Multiscale Computational Models Guided By Emerging Cellular Dynamics Quantification For Predicting Optimum Immune Checkpoint And Targeted Therapy Schedules
- Multiscale Computational Models Guided By Emerging Cellular Dynamics Quantification For Predicting Optimum Immune Checkpoint And Targeted Therapy Schedules
The overall objective of our U01 project is to use mathematical modeling, driven with data extracted from novel cellular quantification experimental technologies, in order to inform the organization of combination immune checkpoint and fibroblast growth factor receptor-3 (FGFR3) inhibitor therapy. Our platform is capable of gathering information on two distinct mechanisms of anti-tumor cytotoxicity by T cells, and of transferring this information into our computationally-intense mathematical models. This project integrates live-cell interaction analysis, spatial evaluation of single-cell level quantitative multiplex immunohistochemistry, and multi-scale mathematical modeling of spatiotemporal tumor dynamics to improve patient outcomes via computationally designed immunotherapy and targeted treatment.
Our overall biological concept of cancer tumor-immune interactions reflects two types of T-cell killing: “rapid” (granule-based) and “contact” (FAS-based). For this biological concept, we hypothesize that the relative proportion of the two killing types, as well as T-cell motility, has a significant effect on the total tumor killing by T-cells, and both checkpoint and FGFR3 targeted therapy modify these proportions. Our overall computational concept of cancer tumor-immune interactions is a multi-scale 3D agent-based model of cancer cell interactions that includes both a novel deterministic-stochastic hybrid strategy to speed computation as well as explicit quantification of tumor-immune interactions via both a novel real-time live-cell imaging pipeline and quantitative multiplex immunohistochemistry, respectively.
We hypothesize that an agent-based model, combined with data from our newly-developed experimental platforms that parameterize cellular representations individually, can
- accurately reflect how different quantities of T cells and proportions of rapid vs. contact killing phenotypes will impact overall tumor growth
- characterize how checkpoint inhibition and anti-FGFR3 treatment enhance the T-cell mediated killing of tumor cells by distinct, complementary mechanisms, a
- optimize a co-treatment schedule with anti-PD-1 and targeted therapy that will have significantly more potent T-cell mediated anti-tumor killing than simultaneous co-treatment.
While based on tumors of the bladder, our platform is easily adaptable for the study of any therapy targeted to immune checkpoint proteins and receptor kinases in any tumor type. The real significance of our work lies in its translational value: our experimental and theoretical studies will be able to test clinically relevant hypotheses regarding the prospect of receptor tyrosine kinase inhibitors to impact the mechanism of tumor cell-kill by immune cells in distinct ways.
Trachette Jackson, Ph.D.
Trachette Jackson, Ph.D. (PI) is Professor of Mathematics and Research Member of the Rogel Cancer Center at the University of Michigan. She is both an Alfred P. Sloan Research Award winner and a James S. McDonnell 21st Century Scientist Award recipient. Motivated by addressing critical challenges associated with molecular cancer therapeutics, developing multiscale mathematical models of targeted cancer treatment strategies is the aim of much of Dr. Jackson’s research. These models allow for the integration tiers of information at precisely the level of detail required to uncover possible hidden mechanisms that mediate potentially counterintuitive therapeutic effects of novel, targeted therapeutics on the multiple cell types responsible for tumor progression.
Alexander T. Pearson, MD, PhD
Alexander T. Pearson, MD, PhD (Co-I) is Assistant Professor of Medicine at the University of Chicago, and is a practicing medical oncologist, statistician, and laboratory researcher. He has developed tracking and analysis tools for evaluating tumor growth based on cell level observations as well as quantitative pathology approaches using deep learning. He aims to integrate quantitative modeling tools into clinical practice.
Randy F Sweis, MD
Randy F Sweis, MD (Co- I) is Assistant Professor of Medicine at the University of Chicago, and is a practicing medical oncologist, tumor immunologist, and clinical pharmacologist. He is a national thought leader in bladder cancer targeted and immunotherapy, and is PI for a combination FGFR3-PD-1 therapy clinical trial.