Open AccessA validated mathematical model of FGFR3‐mediated tumor growth reveals pathways to harness the benefits of combination targeted therapy and immunotherapy in bladder cancer
Author(s) -
Okuneye Kamaldeen,
Bergman Daniel,
Bloodworth Jeffrey C.,
Pearson Alexander T.,
Sweis Randy F.,
Jackson Trachette L.
Publication year - 2021
Publication title -
computational and systems oncology
Language(s) - English
Resource type - Journals
ISSN - 2689-9655
DOI - 10.1002/cso2.1019
Subject(s) - medicine , immunotherapy , bladder cancer , targeted therapy , oncology , immune checkpoint , receptor tyrosine kinase , cancer , malignancy , cancer research , tyrosine kinase inhibitor , monoclonal antibody , immunology , antibody , receptor
Abstract Bladder cancer is a common malignancy with over 80,000 estimated new cases and nearly 18,000 deaths per year in the United States alone. Therapeutic options for metastatic bladder cancer had not evolved much for nearly four decades, until recently, when five immune checkpoint inhibitors were approved by the U.S. Food and Drug Administration (FDA). Despite the activity of these drugs in some patients, the objective response rate for each is less than 25%. At the same time, fibroblast growth factor receptors (FGFRs) have been attractive drug targets for a variety of cancers, and in 2019 the FDA approved the first therapy targeted against FGFR3 for bladder cancer. Given the excitement around these new receptor tyrosine kinase and immune checkpoint targeted strategies, and the challenges they each may face on their own, emerging data suggest that combining these treatment options could lead to improved therapeutic outcomes. In this paper, we develop a mathematical model for FGFR3‐mediated tumor growth and use it to investigate the impact of the combined administration of a small molecule inhibitor of FGFR3 and a monoclonal antibody against the PD‐1/PD‐L1 immune checkpoint. The model is carefully calibrated and validated with experimental data before survival benefits, and dosing schedules are explored. Predictions of the model suggest that FGFR3 mutation reduces the effectiveness of anti‐PD‐L1 therapy, that there are regions of parameter space where each monotherapy can outperform the other, and that pretreatment with anti‐PD‐L1 therapy always results in greater tumor reduction even when anti‐FGFR3 therapy is the more effective monotherapy.