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

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.

Figures

Figure 1. Systematic mapping of binary interactions and co-complex associations between viral and host proteins

a, The virome-to-variome network model proposes that genomic variations (point mutations, amplifications, deletions or translocations) and expression of tumour virus proteins induce related disease states by similarly influencing properties of cellular networks. b, Experimental pipeline for identifying viral-host interactions. Selected cloned viORFs were subjected to yeast two-hybrid (Y2H) screens, and introduced into cell lines for both tandem-affinity purification followed by mass spectrometry (TAP-MS) and microarray analyses. Numbers of viORFs that were successfully processed at each step are indicated in red. c, Left panel: network of binary viral-host interactions identified by Y2H. Right panel: subsets of human target proteins that have significantly more (red dots) or less (black dots) viral interactors than expected based on their degree in HI-2. d, Network of co-complex associations of E6 viral proteins from six HPV types (hexagons, coloured according to disease class) with host proteins (circles). Host proteins that associate with two or more E6 proteins are coloured according to the disease class(es) of the corresponding HPV types. Circle size is proportional to the number of associations between host and viral proteins in the E6 networks. Distribution plots of 1,000 randomised networks and experimentally observed data (green arrows) for the number of host proteins targeted by two or more viral proteins in the corresponding sub-networks (left histogram), or the ratio of the probability of a host protein being targeted by viral proteins from the same class to the probability it is targeted by viral proteins from different classes (right histogram). Insets: representative random networks from these distributions.

Figure 2. Transcriptome perturbations induced by viral protein expression

a, Heatmap of average cluster expression relative to control. Enriched GO terms and KEGG pathways are listed adjacent to the numbered expression clusters. In cluster C1 eight of the nine transcripts are snoRNAs (denoted with #). Upper dendrogram is shaded by viORF grouping. Grey blocks show which viral proteins associate with the indicated host proteins. b, Schematic shows how the viral protein-TF-target gene network was constructed, with three representative networks shown. Null distribution of average fraction of TF target genes differentially expressed in the corresponding cell lines (histogram), along with observed value (green arrow).

Figure 3. The Notch pathway is targeted by multiple DNA tumour virus proteins

a, Western blots of co-immunoprecipitations of HPV E6 proteins in IMR-90 cells. b, Heatmap of expression of Notch pathway responsive genes in IMR-90 cells upon expression of E6 proteins from different HPV types or upon knockdown of MAML1, relative to control cells. c, Representation of viral protein interactions with components of the Notch signalling pathway (as defined in KEGG).

Figure 4. Interpretation of somatic cancer mutations using viral-host network models

a, Schematic describing composition of VirHost (proteins identified by TAP-MS with ≥3 unique peptides, Y2H and TF) and overlap with COSMIC Classic genes. Viral protein (hexagon) perturbations of cancer proteins (circles) classified as oncogenes or tumour suppressors. b, Venn diagram of overlaps of VirHost proteins with COSMIC Classic genes and candidate cancer genes identified through four transposon-based functional genomics screens. c, Venn diagram of overlaps of VirHost proteins with COSMIC Classic genes and with a prioritised set of genes found through somatic mutation analysis. P values: Fisher’s exact test or permutation based. d-f, Venn Diagrams comparing VirHost, GWAS (d), SCNA-AMP (e) and SCNA-DEL (f) data sets for ability to recover COSMIC Classic genes.