Dynamic PD-L1 Regulation Shapes Tumor Immune Escape and Response to Immunotherapy
<b>Background</b>: 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. <b>Methods</b>: 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 (ϵ) and providing a mechanistic basis for both therapeutic synergy and treatment failure. <b>Results</b>: 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. <b>Conclusions</b>: 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.
Authors
- Ardak Kashkynbayev (ORCID: https://orcid.org/0000-0002-1338-2058)
- Bruce Pell (ORCID: https://orcid.org/0000-0003-1358-8061)
- Aisha Tursynkozha
- Yang Kuang (ORCID: https://orcid.org/0000-0003-2774-4133)
- Aigerim Kalizhanova
- Denise Dengi
Institutions
- Lawrence Technological University (US)
- Astana Medical University (KZ)
- Arizona State University (US)
- Nazarbayev University (KZ)
- University of California, Berkeley (US)
Publication Details
- Journal
- Cancers
- Published
- 2025-11-27
- DOI
- https://doi.org/10.3390/cancers17233803
- Primary Topic
- Cancer Immunotherapy and Biomarkers
- Type
- article
- Field-Weighted Citation Impact
- 0.00