title: A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells.
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.
authors.affiliation: Catedra de Bioinformatica, Escuela de Medicina, Tecnologico de Monterrey, Monterrey, Nuevo Leon, Mexico.
authors.forename: Victor
authors.initials: V
authors.lastname: Trevino
authors.affiliation: Department of Methodology and Statistics, Maastricht University, Maastricht, Netherlands.
authors.forename: Alberto
authors.initials: A
authors.lastname: Cassese
authors.affiliation: School of Experimental and Clinical Medicine, University of Birmingham, Edgbaston, Birmingham, United Kingdom.
authors.forename: Zsuzsanna
authors.initials: Z
authors.lastname: Nagy
authors.affiliation: School of Immunity and Infection, University of Birmingham, Edgbaston, Birmingham, United Kingdom.
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Zhuang
authors.affiliation: Institute of Integrative Biology, University of Liverpool, Liverpool, United Kingdom.
authors.forename: John
authors.initials: J
authors.lastname: Herbert
authors.affiliation: Institute of Integrative Biology, University of Liverpool, Liverpool, United Kingdom.
authors.forename: Philipp
authors.initials: P
authors.lastname: Antczak
authors.affiliation: Institute of Integrative Biology, University of Liverpool, Liverpool, United Kingdom.
authors.forename: Kim
authors.initials: K
authors.lastname: Clarke
authors.affiliation: School of Cancer Sciences, College of Medicine and Dentistry, University of Birmingham, Edgbaston, Birmingham, United Kingdom.
authors.forename: Nicholas
authors.initials: N
authors.lastname: Davies
authors.affiliation: School of Pharmacy, Faculty of Science and Engineering, University of Wolverhampton, Wolverhampton, United Kingdom.
authors.forename: Ayesha
authors.initials: A
authors.lastname: Rahman
authors.affiliation: Department of Pharmacology and Therapeutics, Roswell Park Cancer Institute, Buffalo, New York, United States of America.
authors.forename: Moray J
authors.initials: MJ
authors.lastname: Campbell
authors.affiliation: Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
authors.forename: Michele
authors.identifier: 0000-0002-6363-9907
authors.initials: M
authors.lastname: Guindani
authors.affiliation: School of Immunity and Infection, University of Birmingham, Edgbaston, Birmingham, United Kingdom.
authors.forename: Roy
authors.initials: R
authors.lastname: Bicknell
authors.affiliation: Department of Statistics, Rice University, Houston, Texas, United States of America.
authors.forename: Marina
authors.initials: M
authors.lastname: Vannucci
authors.affiliation: Institute of Integrative Biology, University of Liverpool, Liverpool, United Kingdom.
authors.forename: Francesco
authors.initials: F
authors.lastname: Falciani
mesh_terms: D001499:Bayes Theorem; D002450:Cell Communication; D002460:Cell Line; D045744:Cell Line, Tumor; D018920:Coculture Techniques; D019295:Computational Biology; D004847:Epithelial Cells / Q000378:metabolism; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D008297:Male; D008954:Models, Biological; D011467:Prostate / Q000166:cytology* / Q000378:metabolism*; D011471:Prostatic Neoplasms / Q000235:genetics* / Q000378:metabolism / Q000473:pathology*; D015398:Signal Transduction / Q000235:genetics