A combination approach of pseudotime analysis and mathematical modeling for understanding drug-resistant mechanisms

Abstract Cancer cells acquire drug resistance through the following stages: nonresistant, pre-resistant, and resistant. Although the molecular mechanism of drug resistance is well investigated, the process of drug resistance acquisition remains largely unknown. Here we elucidate the molecular mechan...

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Autores principales:	Shigeyuki Magi, Sewon Ki, Masao Ukai, Elisa Domínguez-Hüttinger, Atsuhiko T Naito, Yutaka Suzuki, Mariko Okada
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/edea2815879c453eac28e10bba3cf3e5
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spelling	oai:doaj.org-article:edea2815879c453eac28e10bba3cf3e52021-12-02T15:15:58ZA combination approach of pseudotime analysis and mathematical modeling for understanding drug-resistant mechanisms10.1038/s41598-021-97887-z2045-2322https://doaj.org/article/edea2815879c453eac28e10bba3cf3e52021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97887-zhttps://doaj.org/toc/2045-2322Abstract Cancer cells acquire drug resistance through the following stages: nonresistant, pre-resistant, and resistant. Although the molecular mechanism of drug resistance is well investigated, the process of drug resistance acquisition remains largely unknown. Here we elucidate the molecular mechanisms underlying the process of drug resistance acquisition by sequential analysis of gene expression patterns in tamoxifen-treated breast cancer cells. Single-cell RNA-sequencing indicates that tamoxifen-resistant cells can be subgrouped into two, one showing altered gene expression related to metabolic regulation and another showing high expression levels of adhesion-related molecules and histone-modifying enzymes. Pseudotime analysis showed a cell transition trajectory to the two resistant subgroups that stem from a shared pre-resistant state. An ordinary differential equation model based on the trajectory fitted well with the experimental results of cell growth. Based on the established model, it was predicted and experimentally validated that inhibition of transition to both resistant subtypes would prevent the appearance of tamoxifen resistance.Shigeyuki MagiSewon KiMasao UkaiElisa Domínguez-HüttingerAtsuhiko T NaitoYutaka SuzukiMariko OkadaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shigeyuki Magi Sewon Ki Masao Ukai Elisa Domínguez-Hüttinger Atsuhiko T Naito Yutaka Suzuki Mariko Okada A combination approach of pseudotime analysis and mathematical modeling for understanding drug-resistant mechanisms
description	Abstract Cancer cells acquire drug resistance through the following stages: nonresistant, pre-resistant, and resistant. Although the molecular mechanism of drug resistance is well investigated, the process of drug resistance acquisition remains largely unknown. Here we elucidate the molecular mechanisms underlying the process of drug resistance acquisition by sequential analysis of gene expression patterns in tamoxifen-treated breast cancer cells. Single-cell RNA-sequencing indicates that tamoxifen-resistant cells can be subgrouped into two, one showing altered gene expression related to metabolic regulation and another showing high expression levels of adhesion-related molecules and histone-modifying enzymes. Pseudotime analysis showed a cell transition trajectory to the two resistant subgroups that stem from a shared pre-resistant state. An ordinary differential equation model based on the trajectory fitted well with the experimental results of cell growth. Based on the established model, it was predicted and experimentally validated that inhibition of transition to both resistant subtypes would prevent the appearance of tamoxifen resistance.
format	article
author	Shigeyuki Magi Sewon Ki Masao Ukai Elisa Domínguez-Hüttinger Atsuhiko T Naito Yutaka Suzuki Mariko Okada
author_facet	Shigeyuki Magi Sewon Ki Masao Ukai Elisa Domínguez-Hüttinger Atsuhiko T Naito Yutaka Suzuki Mariko Okada
author_sort	Shigeyuki Magi
title	A combination approach of pseudotime analysis and mathematical modeling for understanding drug-resistant mechanisms
title_short	A combination approach of pseudotime analysis and mathematical modeling for understanding drug-resistant mechanisms
title_full	A combination approach of pseudotime analysis and mathematical modeling for understanding drug-resistant mechanisms
title_fullStr	A combination approach of pseudotime analysis and mathematical modeling for understanding drug-resistant mechanisms
title_full_unstemmed	A combination approach of pseudotime analysis and mathematical modeling for understanding drug-resistant mechanisms
title_sort	combination approach of pseudotime analysis and mathematical modeling for understanding drug-resistant mechanisms
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/edea2815879c453eac28e10bba3cf3e5
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A combination approach of pseudotime analysis and mathematical modeling for understanding drug-resistant mechanisms

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