Jaap Kool

Retroviral insertional mutagenesis: past, present and future

Retroviral insertion mutagenesis screens in mice are powerful tools for efficient identification of oncogenic mutations in an in vivo setting. Many oncogenes identified in these screens have also been shown to play a causal role in the development of human cancers. Sequencing and annotation of the mouse genome, along with recent improvements in insertion site cloning has greatly facilitated identification of oncogenic events in retrovirus-induced tumours. In this review, we discuss the features of retroviral insertion mutagenesis screens, covering the mechanisms by which retroviral insertions mutate cellular genes, the practical aspects of insertion site cloning, the identification and analysis of common insertion sites, and finally we address the potential for use of somatic insertional mutagens in the study of nonhaematopoietic and nonmammary tumour types.

Large-Scale Mutagenesis in p19ARF- and p53-Deficient Mice Identifies Cancer Genes and Their Collaborative Networks

Summary p53 and p19ARF are tumor suppressors frequently mutated in human tumors. In a high-throughput screen in mice for mutations collaborating with either p53 or p19ARF deficiency, we identified 10,806 retroviral insertion sites, implicating over 300 loci in tumorigenesis. This dataset reveals 20 genes that are specifically mutated in either p19ARF-deficient, p53-deficient or wild-type mice (including Flt3, mmu-mir-106a-363, Smg6, and Ccnd3), as well as networks of significant collaborative and mutually exclusive interactions between cancer genes. Furthermore, we found candidate tumor suppressor genes, as well as distinct clusters of insertions within genes like Flt3 and Notch1 that induce mutants with different spectra of genetic interactions. Cross species comparative analysis with aCGH data of human cancer cell lines revealed known and candidate oncogenes (Mmp13, Slamf6, and Rreb1) and tumor suppressors (Wwox and Arfrp2). This dataset should prove to be a rich resource for the study of genetic interactions that underlie tumorigenesis.

A high-throughput splinkerette-PCR method for the isolation and sequencing of retroviral insertion sites.

Insertional mutagens such as viruses and transposons are a useful tool for performing forward genetic screens in mice to discover cancer genes. These screens are most effective when performed using hundreds of mice; however, until recently, the cost-effective isolation and sequencing of insertion sites has been a major limitation to performing screens on this scale. Here we present a method for the high-throughput isolation of insertion sites using a highly efficient splinkerette-PCR method coupled with capillary or 454 sequencing. This protocol includes a description of the procedure for DNA isolation, DNA digestion, linker or splinkerette ligation, primary and secondary PCR amplification, and sequencing. This method, which takes about 1 week to perform, has allowed us to isolate hundreds of thousands of insertion sites from mouse tumors and, unlike other methods, has been specifically optimized for the murine leukemia virus (MuLV), and can easily be performed in a 96-well plate format for the efficient multiplex isolation of insertion sites.

High-throughput insertional mutagenesis screens in mice to identify oncogenic networks

Retroviral insertional mutagenesis screens have been used for many years as a tool for cancer gene discovery. In recent years, completion of the mouse genome sequence as well as improved technologies for cloning and sequencing of retroviral insertions have greatly facilitated the retrieval of more complete data sets from these screens. The concomitant increase of the size of the screens allows researchers to address new questions about the genes and signalling networks involved in tumour development. In addition, the development of new insertional mutagenesis tools such as DNA transposons enables screens for cancer genes in tissues that previously could not be analysed by retroviral insertional mutagenesis.

Detecting Statistically Significant Common Insertion Sites in Retroviral Insertional Mutagenesis Screens

Retroviral insertional mutagenesis screens, which identify genes involved in tumor development in mice, have yielded a substantial number of retroviral integration sites, and this number is expected to grow substantially due to the introduction of high-throughput screening techniques. The data of various retroviral insertional mutagenesis screens are compiled in the publicly available Retroviral Tagged Cancer Gene Database (RTCGD). Integrally analyzing these screens for the presence of common insertion sites (CISs, i.e., regions in the genome that have been hit by viral insertions in multiple independent tumors significantly more than expected by chance) requires an approach that corrects for the increased probability of finding false CISs as the amount of available data increases. Moreover, significance estimates of CISs should be established taking into account both the noise, arising from the random nature of the insertion process, as well as the bias, stemming from preferential insertion sites present in the genome and the data retrieval methodology. We introduce a framework, the kernel convolution (KC) framework, to find CISs in a noisy and biased environment using a predefined significance level while controlling the family-wise error (FWE) (the probability of detecting false CISs). Where previous methods use one, two, or three predetermined fixed scales, our method is capable of operating at any biologically relevant scale. This creates the possibility to analyze the CISs in a scale space by varying the width of the CISs, providing new insights in the behavior of CISs across multiple scales. Our method also features the possibility of including models for background bias. Using simulated data, we evaluate the KC framework using three kernel functions, the Gaussian, triangular, and rectangular kernel function. We applied the Gaussian KC to the data from the combined set of screens in the RTCGD and found that 53% of the CISs do not reach the significance threshold in this combined setting. Still, with the FWE under control, application of our method resulted in the discovery of eight novel CISs, which each have a probability less than 5% of being false detections.

High-throughput semiquantitative analysis of insertional mutations in heterogeneous tumors

Retroviral and transposon-based insertional mutagenesis (IM) screens are widely used for cancer gene discovery in mice. Exploiting the full potential of IM screens requires methods for high-throughput sequencing and mapping of transposon and retroviral insertion sites. Current protocols are based on ligation-mediated PCR amplification of junction fragments from restriction endonuclease-digested genomic DNA, resulting in amplification biases due to uneven genomic distribution of restriction enzyme recognition sites. Consequently, sequence coverage cannot be used to assess the clonality of individual insertions. We have developed a novel method, called shear-splink, for the semiquantitative high-throughput analysis of insertional mutations. Shear-splink employs random fragmentation of genomic DNA, which reduces unwanted amplification biases. Additionally, shear-splink enables us to assess clonality of individual insertions by determining the number of unique ligation points (LPs) between the adapter and genomic DNA. This parameter serves as a semiquantitative measure of the relative clonality of individual insertions within heterogeneous tumors. Mixing experiments with clonal cell lines derived from mouse mammary tumor virus (MMTV)-induced tumors showed that shear-splink enables the semiquantitative assessment of the clonality of MMTV insertions. Further, shear-splink analysis of 16 MMTV- and 127 Sleeping Beauty (SB)-induced tumors showed enrichment for cancer-relevant insertions by exclusion of irrelevant background insertions marked by single LPs, thereby facilitating the discovery of candidate cancer genes. To fully exploit the use of the shear-splink method, we set up the Insertional Mutagenesis Database (iMDB), offering a publicly available web-based application to analyze both retroviral- and transposon-based insertional mutagenesis data.

Novel candidate cancer genes identified by a large-scale cross-species comparative oncogenomics approach.

Comparative genomic hybridization (CGH) can reveal important disease genes but the large regions identified could sometimes contain hundreds of genes. Here we combine high-resolution CGH analysis of 598 human cancer cell lines with insertion sites isolated from 1,005 mouse tumors induced with the murine leukemia virus (MuLV). This cross-species oncogenomic analysis revealed candidate tumor suppressor genes and oncogenes mutated in both human and mouse tumors, making them strong candidates for novel cancer genes. A significant number of these genes contained binding sites for the stem cell transcription factors Oct4 and Nanog. Notably, mice carrying tumors with insertions in or near stem cell module genes, which are thought to participate in cell self-renewal, died significantly faster than mice without these insertions. A comparison of the profile we identified to that induced with the Sleeping Beauty (SB) transposon system revealed significant differences in the profile of recurrently mutated genes. Collectively, this work provides a rich catalogue of new candidate cancer genes for functional analysis.

Insertional Mutagenesis in Mice Deficient for p15Ink4b, p16Ink4a, p21Cip1, and p27Kip1 Reveals Cancer Gene Interactions and Correlations with Tumor Phenotypes

The cyclin dependent kinase (CDK) inhibitors p15, p16, p21, and p27 are frequently deleted, silenced, or downregulated in many malignancies. Inactivation of CDK inhibitors predisposes mice to tumor development, showing that these genes function as tumor suppressors. Here, we describe high-throughput murine leukemia virus insertional mutagenesis screens in mice that are deficient for one or two CDK inhibitors. We retrieved 9,117 retroviral insertions from 476 lymphomas to define hundreds of loci that are mutated more frequently than expected by chance. Many of these loci are skewed toward a specific genetic context of predisposing germline and somatic mutations. We also found associations between these loci with gender, age of tumor onset, and lymphocyte lineage (B or T cell). Comparison of retroviral insertion sites with single nucleotide polymorphisms associated with chronic lymphocytic leukemia revealed a significant overlap between the datasets. Together, our findings highlight the importance of genetic context within large-scale mutation detection studies, and they show a novel use for insertional mutagenesis data in prioritizing disease-associated genes that emerge from genome-wide association studies.

Analysis of tumor heterogeneity and cancer gene networks using deep sequencing of MMTV-induced mouse mammary tumors.

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.

Identifying regulatory mechanisms underlying tumorigenesis using locus expression signature analysis

Significance Each individual tumor harbors a unique combination of genetic lesions, which together are responsible for the aberrant behavior of its cells. Cancer-causing viruses have been used in mice to systematically sample this genetic diversity. Corresponding changes in global gene expression can be monitored using high-throughput technology. In this study, we present a computational strategy that integrates information at the genetic and molecular level to construct a genome-wide signature that captures the effect of an individual genetic lesion on the gene regulatory network of the cell. We show how these signatures can be exploited to gain insight into the processes and regulatory pathways perturbed by each lesion and suggest drugs that can counteract its effect. Retroviral insertional mutagenesis is a powerful tool for identifying putative cancer genes in mice. To uncover the regulatory mechanisms by which common insertion loci affect downstream processes, we supplemented genotyping data with genome-wide mRNA expression profiling data for 97 tumors induced by retroviral insertional mutagenesis. We developed locus expression signature analysis, an algorithm to construct and interpret the differential gene expression signature associated with each common insertion locus. Comparing locus expression signatures to promoter affinity profiles allowed us to build a detailed map of transcription factors whose protein-level regulatory activity is modulated by a particular locus. We also predicted a large set of drugs that might mitigate the effect of the insertion on tumorigenesis. Taken together, our results demonstrate the potential of a locus-specific signature approach for identifying mammalian regulatory mechanisms in a cancer context.