Lee, Junho Kim, Eunjung 2025-08-26T02:30:59Z 2025-08-26T02:30:59Z 2025-08-25 2025-08 2056-7189 https://pubs.kist.re.kr/handle/201004/153041 Cancer-associated fibroblasts (CAFs) are key components of the tumor microenvironment (TME). CAF phenotypes are highly heterogeneous and exert anti- and protumorigenic effects. We present a mathematical model that describes cancer-immune-CAF interactions and exploits the heterogeneity of CAF phenotypes to predict cancer progression and treatment response. By simulating multiple treatment options, including targeted monotherapies alone, two different immunotherapies, and a combination of therapies, we have found that CAF composition can impact treatment outcomes, potentially resulting in comparable effectiveness of single-drug treatments and combinatorial approaches or even the ineffectiveness of multicombination therapies. These findings suggest that CAF composition can be a promising indicator, in some cases guiding the choice towards less invasive therapies without compromising effectiveness. Our model indicates that accounting for CAF characteristics might facilitate the matching of targeted treatments, supporting clinical decision-making. English Systems Biology Instititute | Nature Publishing Group Ordinary differential equation model of cancer-associated fibroblast heterogeneity predicts treatment outcomes Article 10.1038/s41540-025-00578-y 1 npj Systems Biology and Applications, v.11, no.1 npj Systems Biology and Applications 11 1 Y scie scopus 001556310400001 Mathematical & Computational Biology Mathematical & Computational Biology Article INHIBITOR TASELISIB GROWTH REGULATORY T-CELLS