Abstract: “Harnessing the adaptive immune system to recognize and eliminate cancer cells has ushered in T cell-based immunotherapies, which have revolutionized cancer treatment by inducing durable remission for many patients. Due to the immense complexity of T cell immunotherapy, nearly all attempts to improve these approaches and to expand their applications have been driven empirically. To predict treatment failure and optimize current therapies, we must understand the dynamic interplay between an adaptive immune system and an evolving cancer population. My talk will describe stochastic modeling that our group has been actively developing, which include random energy models of the T cell receptor-antigen interface developed to characterize immune recognition potential against tumor antigens, and dynamical models of the interaction between an evolving cancer population and an adaptive immune system.

About the speaker: Jason T. George is an assistant professor in the Department of Biomedical Engineering at Texas A&M University. His research interests lie at the intersection of engineering and applied mathematics, where he focuses on developing dual theoretical and data-driven mathematical models to characterize cancer development and evasion with the goal of improving treatment outcomes. Prior to joining A&M, Dr. George was a postdoctoral fellow in the Center for Theoretical Biological Physics at Rice University. He was a student in the Medical Scientist Training Program where he earned his M.D. at Baylor College of Medicine and Ph.D. in the Department of Bioengineering at Rice University under the mentorship of Herbert Levine. Prior to this, Dr. George completed his undergraduate training at Texas A&M University, where he earned a B.S. in Biomedical Engineering and a B.A. in Mathematics. Dr. George is the recipient of the CPRIT First-Time Tenure-Track Faculty Recruitment Award. His graduate thesis work was funded by a Ruth L. Kirschstein Fellowship through the NIH National Cancer Institute.