Essential Maintenance: All Authorea-powered sites will be offline 9am-10am EDT Tuesday 28 May
and 11pm-1am EDT Tuesday 28-Wednesday 29 May. We apologise for any inconvenience.

loading page

A Validated Mathematical Model of FGFR3-Mediated Tumor Growth Reveals Pathways to Harness the Benefits of Combination Targeted Therapy and Immunotherapy in Bladder Cancer
  • +3
  • Kamaldeen Okuneye,
  • Daniel Bergman,
  • Jeffrey Bloodworth,
  • Alexander Pearson,
  • Randy Sweis,
  • Trachette Jackson
Kamaldeen Okuneye
Applied Biomath
Author Profile
Daniel Bergman
University of Michigan College of Literature Science and the Arts
Author Profile
Jeffrey Bloodworth
University of Chicago
Author Profile
Alexander Pearson
University of Chicago
Author Profile
Randy Sweis
University of Chicago
Author Profile
Trachette Jackson
University of Michigan

Corresponding Author:[email protected]

Author Profile

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 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.
31 Dec 2020Submitted to Computational and Systems Oncology
31 Dec 2020Submission Checks Completed
31 Dec 2020Assigned to Editor
31 Dec 2020Reviewer(s) Assigned
02 Feb 2021Review(s) Completed, Editorial Evaluation Pending
03 Feb 2021Editorial Decision: Revise Major
15 Mar 20211st Revision Received
17 Mar 2021Submission Checks Completed
17 Mar 2021Assigned to Editor
17 Mar 2021Review(s) Completed, Editorial Evaluation Pending
18 Mar 2021Reviewer(s) Assigned
13 Apr 2021Editorial Decision: Accept
Jun 2021Published in Computational and Systems Oncology volume 1 issue 2. https://doi.org/10.1002/cso2.1019