Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins

Citation:

Orit Rozenblatt-Rosen, Rahul C Deo, Megha Padi, Guillaume Adelmant, Michael A Calderwood, Thomas Rolland, Miranda Grace, Amélie Dricot, Manor Askenazi, Maria Tavares, Samuel J Pevzner, Fieda Abderazzaq, Danielle Byrdsong, Anne-Ruxandra Carvunis, Alyce A Chen, Jingwei Cheng, Mick Correll, Melissa Duarte, Changyu Fan, Mariet C Feltkamp, Scott B Ficarro, Rachel Franchi, Brijesh K Garg, Natali Gulbahce, Tong Hao, Amy M Holthaus, Robert James, Anna Korkhin, Larisa Litovchick, Jessica C Mar, Theodore R Pak, Sabrina Rabello, Renee Rubio, Yun Shen, Saurav Singh, Jennifer M Spangle, Murat Tasan, Shelly Wanamaker, James T Webber, Jennifer Roecklein-Canfield, Eric Johannsen, Albert-László Barabási, Rameen Beroukhim, Elliott Kieff, Michael E Cusick, David E Hill, Karl Münger, Jarrod a Marto, John Quackenbush, Frederick P Roth, James A DeCaprio, and Marc Vidal. 2012. “Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins.” Nature, 487, 7408, Pp. 491-5.

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.