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Evolutionary analysis of replicator dynamics about anti-cancer combination therapy
1School of Mathematics, Renmin University of China, Beijing 100872, China.
Mathematical Biosciences and Engineering : MBE
|January 18, 2023
Summary
Combination therapies can manage drug-resistant cancer cells by controlling subpopulations. This study uses a replicator dynamical model to explore adaptive treatment schedules for chemotherapy and p53 vaccines, optimizing cancer evolution outcomes.
Area of Science:
- Mathematical Oncology
- Evolutionary Dynamics
- Cancer Therapy Resistance
Background:
- Drug-resistant cancer cell subpopulations pose a significant challenge to effective anti-cancer treatments.
- Combination therapies are a common strategy to overcome acquired or intrinsic drug resistance.
- Understanding tumor cell population evolution under various treatment schedules is crucial for therapeutic success.
Purpose of the Study:
- To explore the evolutionary outcomes of tumor cell populations under different combination schedules of chemotherapy and p53 vaccine therapy.
- To design adaptive therapy schedules that control cancer subpopulations by analyzing evolutionary dynamics.
- To investigate the supportive effects of sensitive cancer cells on targeted therapy-resistant cells.
Main Methods:
- Construction of a replicator dynamical model incorporating sensitive, chemotherapy-resistant, and p53 vaccine-resistant cancer cell subpopulations.
- Local asymptotic stability analysis of evolutionary stable points to predict population dynamics.
- Application of sequential and periodic combination treatment strategies based on evolutionary velocity and absorbing regions.
- Utilized a novel replicator dynamical model to analyze experimental data from mice studies.
Main Results:
- Different monotherapy and combination schedules can lead to distinct evolutionary stable states, including single subpopulations or coexistence of resistant and sensitive cells.
- Adaptive therapy schedules, employing sequential and periodic treatments, can effectively control cancer subpopulations.
- Sensitive cancer cells can support the evolution of targeted therapy-resistant cells, driving populations towards coexistence.
Conclusions:
- Replicator dynamical modeling provides insights into cancer evolution under combination therapies.
- Adaptive treatment strategies are essential for managing drug resistance and controlling tumor heterogeneity.
- The interplay between sensitive and resistant cancer cells influences treatment outcomes and therapeutic resistance.
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