Dynamic PD-L1 Regulation Shapes Tumor Immune Escape and Response to Immunotherapy
Pell B. Kalizhanova A. Tursynkozha A. Dengi D. Kashkynbayev A. Kuang Y.
December 2025Multidisciplinary Digital Publishing Institute (MDPI)
Cancers
2025#17Issue 23
Background: A major challenge in cancer treatment is the ability of tumor cells to adapt to immunotherapy through immune escape, often mediated by the PD-1/PD-L1 pathway. To investigate this, we adapted an ordinary differential equation model of combination therapy, incorporating the dynamics of the immune checkpoint inhibitor Avelumab and the immunostimulant NHS-muIL12. Methods: Using literature-derived parameter values, we refitted a single parameter across therapies, which showed that PD-L1 expression increased with immunotherapy, while Avelumab blocked its functional signaling, preventing PD-L1 from suppressing T-cell activity. Incorporating therapy-dependent, dynamically regulated PD-L1 expression enabled a biologically grounded mechanism to reproduce experimental observations, leading us to formulate PD-L1 tumor expression as a dynamic variable ((Formula presented.)) and providing a mechanistic basis for both therapeutic synergy and treatment failure. Results: We validated this mechanistic framework by showing that the distinct outcomes observed in two independent cancer datasets (EMT-6 and MC38) can be captured by the same model structure, differing only in the parameterization of tumor-specific parameters and PD-L1 regulatory dynamics. Our results indicate that tumor resistance is linked to dose-dependent upregulation of PD-L1 following NHS-muIL12 treatment, explaining treatment failure, while PD-1/PD-L1 blockade in combination therapy enables effective antitumor immune responses. Conclusions: This work provides a validated mechanistic framework for adaptive resistance in combination immunotherapy. Quantified parameter differences between responder and non-responder phenotypes enable clearer biological interpretation and support the development of predictive tools for optimizing treatment strategies.
adaptive immune response , cancer immunotherapy , differential equations , immunostimulant , math model , modeling , PD-L1
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Department of Mathematics and Computer Science, Lawrence Technological University, Southfield, 48075, MI, United States
Department of Mathematics, Nazarbayev University, Astana, 010000, Kazakhstan
Department of Artificial Intelligence and Data Science, Astana IT University, Astana, 010000, Kazakhstan
Department of Mathematics, University of California Berkeley, Berkeley, 94720, CA, United States
School of Mathematical and Statistical Sciences, Arizona State University, Tempe, 85287, AZ, United States
Department of Mathematics and Computer Science
Department of Mathematics
Department of Artificial Intelligence and Data Science
Department of Mathematics
School of Mathematical and Statistical Sciences
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