A statistical framework for detecting therapy-induced resistance from drug screens
Abstract Resistance to therapy remains a significant challenge in cancer treatment, often due to the presence of a stem-like cell population that drives tumor recurrence post-treatment. Moreover, many anticancer therapies induce plasticity, converting initially drug-sensitive cells to a more resista...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | npj Systems Biology and Applications |
| Online Access: | https://doi.org/10.1038/s41540-025-00560-8 |
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| author | Chenyu Wu Einar Bjarki Gunnarsson Jasmine Foo Kevin Leder |
| author_facet | Chenyu Wu Einar Bjarki Gunnarsson Jasmine Foo Kevin Leder |
| author_sort | Chenyu Wu |
| collection | DOAJ |
| description | Abstract Resistance to therapy remains a significant challenge in cancer treatment, often due to the presence of a stem-like cell population that drives tumor recurrence post-treatment. Moreover, many anticancer therapies induce plasticity, converting initially drug-sensitive cells to a more resistant state, e.g. through epigenetic processes and de-differentiation programs. Understanding the balance between therapeutic anti-tumor effects and induced resistance is critical for identifying treatment strategies. In this study, we present a robust statistical framework leveraging multi-type branching process models to characterize the evolutionary dynamics of tumor cell populations. This approach enables the detection and quantification of therapy-induced resistance using high-throughput drug screening data involving total cell counts, without requiring information on subpopulation counts. The framework is validated using both simulated (in silico) and recent experimental (in vitro) datasets, demonstrating its ability to generate meaningful predictions. |
| format | Article |
| id | doaj-art-e908b5a1473b4b4f8c2879252ec58fce |
| institution | Kabale University |
| issn | 2056-7189 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Systems Biology and Applications |
| spelling | doaj-art-e908b5a1473b4b4f8c2879252ec58fce2025-08-20T03:42:53ZengNature Portfolionpj Systems Biology and Applications2056-71892025-08-0111111910.1038/s41540-025-00560-8A statistical framework for detecting therapy-induced resistance from drug screensChenyu Wu0Einar Bjarki Gunnarsson1Jasmine Foo2Kevin Leder3Department of Industrial and Systems Engineering, University of MinnesotaDivision of Applied Mathematics, Science Institute, University of IcelandSchool of Mathematics, University of MinnesotaDepartment of Industrial and Systems Engineering, University of MinnesotaAbstract Resistance to therapy remains a significant challenge in cancer treatment, often due to the presence of a stem-like cell population that drives tumor recurrence post-treatment. Moreover, many anticancer therapies induce plasticity, converting initially drug-sensitive cells to a more resistant state, e.g. through epigenetic processes and de-differentiation programs. Understanding the balance between therapeutic anti-tumor effects and induced resistance is critical for identifying treatment strategies. In this study, we present a robust statistical framework leveraging multi-type branching process models to characterize the evolutionary dynamics of tumor cell populations. This approach enables the detection and quantification of therapy-induced resistance using high-throughput drug screening data involving total cell counts, without requiring information on subpopulation counts. The framework is validated using both simulated (in silico) and recent experimental (in vitro) datasets, demonstrating its ability to generate meaningful predictions.https://doi.org/10.1038/s41540-025-00560-8 |
| spellingShingle | Chenyu Wu Einar Bjarki Gunnarsson Jasmine Foo Kevin Leder A statistical framework for detecting therapy-induced resistance from drug screens npj Systems Biology and Applications |
| title | A statistical framework for detecting therapy-induced resistance from drug screens |
| title_full | A statistical framework for detecting therapy-induced resistance from drug screens |
| title_fullStr | A statistical framework for detecting therapy-induced resistance from drug screens |
| title_full_unstemmed | A statistical framework for detecting therapy-induced resistance from drug screens |
| title_short | A statistical framework for detecting therapy-induced resistance from drug screens |
| title_sort | statistical framework for detecting therapy induced resistance from drug screens |
| url | https://doi.org/10.1038/s41540-025-00560-8 |
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