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Adaptive Control of Tumor Growth
1Ted Rogers School of Information Technology Management, Toronto Metropolitan University, Toronto, ON, Canada.
Cancer Control : Journal of the Moffitt Cancer Center
|January 31, 2024
Summary
Adaptive cancer therapy uses tumor feedback to predict and control tumor growth, aiming to overcome resistance and improve patient outcomes. This approach considers cancer
Area of Science:
- Oncology
- Mathematical Biology
- Systems Biology
Background:
- Current cancer treatment optimizations often neglect cancer's evolutionary adaptation to therapies.
- Cancer is a complex, adaptive system requiring dynamic treatment strategies.
- Therapeutic resistance and disease management necessitate adaptive treatment approaches.
Purpose of the Study:
- To explore the feasibility of adaptive cancer treatment driven by tumor state feedback.
- To investigate the role of cell adaptive fitness in phenotypic plasticity.
- To utilize pathway entropy as a biomarker for tumor growth trajectory.
Main Methods:
- Development and application of deterministic and stochastic models of tumor growth dynamics.
- Modeling adaptive cancer therapy based on monitored tumor burden and clonal composition.
- Incorporation of clinical outcome setpoints to guide treatment adaptation.
Main Results:
- Adaptive therapeutic strategies may require one-step-ahead prediction of tumor burden.
- Cell adaptive fitness is identified as a key driver of phenotypic plasticity.
- Pathway entropy can serve as a biomarker for predicting tumor growth trajectory.
Conclusions:
- Adaptive cancer treatment driven by tumor state feedback is a feasible approach.
- Understanding cancer's adaptive nature is crucial for long-term disease management.
- Mathematical modeling provides a framework for optimizing adaptive cancer therapies.
Keywords:
adaptive cancer therapyadaptive combination therapyadaptive oncologycancer evolutionary dynamicstumor state feedback controlMore Related Videos
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