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.

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Publication Details

Journal
Cancers
Published
2025-11-27
DOI
https://doi.org/10.3390/cancers17233803
Primary Topic
Cancer Immunotherapy and Biomarkers
Type
article
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article

Dynamic PD-L1 Regulation Shapes Tumor Immune Escape and Response to Immunotherapy

Ardak Kashkynbayev, Bruce Pell, Aisha Tursynkozha, Yang Kuang et al.
Cancers
Cancer Immunotherapy and Biomarkers
article

Dynamic PD-L1 Regulation Shapes Tumor Immune Escape and Response to Immunotherapy

Ardak Kashkynbayev, Bruce Pell, Aisha Tursynkozha, Yang Kuang, Aigerim Kalizhanova, Denise Dengi
article en

Abstract

<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.

CancersVol. 17(23)
Lawrence Technological University (US), Astana Medical University (KZ), Arizona State University (US), Nazarbayev University (KZ), University of California, Berkeley (US)
Openalex Percentile: Top 49%
Cancer Immunotherapy and Biomarkers
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