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Identifying biological mechanisms for favorable cancer prognosis using non-hypothesis-driven iterative survival analysis

Survival analyses based on the Kaplan–Meier estimate have been pervasively used to support or validate the relevance of biological mechanisms in cancer research. Recently, with the appearance of gene expression high-throughput technologies, this kind of analysis has been applied to tumor transcripto...

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Autores principales: Crespo, Isaac, Götz, Lou, Liechti, Robin, Coukos, George, Doucey, Marie-Agnès, Xenarios, Ioannis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5516921/
https://www.ncbi.nlm.nih.gov/pubmed/28725481
http://dx.doi.org/10.1038/npjsba.2016.37
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author Crespo, Isaac
Götz, Lou
Liechti, Robin
Coukos, George
Doucey, Marie-Agnès
Xenarios, Ioannis
author_facet Crespo, Isaac
Götz, Lou
Liechti, Robin
Coukos, George
Doucey, Marie-Agnès
Xenarios, Ioannis
author_sort Crespo, Isaac
collection PubMed
description Survival analyses based on the Kaplan–Meier estimate have been pervasively used to support or validate the relevance of biological mechanisms in cancer research. Recently, with the appearance of gene expression high-throughput technologies, this kind of analysis has been applied to tumor transcriptomics data. In a ‘bottom–up’ approach, gene-expression profiles that are associated with a deregulated pathway hypothetically involved in cancer progression are first identified and then subsequently correlated with a survival effect, which statistically supports or requires the rejection of such a hypothesis. In this work, we propose a ‘top–down’ approach, in which the clinical outcome (survival) is the starting point that guides the identification of deregulated biological mechanisms in cancer by a non-hypothesis-driven iterative survival analysis. We show that the application of our novel method to a population of ~2,000 breast cancer patients of the METABRIC consortium allows the identification of several well-known cancer mechanisms, such as ERBB4, HNF3A and TGFB pathways, and the investigation of their paradoxical dual effect. In addition, several novel biological mechanisms are proposed as potentially involved in cancer progression. The proposed exploratory methodology can be considered both alternative and complementary to classical 'bottom–up' approaches for validation of biological hypotheses. We propose that our method may be used to better characterize cancer, and may therefore impact the future design of therapies that are truly molecularly tailored to individual patients. The method, named SURCOMED, was implemented as a web-based tool, which is publicly available at http://surcomed.vital-it.ch. R scripts are also available at http://surcomed.sourceforge.net).
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spelling pubmed-55169212017-07-19 Identifying biological mechanisms for favorable cancer prognosis using non-hypothesis-driven iterative survival analysis Crespo, Isaac Götz, Lou Liechti, Robin Coukos, George Doucey, Marie-Agnès Xenarios, Ioannis NPJ Syst Biol Appl Article Survival analyses based on the Kaplan–Meier estimate have been pervasively used to support or validate the relevance of biological mechanisms in cancer research. Recently, with the appearance of gene expression high-throughput technologies, this kind of analysis has been applied to tumor transcriptomics data. In a ‘bottom–up’ approach, gene-expression profiles that are associated with a deregulated pathway hypothetically involved in cancer progression are first identified and then subsequently correlated with a survival effect, which statistically supports or requires the rejection of such a hypothesis. In this work, we propose a ‘top–down’ approach, in which the clinical outcome (survival) is the starting point that guides the identification of deregulated biological mechanisms in cancer by a non-hypothesis-driven iterative survival analysis. We show that the application of our novel method to a population of ~2,000 breast cancer patients of the METABRIC consortium allows the identification of several well-known cancer mechanisms, such as ERBB4, HNF3A and TGFB pathways, and the investigation of their paradoxical dual effect. In addition, several novel biological mechanisms are proposed as potentially involved in cancer progression. The proposed exploratory methodology can be considered both alternative and complementary to classical 'bottom–up' approaches for validation of biological hypotheses. We propose that our method may be used to better characterize cancer, and may therefore impact the future design of therapies that are truly molecularly tailored to individual patients. The method, named SURCOMED, was implemented as a web-based tool, which is publicly available at http://surcomed.vital-it.ch. R scripts are also available at http://surcomed.sourceforge.net). Nature Publishing Group 2016-12-22 /pmc/articles/PMC5516921/ /pubmed/28725481 http://dx.doi.org/10.1038/npjsba.2016.37 Text en Copyright © 2016 The Author(s) http://creativecommons.org/licenses/by-nc-sa/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 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-nc-sa/4.0/
spellingShingle Article
Crespo, Isaac
Götz, Lou
Liechti, Robin
Coukos, George
Doucey, Marie-Agnès
Xenarios, Ioannis
Identifying biological mechanisms for favorable cancer prognosis using non-hypothesis-driven iterative survival analysis
title Identifying biological mechanisms for favorable cancer prognosis using non-hypothesis-driven iterative survival analysis
title_full Identifying biological mechanisms for favorable cancer prognosis using non-hypothesis-driven iterative survival analysis
title_fullStr Identifying biological mechanisms for favorable cancer prognosis using non-hypothesis-driven iterative survival analysis
title_full_unstemmed Identifying biological mechanisms for favorable cancer prognosis using non-hypothesis-driven iterative survival analysis
title_short Identifying biological mechanisms for favorable cancer prognosis using non-hypothesis-driven iterative survival analysis
title_sort identifying biological mechanisms for favorable cancer prognosis using non-hypothesis-driven iterative survival analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5516921/
https://www.ncbi.nlm.nih.gov/pubmed/28725481
http://dx.doi.org/10.1038/npjsba.2016.37
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