Masud, M.A. Kim, Jae-Young Kim, Eunjung 2024-01-12T06:34:53Z 2024-01-12T06:34:53Z 2023-06-05 2023-08 0010-4825 https://pubs.kist.re.kr/handle/201004/79867 Adaptive therapy (AT) is an evolution-based treatment strategy that exploits cell？cell competition. Acquired resistance can change the competitive nature of cancer cells in a tumor, impacting AT outcomes. We aimed to determine if adaptive therapy can still be effective with cell’s acquiring resistance. We developed an agent-based model for spatial tumor growth considering three different types of acquired resistance: random genetic mutations during cell division, drug-induced reversible (plastic) phenotypic changes, and drug-induced irreversible phenotypic changes. These three resistance mechanisms lead to different spatial distributions of resistant cells. To quantify the spatial distribution, we propose an extension of Ripley’s K-function, Sampled Ripley’s K-function (SRKF), which calculates the non-randomness of the resistance distribution over the tumor domain. Our model predicts that the emergent spatial distribution of resistance can determine the time to progression under both adaptive and continuous therapy (CT). Notably, a high rate of random genetic mutations leads to quicker progression under AT than CT due to the emergence of many small clumps of resistant cells. Drug-induced phenotypic changes accelerate tumor progression irrespective of the treatment strategy. Low-rate switching to a sensitive state reduces the benefits of AT compared to CT. Furthermore, we also demonstrated that drug-induced resistance necessitates aggressive treatment under CT, regardless of the presence of cancer-associated fibroblasts. However, there is an optimal dose that can most effectively delay tumor relapse under AT by suppressing resistance. In conclusion, this study demonstrates that diverse resistance mechanisms can shape the distribution of resistance and thus determine the efficacy of adaptive therapy. English Pergamon Press Ltd. Modeling the effect of acquired resistance on cancer therapy outcomes Article 10.1016/j.compbiomed.2023.107035 1 Computers in Biology and Medicine, v.162 Computers in Biology and Medicine 162 N scie scopus 001014380000001 Biology Computer Science, Interdisciplinary Applications Engineering, Biomedical Mathematical & Computational Biology Life Sciences & Biomedicine - Other Topics Computer Science Engineering Mathematical & Computational Biology Article TO-MESENCHYMAL TRANSITION DRUG-RESISTANCE DNA METHYLATION FIBROBLASTS HETEROGENEITY RECEPTORS Drug-induced resistance Intrinsic resistance Plasticity Spatial interactions Agent-based model Optimal dose Spatial cluster formation Point pattern analysis