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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Impact Journals LLC
2018
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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 |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-5929420 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Impact Journals LLC |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT bordelsergio constraintbasedmodelingofmetabolismallowsfindingmetaboliccancerhallmarksandidentifyingpersonalizedtherapeuticwindows |