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Molecular features that predict the response to antimetabolite chemotherapies

BACKGROUND: Antimetabolite chemotherapeutic agents that target cellular metabolism are widely used in the clinic and are thought to exert their anti-cancer effects mainly through non-specific cytotoxic effects. However, patients vary dramatically with respect to treatment outcome, and the sources of...

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Autores principales: Mehrmohamadi, Mahya, Jeong, Seong Ho, Locasale, Jason W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5627437/
https://www.ncbi.nlm.nih.gov/pubmed/29026541
http://dx.doi.org/10.1186/s40170-017-0170-3
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author Mehrmohamadi, Mahya
Jeong, Seong Ho
Locasale, Jason W.
author_facet Mehrmohamadi, Mahya
Jeong, Seong Ho
Locasale, Jason W.
author_sort Mehrmohamadi, Mahya
collection PubMed
description BACKGROUND: Antimetabolite chemotherapeutic agents that target cellular metabolism are widely used in the clinic and are thought to exert their anti-cancer effects mainly through non-specific cytotoxic effects. However, patients vary dramatically with respect to treatment outcome, and the sources of heterogeneity remain largely unknown. METHODS: Here, we introduce a computational method for identifying gene expression signatures of response to chemotherapies and apply it to human tumors and cancer cell lines. Furthermore, we characterize a set of 17 antimetabolite agents in various contexts to investigate determinants of sensitivity to these agents. RESULTS: We identify distinct favorable and unfavorable metabolic expression signatures for 5-FU and Gemcitabine. Importantly, we find that metabolic pathways targeted by each of these antimetabolites are specifically enriched in its expression signatures. We provide evidence against the common notion about non-specific cytotoxic functions of antimetabolite drugs. CONCLUSIONS: This study demonstrates through unbiased analyses that the activities of metabolic pathways likely contribute to therapeutic response. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40170-017-0170-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-56274372017-10-12 Molecular features that predict the response to antimetabolite chemotherapies Mehrmohamadi, Mahya Jeong, Seong Ho Locasale, Jason W. Cancer Metab Research BACKGROUND: Antimetabolite chemotherapeutic agents that target cellular metabolism are widely used in the clinic and are thought to exert their anti-cancer effects mainly through non-specific cytotoxic effects. However, patients vary dramatically with respect to treatment outcome, and the sources of heterogeneity remain largely unknown. METHODS: Here, we introduce a computational method for identifying gene expression signatures of response to chemotherapies and apply it to human tumors and cancer cell lines. Furthermore, we characterize a set of 17 antimetabolite agents in various contexts to investigate determinants of sensitivity to these agents. RESULTS: We identify distinct favorable and unfavorable metabolic expression signatures for 5-FU and Gemcitabine. Importantly, we find that metabolic pathways targeted by each of these antimetabolites are specifically enriched in its expression signatures. We provide evidence against the common notion about non-specific cytotoxic functions of antimetabolite drugs. CONCLUSIONS: This study demonstrates through unbiased analyses that the activities of metabolic pathways likely contribute to therapeutic response. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40170-017-0170-3) contains supplementary material, which is available to authorized users. BioMed Central 2017-10-03 /pmc/articles/PMC5627437/ /pubmed/29026541 http://dx.doi.org/10.1186/s40170-017-0170-3 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Mehrmohamadi, Mahya
Jeong, Seong Ho
Locasale, Jason W.
Molecular features that predict the response to antimetabolite chemotherapies
title Molecular features that predict the response to antimetabolite chemotherapies
title_full Molecular features that predict the response to antimetabolite chemotherapies
title_fullStr Molecular features that predict the response to antimetabolite chemotherapies
title_full_unstemmed Molecular features that predict the response to antimetabolite chemotherapies
title_short Molecular features that predict the response to antimetabolite chemotherapies
title_sort molecular features that predict the response to antimetabolite chemotherapies
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5627437/
https://www.ncbi.nlm.nih.gov/pubmed/29026541
http://dx.doi.org/10.1186/s40170-017-0170-3
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