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Predicting chemotherapeutic drug combinations through gene network profiling

Contemporary chemotherapeutic treatments incorporate the use of several agents in combination. However, selecting the most appropriate drugs for such therapy is not necessarily an easy or straightforward task. Here, we describe a targeted approach that can facilitate the reliable selection of chemot...

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Autores principales: Nguyen, Thi Thuy Trang, Chua, Jacqueline Kia Kee, Seah, Kwi Shan, Koo, Seok Hwee, Yee, Jie Yin, Yang, Eugene Guorong, Lim, Kim Kiat, Pang, Shermaine Yu Wen, Yuen, Audrey, Zhang, Louxin, Ang, Wee Han, Dymock, Brian, Lee, Edmund Jon Deoon, Chen, Ee Sin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4726371/
https://www.ncbi.nlm.nih.gov/pubmed/26791325
http://dx.doi.org/10.1038/srep18658
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author Nguyen, Thi Thuy Trang
Chua, Jacqueline Kia Kee
Seah, Kwi Shan
Koo, Seok Hwee
Yee, Jie Yin
Yang, Eugene Guorong
Lim, Kim Kiat
Pang, Shermaine Yu Wen
Yuen, Audrey
Zhang, Louxin
Ang, Wee Han
Dymock, Brian
Lee, Edmund Jon Deoon
Chen, Ee Sin
author_facet Nguyen, Thi Thuy Trang
Chua, Jacqueline Kia Kee
Seah, Kwi Shan
Koo, Seok Hwee
Yee, Jie Yin
Yang, Eugene Guorong
Lim, Kim Kiat
Pang, Shermaine Yu Wen
Yuen, Audrey
Zhang, Louxin
Ang, Wee Han
Dymock, Brian
Lee, Edmund Jon Deoon
Chen, Ee Sin
author_sort Nguyen, Thi Thuy Trang
collection PubMed
description Contemporary chemotherapeutic treatments incorporate the use of several agents in combination. However, selecting the most appropriate drugs for such therapy is not necessarily an easy or straightforward task. Here, we describe a targeted approach that can facilitate the reliable selection of chemotherapeutic drug combinations through the interrogation of drug-resistance gene networks. Our method employed single-cell eukaryote fission yeast (Schizosaccharomyces pombe) as a model of proliferating cells to delineate a drug resistance gene network using a synthetic lethality workflow. Using the results of a previous unbiased screen, we assessed the genetic overlap of doxorubicin with six other drugs harboring varied mechanisms of action. Using this fission yeast model, drug-specific ontological sub-classifications were identified through the computation of relative hypersensitivities. We found that human gastric adenocarcinoma cells can be sensitized to doxorubicin by concomitant treatment with cisplatin, an intra-DNA strand crosslinking agent, and suberoylanilide hydroxamic acid, a histone deacetylase inhibitor. Our findings point to the utility of fission yeast as a model and the differential targeting of a conserved gene interaction network when screening for successful chemotherapeutic drug combinations for human cells.
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spelling pubmed-47263712016-01-27 Predicting chemotherapeutic drug combinations through gene network profiling Nguyen, Thi Thuy Trang Chua, Jacqueline Kia Kee Seah, Kwi Shan Koo, Seok Hwee Yee, Jie Yin Yang, Eugene Guorong Lim, Kim Kiat Pang, Shermaine Yu Wen Yuen, Audrey Zhang, Louxin Ang, Wee Han Dymock, Brian Lee, Edmund Jon Deoon Chen, Ee Sin Sci Rep Article Contemporary chemotherapeutic treatments incorporate the use of several agents in combination. However, selecting the most appropriate drugs for such therapy is not necessarily an easy or straightforward task. Here, we describe a targeted approach that can facilitate the reliable selection of chemotherapeutic drug combinations through the interrogation of drug-resistance gene networks. Our method employed single-cell eukaryote fission yeast (Schizosaccharomyces pombe) as a model of proliferating cells to delineate a drug resistance gene network using a synthetic lethality workflow. Using the results of a previous unbiased screen, we assessed the genetic overlap of doxorubicin with six other drugs harboring varied mechanisms of action. Using this fission yeast model, drug-specific ontological sub-classifications were identified through the computation of relative hypersensitivities. We found that human gastric adenocarcinoma cells can be sensitized to doxorubicin by concomitant treatment with cisplatin, an intra-DNA strand crosslinking agent, and suberoylanilide hydroxamic acid, a histone deacetylase inhibitor. Our findings point to the utility of fission yeast as a model and the differential targeting of a conserved gene interaction network when screening for successful chemotherapeutic drug combinations for human cells. Nature Publishing Group 2016-01-21 /pmc/articles/PMC4726371/ /pubmed/26791325 http://dx.doi.org/10.1038/srep18658 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Nguyen, Thi Thuy Trang
Chua, Jacqueline Kia Kee
Seah, Kwi Shan
Koo, Seok Hwee
Yee, Jie Yin
Yang, Eugene Guorong
Lim, Kim Kiat
Pang, Shermaine Yu Wen
Yuen, Audrey
Zhang, Louxin
Ang, Wee Han
Dymock, Brian
Lee, Edmund Jon Deoon
Chen, Ee Sin
Predicting chemotherapeutic drug combinations through gene network profiling
title Predicting chemotherapeutic drug combinations through gene network profiling
title_full Predicting chemotherapeutic drug combinations through gene network profiling
title_fullStr Predicting chemotherapeutic drug combinations through gene network profiling
title_full_unstemmed Predicting chemotherapeutic drug combinations through gene network profiling
title_short Predicting chemotherapeutic drug combinations through gene network profiling
title_sort predicting chemotherapeutic drug combinations through gene network profiling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4726371/
https://www.ncbi.nlm.nih.gov/pubmed/26791325
http://dx.doi.org/10.1038/srep18658
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