Russell Rockne

Synopsis: In this talk, I will present how mathematical modeling, developed through close interdisciplinary collaboration with biologists and clinicians, can be used to predict cancer progression and guide therapeutic decision-making. I will share examples from three areas of translational research: CAR T-cell immunotherapy for solid tumors, targeted radionuclide therapy for multiple myeloma, and tyrosine kinase inhibitor therapy for acute myeloid leukemia. Each case study will focus on a specific clinical challenge, the modeling approach used to address it, and how the results have informed or could inform patient care.

The goal is to demonstrate how quantitative frameworks can enhance our understanding of treatment dynamics, resistance mechanisms, and therapeutic optimization.

I will conclude with a forward-looking perspective on integrating artificial intelligence with mechnistic modeling to accelerate the development of personalized cancer therapies.