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

2.50
Hdl Handle:
http://hdl.handle.net/2436/614442
Title:
A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells.
Authors:
Trevino, Victor; Cassese, Alberto; Nagy, Zsuzsanna; Zhuang, Xiaodong; Herbert, John; Antzack, Philipp; Clarke, Kim; Davies, Nicholas; Rahman, Ayesha; Campbell, Moray J; Guindani, Michele; Bicknell, Roy; Vannucci, Marina; Falciani, Francesco
Abstract:
The 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.
Citation:
A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells. 2016, 12 (4):e1004884 PLoS Comput. Biol.
Publisher:
Public Library of Science (United States)
Journal:
PLoS computational biology
Issue Date:
Apr-2016
URI:
http://hdl.handle.net/2436/614442
DOI:
10.1371/journal.pcbi.1004884
PubMed ID:
27124473
Additional Links:
http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004884
Type:
Article
Language:
en
ISSN:
1553-7358
Sponsors:
Cancer research UK, BBSRC, NIH
Appears in Collections:
Pharmacy and Natural Products Research Group

Full metadata record

DC FieldValue Language
dc.contributor.authorTrevino, Victoren
dc.contributor.authorCassese, Albertoen
dc.contributor.authorNagy, Zsuzsannaen
dc.contributor.authorZhuang, Xiaodongen
dc.contributor.authorHerbert, Johnen
dc.contributor.authorAntzack, Philippen
dc.contributor.authorClarke, Kimen
dc.contributor.authorDavies, Nicholasen
dc.contributor.authorRahman, Ayeshaen
dc.contributor.authorCampbell, Moray Jen
dc.contributor.authorGuindani, Micheleen
dc.contributor.authorBicknell, Royen
dc.contributor.authorVannucci, Marinaen
dc.contributor.authorFalciani, Francescoen
dc.date.accessioned2016-06-23T13:22:34Zen
dc.date.available2016-06-23T13:22:34Zen
dc.date.issued2016-04en
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.en
dc.identifier.issn1553-7358en
dc.identifier.pmid27124473en
dc.identifier.doi10.1371/journal.pcbi.1004884en
dc.identifier.urihttp://hdl.handle.net/2436/614442en
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
dc.description.sponsorshipCancer research UK, BBSRC, NIHen
dc.language.isoenen
dc.publisherPublic Library of Science (United States)en
dc.relation.urlhttp://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004884en
dc.rightsArchived with thanks to PLoS computational biologyen
dc.subjectNetwork biology,en
dc.subjectprostate canceren
dc.titleA Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells.en
dc.typeArticleen
dc.identifier.journalPLoS computational biologyen

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