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The ability of retroviruses and transposons to insert their genetic material into host DNA makes them widely used tools in molecular biology, cancer research and gene therapy. However, these systems have biases that may strongly affect research outcomes. To address this issue, we generated very large datasets consisting of to unselected integrations in the mouse genome for the Sleeping Beauty (SB) and piggyBac (PB) transposons, and the Mouse Mammary Tumor Virus (MMTV). We analyzed (epi)genomic features to generate bias maps at both local and genome-wide scales. MMTV showed a remarkably uniform distribution of integrations across the genome. More distinct preferences were observed for the two transposons, with PB showing remarkable resemblance to bias profiles of the Murine Leukemia Virus. Furthermore, we present a model where target site selection is directed at multiple scales. At a large scale, target site selection is similar across systems, and defined by domain-oriented features, namely expression of proximal genes, proximity to CpG islands and to genic features, chromatin compaction and replication timing. Notable differences between the systems are mainly observed at smaller scales, and are directed by a diverse range of features. To study the effect of these biases on integration sites occupied under selective pressure, we turned to insertional mutagenesis (IM) screens. In IM screens, putative cancer genes are identified by finding frequently targeted genomic regions, or Common Integration Sites (CISs). Within three recently completed IM screens, we identified 7%–33% putative false positive CISs, which are likely not the result of the oncogenic selection process. Moreover, results indicate that PB, compared to SB, is more suited to tag oncogenes., Intelligent Systems, Electrical Engineering, Mathematics and Computer Science

The ability of retroviruses and transposons to insert their genetic material into host DNA makes them widely used tools in molecular biology, cancer research and gene therapy. However, these systems have biases that may strongly affect research outcomes. To address this issue, we generated very large datasets consisting of to unselected integrations in the mouse genome for the Sleeping Beauty (SB) and piggyBac (PB) transposons, and the Mouse Mammary Tumor Virus (MMTV). We analyzed (epi)genomic features to generate bias maps at both local and genome-wide scales. MMTV showed a remarkably uniform distribution of integrations across the genome. More distinct preferences were observed for the two transposons, with PB showing remarkable resemblance to bias profiles of the Murine Leukemia Virus. Furthermore, we present a model where target site selection is directed at multiple scales. At a large scale, target site selection is similar across systems, and defined by domain-oriented features, namely expression of proximal genes, proximity to CpG islands and to genic features, chromatin compaction and replication timing. Notable differences between the systems are mainly observed at smaller scales, and are directed by a diverse range of features. To study the effect of these biases on integration sites occupied under selective pressure, we turned to insertional mutagenesis (IM) screens. In IM screens, putative cancer genes are identified by finding frequently targeted genomic regions, or Common Integration Sites (CISs). Within three recently completed IM screens, we identified 7%–33% putative false positive CISs, which are likely not the result of the oncogenic selection process. Moreover, results indicate that PB, compared to SB, is more suited to tag oncogenes., Intelligent Systems, Electrical Engineering, Mathematics and Computer Science

Cancer develops through a multistep process in which normal cells progress to malignant tumors via the evolution of their genomes as a result of the acquisition of mutations in cancer driver genes. The number, identity and mode of action of cancer driver genes, and how they contribute to tumor evolution is largely unknown. This study deployed the Mouse Mammary Tumor Virus (MMTV) as an insertional mutagen to find both the driver genes and the networks in which they function. Using deep insertion site sequencing we identified around 31000 retroviral integration sites in 604 MMTV-induced mammary tumors from mice with mammary gland-specific deletion of Trp53, Pten heterozygous knockout mice, or wildtype strains. We identified 18 known common integration sites (CISs) and 12 previously unknown CISs marking new candidate cancer genes. Members of the Wnt, Fgf, Fgfr, Rspo and Pdgfr gene families were commonly mutated in a mutually exclusive fashion. The sequence data we generated yielded also information on the clonality of insertions in individual tumors, allowing us to develop a data-driven model of MMTV-induced tumor development. Insertional mutations near Wnt and Fgf genes mark the earliest ‘‘initiating’’ events in MMTV induced tumorigenesis, whereas Fgfr genes are targeted later during tumor progression. Our data shows that insertional mutagenesis can be used to discover the mutational networks, the timing of mutations, and the genes that initiate and drive tumor evolution., Computer Science, Electrical Engineering, Mathematics and Computer Science

Cancer develops through a multistep process in which normal cells progress to malignant tumors via the evolution of their genomes as a result of the acquisition of mutations in cancer driver genes. The number, identity and mode of action of cancer driver genes, and how they contribute to tumor evolution is largely unknown. This study deployed the Mouse Mammary Tumor Virus (MMTV) as an insertional mutagen to find both the driver genes and the networks in which they function. Using deep insertion site sequencing we identified around 31000 retroviral integration sites in 604 MMTV-induced mammary tumors from mice with mammary gland-specific deletion of Trp53, Pten heterozygous knockout mice, or wildtype strains. We identified 18 known common integration sites (CISs) and 12 previously unknown CISs marking new candidate cancer genes. Members of the Wnt, Fgf, Fgfr, Rspo and Pdgfr gene families were commonly mutated in a mutually exclusive fashion. The sequence data we generated yielded also information on the clonality of insertions in individual tumors, allowing us to develop a data-driven model of MMTV-induced tumor development. Insertional mutations near Wnt and Fgf genes mark the earliest ‘‘initiating’’ events in MMTV induced tumorigenesis, whereas Fgfr genes are targeted later during tumor progression. Our data shows that insertional mutagenesis can be used to discover the mutational networks, the timing of mutations, and the genes that initiate and drive tumor evolution., Computer Science, Electrical Engineering, Mathematics and Computer Science

Insertional mutagenesis is a potent forward genetic screening technique used to identify candidate cancer genes in mouse model systems. An important, yet unresolved issue in the analysis of these screens, is the identification of the genes affected by the insertions. To address this, we developed Kernel Convolved Rule Based Mapping (KC-RBM). KC-RBM exploits distance, orientation and insertion density across tumors to automatically map integration sites to target genes. We perform the first genome-wide evaluation of the association of insertion occurrences with aberrant gene expression of the predicted targets in both retroviral and transposon data sets. We demonstrate the efficiency of KC-RBM by showing its superior performance over existing approaches in recovering true positives from a list of independently, manually curated cancer genes. The results of this work will significantly enhance the accuracy and speed of cancer gene discovery in forward genetic screens. KC-RBM is available as R-package., Mediamatics, Electrical Engineering, Mathematics and Computer Science

Insertional mutagenesis is a potent forward genetic screening technique used to identify candidate cancer genes in mouse model systems. An important, yet unresolved issue in the analysis of these screens, is the identification of the genes affected by the insertions. To address this, we developed Kernel Convolved Rule Based Mapping (KC-RBM). KC-RBM exploits distance, orientation and insertion density across tumors to automatically map integration sites to target genes. We perform the first genome-wide evaluation of the association of insertion occurrences with aberrant gene expression of the predicted targets in both retroviral and transposon data sets. We demonstrate the efficiency of KC-RBM by showing its superior performance over existing approaches in recovering true positives from a list of independently, manually curated cancer genes. The results of this work will significantly enhance the accuracy and speed of cancer gene discovery in forward genetic screens. KC-RBM is available as R-package., Mediamatics, Electrical Engineering, Mathematics and Computer Science