Computational identification of insertional mutagenesis targets for cancer gene discovery

Restoration of T and B Cell Differentiation after RAG1 Gene Transfer in Human RAG1 Defective Hematopoietic Stem Cells

Recombinase-activating gene (RAG)-deficient SCID patients lack B and T lymphocytes due to the inability to rearrange immunoglobulin and T cell receptor genes. The two RAG genes act as a required dimer to initiate gene recombination. Gene therapy is a valid treatment alternative for RAG-SCID patients who lack a suitable bone marrow donor, but developing such therapy for RAG1/2 has proven challenging. Using a clinically approved lentiviral vector with a codon-optimized RAG1 gene, we report here preclinical studies using CD34+ cells from four RAG1-SCID patients. We used in vitro T cell developmental assays and in vivo assays in xenografted NSG mice. The RAG1-SCID patient CD34+ cells transduced with the RAG1 vector and transplanted into NSG mice led to restored human B and T cell development. Together with favorable safety data on integration sites, these results substantiate an ongoing phase I/II clinical trial for RAG1-SCID.

Transmicron: accurate prediction of insertion probabilities improves detection of cancer driver genes from transposon mutagenesis screens

Transposon screens are powerful in vivo assays used to identify loci driving carcinogenesis. These loci are identified as Common Insertion Sites (CISs), i.e. regions with more transposon insertions than expected by chance. However, the identification of CISs is affected by biases in the insertion behaviour of transposon systems. Here, we introduce Transmicron, a novel method that differs from previous methods by (i) modelling neutral insertion rates based on chromatin accessibility, transcriptional activity and sequence context and (ii) estimating oncogenic selection for each genomic region using Poisson regression to model insertion counts while controlling for neutral insertion rates. To assess the benefits of our approach, we generated a dataset applying two different transposon systems under comparable conditions. Benchmarking for enrichment of known cancer genes showed improved performance of Transmicron against state-of-the-art methods. Modelling neutral insertion rates allowed for better control of false positives and stronger agreement of the results between transposon systems. Moreover, using Poisson regression to consider intra-sample and inter-sample information proved beneficial in small and moderately-sized datasets. Transmicron is open-source and freely available. Overall, this study contributes to the understanding of transposon biology and introduces a novel approach to use this knowledge for discovering cancer driver genes.

Forward and Reverse Genetics of B Cell Malignancies: From Insertional Mutagenesis to CRISPR-Cas

Cancer genome sequencing has identified dozens of mutations with a putative role in lymphomagenesis and leukemogenesis. Validation of driver mutations responsible for B cell neoplasms is complicated by the volume of mutations worthy of investigation and by the complex ways that multiple mutations arising from different stages of B cell development can cooperate. Forward and reverse genetic strategies in mice can provide complementary validation of human driver genes and in some cases comparative genomics of these models with human tumors has directed the identification of new drivers in human malignancies. We review a collection of forward genetic screens performed using insertional mutagenesis, chemical mutagenesis and exome sequencing and discuss how the high coverage of subclonal mutations in insertional mutagenesis screens can identify cooperating mutations at rates not possible using human tumor genomes. We also compare a set of independently conducted screens from Pax5 mutant mice that converge upon a common set of mutations observed in human acute lymphoblastic leukemia (ALL). We also discuss reverse genetic models and screens that use CRISPR-Cas, ORFs and shRNAs to provide high throughput in vivo proof of oncogenic function, with an emphasis on models using adoptive transfer of ex vivo cultured cells. Finally, we summarize mouse models that offer temporal regulation of candidate genes in an in vivo setting to demonstrate the potential of their encoded proteins as therapeutic targets.

LUMI-PCR: an Illumina platform ligation-mediated PCR protocol for integration site cloning, provides molecular quantitation of integration sites

BACKGROUND

Ligation-mediated PCR protocols have diverse uses including the identification of integration sites of insertional mutagens, integrating vectors and naturally occurring mobile genetic elements. For approaches that employ NGS sequencing, the relative abundance of integrations within a complex mixture is typically determined through the use of read counts or unique fragment lengths from a ligation of sheared DNA; however, these estimates may be skewed by PCR amplification biases and saturation of sequencing coverage.

RESULTS

Here we describe a modification of our previous splinkerette based ligation-mediated PCR using a novel Illumina-compatible adapter design that prevents amplification of non-target DNA and incorporates unique molecular identifiers. This design reduces the number of PCR cycles required and improves relative quantitation of integration abundance for saturating sequencing coverage. By inverting the forked adapter strands from a standard orientation, the integration-genome junction can be sequenced without affecting the sequence diversity required for cluster generation on the flow cell. Replicate libraries of murine leukemia virus-infected spleen samples yielded highly reproducible quantitation of clonal integrations as well as a deep coverage of subclonal integrations. A dilution series of DNAs bearing integrations of MuLV or piggyBac transposon shows linearity of the quantitation over a range of concentrations.

CONCLUSIONS

Merging ligation and library generation steps can reduce total PCR amplification cycles without sacrificing coverage or fidelity. The protocol is robust enough for use in a 96 well format using an automated liquid handler and we include programs for use of a Beckman Biomek liquid handling workstation. We also include an informatics pipeline that maps reads, builds integration contigs and quantitates integration abundance using both fragment lengths and unique molecular identifiers. Suggestions for optimizing the protocol to other target DNA sequences are included. The reproducible distinction of clonal and subclonal integration sites from each other allows for analysis of populations of cells undergoing selection, such as those found in insertional mutagenesis screens.

Mouse models in the era of large human tumour sequencing studies

Cancer is a complex disease in which cells progressively accumulate mutations disrupting their cellular processes. A fraction of these mutations drive tumourigenesis by affecting oncogenes or tumour suppressor genes, but many mutations are passengers with no clear contribution to tumour development. The advancement of DNA and RNA sequencing technologies has enabled in-depth analysis of thousands of human tumours from various tissues to perform systematic characterization of their (epi)genomes and transcriptomes in order to identify (epi)genetic changes associated with cancer. Combined with considerable progress in algorithmic development, this expansion in scale has resulted in the identification of many cancer-associated mutations, genes and pathways that are considered to be potential drivers of tumour development. However, it remains challenging to systematically identify drivers affected by complex genomic rearrangements and drivers residing in non-coding regions of the genome or in complex amplicons or deletions of copy-number driven tumours. Furthermore, functional characterization is challenging in the human context due to the lack of genetically tractable experimental model systems in which the effects of mutations can be studied in the context of their tumour microenvironment. In this respect, mouse models of human cancer provide unique opportunities for pinpointing novel driver genes and their detailed characterization. In this review, we provide an overview of approaches for complementing human studies with data from mouse models. We also discuss state-of-the-art technological developments for cancer gene discovery and validation in mice.

Subclonal mutation selection in mouse lymphomagenesis identifies known cancer loci and suggests novel candidates

Determining whether recurrent but rare cancer mutations are bona fide driver mutations remains a bottleneck in cancer research. Here we present the most comprehensive analysis of murine leukemia virus-driven lymphomagenesis produced to date, sequencing 700,000 mutations from >500 malignancies collected at time points throughout tumor development. This scale of data allows novel statistical approaches for identifying selected mutations and yields a high-resolution, genome-wide map of the selective forces surrounding cancer gene loci. We also demonstrate negative selection of mutations that may be deleterious to tumor development indicating novel avenues for therapy. Screening of two BCL2 transgenic models confirmed known drivers of human non-Hodgkin lymphoma, and implicates novel candidates including modifiers of immunosurveillance and MHC loci. Correlating mutations with genotypic and phenotypic features independently of local variance in mutation density also provides support for weakly evidenced cancer genes. An online resource http://mulvdb.org allows customized queries of the entire dataset.

Evidence implicating cancer drivers can be sparse when limited to clonal events. Here, the authors present a retrovirus driven in vivo lymphomagenesis time course including hundreds of thousands of subclonal mutations and demonstrate the utility of these in mapping the selective forces affecting cancer gene loci, including negatively selected mutations.

Identifying transposon insertions and their effects from RNA-sequencing data

Insertional mutagenesis using engineered transposons is a potent forward genetic screening technique used to identify cancer genes in mouse model systems. In the analysis of these screens, transposon insertion sites are typically identified by targeted DNA-sequencing and subsequently assigned to predicted target genes using heuristics. As such, these approaches provide no direct evidence that insertions actually affect their predicted targets or how transcripts of these genes are affected. To address this, we developed IM-Fusion, an approach that identifies insertion sites from gene-transposon fusions in standard single- and paired-end RNA-sequencing data. We demonstrate IM-Fusion on two separate transposon screens of 123 mammary tumors and 20 B-cell acute lymphoblastic leukemias, respectively. We show that IM-Fusion accurately identifies transposon insertions and their true target genes. Furthermore, by combining the identified insertion sites with expression quantification, we show that we can determine the effect of a transposon insertion on its target gene(s) and prioritize insertions that have a significant effect on expression. We expect that IM-Fusion will significantly enhance the accuracy of cancer gene discovery in forward genetic screens and provide initial insight into the biological effects of insertions on candidate cancer genes.

Insertional mutagenesis identifies drivers of a novel oncogenic pathway in invasive lobular breast carcinoma

Invasive lobular carcinoma (ILC) is the second most common breast cancer subtype and accounts for 8-14% of all cases. Although the majority of human ILCs are characterized by the functional loss of E-cadherin (encoded by CDH1), inactivation of Cdh1 does not predispose mice to develop mammary tumors, implying that mutations in additional genes are required for ILC formation in mice. To identify these genes, we performed an insertional mutagenesis screen using the Sleeping Beauty transposon system in mice with mammary-specific inactivation of Cdh1. These mice developed multiple independent mammary tumors of which the majority resembled human ILC in terms of morphology and gene expression. Recurrent and mutually exclusive transposon insertions were identified in Myh9, Ppp1r12a, Ppp1r12b and Trp53bp2, whose products have been implicated in the regulation of the actin cytoskeleton. Notably, MYH9, PPP1R12B and TP53BP2 were also frequently aberrated in human ILC, highlighting these genes as drivers of a novel oncogenic pathway underlying ILC development.

The small FOXP1 isoform predominantly expressed in activated B cell-like diffuse large B-cell lymphoma and full-length FOXP1 exert similar oncogenic and transcriptional activity in human B cells

The forkhead transcription factor FOXP1 is generally regarded as an oncogene in activated B cell-like diffuse large B-cell lymphoma. Previous studies have suggested that a small isoform of FOXP1 rather than full-length FOXP1, may possess this oncogenic activity. Corroborating those studies, we herein show that activated B cell-like diffuse large B-cell lymphoma cell lines and primary activated B cell-like diffuse large B-cell lymphoma cells predominantly express a small FOXP1 isoform, and that the 5′-end of the Foxp1 gene is a common insertion site in murine lymphomas in leukemia virus- and transposon-mediated insertional mutagenesis screens. By combined mass spectrometry, (quantative) reverse transcription polymerase chain reaction/sequencing, and small interfering ribonucleic acid-mediated gene silencing, we determined that the small FOXP1 isoform predominantly expressed in activated B cell-like diffuse large B-cell lymphoma lacks the N-terminal 100 amino acids of full-length FOXP1. Aberrant overexpression of this FOXP1 isoform (ΔN100) in primary human B cells revealed its oncogenic capacity; it repressed apoptosis and plasma cell differentiation. However, no difference in potency was found between this small FOXP1 isoform and full-length FOXP1. Furthermore, overexpression of full-length FOXP1 or this small FOXP1 isoform in primary B cells and diffuse large B-cell lymphoma cell lines resulted in similar gene regulation. Taken together, our data indicate that this small FOXP1 isoform and full-length FOXP1 have comparable oncogenic and transcriptional activity in human B cells, suggesting that aberrant expression or overexpression of FOXP1, irrespective of the specific isoform, contributes to lymphomagenesis. These novel insights further enhance the value of FOXP1 for the diagnostics, prognostics, and treatment of diffuse large B-cell lymphoma patients.

A Graph Based Framework to Model Virus Integration Sites

With next generation sequencing thousands of virus and viral vector integration genome targets are now under investigation to uncover specific integration preferences and to define clusters of integration, termed common integration sites (CIS), that may allow to assess gene therapy safety or to detect disease related genomic features such as oncogenes.

Here, we addressed the challenge to: 1) define the notion of CIS on graph models, 2) demonstrate that the structure of CIS enters in the category of scale-free networks and 3) show that our network approach analyzes CIS dynamically in an integrated systems biology framework using the Retroviral Transposon Tagged Cancer Gene Database (RTCGD) as a testing dataset.

Mislocalization-related disease gene discovery using gene expression based computational protein localization prediction

Protein sorting is an important mechanism for transporting proteins to their target subcellular locations after their synthesis. Mutations on genes may disrupt the well regulated protein sorting process, leading to a variety of mislocation related diseases. This paper proposes a methodology to discover such disease genes based on gene expression data and computational protein localization prediction. A kernel logistic regression based algorithm is used to successfully identify several candidate cancer genes which may cause cancers due to their mislocation within the cell. Our results also showed that compared to the gene co-expression network defined on Pearson correlation coefficients, the nonlinear Maximum Correlation Coefficients (MIC) based co-expression network give better results for subcellular localization prediction.

Applications of DNA integrating elements: Facing the bias bully

Retroviruses and DNA transposons are an important part of molecular biologists' toolbox. The applications of these elements range from functional genomics to oncogene discovery and gene therapy. However, these elements do not integrate uniformly across the genome, which is an important limitation to their use. A number of genetic and epigenetic factors have been shown to shape the integration preference of these elements. Insight into integration bias can significantly enhance the analysis and interpretation of results obtained using these elements. For three different applications, we outline how bias can affect results, and can potentially be addressed.

3D hotspots of recurrent retroviral insertions reveal long-range interactions with cancer genes

Genomically distal mutations can contribute to the deregulation of cancer genes by engaging in chromatin interactions. To study this, we overlay viral cancer-causing insertions obtained in a murine retroviral insertional mutagenesis screen with genome-wide chromatin conformation capture data. Here we find that insertions tend to cluster in 3D hotspots within the nucleus. The identified hotspots are significantly enriched for known cancer genes, and bear the expected characteristics of bona fide regulatory interactions, such as enrichment for transcription factor-binding sites. In addition, we observe a striking pattern of mutual exclusive integration. This is an indication that insertions in these loci target the same gene, either in their linear genomic vicinity or in their 3D spatial vicinity. Our findings shed new light on the repertoire of targets obtained from insertional mutagenesis screening and underline the importance of considering the genome as a 3D structure when studying effects of genomic perturbations.

Chromatin landscapes of retroviral and transposon integration profiles

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.

Retroviruses and transposons are widely used in cancer research and gene therapy. However, these systems show integration biases that may strongly affect results. To address this issue, we generated very large datasets consisting of to unselected integrations for the Sleeping Beauty and piggyBac transposons, and the Mouse Mammary Tumor Virus (MMTV). We analyzed (epi)genomic features to generate bias maps at local and genome-wide scales. MMTV showed a remarkably uniform distribution of integrations across the genome, and a striking similarity was observed between piggyBac and the Murine Leukemia Virus. Moreover, we find that target site selection is directed at multiple scales. At larger scales, it is similar across systems, and directed by a set of domain-oriented features, including chromatin compaction, replication timing, and CpG islands. Notable differences between systems are defined at smaller scales by a diverse range of epigenetic features. As a practical application of our findings, we determined that three recent insertional mutagenesis screens - commonly used for cancer gene discovery - contained 7%–33% putative false positive integration hotspots.

Identifying regulatory mechanisms underlying tumorigenesis using locus expression signature analysis

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.

Insertional mutagenesis and deep profiling reveals gene hierarchies and a Myc/p53-dependent bottleneck in lymphomagenesis

Retroviral insertional mutagenesis (RIM) is a powerful tool for cancer genomics that was combined in this study with deep sequencing (RIM/DS) to facilitate a comprehensive analysis of lymphoma progression. Transgenic mice expressing two potent collaborating oncogenes in the germ line (CD2-MYC, -Runx2) develop rapid onset tumours that can be accelerated and rendered polyclonal by neonatal Moloney murine leukaemia virus (MoMLV) infection. RIM/DS analysis of 28 polyclonal lymphomas identified 771 common insertion sites (CISs) defining a 'progression network' that encompassed a remarkably large fraction of known MoMLV target genes, with further strong indications of oncogenic selection above the background of MoMLV integration preference. Progression driven by RIM was characterised as a Darwinian process of clonal competition engaging proliferation control networks downstream of cytokine and T-cell receptor signalling. Enhancer mode activation accounted for the most efficiently selected CIS target genes, including Ccr7 as the most prominent of a set of chemokine receptors driving paracrine growth stimulation and lymphoma dissemination. Another large target gene subset including candidate tumour suppressors was disrupted by intragenic insertions. A second RIM/DS screen comparing lymphomas of wild-type and parental transgenics showed that CD2-MYC tumours are virtually dependent on activation of Runx family genes in strong preference to other potent Myc collaborating genes (Gfi1, Notch1). Ikzf1 was identified as a novel collaborating gene for Runx2 and illustrated the interface between integration preference and oncogenic selection. Lymphoma target genes for MoMLV can be classified into (a) a small set of master regulators that confer self-renewal; overcoming p53 and other failsafe pathways and (b) a large group of progression genes that control autonomous proliferation in transformed cells. These findings provide insights into retroviral biology, human cancer genetics and the safety of vector-mediated gene therapy.

Cancer of mice and men: old twists and new tails

In this review we set out to celebrate the contribution that mouse models of human cancer have made to our understanding of the fundamental mechanisms driving tumourigenesis. We take the opportunity to look forward to how the mouse will be used to model cancer and the tools and technologies that will be applied, and indulge in looking back at the key advances the mouse has made possible.

Evaluating risks of insertional mutagenesis by DNA transposons in gene therapy

Investigational therapy can be successfully undertaken using viral- and nonviral-mediated ex vivo gene transfer. Indeed, recent clinical trials have established the potential for genetically modified T cells to improve and restore health. Recently, the Sleeping Beauty (SB) transposon/transposase system has been applied in clinical trials to stably insert a chimeric antigen receptor (CAR) to redirect T-cell specificity. We discuss the context in which the SB system can be harnessed for gene therapy and describe the human application of SB-modified CAR(+) T cells. We have focused on theoretical issues relating to insertional mutagenesis in the context of human genomes that are naturally subjected to remobilization of transposons and the experimental evidence over the last decade of employing SB transposons for defining genes that induce cancer. These findings are put into the context of the use of SB transposons in the treatment of human disease.

Detecting recurrent gene mutation in interaction network context using multi-scale graph diffusion

Background

Delineating the molecular drivers of cancer, i.e. determining cancer genes and the pathways which they deregulate, is an important challenge in cancer research. In this study, we aim to identify pathways of frequently mutated genes by exploiting their network neighborhood encoded in the protein-protein interaction network. To this end, we introduce a multi-scale diffusion kernel and apply it to a large collection of murine retroviral insertional mutagenesis data. The diffusion strength plays the role of scale parameter, determining the size of the network neighborhood that is taken into account. As a result, in addition to detecting genes with frequent mutations in their genomic vicinity, we find genes that harbor frequent mutations in their interaction network context.

Results

We identify densely connected components of known and putatively novel cancer genes and demonstrate that they are strongly enriched for cancer related pathways across the diffusion scales. Moreover, the mutations in the clusters exhibit a significant pattern of mutual exclusion, supporting the conjecture that such genes are functionally linked. Using multi-scale diffusion kernel, various infrequently mutated genes are found to harbor significant numbers of mutations in their interaction network neighborhood. Many of them are well-known cancer genes.

Conclusions

The results demonstrate the importance of defining recurrent mutations while taking into account the interaction network context. Importantly, the putative cancer genes and networks detected in this study are found to be significant at different diffusion scales, confirming the necessity of a multi-scale analysis.

Dosage-dependent tumor suppression by histone deacetylases 1 and 2 through regulation of c-Myc collaborating genes and p53 function

Histone deacetylases (HDACs) are epigenetic erasers of lysine-acetyl marks. Inhibition of HDACs using small molecule inhibitors (HDACi) is a potential strategy in the treatment of various diseases and is approved for treating hematological malignancies. Harnessing the therapeutic potential of HDACi requires knowledge of HDAC-function in vivo. Here, we generated a thymocyte-specific gradient of HDAC-activity using compound conditional knockout mice for Hdac1 and Hdac2. Unexpectedly, gradual loss of HDAC-activity engendered a dosage-dependent accumulation of immature thymocytes and correlated with the incidence and latency of monoclonal lymphoblastic thymic lymphomas. Strikingly, complete ablation of Hdac1 and Hdac2 abrogated lymphomagenesis due to a block in early thymic development. Genomic, biochemical and functional analyses of pre-leukemic thymocytes and tumors revealed a critical role for Hdac1/Hdac2-governed HDAC-activity in regulating a p53-dependent barrier to constrain Myc-overexpressing thymocytes from progressing into lymphomas by regulating Myc-collaborating genes. One Myc-collaborating and p53-suppressing gene, Jdp2, was derepressed in an Hdac1/2-dependent manner and critical for the survival of Jdp2-overexpressing lymphoma cells. Although reduced HDAC-activity facilitates oncogenic transformation in normal cells, resulting tumor cells remain highly dependent on HDAC-activity, indicating that a critical level of Hdac1 and Hdac2 governed HDAC-activity is required for tumor maintenance.

Transposable elements and human cancer: a causal relationship?

Transposable elements are present in almost all genomes including that of humans. These mobile DNA sequences are capable of invading genomes and their impact on genome evolution is substantial as they contribute to the genetic diversity of organisms. The mobility of transposable elements can cause deleterious mutations, gene disruption and chromosome rearrangements that may lead to several pathologies including cancer. This mini-review aims to give a brief overview of the relationship that transposons and retrotransposons may have in the genetic cause of human cancer onset, or conversely creating protection against cancer. Finally, the cause of TE mobility may also be the cancer cell environment itself.

Cancer gene discovery in the mouse

Developments in high-throughput genome analysis and in computational tools have made it possible to rapidly profile entire cancer genomes with basepair resolution. In parallel with these advances, mouse models of cancer have evolved into powerful tools for cancer gene discovery. Here we discuss some of the approaches that may be used for cancer gene identification in the mouse and discuss how a cross-species 'oncogenomics' approach to cancer gene discovery represents a powerful strategy for finding genes that drive tumorigenesis.