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A 3D Organotypic Melanoma Spheroid Skin Model
Published on: May 18, 2018
Adaptive Therapy for Metastatic Melanoma: Predictions from Patient Calibrated Mathematical Models
Eunjung Kim1, Joel S Brown2, Zeynep Eroglu3
1Natural Product Research Center, Korea Institute of Science and Technology, Gangneung 25451, Korea.
Abstract:
Adaptive therapy is an evolution-based treatment approach that aims to maintain tumor volume by employing minimum effective drug doses or timed drug holidays. For successful adaptive therapy outcomes, it is critical to find the optimal timing of treatment switch points in a patient-specific manner. Here we develop a combination of mathematical models that examine interactions between drug-sensitive and resistant cells to facilitate melanoma adaptive therapy dosing and switch time points. The first model assumes genetically fixed drug-sensitive and -resistant popul tions that compete for limited resources. The second model considers phenotypic switching between drug-sensitive and -resistant cells. We calibrated each model to fit melanoma patient biomarker changes over time and predicted patient-specific adaptive therapy schedules. Overall, the models predict that adaptive therapy would have delayed time to progression by 6-25 months compared to continuous therapy with dose rates of 6-74% relative to continuous therapy. We identified predictive factors driving the clinical time gained by adaptive therapy, such as the number of initial sensitive cells, competitive effect, switching rate from resistant to sensitive cells, and sensitive cell growth rate. This study highlights that there is a range of potential patient-specific benefits of adaptive therapy and identifies parameters that modulate this benefit.
Insights
Adaptive therapy for melanoma uses tailored drug doses to delay disease progression. Mathematical models predict this approach can extend progression-free survival by 6-25 months compared to continuous treatment.
Area of Science:
- Mathematical Oncology
- Evolutionary Biology
- Cancer Therapeutics
Background:
- Adaptive therapy is an evolution-based strategy for cancer treatment.
- It aims to maintain tumor control by using minimal effective drug doses or scheduled treatment interruptions.
- Optimizing treatment switch points is crucial for patient-specific adaptive therapy success.
Purpose of the Study:
- To develop mathematical models simulating drug-sensitive and resistant cell interactions for melanoma adaptive therapy.
- To predict optimal dosing and switch time points for patient-specific adaptive therapy schedules.
- To identify factors influencing the clinical benefit of adaptive therapy.
Main Methods:
- Developed two mathematical models: one with fixed sensitive/resistant populations and another with phenotypic switching.
- Calibrated models using melanoma patient biomarker data over time.
- Predicted patient-specific adaptive therapy schedules and outcomes.
Main Results:
- Models predict adaptive therapy can delay time to progression by 6-25 months versus continuous therapy.
- Adaptive therapy doses may range from 6-74% relative to continuous therapy.
- Identified key predictive factors for clinical benefit: initial sensitive cell count, competition, resistance-to-sensitive switching rate, and sensitive cell growth rate.
Conclusions:
- Adaptive therapy offers a range of potential patient-specific benefits in melanoma treatment.
- Specific parameters significantly modulate the clinical gains achieved with adaptive therapy.
- This modeling approach facilitates personalized adaptive therapy strategies.
