Abstract 4940: A hybrid mathematical model: Emergent CAF structures and their impact on cancer progression

Abstract Cancer-associated fibroblasts (CAFs) are key regulators of the tumor microenvironment (TME), exhibiting diverse phenotypes that exert both anti- and pro-tumorigenic effects. To investigate how this heterogeneity shapes cancer progression and therapy response, we first developed an ordinary differential equation (ODE) model, which assumes a well-mixed, homogeneous, and non-spatial TME. This mean-field framework captures cancer-immune-CAF interactions and predicts that CAF composition strongly influences treatment outcomes sometimes making single-agent therapies as effective as multi-drug combinations, or conversely rendering even triple-combination therapies ineffective. These results highlight CAF composition as a potential biomarker for guiding less invasive, CAF-informed therapeutic strategies. To overcome the limitations of non-spatial ODE models, we developed a hybrid Agent-Based Model-Partial Differential Equation-Ordinary Differential Equation (ABM-PDE-ODE) framework. In this spatial model, cancer cells, T cells, and CAFs are represented as individual agents that migrate, proliferate, or die based on local environmental cues. Diffusible factors including oxygen, CXCL, IFN-gamma, and TGF-beta are modeled by PDEs to generate spatial gradients, while each T cell independently solves an ODE-based PD-1/PD-L1 binding equation to determine its activation or exhaustion state. This multiscale model resolves spatial heterogeneity, local immunosuppressive niches, and CAF-driven barriers to immune infiltration phenomena that cannot be captured in the well-mixed ODE system. Together, these complementary modeling approaches provide a unified multiscale framework to evaluate how CAF phenotypic diversity shapes cancer dynamics and treatment response, and they offer new opportunities for CAF-informed, spatially guided personalized therapy design. Citation Format: Junho Lee, Eunjung Kim, . A hybrid mathematical model: Emergent CAF structures and their impact on cancer progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4940.

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Journal
Cancer Research
Published
2026-04-03
DOI
https://doi.org/10.1158/1538-7445.am2026-4940
Primary Topic
Mathematical Biology Tumor Growth
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article
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article

Abstract 4940: A hybrid mathematical model: Emergent CAF structures and their impact on cancer progression

Jongho Lee, Eunjung Kim
Cancer Research
Mathematical Biology Tumor Growth
article

Abstract 4940: A hybrid mathematical model: Emergent CAF structures and their impact on cancer progression

Jongho Lee, Eunjung Kim
article en

Abstract

Abstract Cancer-associated fibroblasts (CAFs) are key regulators of the tumor microenvironment (TME), exhibiting diverse phenotypes that exert both anti- and pro-tumorigenic effects. To investigate how this heterogeneity shapes cancer progression and therapy response, we first developed an ordinary differential equation (ODE) model, which assumes a well-mixed, homogeneous, and non-spatial TME. This mean-field framework captures cancer-immune-CAF interactions and predicts that CAF composition strongly influences treatment outcomes sometimes making single-agent therapies as effective as multi-drug combinations, or conversely rendering even triple-combination therapies ineffective. These results highlight CAF composition as a potential biomarker for guiding less invasive, CAF-informed therapeutic strategies. To overcome the limitations of non-spatial ODE models, we developed a hybrid Agent-Based Model-Partial Differential Equation-Ordinary Differential Equation (ABM-PDE-ODE) framework. In this spatial model, cancer cells, T cells, and CAFs are represented as individual agents that migrate, proliferate, or die based on local environmental cues. Diffusible factors including oxygen, CXCL, IFN-gamma, and TGF-beta are modeled by PDEs to generate spatial gradients, while each T cell independently solves an ODE-based PD-1/PD-L1 binding equation to determine its activation or exhaustion state. This multiscale model resolves spatial heterogeneity, local immunosuppressive niches, and CAF-driven barriers to immune infiltration phenomena that cannot be captured in the well-mixed ODE system. Together, these complementary modeling approaches provide a unified multiscale framework to evaluate how CAF phenotypic diversity shapes cancer dynamics and treatment response, and they offer new opportunities for CAF-informed, spatially guided personalized therapy design. Citation Format: Junho Lee, Eunjung Kim, . A hybrid mathematical model: Emergent CAF structures and their impact on cancer progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4940.

Cancer ResearchVol. 86(7_Supplement)
Korea Institute of Science and Technology (KR)
Openalex Percentile: Top 5%
Mathematical Biology Tumor Growth
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