Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins
Orit Rozenblatt-Rosen,Rahul C. Deo,Rahul C. Deo,Megha Padi,Guillaume Adelmant,Michael A. Calderwood,Thomas Rolland,Miranda Grace,Miranda Grace,Amélie Dricot,Manor Askenazi,Maria Tavares,Sam Pevzner,Sam Pevzner,Fieda Abderazzaq,Danielle Byrdsong,Anne-Ruxandra Carvunis,Alyce A. Chen,Alyce A. Chen,Jingwei Cheng,Jingwei Cheng,Mick Correll,Melissa Duarte,Melissa Duarte,Changyu Fan,Mariet C.W. Feltkamp,Scott B. Ficarro,Rachel Franchi,Rachel Franchi,Brijesh K. Garg,Natali Gulbahce,Tong Hao,Amy M. Holthaus,Amy M. Holthaus,Robert James,Anna Korkhin,Anna Korkhin,Larisa Litovchick,Larisa Litovchick,Jessica C. Mar,Theodore R. Pak,Sabrina Rabello,Renee Rubio,Yun Shen,Saurav Singh,Jennifer M. Spangle,Jennifer M. Spangle,Murat Tasan,Murat Tasan,Shelly Wanamaker,Shelly Wanamaker,James T. Webber,Jennifer Roecklein-Canfield,Jennifer Roecklein-Canfield,Eric Johannsen,Eric Johannsen,Albert-László Barabási,Rameen Beroukhim,Rameen Beroukhim,Rameen Beroukhim,Elliott Kieff,Elliott Kieff,Michael E. Cusick,David E. Hill,Karl Münger,Karl Münger,Jarrod A. Marto,John Quackenbush,Frederick P. Roth,James A. DeCaprio,James A. DeCaprio,Marc Vidal +71 more
TLDR
It is shown that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on a par with their identification through functional genomics and large-scale cataloguing of tumour mutations, to increase the specificity of cancer gene identification.Abstract:
Genotypic differences greatly influence susceptibility and resistance to disease. Understanding genotype-phenotype relationships requires that phenotypes be viewed as manifestations of network properties, rather than simply as the result of individual genomic variations. Genome sequencing efforts have identified numerous germline mutations, and large numbers of somatic genomic alterations, associated with a predisposition to cancer. However, it remains difficult to distinguish background, or 'passenger', cancer mutations from causal, or 'driver', mutations in these data sets. Human viruses intrinsically depend on their host cell during the course of infection and can elicit pathological phenotypes similar to those arising from mutations. Here we test the hypothesis that genomic variations and tumour viruses may cause cancer through related mechanisms, by systematically examining host interactome and transcriptome network perturbations caused by DNA tumour virus proteins. The resulting integrated viral perturbation data reflects rewiring of the host cell networks, and highlights pathways, such as Notch signalling and apoptosis, that go awry in cancer. We show that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on a par with their identification through functional genomics and large-scale cataloguing of tumour mutations. Together, these complementary approaches increase the specificity of cancer gene identification. Combining systems-level studies of pathogen-encoded gene products with genomic approaches will facilitate the prioritization of cancer-causing driver genes to advance the understanding of the genetic basis of human cancer.read more
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Interactome networks and human disease
TL;DR: Why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease are detailed.
Journal ArticleDOI
A proteome-scale map of the human interactome network
Thomas Rolland,Murat Tasan,Benoit Charloteaux,Samuel J. Pevzner,Quan Zhong,Quan Zhong,Nidhi Sahni,Song Yi,Irma Lemmens,Celia Fontanillo,Roberto Mosca,Atanas Kamburov,Susan Dina Ghiassian,Susan Dina Ghiassian,Xinping Yang,Lila Ghamsari,Dawit Balcha,Bridget E. Begg,Pascal Braun,Marc Brehme,Martin P. Broly,Anne-Ruxandra Carvunis,Dan Convery-Zupan,Roser Corominas,Jasmin Coulombe-Huntington,Jasmin Coulombe-Huntington,Elizabeth Dann,Matija Dreze,Amélie Dricot,Changyu Fan,Eric A. Franzosa,Eric A. Franzosa,Fana Gebreab,Bryan J. Gutierrez,Madeleine F. Hardy,Mike Jin,Shuli Kang,Ruth Kiros,Guan Ning Lin,Katja Luck,Andrew MacWilliams,Jörg Menche,Jörg Menche,Ryan R. Murray,Alexandre Palagi,Matthew M. Poulin,Xavier Rambout,Xavier Rambout,John Rasla,Patrick Reichert,Viviana Romero,Elien Ruyssinck,Julie M. Sahalie,Annemarie Scholz,Akash A. Shah,Amitabh Sharma,Amitabh Sharma,Yun Shen,Kerstin Spirohn,Stanley Tam,Alexander O. Tejeda,Shelly A. Trigg,Jean-Claude Twizere,Jean-Claude Twizere,Kerwin Vega,Jennifer M. Walsh,Michael E. Cusick,Yu Xia,Yu Xia,Albert-László Barabási,Albert-László Barabási,Albert-László Barabási,Lilia M. Iakoucheva,Patrick Aloy,Javier De Las Rivas,Jan Tavernier,Michael A. Calderwood,David E. Hill,Tong Hao,Frederick P. Roth,Marc Vidal +80 more
TL;DR: The map uncovers significant interconnectivity between known and candidate cancer gene products, providing unbiased evidence for an expanded functional cancer landscape, while demonstrating how high-quality interactome models will help "connect the dots" of the genomic revolution.
Journal ArticleDOI
The BioGRID interaction database: 2013 update
Andrew Chatr-aryamontri,Bobby-Joe Breitkreutz,Sven Heinicke,Lorrie Boucher,Andrew G. Winter,Chris Stark,Julie Nixon,Lindsay Ramage,Nadine Kolas,Lara O'Donnell,Teresa Reguly,Ashton Breitkreutz,Adnane Sellam,Daici Chen,Christie S. Chang,Jennifer M. Rust,Michael S. Livstone,Rose Oughtred,Kara Dolinski,Mike Tyers +19 more
TL;DR: The Biological General Repository for Interaction Datasets (BioGRID) is an open access archive of genetic and protein interactions that are curated from the primary biomedical literature for all major model organism species.
Journal ArticleDOI
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review
Peter Csermely,Tamas Korcsmaros,Tamas Korcsmaros,Huba Kiss,Gábor London,Ruth Nussinov,Ruth Nussinov +6 more
TL;DR: It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates and an optimized protocol of network-aided drug development is suggested, and a list of systems-level hallmarks of drug quality is provided.
Journal ArticleDOI
The BioGRID interaction database: 2015 update
Andrew Chatr-aryamontri,Bobby-Joe Breitkreutz,Rose Oughtred,Lorrie Boucher,Sven Heinicke,Daici Chen,Chris Stark,Ashton Breitkreutz,Nadine Kolas,Lara O'Donnell,Teresa Reguly,Julie Nixon,Lindsay Ramage,Andrew G. Winter,Adnane Sellam,Christie S. Chang,Jodi E. Hirschman,Chandra L. Theesfeld,Jennifer M. Rust,Michael S. Livstone,Kara Dolinski,Mike Tyers +21 more
TL;DR: The BioGRID architecture has been improved to support a broader range of interaction and post-translational modification types, to allow the representation of more complex multi-gene/protein interactions, to account for cellular phenotypes through structured ontologies, to expedite curation through semi-automated text-mining approaches, and to enhance curation quality control.
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TL;DR: The Cancer Genome Atlas project has analyzed messenger RNA expression, microRNA expression, promoter methylation and DNA copy number in 489 high-grade serous ovarian adenocarcinomas and the DNA sequences of exons from coding genes in 316 of these tumours as mentioned in this paper.
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