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Stochastic competitive release and adaptive chemotherapy.

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This study introduces a finite-cell model for tumor evolution, demonstrating that adaptive chemotherapy can delay drug resistance by managing cell competition. Stochastic adaptive schedules outperform standard treatments.

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Area of Science:

  • Mathematical Biology
  • Evolutionary Dynamics
  • Cancer Research

Background:

  • Tumor evolution is driven by natural selection, leading to chemoresistance.
  • Adaptive chemotherapy aims to manage tumor cell populations and delay resistance.
  • Stochastic fluctuations play a significant role in evolutionary processes.

Purpose of the Study:

  • To develop and analyze a finite-cell model of tumor natural selection under adaptive chemotherapy.
  • To investigate the impact of stochastic fluctuations on the efficacy of adaptive therapy schedules.
  • To compare adaptive schedules with standard chemotherapy regimens in delaying resistance.

Main Methods:

  • Developed a finite-cell model based on a three-component evolutionary game (Healthy, Sensitive, Resistant cells).
  • Utilized a stochastic frequency-dependent Moran process model (N=10K-50K cells).
  • Designed adaptive chemotherapy schedules C(t) based on a deterministic replicator dynamical system.

Main Results:

  • Quantified stochastic fixation probability regions for resistant and sensitive cell populations.
  • Showed that increasing chemotherapy control parameter C increases the resistant population's region.
  • Demonstrated that stochastic adaptive schedules are more effective at delaying resistance than MTD and metronomic schedules.

Conclusions:

  • Finite-cell models provide insights into managing tumor subpopulations and avoiding chemoresistance.
  • Adaptive therapies can be effective in stochastic environments by balancing competing cell populations.
  • The model highlights the importance of considering evolutionary dynamics in cancer treatment strategies.