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Preventing evolutionary rescue in cancer.

Srishti Patil1,2, Armaan Ahmed3,4, Yannick Viossat5

  • 1Indian Institute of Science Education and Research, Pune, India.

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Summary

Optimizing cancer therapy involves switching treatments when tumors are smallest, ideally near their minimum size before relapse. This "extinction therapy" strategy, based on evolutionary rescue theory, aims to maximize tumor eradication probability.

Keywords:
cancer treatmentevolutionary rescueevolutionary therapymathematical oncologytherapeutic resistance

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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.