Multiscale Models Guided By Cellular Dynamics Quantification for Predicting Optimum Immune and Targeted Therapy Schedules
The principal goal of this proposal is to combine multiscale mathematical modeling with novel model-driven experimental platforms in order to gain a deeper, more robust understanding of tumor-immune dynamics. Using bladder cancer as our platform, we are developing a comprehensive and predictive 3D computational framework that will be used to optimize combination immunotherapy and receptor kinase targeted therapy. Our experimentally-driven multiscale approach is poised to significantly enhance the current understanding of differential cell-kill mechanisms on tumor-immune outcomes and to improve the ability to combine promising drugs for clinical trials.
Given the poor outcomes with chemotherapy in advanced cancers, immunotherapy has emerged as an exciting domain for exploration. Monoclonal antibodies targeting the PD-1/PD-L1 “immune checkpoint” pathway have resulted in favorable outcomes in advanced bladder cancer, and 5 drugs targeting this pathway have been approved in the past two years. Unfortunately, the objective response rates of current FDA approved immunotherapy drugs remain less than 25%.
Checkpoint proteins, such as PD-L1 on tumor cells and PD-1 on T cells, help keep immune responses in check. The binding of PD-L1 to PD-1 keeps T cells from killing tumor cells in the body (left panel). Blocking the binding of PD-L1 to PD-1 with an immune checkpoint inhibitor (anti-PD-L1 or anti-PD-1) allows the T cells to kill tumor cells (right panel). Credit: NCI – https://www.cancer.gov/about-cancer/treatment/types/immunotherapy/checkpoint-inhibitors
Under normal conditions, heparin (pink)-bound FGF mediates FGFR dimerization, leading to cell proliferation and cell survival. In cancer, activating mutations or gene amplification, common causes of deregulated FGFR signaling, lead to constitutive activation of downstream signaling pathways and aberrant cell proliferation and increased cell survival. FGFR-specific monoclonal antibodies, TKI, and SMIs can counteract these effects. Credit: Haynes and Day– https://cancerres.aacrjournals.org/content/70/13/5199
A powerful and practical way to optimize novel drug combinations for clinical cancer treatment is to use sophisticated, data-driven computational models. Our agent-based modeling platform will both aid in the characterization of tumor-immune dynamics and also suggest the best strategies for administering therapeutic combinations of immune-checkpoint and receptor kinase inhibitors. The model will be parameterized at the molecular and cellular scales by an innovative high-throughput image quantification pipeline that allows T-cell or cancer cell behaviors and interactions to be observed, tracked and quantified.
Our experimentally-driven multiscale approach is posed to (1) significantly enhance the current understanding of the impact of differential cell-kill mechanisms on tumor-immune outcomes; (2) optimize the administration of combination therapy and maximize tumor response; and (3) to improve the ability to select the most promising drugs for the clinical trials. While based on tumors of the bladder, the platform that we are developing is easily adaptable for the study of any therapy targeted to immune checkpoint proteins and receptor kinases in any tumor type.
This page is under construction… Stay tuned!