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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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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. |
format | Online Article Text |
id | pubmed-5627437 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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