Kateřina Staňková

This study evaluates the clinical feasibility of evolutionary therapy for NSCLC by simulating its performance under real-world constraints like measurement error and appointment delays.

This study develops an evolutionary game theory model to analyze how persister cells influence cancer dynamics under treatment, showing they can stabilize or destabilize tumors based on transition rates and competition.

This study uses deep reinforcement learning to design treatment protocols for NSCLC that improve robustness, quality of life, and time to progression compared to standard adaptive therapy and maximum tolerable dose approaches.

This study shows that initial tumor composition influences resistance evolution and treatment outcomes in NSCLC, with resistance fitness effects varying based on initial resistant cell proportion.

The paper fits evolutionary game-theoretic models to NSCLC in-vitro data to identify the best model for capturing tumor growth and treatment response dynamics.

This study proposes integrating migrastatics into adaptive therapy to delay metastasis and improve cancer treatment outcomes.