University of Oxford - Oxford maths student: My research on predicting how cancer tumours will respond to treatment
The speaker, a third-year doctoral student in mathematical biology, focuses on using mathematical models to predict how cancer tumors respond to treatments. The goal is to optimize treatment schedules to improve patient outcomes. Traditional cancer therapy involves continuous drug administration, which initially reduces tumor size by killing sensitive cells but often leads to relapse as resistant cells grow. To counter this, metronomic therapy is proposed, involving scheduled breaks in treatment to resensitize tumors. This approach is mathematically characterized in phase space, showing how treatment pauses can alter tumor composition and regain drug sensitivity. The speaker employs control theory and statistics to optimize treatment duration and collaborates with the Moffitt Cancer Center to personalize treatments based on individual patient data, aiming to extend patient lifetimes using existing drugs.
Key Points:
- Mathematical models predict tumor response to optimize treatment schedules.
- Continuous drug administration can lead to tumor relapse due to resistant cells.
- Metronomic therapy involves treatment breaks to resensitize tumors.
- Phase space analysis shows how treatment pauses affect tumor composition.
- Collaboration with Moffitt Cancer Center personalizes treatment to extend patient lifetimes.
Details:
1. 🎓 Introduction to Mathematical Oncology
- Mathematical models are crucial for predicting cancer tumor responses to various treatments, providing insights into the biological processes of cancer growth and decay.
- These models enable the exploration of optimal treatment schedules, aiming to maximize therapeutic efficacy while minimizing side effects.
- A key goal of mathematical oncology is to improve patient outcomes by tailoring treatment plans based on model predictions, potentially reducing treatment times and improving survival rates.
- Examples of successful applications include adjusting chemotherapy doses and timing to avoid resistance and enhance treatment response.
- The integration of data from clinical trials into these models helps in refining predictions and improving their accuracy.
- Continuous development and validation of models through real-world data are essential to advance their reliability and application in clinical settings.
2. 🔬 Understanding Tumor Dynamics
- The fraction of tumor cells sensitive or resistant to a particular drug mechanism is critical for determining treatment success.
- In standard cancer therapy, continuous drug administration is a common practice aimed at managing tumor size.
- Continuous drug administration often leads to an initial reduction in tumor size, highlighting the importance of understanding tumor cell sensitivity.
- Drug sensitivity in tumor cells significantly impacts treatment outcomes, necessitating precise measurement and monitoring.
- Case studies show that personalized treatment strategies, taking into account varying drug sensitivities, can improve patient outcomes.
3. 📉 Visualizing Cancer Progression
- Visualization techniques play a crucial role in tracking cancer progression by distinguishing between drug-sensitive and drug-resistant cells, crucial for guiding treatment decisions.
- Initially, sensitive cells (marked in green) respond to treatment, but over time, resistant cells (marked in red) proliferate, leading to tumor relapse.
- The visualization process highlights the transition from a predominantly green (sensitive) tumor to one dominated by red (resistant), indicating the need for treatment adjustment.
- Understanding these transitions through visualization aids researchers and clinicians in developing strategies to combat drug resistance and improve patient outcomes.
4. 💊 Innovating Treatment with Metronomic Therapy
- Metronomic therapy uses scheduled drug treatment breaks to resensitize tumors, enhancing treatment efficacy.
- Mathematical modeling in Phase space illustrates tumor composition changes over time, with predictive trajectories depicted.
- Treatment pauses shift tumor trajectories in Phase space, suggesting resensitization and potential for improved outcomes.
5. 🔍 Personalizing Cancer Treatment Strategies
- The collaboration with Mofet Cancer Center in Florida focuses on personalizing treatment approaches to individual patients.
- Utilizing patient data to tailor treatments has led to increased patient lifetimes using existing drugs.
- Optimization of treatment response time is a key focus area, aiming to maximize how long patients respond positively to treatment.