Epigenetic state inheritance drivers drug-tolerant persister-induced resistance in solid tumors: A stochastic agent-based model
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| Format: | Preprint |
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2026
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| _version_ | 1866910005575286784 |
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| author | Liang, Xiyin Lei, Jinzhi |
| author_facet | Liang, Xiyin Lei, Jinzhi |
| contents | The efficacy of anti-cancer therapies is severely limited by the emergence of drug resistance. While genetic drivers are well-characterized, growing evidence suggests that non-genetic mechanisms, particularly those involving drug-tolerant persisters (DTPs), play a pivotal role in solid tumor relapse. To elucidate the evolutionary dynamics of DTP-induced resistance, we develop a stochastic agent-based model (ABM) of solid tumor evolution that couples macroscopic population dynamics with microscopic epigenetic state inheritance during the cell cycle. Our simulations accurately reproduce the temporal progression of relapse observed in experimental studies, capturing the dynamic transition from sensitive cells to DTPs, and ultimately to stable resistant phenotypes under prolonged therapy. By explicitly modeling the epigenetic plasticity of individual cells, our model bridges the gap between cellular heterogeneity and population-level tumor evolution. Furthermore, we performed \textit{in silico} clinical trials using virtual patient cohorts to evaluate therapeutic outcomes, demonstrating that optimized adaptive treatment strategies can significantly delay tumor relapse compared to standard dosing. This study provides a quantitative framework for dissecting DTP-driven resistance mechanisms and designing more effective, biologically informed therapeutic strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_22619 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Epigenetic state inheritance drivers drug-tolerant persister-induced resistance in solid tumors: A stochastic agent-based model Liang, Xiyin Lei, Jinzhi Populations and Evolution The efficacy of anti-cancer therapies is severely limited by the emergence of drug resistance. While genetic drivers are well-characterized, growing evidence suggests that non-genetic mechanisms, particularly those involving drug-tolerant persisters (DTPs), play a pivotal role in solid tumor relapse. To elucidate the evolutionary dynamics of DTP-induced resistance, we develop a stochastic agent-based model (ABM) of solid tumor evolution that couples macroscopic population dynamics with microscopic epigenetic state inheritance during the cell cycle. Our simulations accurately reproduce the temporal progression of relapse observed in experimental studies, capturing the dynamic transition from sensitive cells to DTPs, and ultimately to stable resistant phenotypes under prolonged therapy. By explicitly modeling the epigenetic plasticity of individual cells, our model bridges the gap between cellular heterogeneity and population-level tumor evolution. Furthermore, we performed \textit{in silico} clinical trials using virtual patient cohorts to evaluate therapeutic outcomes, demonstrating that optimized adaptive treatment strategies can significantly delay tumor relapse compared to standard dosing. This study provides a quantitative framework for dissecting DTP-driven resistance mechanisms and designing more effective, biologically informed therapeutic strategies. |
| title | Epigenetic state inheritance drivers drug-tolerant persister-induced resistance in solid tumors: A stochastic agent-based model |
| topic | Populations and Evolution |
| url | https://arxiv.org/abs/2601.22619 |