The mammalian Retinoblastoma (RB) family including pRB, p107, and p130 represses E2F target genes through mechanisms that are not fully understood. In D. melanogaster, RB-dependent repression is mediated in part by the multisubunit protein complex Drosophila RBF, E2F, and Myb (dREAM) that contains homologs of the C. elegans synthetic multivulva class B (synMuvB) gene products. Using an integrated approach combining proteomics, genomics, and bioinformatic analyses, we identified a p130 complex termed DP, RB-like, E2F, and MuvB (DREAM) that contains mammalian homologs of synMuvB proteins LIN-9, LIN-37, LIN-52, LIN-54, and LIN-53/RBBP4. DREAM bound to more than 800 human promoters in G0 and was required for repression of E2F target genes. In S phase, MuvB proteins dissociated from p130 and formed a distinct submodule that bound MYB. This work reveals an evolutionarily conserved multisubunit protein complex that contains p130 and E2F4, but not pRB, and mediates the repression of cell cycle-dependent genes in quiescence.

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 associated with cancer predisposition and large numbers of somatic genomic alterations2. 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 mutations3. 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.