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Identification of Potential Drug Targets in Cancer Signaling Pathways using Stochastic Logical Models

The investigation of vulnerable components in a signaling pathway can contribute to development of drug therapy addressing aberrations in that pathway. Here, an original signaling pathway is derived from the published literature on breast cancer models. New stochastic logical models are then develop...

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Autores principales: Zhu, Peican, Aliabadi, Hamidreza Montazeri, Uludağ, Hasan, Han, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4796823/
https://www.ncbi.nlm.nih.gov/pubmed/26988076
http://dx.doi.org/10.1038/srep23078
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author Zhu, Peican
Aliabadi, Hamidreza Montazeri
Uludağ, Hasan
Han, Jie
author_facet Zhu, Peican
Aliabadi, Hamidreza Montazeri
Uludağ, Hasan
Han, Jie
author_sort Zhu, Peican
collection PubMed
description The investigation of vulnerable components in a signaling pathway can contribute to development of drug therapy addressing aberrations in that pathway. Here, an original signaling pathway is derived from the published literature on breast cancer models. New stochastic logical models are then developed to analyze the vulnerability of the components in multiple signalling sub-pathways involved in this signaling cascade. The computational results are consistent with the experimental results, where the selected proteins were silenced using specific siRNAs and the viability of the cells were analyzed 72 hours after silencing. The genes elF4E and NFkB are found to have nearly no effect on the relative cell viability and the genes JAK2, Stat3, S6K, JUN, FOS, Myc, and Mcl1 are effective candidates to influence the relative cell growth. The vulnerabilities of some targets such as Myc and S6K are found to vary significantly depending on the weights of the sub-pathways; this will be indicative of the chosen target to require customization for therapy. When these targets are utilized, the response of breast cancers from different patients will be highly variable because of the known heterogeneities in signaling pathways among the patients. The targets whose vulnerabilities are invariably high might be more universally acceptable targets.
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spelling pubmed-47968232016-03-18 Identification of Potential Drug Targets in Cancer Signaling Pathways using Stochastic Logical Models Zhu, Peican Aliabadi, Hamidreza Montazeri Uludağ, Hasan Han, Jie Sci Rep Article The investigation of vulnerable components in a signaling pathway can contribute to development of drug therapy addressing aberrations in that pathway. Here, an original signaling pathway is derived from the published literature on breast cancer models. New stochastic logical models are then developed to analyze the vulnerability of the components in multiple signalling sub-pathways involved in this signaling cascade. The computational results are consistent with the experimental results, where the selected proteins were silenced using specific siRNAs and the viability of the cells were analyzed 72 hours after silencing. The genes elF4E and NFkB are found to have nearly no effect on the relative cell viability and the genes JAK2, Stat3, S6K, JUN, FOS, Myc, and Mcl1 are effective candidates to influence the relative cell growth. The vulnerabilities of some targets such as Myc and S6K are found to vary significantly depending on the weights of the sub-pathways; this will be indicative of the chosen target to require customization for therapy. When these targets are utilized, the response of breast cancers from different patients will be highly variable because of the known heterogeneities in signaling pathways among the patients. The targets whose vulnerabilities are invariably high might be more universally acceptable targets. Nature Publishing Group 2016-03-18 /pmc/articles/PMC4796823/ /pubmed/26988076 http://dx.doi.org/10.1038/srep23078 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
Zhu, Peican
Aliabadi, Hamidreza Montazeri
Uludağ, Hasan
Han, Jie
Identification of Potential Drug Targets in Cancer Signaling Pathways using Stochastic Logical Models
title Identification of Potential Drug Targets in Cancer Signaling Pathways using Stochastic Logical Models
title_full Identification of Potential Drug Targets in Cancer Signaling Pathways using Stochastic Logical Models
title_fullStr Identification of Potential Drug Targets in Cancer Signaling Pathways using Stochastic Logical Models
title_full_unstemmed Identification of Potential Drug Targets in Cancer Signaling Pathways using Stochastic Logical Models
title_short Identification of Potential Drug Targets in Cancer Signaling Pathways using Stochastic Logical Models
title_sort identification of potential drug targets in cancer signaling pathways using stochastic logical models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4796823/
https://www.ncbi.nlm.nih.gov/pubmed/26988076
http://dx.doi.org/10.1038/srep23078
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