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

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Abstract
Combining analysis of host proteome and transcriptome perturbations induced by tumour virus proteins with ongoing genome-wide studies of cancer facilitates the prioritization of cancer genes. This systematic search for host targets of tumour viruses, using proteome and transcriptome analyses of viral proteins from mammalian DNA viruses with transforming or tumorigenic properties, provides an extensive catalogue of changes in genetic and protein expression that can be screened against genome-wide studies of cancer. The study focuses on human papillomavirus, Epstein–Barr virus, adenovirus and polyomavirus. The resulting list of transforming viral protein targets identifies causal genes within both somatic and Mendelian cancer-associated loci. 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 variations1. Genome sequencing efforts have identified numerous germline mutations, and large numbers of somatic genomic alterations, associated with a predisposition to cancer2. 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 mutations3. 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.