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Interpreting Cancer Genomes Using Systematic Host Perturbations By LETTER doi:10.1038/nature11288 Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins Orit Rozenblatt-Rosen1,2,3*, Rahul C. Deo1,4,5*,MeghaPadi1,6,7*, Guillaume Adelmant1,4,8*, Michael A. Calderwood1,9,10,11, Thomas Rolland1,9,10, Miranda Grace1,12,Ame´lie Dricot1,9,10, Manor Askenazi1,4,8, Maria Tavares1,2,3,8,SamuelJ.Pevzner9,10,13, Fieda Abderazzaq1,6, Danielle Byrdsong1,9,10, Anne-Ruxandra Carvunis9,10, Alyce A. Chen1,12, Jingwei Cheng2,3,MickCorrell6, Melissa Duarte1,9,11,ChangyuFan1,9,10, Mariet C. Feltkamp14, Scott B. Ficarro1,4,8, Rachel Franchi1,9,15, Brijesh K. Garg1,4,8, Natali Gulbahce1,9,16,17,TongHao1,9,10, Amy M. Holthaus1,11,RobertJames1,9,10, Anna Korkhin1,2,3, Larisa Litovchick1,2,3, Jessica C. Mar1,6,7,TheodoreR.Pak18, Sabrina Rabello1,3,9,16, Renee Rubio1,6,YunShen1,9,10, Saurav Singh4,8, Jennifer M. Spangle1,12, Murat Tasan1,4,18, Shelly Wanamaker9,10,15, James T. Webber4,8, Jennifer Roecklein-Canfield9,15,EricJohannsen1,11, Albert-La´szlo´ Baraba´si1,3,9,16,RameenBeroukhim3,19,20, Elliott Kieff1,11, Michael E. Cusick1,9,10, David E. Hill1,9,10, Karl Mu¨nger1,12, Jarrod A. Marto1,4,8,JohnQuackenbush1,6,7, Frederick P. Roth1,4,9,18, James A. DeCaprio1,2,3 & Marc Vidal1,9,10 Genotypic differences greatly influence susceptibility and resist- which DNA tumour virus proteins physically target the products of ance to disease. Understanding genotype–phenotype relationships RB1 or TP53, two well-established germline-inherited and somatically requires that phenotypes be viewed as manifestations of network inactivated tumour-suppressor genes6. We propose that viruses and properties, rather than simply as the result of individual genomic genomic variations alter local and global properties of cellular net- variations1. Genome sequencing efforts have identified numerous works in similar ways to cause pathological states. Models derived from germline mutations, and large numbers of somatic genomic altera- host perturbations mediated by viral proteins representing the virome7 tions, associated with a predisposition to cancer2. However, it should serve as surrogates for network perturbations that result from remains difficult to distinguish background, or ‘passenger’, cancer large numbers of genomic variations, or the variome8 (Fig. 1a). mutations from causal, or ‘driver’, mutations in these data sets. We developed an integrated pipeline to systematically investigate Human viruses intrinsically depend on their host cell during the perturbations of host interactome and transcriptome networks induced course of infection and can elicit pathological phenotypes similar by gene products of four functionally related, yet biologically distinct, to those arising from mutations3. Here we test the hypothesis that families of DNA tumour viruses: human papillomavirus (HPV), genomic variations and tumour viruses may cause cancer through Epstein–Barr virus (EBV), adenovirus (Ad5) and polyomavirus (PyV) related mechanisms, by systematically examining host interactome (Fig. 1b, Supplementary Table 1 and Supplementary Fig. 1). and transcriptome network perturbations caused by DNA tumour Applying a stringent implementation of the yeast two-hybrid (Y2H) virus proteins. The resulting integrated viral perturbation data system9, 123 viral open reading frames (viral ORFs) were screened reflects rewiring of the host cell networks, and highlights pathways, against a collection of about 13,000 human ORFs10, resulting in a such as Notch signalling and apoptosis, that go awry in cancer. We validated virus–host interaction network of 454 binary interactions show that systematic analyses of host targets of viral proteins can involving 53 viral proteins and 307 human proteins (Fig. 1c and identify cancer genes with a success rate on a par with their iden- Supplementary Table 2). Analysis of our binary interaction map tification through functional genomics and large-scale cataloguing identified 31 host target proteins that showed more binary interactions of tumour mutations. Together, these complementary approaches with viral proteins than would be expected given their ‘degree’ increase the specificity of cancer gene identification. Combining (number of interactors) in our current binary map of the human systems-level studies of pathogen-encoded gene products with interactome network (HI-2)11 (Fig. 1c, Supplementary Table 3 and genomic approaches will facilitate the prioritization of cancer- Supplementary Notes), suggesting a set of common mechanisms by causing driver genes to advance the understanding of the genetic which different viral proteins rewire the host interactome network. basis of human cancer. To examine perturbations in both the interactome and transcriptome Integrative studies of viral proteins have identified host perturba- networks directly in human cells, we generated expression constructs tions relevant to the aetiology of viral disease4,5. We examined whether fusing each viral ORF to a tandem epitope tag and introduced each such a strategy, extended systematically across a range of tumour construct into IMR-90 normal human diploid fibroblasts. Interactions viruses, could shed light on cancers even beyond those directly caused between viral proteins and the host proteome were identified by tandem by these pathogens. Our hypothesis is inspired by classical examples in affinity purification followed by mass spectrometry (TAP–MS)12. The 1Genomic Analysis of Network Perturbations Center of Excellence in Genomic Science, Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA. 2Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA. 3Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA. 4Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts 02115, USA. 5Cardiovascular Research Institute, Department of Medicine and Institute for Human Genetics, University of California, San Francisco, California 94143, USA. 6Center for Cancer Computational Biology (CCCB), Department of Biostatistics and Computational Biology and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA. 7Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA. 8Blais Proteomics Center and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA. 9Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA. 10Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA. 11Infectious Diseases Division, The Channing Laboratory, Brigham and Women’s Hospital and Departments of Medicine and of Microbiology and Immunobiology, Harvard Medical School, Boston, Massachusetts 02115, USA. 12Division of Infectious Diseases, Brigham and Women’s Hospital and Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA. 13Biomedical Engineering Department, Boston University and Boston University School of Medicine, Boston, Massachusetts 02118, USA. 14Department of Medical Microbiology, Leiden University Medical Center, 2300 RC Leiden, The Netherlands. 15Department of Chemistry, Simmons College, Boston, Massachusetts 02115, USA. 16Center for Complex Networks Research (CCNR) and Department of Physics, Northeastern University, Boston, Massachusetts 02115, USA. 17Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California 94158, USA. 18Donnelly Centre and Departments of Molecular Genetics and Computer Science, University of Toronto and Lunenfeld Research Institute, Mt Sinai Hospital, Toronto, Ontario M5G 1X5, Canada. 19Department of Medical Oncology and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA. 20The Broad Institute, Cambridge, Massachusetts 02142, USA. *These authors contributed equally to this work. 26 JULY 2012 | VOL 487 | NATURE | 491 ©2012 Macmillan Publishers Limited. All rights reserved RESEARCH LETTER abHPV HPV types representing three different disease classes: high-risk PyV Ad5 Perturbations Cellular networks Phenotypes Viral ORFs: 144 mucosal, low-risk mucosal and cutaneous. E6 and E7 proteins encoded EBV by high-risk mucosal HPVs are strongly oncogenic6. Multiple host Y2H vectors: 123 Retroviral vectors: 87 proteins were found to associate with E6 proteins encoded by two or Transduction more different HPV types (P , 0.001; Fig. 1d), including the known E6 Human 13 Variome ORFeome IMR-90 cell lines: 75 target UBE3A (E6AP) . Among these we observed a statistically sig- 5.1 QC: 64 nificant subgroup of host proteins targeted only by E6 proteins from Protein RNA the same disease class (P , 0.001). The transcriptional regulators CREB-binding protein (CREBBP) and EP300 were found to associate Virome TAP–MS: 54 Microarray: 63 Y2H: 53 with E6 proteins from both cutaneous HPV types, but not with those Protein–protein interactions Expression c from the mucosal classes. In contrast with E6 proteins, no group of EBV 5 HPV host proteins showed class-specific targeting by HPV E7 proteins Adenovirus 4 Polyomavirus (Supplementary Fig. 4). These differential associations reflect how 3 rewiring of virus–host interactome networks may relate to the 2 Degree in Y2H Degree aetiology of viral disease. 1 virus−host interactome 1 10 50 100 200
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