Data-based modeling of breast cancer and optimal therapy

Area of Science:

• Oncology
• Mathematical Biology
• Immunotherapy

Background:

• Aberrant accumulation of β-catenin proteins is a key driver in breast cancer development.
• Current immunotherapy and targeted mRNA treatments for β-catenin are often costly and complex.
• Understanding tumor microenvironment interactions is crucial for effective breast cancer therapy.

Purpose of the Study:

• To develop novel mathematical models for breast cancer treatment strategies.
• To calibrate models using clinical data for faster, lower-cost therapy selection and evaluation.
• To explore combined RNA interference and immune checkpoint inhibitor therapies targeting β-catenin.

Main Methods:

• Construction of breast cancer treatment models incorporating RNA interference and immune checkpoint inhibitors.
• Parameter estimation using Markov Chain Monte Carlo (MCMC) methods.
• Optimization of therapy schemes using gradient descent and particle swarm algorithms.

Main Results:

• Models were validated through simulations aligning with experimental results.
• Optimized therapy schemes demonstrated potential for tumor growth inhibition and cost reduction.
• Simulations highlighted the challenge of drug resistance and subsequent cancer relapse.
• Parametric sensitivity analysis identified dendritic cell injection as a promising complementary treatment.

Conclusions:

• Mathematical modeling offers a cost-effective approach to guide clinical trials and treatment selection for breast cancer.
• Combined therapies targeting β-catenin and enhancing immunity show promise.
• Future research should consider novel approaches like dendritic cell therapy to overcome drug resistance and improve long-term outcomes.