tumor extinction.
• Investigated the impact of treatment timing, dose, and tumor parameters on eradication probability.

Main Results:

• Confirmed that the optimal time to switch treatment is near the tumor's minimum size (nadir) before relapse.
• Identified windows of high extinction probability around the optimal switching point, suggesting later switching is often preferable to switching too early.
• Demonstrated that treatment dose, tumor demographics, and evolutionary parameters significantly influence outcomes, and provided guidance for scheduling drugs with unequal efficacy.

Conclusions:

• An evolutionarily-informed 'extinction therapy' strategy, focusing on exploiting tumor vulnerability at its smallest size, offers a theoretical basis for optimizing sequential cancer treatment.
• Switching treatments around the tumor nadir, with a preference for switching after relapse initiation if the exact nadir is uncertain, can enhance eradication probability.
• This study provides a foundation for experimental and clinical investigations into optimizing cancer treatment scheduling based on evolutionary principles.