Interpreting Cancer Genomes Using Systematic Host Perturbations by Tumour Virus Proteins
Deo, Rahul C.
Feltkamp, Mariet C.
Garg, Brijesh K.
Mar, Jessica C.
Pak, Theodore R.
Webber, James T.
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CitationRozenblatt-Rosen, Orit, Rahul C. Deo, Megha Padi, Guillaume Adelmant, Michael A. Calderwood, Thomas Rolland, Miranda Grace, et al. 2012. Interpreting cancer genomes using systematic host perturbations by tumour virus proteins. Nature 487(7408): 491-495.
AbstractGenotypic 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 associated with cancer predisposition and large numbers of somatic genomic alterations. However, it remains challenging to distinguish between background, or “passenger” and causal, or “driver” cancer mutations in these datasets. Human viruses intrinsically depend on their host cell during the course of infection and can elicit pathological phenotypes similar to those arising from mutations. To test the hypothesis that genomic variations and tumour viruses may cause cancer via related mechanisms, we systematically examined 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 that go awry in cancer, such as Notch signalling and apoptosis. We show that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on par with their identification through functional genomics and large-scale cataloguing of tumour mutations. Together, these complementary approaches result in increased specificity for cancer gene identification. Combining systems-level studies of pathogen-encoded gene products with genomic approaches will facilitate prioritization of cancer-causing driver genes so as to advance understanding of the genetic basis of human cancer.
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