Boolean network model for cancer pathways: predicting carcinogenesis and targeted therapy outcomes.

A Boolean dynamical system integrating the main signaling pathways involved in cancer is constructed based on the currently known protein-protein interaction network. This system exhibits stationary protein activation patterns--attractors--dependent on the cell's microenvironment. These dynamic...

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Autores principales:	Herman F Fumiã, Marcelo L Martins
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/cb38104c583c4bc99e6478986309aad5
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spelling	oai:doaj.org-article:cb38104c583c4bc99e6478986309aad52021-11-18T09:02:48ZBoolean network model for cancer pathways: predicting carcinogenesis and targeted therapy outcomes.1932-620310.1371/journal.pone.0069008https://doaj.org/article/cb38104c583c4bc99e6478986309aad52013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23922675/?tool=EBIhttps://doaj.org/toc/1932-6203A Boolean dynamical system integrating the main signaling pathways involved in cancer is constructed based on the currently known protein-protein interaction network. This system exhibits stationary protein activation patterns--attractors--dependent on the cell's microenvironment. These dynamical attractors were determined through simulations and their stabilities against mutations were tested. In a higher hierarchical level, it was possible to group the network attractors into distinct cell phenotypes and determine driver mutations that promote phenotypic transitions. We find that driver nodes are not necessarily central in the network topology, but at least they are direct regulators of central components towards which converge or through which crosstalk distinct cancer signaling pathways. The predicted drivers are in agreement with those pointed out by diverse census of cancer genes recently performed for several human cancers. Furthermore, our results demonstrate that cell phenotypes can evolve towards full malignancy through distinct sequences of accumulated mutations. In particular, the network model supports routes of carcinogenesis known for some tumor types. Finally, the Boolean network model is employed to evaluate the outcome of molecularly targeted cancer therapies. The major find is that monotherapies were additive in their effects and that the association of targeted drugs is necessary for cancer eradication.Herman F FumiãMarcelo L MartinsPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 7, p e69008 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Herman F Fumiã Marcelo L Martins Boolean network model for cancer pathways: predicting carcinogenesis and targeted therapy outcomes.
description	A Boolean dynamical system integrating the main signaling pathways involved in cancer is constructed based on the currently known protein-protein interaction network. This system exhibits stationary protein activation patterns--attractors--dependent on the cell's microenvironment. These dynamical attractors were determined through simulations and their stabilities against mutations were tested. In a higher hierarchical level, it was possible to group the network attractors into distinct cell phenotypes and determine driver mutations that promote phenotypic transitions. We find that driver nodes are not necessarily central in the network topology, but at least they are direct regulators of central components towards which converge or through which crosstalk distinct cancer signaling pathways. The predicted drivers are in agreement with those pointed out by diverse census of cancer genes recently performed for several human cancers. Furthermore, our results demonstrate that cell phenotypes can evolve towards full malignancy through distinct sequences of accumulated mutations. In particular, the network model supports routes of carcinogenesis known for some tumor types. Finally, the Boolean network model is employed to evaluate the outcome of molecularly targeted cancer therapies. The major find is that monotherapies were additive in their effects and that the association of targeted drugs is necessary for cancer eradication.
format	article
author	Herman F Fumiã Marcelo L Martins
author_facet	Herman F Fumiã Marcelo L Martins
author_sort	Herman F Fumiã
title	Boolean network model for cancer pathways: predicting carcinogenesis and targeted therapy outcomes.
title_short	Boolean network model for cancer pathways: predicting carcinogenesis and targeted therapy outcomes.
title_full	Boolean network model for cancer pathways: predicting carcinogenesis and targeted therapy outcomes.
title_fullStr	Boolean network model for cancer pathways: predicting carcinogenesis and targeted therapy outcomes.
title_full_unstemmed	Boolean network model for cancer pathways: predicting carcinogenesis and targeted therapy outcomes.
title_sort	boolean network model for cancer pathways: predicting carcinogenesis and targeted therapy outcomes.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/cb38104c583c4bc99e6478986309aad5
work_keys_str_mv	AT hermanffumia booleannetworkmodelforcancerpathwayspredictingcarcinogenesisandtargetedtherapyoutcomes AT marcelolmartins booleannetworkmodelforcancerpathwayspredictingcarcinogenesisandtargetedtherapyoutcomes
_version_	1718420990371823616

Boolean network model for cancer pathways: predicting carcinogenesis and targeted therapy outcomes.

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