Speakers
Description
Chimeric Antigen Receptor (CAR) T-cell therapy has emerged as a transformative approach in cancer treatment, particularly for hematological malignancies, and is increasingly explored for solid tumors. Patient responses, however, are highly variable, reflecting the complexity of tumor-immune interactions, antigen heterogeneity, immune exhaustion, and microenvironmental influences.
This minisymposium focuses on mathematical and data-driven modeling approaches that aim to understand, predict, and optimize CAR T-cell therapy. Mechanistic and computational models, informed by clinical and experimental data, provide quantitative insights into tumor and CAR T-cell dynamics, the emergence of resistance, and the factors that influence therapeutic efficacy. By integrating theoretical and empirical perspectives, these models offer guidance for treatment design, biomarker identification, and personalized therapy strategies.
Attendees will gain a comprehensive view of how mathematical and data-driven modeling can support the development and optimization of CAR T-cell therapy, bridging the gap between quantitative theory and clinical application.
