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Constraint based modeling of metabolism allows finding metabolic cancer hallmarks and identifying personalized therapeutic windows

In order to choose optimal personalized anticancer treatments, transcriptomic data should be analyzed within the frame of biological networks. The best known human biological network (in terms of the interactions between its different components) is metabolism. Cancer cells have been known to have s...

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Detalles Bibliográficos
Autor principal: Bordel, Sergio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals LLC 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5929420/
https://www.ncbi.nlm.nih.gov/pubmed/29731977
http://dx.doi.org/10.18632/oncotarget.24805
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author Bordel, Sergio
author_facet Bordel, Sergio
author_sort Bordel, Sergio
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description In order to choose optimal personalized anticancer treatments, transcriptomic data should be analyzed within the frame of biological networks. The best known human biological network (in terms of the interactions between its different components) is metabolism. Cancer cells have been known to have specific metabolic features for a long time and currently there is a growing interest in characterizing new cancer specific metabolic hallmarks. In this article it is presented a method to find personalized therapeutic windows using RNA-seq data and Genome Scale Metabolic Models. This method is implemented in the python library, pyTARG. Our predictions showed that the most anticancer selective (affecting 27 out of 34 considered cancer cell lines and only 1 out of 6 healthy mesenchymal stem cell lines) single metabolic reactions are those involved in cholesterol biosynthesis. Excluding cholesterol biosynthesis, all the considered cell lines can be selectively affected by targeting different combinations (from 1 to 5 reactions) of only 18 metabolic reactions, which suggests that a small subset of drugs or siRNAs combined in patient specific manners could be at the core of metabolism based personalized treatments.
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spelling pubmed-59294202018-05-04 Constraint based modeling of metabolism allows finding metabolic cancer hallmarks and identifying personalized therapeutic windows Bordel, Sergio Oncotarget Research Paper In order to choose optimal personalized anticancer treatments, transcriptomic data should be analyzed within the frame of biological networks. The best known human biological network (in terms of the interactions between its different components) is metabolism. Cancer cells have been known to have specific metabolic features for a long time and currently there is a growing interest in characterizing new cancer specific metabolic hallmarks. In this article it is presented a method to find personalized therapeutic windows using RNA-seq data and Genome Scale Metabolic Models. This method is implemented in the python library, pyTARG. Our predictions showed that the most anticancer selective (affecting 27 out of 34 considered cancer cell lines and only 1 out of 6 healthy mesenchymal stem cell lines) single metabolic reactions are those involved in cholesterol biosynthesis. Excluding cholesterol biosynthesis, all the considered cell lines can be selectively affected by targeting different combinations (from 1 to 5 reactions) of only 18 metabolic reactions, which suggests that a small subset of drugs or siRNAs combined in patient specific manners could be at the core of metabolism based personalized treatments. Impact Journals LLC 2018-04-13 /pmc/articles/PMC5929420/ /pubmed/29731977 http://dx.doi.org/10.18632/oncotarget.24805 Text en Copyright: © 2018 Bordel http://creativecommons.org/licenses/by/3.0/ This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) (CC-BY), which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Research Paper
Bordel, Sergio
Constraint based modeling of metabolism allows finding metabolic cancer hallmarks and identifying personalized therapeutic windows
title Constraint based modeling of metabolism allows finding metabolic cancer hallmarks and identifying personalized therapeutic windows
title_full Constraint based modeling of metabolism allows finding metabolic cancer hallmarks and identifying personalized therapeutic windows
title_fullStr Constraint based modeling of metabolism allows finding metabolic cancer hallmarks and identifying personalized therapeutic windows
title_full_unstemmed Constraint based modeling of metabolism allows finding metabolic cancer hallmarks and identifying personalized therapeutic windows
title_short Constraint based modeling of metabolism allows finding metabolic cancer hallmarks and identifying personalized therapeutic windows
title_sort constraint based modeling of metabolism allows finding metabolic cancer hallmarks and identifying personalized therapeutic windows
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5929420/
https://www.ncbi.nlm.nih.gov/pubmed/29731977
http://dx.doi.org/10.18632/oncotarget.24805
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