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dc.contributor.authorTrevino, Victor
dc.contributor.authorCassese, Alberto
dc.contributor.authorNagy, Zsuzsanna
dc.contributor.authorZhuang, Xiaodong
dc.contributor.authorHerbert, John
dc.contributor.authorAntzack, Philipp
dc.contributor.authorClarke, Kim
dc.contributor.authorDavies, Nicholas
dc.contributor.authorRahman, Ayesha
dc.contributor.authorCampbell, Moray J
dc.contributor.authorGuindani, Michele
dc.contributor.authorBicknell, Roy
dc.contributor.authorVannucci, Marina
dc.contributor.authorFalciani, Francesco
dc.date.accessioned2016-06-23T13:22:34Zen
dc.date.available2016-06-23T13:22:34Zen
dc.date.issued2016-04-28
dc.identifier.citationA Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells. 2016, 12 (4):e1004884 PLoS Comput. Biol.
dc.identifier.issn1553-7358
dc.identifier.pmid27124473
dc.identifier.doi10.1371/journal.pcbi.1004884
dc.identifier.urihttp://hdl.handle.net/2436/614442
dc.description© 2016 The Authors. Published by Public Library of Science. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1371/journal.pcbi.1004884
dc.description.abstractThe advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell communication networks in a wide spectrum of biological systems.
dc.description.sponsorshipCancer research UK, BBSRC, NIH
dc.language.isoen
dc.publisherPublic Library of Science (United States)
dc.relation.urlhttp://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004884
dc.subjectNetwork biology,
dc.subjectprostate cancer
dc.titleA Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells
dc.typeJournal article
dc.identifier.journalPLoS computational biology
dc.identifier.articlenumbere1004884
dc.date.accepted2016-03-24
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
rioxxterms.licenseref.startdate2016-06-23
dc.source.volume12
dc.source.issue4
refterms.dateFOA2020-04-30T06:58:02Z
html.description.abstractThe advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell communication networks in a wide spectrum of biological systems.


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