Quantitative Modeling of Metastasis: Cancer at the Organism Scale

Explore cutting-edge mathematical modeling techniques for understanding and predicting metastasis in cancer through this 48-minute lecture by Sébastien Benzekry from Inria Sophia Antipolis-Méditerranée. Delve into the development of quantitative models that describe and predict metastatic progression, focusing on solid cancers where secondary tumors are the primary cause of mortality. Learn how these models can test biological theories, provide mechanistic insights, and create predictive tools for metastatic relapse and therapy impacts. Examine research on spontaneous metastasis development post-surgery, theoretical models of metastatic spread and growth, and tumor-tumor systemic interactions. Discover recent findings on paradoxical pro-metastatic effects of anti-angiogenic therapies. Gain insights into the practical applications of these models in clinical settings, including brain metastasis from non-small cell lung cancer and predicting metastatic relapse in early-stage breast cancer. Understand how this research contributes to the integration of mathematical modeling in metastasis research and its potential as a predictive tool for personalized oncology.