A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells

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dc.citation.articleNumbere1004884en_US
dc.citation.issueNumber4en_US
dc.citation.journalTitlePLoS Computational Biologyen_US
dc.citation.volumeNumber12en_US
dc.contributor.authorTrevino, Victoren_US
dc.contributor.authorCassese, Albertoen_US
dc.contributor.authorNagy, Zsuzsannaen_US
dc.contributor.authorZhuang, Xiaodongen_US
dc.contributor.authorHerbert, Johnen_US
dc.contributor.authorAntzack, Philippen_US
dc.contributor.authorClarke, Kimen_US
dc.contributor.authorDavies, Nicholasen_US
dc.contributor.authorRahman, Ayeshaen_US
dc.contributor.authorCampbell, Moray J.en_US
dc.contributor.authorGuindani, Micheleen_US
dc.contributor.authorBicknell, Royen_US
dc.contributor.authorVannucci, Marinaen_US
dc.contributor.authorFalciani, Francescoen_US
dc.date.accessioned2017-05-03T19:58:19Zen_US
dc.date.available2017-05-03T19:58:19Zen_US
dc.date.issued2016en_US
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.en_US
dc.identifier.citationTrevino, Victor, Cassese, Alberto, Nagy, Zsuzsanna, et al.. "A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells." <i>PLoS Computational Biology,</i> 12, no. 4 (2016) Public Library of Science: https://doi.org/10.1371/journal.pcbi.1004884.en_US
dc.identifier.doihttps://doi.org/10.1371/journal.pcbi.1004884en_US
dc.identifier.urihttps://hdl.handle.net/1911/94147en_US
dc.language.isoengen_US
dc.publisherPublic Library of Scienceen_US
dc.rightsThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleA Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cellsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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