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ScienceDirect Sleeping Beauty mutagenesis: exploiting forward genetic screens for cancer gene discovery Michael B Mann, Nancy A Jenkins, Neal G Copeland and Karen M Mann Sleeping Beauty (SB) is a powerful insertional mutagen used in somatic forward genetic screens to identify novel candidate cancer genes. In the past two years, SB has become widely adopted to model human pancreatic, hepatocellular, colorectal and neurological cancers to identify loci that participate in tumor initiation, progression and metastasis. Oncogenomic approaches have directly linked hundreds of genes identified by SB with human cancers, many with prognostic implications. These SB candidate cancer genes are aiding to prioritize punitive human cancer genes for follow-up studies and as possible biomarkers or therapeutic targets. This review highlights recent advances in SB cancer gene discovery, approaches to validate candidate cancer genes, and efforts to integrate SB data across all tumor types to prioritize drug development and tumor specificity. Addresses Cancer Research Program, Houston Methodist Research Institute, 6670 Bertner Avenue, Houston, TX 77030, United States Corresponding author: Mann, Karen M ([email protected])
Current Opinion in Genetics & Development 2014, 24:16–22 This review comes from a themed issue on Cancer genomics Edited by David J Adams and Ultan McDermott For a complete overview see the Issue and the Editorial Available online 20th December 2013 0959-437X/$ – see front matter, # 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.gde.2013.11.004
Sleeping Beauty models of cancer Insertional mutagenesis for gene discovery
SB is so efficient at identifying cancer genes because it contains elements that can drive expression of in-frame genomic sequences (oncogenes) or disrupt gene expression (tumor suppressor genes), depending on the selected transposon insertion orientation and location (see [3] for review). Transgenic transposon donor strains contain concatamers of transposons, ranging from 30 to 300 copies, integrated in a single donor site in the mouse genome. The numbers of transposon copies, as well as the internal promoter elements, appear to influence tumor type formation with constitutive whole-body transposition [1,2,4]. An inducible allele of the transposase targeted to the dispensable Rosa26 locus allowed for control of SB activation in space and time [4,5], and transposase alleles have been engineered with varying activity in the mouse genome (see [6] for review). Each transposon is flanked by inverted repeats that are recognized by the transposase, which then induces doublestrand breaks, liberating the transposon from the donor site in the mouse genome. The transposon can then reintegrate into another location in the genome or it may be lost (see [3] for review). The process of integration-excision-reintegration of transposons throughout the mouse genome is continuous. Transposon insertions are ‘fixed’ when they offer a selective advantage to a cell. The accumulation of fixed transposon insertions in sub-populations of cells leads to the formation of cancer. Statistical pipelines define candidate cancer genes
Introduction Sleeping Beauty (SB) was first reported in 2005 as a DNA transposon-based somatic insertional mutagenesis system capable of inducing hematopoietic and solid tumors in the mouse [1,2]. Subsequently, forward genetic screens in both hematopoietic and solid tumor models using SB have identified hundreds of candidate cancer genes. SB mutagenesis proved to be advantageous over classic retroviral mutagenesis approaches due to its short-acting effects on targeted genes and its ability to mutate all cells of the body. Recent reports demonstrate the ability of SB to drive metastatic disease, which is particularly challenging to study in human cancer patients. This review highlights recent advances in SB cancer gene discovery published in the last two years, ongoing efforts to validate candidate cancer genes, and possibilities for integration of SB data across all tumor types to prioritize drug development and tumor specificity. Current Opinion in Genetics & Development 2014, 24:16–22
As SB data sets grew in size and complexity due to advances in next generation sequencing (NGS) platforms over the past few years, so too have the methods used to define candidate cancer genes from SB mutagenized cancer genomes. The field has moved away from simply listing genes that contain two or more SB insertions within the vicinity of a gene. Now, several new statistical frameworks define candidate cancer genes. The availability of these tools standardizes identification of loci in tumor genomes enriched for transposon insertions, termed common insertion sites (CISs). The most frequently used methods to identify CISs are Monte Carlo (MC) simulation [5], Gaussian Kernel Convolutions (GKC) [7], gene-centric CIS (gCIS) analysis [8] and Poisson Regression Insertion Modeling (PRIM) [9] algorithms (see [6] for review). Two recent studies integrated the various analysis platforms for comprehensive candidate cancer gene detection [10,11]. Future efforts www.sciencedirect.com
Sleeping Beauty mouse models for cancer gene discovery Mann et al. 17
will focus on building databases of existing data sets to permit meta-analysis studies that may identify still more candidate cancer genes.
SB identifies candidate cancer genes relevant to human cancers Hematopoietic and skin cancers
Hematopoietic malignancies and squamous cell carcinoma (SCC) were the first reported cancers induced by constitutive SB mutagenesis [1,2,4]. Recent studies have used SB to identify mutations that cooperate with mouse orthologs of commonly mutated loci in human hematopoietic malignancies, including Trp53 [12], Cadm1 [13] and Runx2 [14] in B-cell lymphoma, Jak2 in myeloproliferative disease [15] and NFKB in chronic lymphoblastic leukemia [16]. Berquam-Vrieze and colleagues demonstrated that cell-of-origin and timing of mutagenesis initiation influences transposon selection and disease outcome [17]. Quintana et al. identified loci that participate in multiple non-melanoma skin cancers, including basal-cell carcinoma (BCC), keratoacanthoma and SCC, and confirmed that Notch downregulation is a feature in both SB-driven and human non-melanoma skin tumors [18]. Roger et al. [19] validated the ability of N-terminal truncated Zmiz1 to induce invasive keratoacanthoma and SCC in skin when overexpressed, an observation first made by Dupuy and colleagues in an SB screen [4]. Digestive cancers
Hepatocellular carcinoma (HCC) is the most frequently modeled solid tumor using SB [4,20–22]. O’Donnell et al. [22] recently identified and validated three tumor suppressors that cooperate with Myc to induce HCC. Riordan et al. [23] confirmed that Rtl1, which maps to the Rian locus, is a causative oncogene in HCC. Keng and colleagues identified Egfr in an HCC SB model and showed an association between polysomy of the EGFR locus in human males and HCC [20], potentially providing a link to the male sex bias in HCC. March and colleagues reported a large-scale SB screen for intestinal tumors using two different alleles of Apc as sensitizing mutations [7]. This SB screen was the largest reported, with nearly 450 tumors sequenced, and was the first to take an oncogenomics approach to integrate SB candidate cancer genes (CCGs) with human mutation data from colorectal cancer (CRC). Mann et al. and Perez-Mancera and colleagues used SB to identify cooperating mutations in an oncogenic Kras model of pancreatic cancer [10,24]. SB-induced a highly invasive pancreatic cancer with metastases to liver, lung, peritoneum and surrounding lymph nodes [10,24], and SB-driven adenocarcinomas exhibited many hallmarks of human pancreatic adenocarcinoma (PDAC), including a major stromal component. Importantly, both studies identified known cancer genes previously implicated in pancreatic cancer, including www.sciencedirect.com
Cdkn2a, Smad4 and Acvr1b, as well as a number of genes included on the Cancer Gene Census (n = 84, P < 0.001). Three hundred and thirty-four CCGs from the two studies overlapped, and these represented the genes most statistically enriched for transposon inserts, including Usp9x, Pten, Ctnna1 and Setd5, many of which have not been previously implicated in pancreatic cancer. One of the major findings from the colorectal and pancreatic SB screens is the significant concordance between the CCGs and the human orthologs with non-synonymous mutations or copy number variation in human colorectal and pancreatic cancers. Many of the mutations identified in human PDAC exomes occur at low frequency [25,26]; however, enrichment of SB insertions in orthologous mouse genes suggests that these mutations are not passenger events in pancreatic cancer. March and colleagues implicated one-third of their CCGs in human CRC. An extension of mutational overlap is the concordance of signaling pathways and processes perturbed in both human and SB-driven colorectal and pancreatic cancers. TGF-Beta, MAP kinase, PI3K/AKT and Wnt signaling pathways were significantly enriched for SB CCGs in both CRC and PDAC models [7,10]. March and colleagues functionally validated several new modulators of Wnt signaling. The SB PDAC screens also showed enrichment for CCGs in genes implicated in axon guidance and chromatin remodeling, two processes highlighted by Biankin et al. for enrichment of exomic mutations in human PDAC [25]. These findings reaffirm that SB cancer screens complement ongoing human cancer sequencing efforts and may serve to help prioritize human mutation data for further validation.
Nervous system cancers Recently, SB has been successfully used to model tumors of the nervous system. Koso et al. reported an in vitro mutagenesis strategy for identifying candidate cancer genes that contribute to glioma-initiating cells. Mobilization of SB transposons in neural stem cells in culture permitted immortalization of astroglial-like cells, which were able to generate glioblastoma multiforme (GBM) mesenchymal tumors upon transplantation [27]. Several well-known GBM driver mutations were identified and appear to cooperate with several receptor tyrosine kinase genes. Rahrmann et al. modeled malignant peripheral nerve sheath tumors (MPNST) and neurofibromas using SB with four different sensitizing alleles (Table 1), including EGFR overexpression alone or in combination with mutant Trp53 [11]. CIS analysis of the SB-induced tumors confirmed known orthologous drivers of human MPNST (Nf1 and Pten) and new candidate cancer genes, including Foxr2. Wu et al. [28] modeled medulloblastoma using two different SB models, one sensitized with a Ptch Current Opinion in Genetics & Development 2014, 24:16–22
18 Cancer genomics
Table 1 Tumor types modeled by Sleeping Beauty mutagenesis SB model system cohort and allele summary
Tumor
Study
Hematopoietic cancers Rosa26SBase/+; T2/Onc2(TG.6070 or TG.6113) Vav1-cre; Rosa26SBase/+; T2/Onc2(TG.6070 or TG.6113) Lck-cre; Rosa26SBase/+; T2/Onc2(TG.6070 or TG.6113) CD4-cre; Rosa26SBase/+; T2/Onc2(TG.6070 or TG.6113) Etv6+/RUNX1::HSB5; Rosa26SB11/+; T2/Onc(TG.76); Rosa26SB11/+; T2/Onc(TG.76); Rassf1a / Rosa26SB11/+; T2/Onc(TG.76); Cadm1 / Rosa26SB11/+; T2/Onc(TG.76); Trp53 / Vav1-cre; Rosa26LSL SB11/+; T2/Onc2(TG.6113); Jak2V617F Em-TCL1; CD19-cre; Rosa26LSL SB11/+; T2/Onc2(TG.6070 or TG.6113)
[1] [17] [17] [17] [33] [14] [13] [12] [15] [16]
Rosa26SBase/+; T2/Onc3(TG.12740 or TG.12775) Rosa26SBase/+; T2/Onc2(TG.12740 or TG.12775); Rag2 K5-SB11; T2/Onc2(TG.6070); Tg.AC
[4] [36] [18]
Squamous cell carcinoma/keratoacanthoma /
Hepatocellular adenoma/hepatocellular carcinoma Alb-cre; Rosa26LSL SB11/+; T2/Onc(TG.68); Trp53R270H/+ Rosa26SB11/+; T2/Onc3(TG.12740 or TG.12775) Rosa26SBase/+; T2/Onc2(TG.12740 or TG.12775); Rag2 / Rosa26SB11/+; T2/Onc(TG.68); tet-O-MYC; LAPtTA
[21] [4] [36] [22]
Villin-cre; Rosa26LSL Ah-cre; Rosa26Lox66 Ah-cre; Rosa26Lox66
[5] [7] [7]
Colorectal cancer SB11/+
; T2/Onc(TG.68) ; T2/Onc(TG.76); ApcFloxed/+ Lox71/+ ; T2/Onc(TG.76); ApcMin/+
SB11 Lox71/+ SB11
Pancreatic ductal adenocarcinoma Pdx1-cre; Rosa26LSL Pdx1-cre; Rosa26LSL Pdx1-cre; Rosa26LSL
SB11/+
; T2/Onc2(TG.6113); KrasLSL G12D/+ ; T2/Onc3(TG.12740); KrasLSL G12D/+ SB13/+ ; T2/Onc(TG.76); KrasLSL G12D/+ SB11/+
[10] [10] [24]
Medulloblastoma Math1-SB11; T2/Onc(TG.68); Ptch1+/ Math1-SB11; T2/Onc(TG.68); Trp53Mut
[28] [28]
Nestin-cre; Rosa26LSL Nestin-cre; Rosa26LSL
[27] [27]
Glioblastoma multiforme SB11/+
; T2/Onc2(TG.6113); Trp53R172H/+ ; T2/Onc3(TG.12740); Trp53R172H/+
SB11/+
Malignant peripheral nerve sheath tumor Cnp-cre; Rosa26LSL Cnp-cre; Rosa26LSL Cnp-cre; Rosa26LSL
SB11/+
; T2/Onc(TG.68); Trp53R270H/+ ; T2/Onc(TG.68); Cnp-EGFR SB11/+ ; T2/Onc(TG.68); Cnp-EGFR; Trp53R270H/+ SB11/+
[11] [11] [11]
SB concatamer allele mapping information: T2/Onc(TG.76) on Mmu1; T2/Onc(TG.68) on Mmu15; T2/Onc2(TG.6113) on Mmu1; T2/Onc2(TG.6070) on Mmu4; T2/Onc2(TG.12740) on Mmu9; T2/Onc2(TG.12775) on Mmu12; HSB5, hyperactive SB variant; () cohorts both with or without Trp53 mutation.
hemizygous null mutation (Ptch+/ ) and one sensitized by Trp53 loss (Trp53mut), and characterized the relationship between the transposon events in primary tumors and metastases. Perhaps surprisingly, fewer than 10% of primary CCGs overlapped with metastasis CCGs on a population level for either cohort. Amplification of specific transposon insertion sites in both metastases and related primary tumors by PCR indicated that SB insertion events might have arisen in a rare subclone in the primary tumor or were de novo events in the metastases. Equally plausible is the possibility that limitations in tumor sampling and depth of sequencing failed to capture all transposon events in the primary tumors. Comparisons between human medulloblastomas and matched metastases using promoter CpG methylation, copy number alterations and single nucleotide variants revealed variability in the relatedness of metastases to the Current Opinion in Genetics & Development 2014, 24:16–22
primary tumors and to each other. Systematic sequencing of primary tumor regions from SB tumors will be required in order to capture the intra-tumor heterogeneity of transposon insertions throughout the tumors, accompanied by increasing sequencing depth to achieve robust representation of insertional events. Multiple metastases must also be sequenced to saturation in order to capture the inter-tumor heterogeneity within a single mouse and across a population of animals with metastatic burden. This information will provide insight into metastatic potential and perhaps elucidate genes required for metastasis expansion after seeding.
Validation of candidate cancer genes Demonstrating biological relevance of identified CCGs, particularly with respect to human cancers, is an integral part of cancer gene discovery in both mouse models and www.sciencedirect.com
Sleeping Beauty mouse models for cancer gene discovery Mann et al. 19
Table 2 Validation studies of candidate cancer genes identified by Sleeping Beauty mutagenesis screens Candidate cancer gene(s)
Tumor
Summary
Bnip2, Esp8, Ncoa5, Tcf12
CRC
Four new positive regulators (punitive oncogenes) of Wnt signaling in colorectal cancer
Bcl11b, Btbd3, Crkl, Csnk2a1, Mbnl2, Nedd4, Numb, Onecut2, Rbm9, Rcor1, Rlbp1, Rrbp1, Sema4b, Tcf7l2, Ywhae, Zfpm1 Ctnnd1, Gnaq
CRC
16 new negative regulators (punitive tumor suppressors) of Wnt signaling in colorectal cancer
PDAC
2 new tumor suppressors in pancreatic cancer
Acvr2a, Aff4, Ap1g1, Map2k4, Meis2, Mkln1, Thsd7a
PDAC
7 new cancer genes in pancreatic cancer
Usp9x
PDAC
1 new tumor suppressor in pancreatic cancer
Dtnb, Ncoa2, Zfx
HCC
3 new tumor suppressors in liver cancer
Rtl1
HCC
1 new oncogene in liver cancer
Zmiz1
KA
1 new oncogene in skin cancer
Ccrk, Eras, Lhx1
MB
3 new metastasis-inducing oncogenes in Shh-driven malignant medulloblastoma
Foxr2
MPNST
1 new oncogene in malignant peripheral nerve sheath cancer
Validation platform(s) and Summary
Study
Knock-down of candidate CISs with shRNAs in SW480 cancer cell line using LEF/TCF-bla Wnt reporter system Knock-down of candidate CISs with shRNAs in SW480 cancer cell line using LEF/TCF-bla Wnt reporter system
[7]
Absent CTNND1 and reduced GNAQ protein levels correlated with poor survival outcomes in advance human PDAC patients sample tissue array Damaging mutations in ACVR2A, AFF4, AP1G1, MAP2K4, MEIS2, MKLN1, and THSD7A discovered by deep resequencing of human PDAC genomes Low expression of USP9X correlated with poor survival after surgery; Usp9x-deficiency enhances transformation of KrasG12D-sensitized pancreatic ductal cells into mPanIN shRNA knock-down of candidate CISs in Myc-immortalized hepatoblasts from Trp53-null mice promoted tumor formation in nude mice; Ncoa2-deficient mice increase liver tumor multiplicity and maximal diameter Hydrodynamic gene delivery of Rtl1 overexpression constructs into the livers of adult mice drives HCC Transgenic overexpression of Ntuncated Zmiz1 in skin produced invasive keratoacanthoma Overexpression of candidate CISs with RCAS retroviral vectors in Shhexpressing, Nestin-positive in mouse cerebellar nerual progenitor cells and RCAS/tv-a system promoted leptomeningeal dissemination FOXR2 overexpression in immortalized human Schwann cells permitted xenograft tumor growth; shRNA knock-down in STS26T human MPNST cell line prevented xenograft tumor formation
[10]
[7]
[10]
[24]
[22]
[23]
[19]
[29]
[11]
CRC, colorectal cancer; MB, medulloblastoma; MPNST, malignant peripheral nerve sheath tumor; PDAC, pancreatic ductal adenocarcinoma; HCC, hepatocellular carcinoma; KA, keratoacanthoma; RCAS, Replication-Competent ASLV long terminal repeat (LTR) with a Splice acceptor; mPanIN, mouse pancreatic intraepithelial neoplasia.
human patients. A major finding from the SB PDAC screens was the large number of statistically defined CCGs with no known mutations in human pancreatic cancer. Most of these genes are predicted tumor suppressors based on the transposon insertion orientation. PerezMancera et al. [24] demonstrated that in the absence of mutation, low expression of USP9X, the human ortholog www.sciencedirect.com
of the top SB candidate cancer gene Usp9x, in pancreatic cancer patients correlated with poor survival after surgery in one cohort and inversely correlated with widespread metastases in a second independent cohort. Mann et al. [10] performed targeted deep sequencing to confirm damaging mutations in seven new CCGs (see Table 2). They also used expression data from human PDAC Current Opinion in Genetics & Development 2014, 24:16–22
20 Cancer genomics
patients to identify orthologs of SB candidate cancer genes that significantly associated with patient survival, only two of which had identified mutations in human pancreatic cancer. CTNND1 and GNAQ protein levels were decreased or absent in advanced adenocarcinoma by immunohistochemical analysis of a human PDAC tissue microarray and were associated with decreased patient survival in these patients. Mumert and colleagues provided convincing evidence for a role of SB metastasis-specific genes in medulloblastoma tumor progression in a follow-up manuscript where they overexpressed three predicted metastasis-specific oncogenes, Ccrk, Eras, and Lhx1 using retroviruses in the mouse cerebellum [29]. In combination with Shh activation, these genes promoted leptomeningeal dissemination. These examples highlight a few of the current approaches to characterize roles for CCGs; others are listed in Table 2. Functional studies, particularly to understand how the plethora of predicted tumor suppressor genes function to drive cancer, will undoubtedly involve both in vitro and in vivo strategies using mouse models.
Integration of candidate cancer genes across SB models and beyond From the SB screens that have been published thus far, it is clear that the major ‘drivers,’ statistically defined to be the top-ranked CCGs, are unique to individual tumor types. Among top-ranked CCGs from each study surveyed here (Table 1), Usp9x appears to be uniquely and specifically associated with PDAC [10,24], Apc with CRC [5,7], Nf1 with nervous system tumors [11,27], Zmiz1 with KA/SCC [4,19], and Rtl3 (within the Rian locus) with HCC [4,23,30]. Recurrently identifying CCGs in independent SB screens for a particular tumor type lends increased confidence for their role in driving tumorigenesis, especially when different statistical algorithms are employed. Often, SB infrequently mutates top drivers from one cancer in other tumor types, where they are not likely to significantly contribute to tumorigenesis on their own. This raises the possibility that these so-called ‘private’ CCGs, and the biological pathways and processes in which they participate, may hold clues to identify important therapeutic intervention points. It is notable that some CCGs (including Crebbp, Dmd, Jmy, Myst2 and Ppp6r3) are recurrently identified in SB tumor screens. These so-called ‘public’ CCGs, found across various SB cancer models at appreciable frequencies, might represent new cancer genes that have a general role in promoting cancer initiation and/or progression. Another possibility is that these loci represent SB hotspots and are commonly found because SB insertions occur at those sites more often than predicted by chance. However, the latter seems unlikely since March Current Opinion in Genetics & Development 2014, 24:16–22
et al. showed that unselected SB insertion sites from premalignant cells of the intestinal epithelium do not identify many CISs [7]. The rapid success of adapting Sleeping Beauty mutagenesis to cancer gene discovery since its reawakening in 1997 [31] has sparked intense interest into identifying additional insertional mutagenesis platforms. The three other transposons able to mobilize in mouse cells are piggyBac (PB), Tol2 and Minos (see Copeland and Jenkins, 2010 for review). Recently, PB has emerged as a potent alternative mutagen for cancer gene discovery in the mouse [32,33]. PB transposase activity is more efficient than SB, and excision of the transposon from genomic DNA leaves no major mutagenic footprint in contrast to SB, which leaves behind the 5 bp tag TACTG. One useful attribute of PB is its ability to carry up to 9.1 kb of cloned DNA sequence between the inverted repeats of the transposon (SB is constrained at 2.1 kb of sequence for optimal transposition efficiency). The larger cargo capacity of PB allows flexibility to add reporters or other DNA elements of interest [33]. Importantly, PB has significantly fewer local hopping events than SB, allowing for greater genomic mutational coverage with a single transposon donor. However, PB prefers the sequence TTAA to insert in the mouse genome, which is 11-fold less frequent in the mouse genome than the TA dinucleotide used by SB. Therefore, the number of possible mutational events driven by PB is less than SB. Nevertheless, PB insertional mutagenesis is a valuable complement to SB insertional mutagenesis. As more SB and PB screens are published from new tumor types, we will be able to differentiate CCGs that function broadly as cancer susceptibility loci from CCGs that have a profound role in a particular tumor type.
Concluding remarks SB mutagenesis has proven to be a more powerful system for cancer gene discovery than anyone could have predicted. SB models of cancer recapitulate the anatomical and histological features of the human cancers they model, including metastases in SB models of pancreatic cancer and medulloblastoma. Sophisticated statistical pipelines have been developed by multiple groups to annotate and prioritize the plethora of data gleaned from mapping transposon insertions in SB tumors [7,8,34,35]. Most importantly, comparisons of SB candidate cancer genes to sequencing data generated by The Cancer Gene Atlas (TCGA) and International Cancer Gene Consortium (ICGC), two publically funded efforts to characterize human cancer genomes, has revealed striking overlap of mutated genes and perturbed signaling pathways between human cancers and their SB-driven mouse models. New associations of genes and signaling pathways with particular cancers have been simultaneously discovered in both human cancer and SB models, and many genes www.sciencedirect.com
Sleeping Beauty mouse models for cancer gene discovery Mann et al. 21
identified by SB are not mutated in human cancers but do exhibit expression changes with prognostic implications. SB mutagenesis has the potential to provide great insight into clonal evolution of primary tumors and their metastases, potentially distinguishing gatekeeper genes, present in both primary and metastatic lesions, from metastasis-promoting genes. Identifying both classes of genes are of particular interest for broadening our knowledge of targeted therapy design. This is an exciting time in the SB mutagenesis field, where the potential to find new cancer genes is being realized and the data from the mouse models is influencing the validation priorities for human cancer genes.
Acknowledgements We apologize to those research groups whose work was not discussed due to space limitations. NGC and NAJ are Cancer Prevention Research Institute of Texas (CPRIT) Scholars in Cancer Research.
References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as: of special interest of outstanding interest 1.
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22 Cancer genomics
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24. Perez-Mancera PA, Rust AG, van der Weyden L, Kristiansen G, Li A, Sarver AL, Silverstein KA, Grutzmann R, Aust D, Rummele P et al.: The deubiquitinase USP9X suppresses pancreatic ductal adenocarcinoma. Nature 2012, 486:266-270. This letter was among the first reports of metastatic disease driven by SB mutagenesis and validated Usp9x as a new tumor suppressor gene in PDAC.
31. Ivics Z, Hackett PB, Plasterk RH, Izsvak Z: Molecular reconstruction of Sleeping Beauty, a Tc1-like transposon from fish, and its transposition in human cells. Cell 1997, 91:501-510.
25. Biankin AV, Waddell N, Kassahn KS, Gingras MC, Muthuswamy LB, Johns AL, Miller DK, Wilson PJ, Patch AM, Wu J et al.: Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes. Nature 2012, 491:399-405.
32. Landrette SF, Cornett JC, Ni TK, Bosenberg MW: Xu T: piggyBac transposon somatic mutagenesis with an activated reporter and tracker (PB-SMART) for genetic screens in mice. PLoS One 2011, 6:e26650.
26. Jones S, Zhang X, Parsons DW, Lin JC, Leary RJ, Angenendt P, Mankoo P, Carter H, Kamiyama H, Jimeno A et al.: Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science 2008, 321:1801-1806.
33. Rad R, Rad L, Wang W, Cadinanos J, Vassiliou G, Rice S, Campos LS, Yusa K, Banerjee R, Li MA et al.: PiggyBac transposon mutagenesis: a tool for cancer gene discovery in mice. Science 2010, 330:1104-1107.
27. Koso H, Takeda H, Yew CC, Ward JM, Nariai N, Ueno K, Nagasaki M, Watanabe S, Rust AG, Adams DJ et al.: Transposon mutagenesis identifies genes that transform neural stem cells into glioma-initiating cells. Proc Natl Acad Sci U S A 2012, 109:E2998-E3007.
34. de Ridder J, Uren A, Kool J, Reinders M, Wessels L: Detecting statistically significant common insertion sites in retroviral insertional mutagenesis screens. PLoS Comput Biol 2006, 2:e166.
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Current Opinion in Genetics & Development 2014, 24:16–22
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ScienceDirect Sleeping Beauty mutagenesis: exploiting forward genetic screens for cancer gene discovery Michael B Mann, Nancy A Jenkins, Neal G Copeland and Karen M Mann Sleeping Beauty (SB) is a powerful insertional mutagen used in somatic forward genetic screens to identify novel candidate cancer genes. In the past two years, SB has become widely adopted to model human pancreatic, hepatocellular, colorectal and neurological cancers to identify loci that participate in tumor initiation, progression and metastasis. Oncogenomic approaches have directly linked hundreds of genes identified by SB with human cancers, many with prognostic implications. These SB candidate cancer genes are aiding to prioritize punitive human cancer genes for follow-up studies and as possible biomarkers or therapeutic targets. This review highlights recent advances in SB cancer gene discovery, approaches to validate candidate cancer genes, and efforts to integrate SB data across all tumor types to prioritize drug development and tumor specificity. Addresses Cancer Research Program, Houston Methodist Research Institute, 6670 Bertner Avenue, Houston, TX 77030, United States Corresponding author: Mann, Karen M ([email protected])
Current Opinion in Genetics & Development 2014, 24:16–22 This review comes from a themed issue on Cancer genomics Edited by David J Adams and Ultan McDermott For a complete overview see the Issue and the Editorial Available online 20th December 2013 0959-437X/$ – see front matter, # 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.gde.2013.11.004
Sleeping Beauty models of cancer Insertional mutagenesis for gene discovery
SB is so efficient at identifying cancer genes because it contains elements that can drive expression of in-frame genomic sequences (oncogenes) or disrupt gene expression (tumor suppressor genes), depending on the selected transposon insertion orientation and location (see [3] for review). Transgenic transposon donor strains contain concatamers of transposons, ranging from 30 to 300 copies, integrated in a single donor site in the mouse genome. The numbers of transposon copies, as well as the internal promoter elements, appear to influence tumor type formation with constitutive whole-body transposition [1,2,4]. An inducible allele of the transposase targeted to the dispensable Rosa26 locus allowed for control of SB activation in space and time [4,5], and transposase alleles have been engineered with varying activity in the mouse genome (see [6] for review). Each transposon is flanked by inverted repeats that are recognized by the transposase, which then induces doublestrand breaks, liberating the transposon from the donor site in the mouse genome. The transposon can then reintegrate into another location in the genome or it may be lost (see [3] for review). The process of integration-excision-reintegration of transposons throughout the mouse genome is continuous. Transposon insertions are ‘fixed’ when they offer a selective advantage to a cell. The accumulation of fixed transposon insertions in sub-populations of cells leads to the formation of cancer. Statistical pipelines define candidate cancer genes
Introduction Sleeping Beauty (SB) was first reported in 2005 as a DNA transposon-based somatic insertional mutagenesis system capable of inducing hematopoietic and solid tumors in the mouse [1,2]. Subsequently, forward genetic screens in both hematopoietic and solid tumor models using SB have identified hundreds of candidate cancer genes. SB mutagenesis proved to be advantageous over classic retroviral mutagenesis approaches due to its short-acting effects on targeted genes and its ability to mutate all cells of the body. Recent reports demonstrate the ability of SB to drive metastatic disease, which is particularly challenging to study in human cancer patients. This review highlights recent advances in SB cancer gene discovery published in the last two years, ongoing efforts to validate candidate cancer genes, and possibilities for integration of SB data across all tumor types to prioritize drug development and tumor specificity. Current Opinion in Genetics & Development 2014, 24:16–22
As SB data sets grew in size and complexity due to advances in next generation sequencing (NGS) platforms over the past few years, so too have the methods used to define candidate cancer genes from SB mutagenized cancer genomes. The field has moved away from simply listing genes that contain two or more SB insertions within the vicinity of a gene. Now, several new statistical frameworks define candidate cancer genes. The availability of these tools standardizes identification of loci in tumor genomes enriched for transposon insertions, termed common insertion sites (CISs). The most frequently used methods to identify CISs are Monte Carlo (MC) simulation [5], Gaussian Kernel Convolutions (GKC) [7], gene-centric CIS (gCIS) analysis [8] and Poisson Regression Insertion Modeling (PRIM) [9] algorithms (see [6] for review). Two recent studies integrated the various analysis platforms for comprehensive candidate cancer gene detection [10,11]. Future efforts www.sciencedirect.com
Sleeping Beauty mouse models for cancer gene discovery Mann et al. 17
will focus on building databases of existing data sets to permit meta-analysis studies that may identify still more candidate cancer genes.
SB identifies candidate cancer genes relevant to human cancers Hematopoietic and skin cancers
Hematopoietic malignancies and squamous cell carcinoma (SCC) were the first reported cancers induced by constitutive SB mutagenesis [1,2,4]. Recent studies have used SB to identify mutations that cooperate with mouse orthologs of commonly mutated loci in human hematopoietic malignancies, including Trp53 [12], Cadm1 [13] and Runx2 [14] in B-cell lymphoma, Jak2 in myeloproliferative disease [15] and NFKB in chronic lymphoblastic leukemia [16]. Berquam-Vrieze and colleagues demonstrated that cell-of-origin and timing of mutagenesis initiation influences transposon selection and disease outcome [17]. Quintana et al. identified loci that participate in multiple non-melanoma skin cancers, including basal-cell carcinoma (BCC), keratoacanthoma and SCC, and confirmed that Notch downregulation is a feature in both SB-driven and human non-melanoma skin tumors [18]. Roger et al. [19] validated the ability of N-terminal truncated Zmiz1 to induce invasive keratoacanthoma and SCC in skin when overexpressed, an observation first made by Dupuy and colleagues in an SB screen [4]. Digestive cancers
Hepatocellular carcinoma (HCC) is the most frequently modeled solid tumor using SB [4,20–22]. O’Donnell et al. [22] recently identified and validated three tumor suppressors that cooperate with Myc to induce HCC. Riordan et al. [23] confirmed that Rtl1, which maps to the Rian locus, is a causative oncogene in HCC. Keng and colleagues identified Egfr in an HCC SB model and showed an association between polysomy of the EGFR locus in human males and HCC [20], potentially providing a link to the male sex bias in HCC. March and colleagues reported a large-scale SB screen for intestinal tumors using two different alleles of Apc as sensitizing mutations [7]. This SB screen was the largest reported, with nearly 450 tumors sequenced, and was the first to take an oncogenomics approach to integrate SB candidate cancer genes (CCGs) with human mutation data from colorectal cancer (CRC). Mann et al. and Perez-Mancera and colleagues used SB to identify cooperating mutations in an oncogenic Kras model of pancreatic cancer [10,24]. SB-induced a highly invasive pancreatic cancer with metastases to liver, lung, peritoneum and surrounding lymph nodes [10,24], and SB-driven adenocarcinomas exhibited many hallmarks of human pancreatic adenocarcinoma (PDAC), including a major stromal component. Importantly, both studies identified known cancer genes previously implicated in pancreatic cancer, including www.sciencedirect.com
Cdkn2a, Smad4 and Acvr1b, as well as a number of genes included on the Cancer Gene Census (n = 84, P < 0.001). Three hundred and thirty-four CCGs from the two studies overlapped, and these represented the genes most statistically enriched for transposon inserts, including Usp9x, Pten, Ctnna1 and Setd5, many of which have not been previously implicated in pancreatic cancer. One of the major findings from the colorectal and pancreatic SB screens is the significant concordance between the CCGs and the human orthologs with non-synonymous mutations or copy number variation in human colorectal and pancreatic cancers. Many of the mutations identified in human PDAC exomes occur at low frequency [25,26]; however, enrichment of SB insertions in orthologous mouse genes suggests that these mutations are not passenger events in pancreatic cancer. March and colleagues implicated one-third of their CCGs in human CRC. An extension of mutational overlap is the concordance of signaling pathways and processes perturbed in both human and SB-driven colorectal and pancreatic cancers. TGF-Beta, MAP kinase, PI3K/AKT and Wnt signaling pathways were significantly enriched for SB CCGs in both CRC and PDAC models [7,10]. March and colleagues functionally validated several new modulators of Wnt signaling. The SB PDAC screens also showed enrichment for CCGs in genes implicated in axon guidance and chromatin remodeling, two processes highlighted by Biankin et al. for enrichment of exomic mutations in human PDAC [25]. These findings reaffirm that SB cancer screens complement ongoing human cancer sequencing efforts and may serve to help prioritize human mutation data for further validation.
Nervous system cancers Recently, SB has been successfully used to model tumors of the nervous system. Koso et al. reported an in vitro mutagenesis strategy for identifying candidate cancer genes that contribute to glioma-initiating cells. Mobilization of SB transposons in neural stem cells in culture permitted immortalization of astroglial-like cells, which were able to generate glioblastoma multiforme (GBM) mesenchymal tumors upon transplantation [27]. Several well-known GBM driver mutations were identified and appear to cooperate with several receptor tyrosine kinase genes. Rahrmann et al. modeled malignant peripheral nerve sheath tumors (MPNST) and neurofibromas using SB with four different sensitizing alleles (Table 1), including EGFR overexpression alone or in combination with mutant Trp53 [11]. CIS analysis of the SB-induced tumors confirmed known orthologous drivers of human MPNST (Nf1 and Pten) and new candidate cancer genes, including Foxr2. Wu et al. [28] modeled medulloblastoma using two different SB models, one sensitized with a Ptch Current Opinion in Genetics & Development 2014, 24:16–22
18 Cancer genomics
Table 1 Tumor types modeled by Sleeping Beauty mutagenesis SB model system cohort and allele summary
Tumor
Study
Hematopoietic cancers Rosa26SBase/+; T2/Onc2(TG.6070 or TG.6113) Vav1-cre; Rosa26SBase/+; T2/Onc2(TG.6070 or TG.6113) Lck-cre; Rosa26SBase/+; T2/Onc2(TG.6070 or TG.6113) CD4-cre; Rosa26SBase/+; T2/Onc2(TG.6070 or TG.6113) Etv6+/RUNX1::HSB5; Rosa26SB11/+; T2/Onc(TG.76); Rosa26SB11/+; T2/Onc(TG.76); Rassf1a / Rosa26SB11/+; T2/Onc(TG.76); Cadm1 / Rosa26SB11/+; T2/Onc(TG.76); Trp53 / Vav1-cre; Rosa26LSL SB11/+; T2/Onc2(TG.6113); Jak2V617F Em-TCL1; CD19-cre; Rosa26LSL SB11/+; T2/Onc2(TG.6070 or TG.6113)
[1] [17] [17] [17] [33] [14] [13] [12] [15] [16]
Rosa26SBase/+; T2/Onc3(TG.12740 or TG.12775) Rosa26SBase/+; T2/Onc2(TG.12740 or TG.12775); Rag2 K5-SB11; T2/Onc2(TG.6070); Tg.AC
[4] [36] [18]
Squamous cell carcinoma/keratoacanthoma /
Hepatocellular adenoma/hepatocellular carcinoma Alb-cre; Rosa26LSL SB11/+; T2/Onc(TG.68); Trp53R270H/+ Rosa26SB11/+; T2/Onc3(TG.12740 or TG.12775) Rosa26SBase/+; T2/Onc2(TG.12740 or TG.12775); Rag2 / Rosa26SB11/+; T2/Onc(TG.68); tet-O-MYC; LAPtTA
[21] [4] [36] [22]
Villin-cre; Rosa26LSL Ah-cre; Rosa26Lox66 Ah-cre; Rosa26Lox66
[5] [7] [7]
Colorectal cancer SB11/+
; T2/Onc(TG.68) ; T2/Onc(TG.76); ApcFloxed/+ Lox71/+ ; T2/Onc(TG.76); ApcMin/+
SB11 Lox71/+ SB11
Pancreatic ductal adenocarcinoma Pdx1-cre; Rosa26LSL Pdx1-cre; Rosa26LSL Pdx1-cre; Rosa26LSL
SB11/+
; T2/Onc2(TG.6113); KrasLSL G12D/+ ; T2/Onc3(TG.12740); KrasLSL G12D/+ SB13/+ ; T2/Onc(TG.76); KrasLSL G12D/+ SB11/+
[10] [10] [24]
Medulloblastoma Math1-SB11; T2/Onc(TG.68); Ptch1+/ Math1-SB11; T2/Onc(TG.68); Trp53Mut
[28] [28]
Nestin-cre; Rosa26LSL Nestin-cre; Rosa26LSL
[27] [27]
Glioblastoma multiforme SB11/+
; T2/Onc2(TG.6113); Trp53R172H/+ ; T2/Onc3(TG.12740); Trp53R172H/+
SB11/+
Malignant peripheral nerve sheath tumor Cnp-cre; Rosa26LSL Cnp-cre; Rosa26LSL Cnp-cre; Rosa26LSL
SB11/+
; T2/Onc(TG.68); Trp53R270H/+ ; T2/Onc(TG.68); Cnp-EGFR SB11/+ ; T2/Onc(TG.68); Cnp-EGFR; Trp53R270H/+ SB11/+
[11] [11] [11]
SB concatamer allele mapping information: T2/Onc(TG.76) on Mmu1; T2/Onc(TG.68) on Mmu15; T2/Onc2(TG.6113) on Mmu1; T2/Onc2(TG.6070) on Mmu4; T2/Onc2(TG.12740) on Mmu9; T2/Onc2(TG.12775) on Mmu12; HSB5, hyperactive SB variant; () cohorts both with or without Trp53 mutation.
hemizygous null mutation (Ptch+/ ) and one sensitized by Trp53 loss (Trp53mut), and characterized the relationship between the transposon events in primary tumors and metastases. Perhaps surprisingly, fewer than 10% of primary CCGs overlapped with metastasis CCGs on a population level for either cohort. Amplification of specific transposon insertion sites in both metastases and related primary tumors by PCR indicated that SB insertion events might have arisen in a rare subclone in the primary tumor or were de novo events in the metastases. Equally plausible is the possibility that limitations in tumor sampling and depth of sequencing failed to capture all transposon events in the primary tumors. Comparisons between human medulloblastomas and matched metastases using promoter CpG methylation, copy number alterations and single nucleotide variants revealed variability in the relatedness of metastases to the Current Opinion in Genetics & Development 2014, 24:16–22
primary tumors and to each other. Systematic sequencing of primary tumor regions from SB tumors will be required in order to capture the intra-tumor heterogeneity of transposon insertions throughout the tumors, accompanied by increasing sequencing depth to achieve robust representation of insertional events. Multiple metastases must also be sequenced to saturation in order to capture the inter-tumor heterogeneity within a single mouse and across a population of animals with metastatic burden. This information will provide insight into metastatic potential and perhaps elucidate genes required for metastasis expansion after seeding.
Validation of candidate cancer genes Demonstrating biological relevance of identified CCGs, particularly with respect to human cancers, is an integral part of cancer gene discovery in both mouse models and www.sciencedirect.com
Sleeping Beauty mouse models for cancer gene discovery Mann et al. 19
Table 2 Validation studies of candidate cancer genes identified by Sleeping Beauty mutagenesis screens Candidate cancer gene(s)
Tumor
Summary
Bnip2, Esp8, Ncoa5, Tcf12
CRC
Four new positive regulators (punitive oncogenes) of Wnt signaling in colorectal cancer
Bcl11b, Btbd3, Crkl, Csnk2a1, Mbnl2, Nedd4, Numb, Onecut2, Rbm9, Rcor1, Rlbp1, Rrbp1, Sema4b, Tcf7l2, Ywhae, Zfpm1 Ctnnd1, Gnaq
CRC
16 new negative regulators (punitive tumor suppressors) of Wnt signaling in colorectal cancer
PDAC
2 new tumor suppressors in pancreatic cancer
Acvr2a, Aff4, Ap1g1, Map2k4, Meis2, Mkln1, Thsd7a
PDAC
7 new cancer genes in pancreatic cancer
Usp9x
PDAC
1 new tumor suppressor in pancreatic cancer
Dtnb, Ncoa2, Zfx
HCC
3 new tumor suppressors in liver cancer
Rtl1
HCC
1 new oncogene in liver cancer
Zmiz1
KA
1 new oncogene in skin cancer
Ccrk, Eras, Lhx1
MB
3 new metastasis-inducing oncogenes in Shh-driven malignant medulloblastoma
Foxr2
MPNST
1 new oncogene in malignant peripheral nerve sheath cancer
Validation platform(s) and Summary
Study
Knock-down of candidate CISs with shRNAs in SW480 cancer cell line using LEF/TCF-bla Wnt reporter system Knock-down of candidate CISs with shRNAs in SW480 cancer cell line using LEF/TCF-bla Wnt reporter system
[7]
Absent CTNND1 and reduced GNAQ protein levels correlated with poor survival outcomes in advance human PDAC patients sample tissue array Damaging mutations in ACVR2A, AFF4, AP1G1, MAP2K4, MEIS2, MKLN1, and THSD7A discovered by deep resequencing of human PDAC genomes Low expression of USP9X correlated with poor survival after surgery; Usp9x-deficiency enhances transformation of KrasG12D-sensitized pancreatic ductal cells into mPanIN shRNA knock-down of candidate CISs in Myc-immortalized hepatoblasts from Trp53-null mice promoted tumor formation in nude mice; Ncoa2-deficient mice increase liver tumor multiplicity and maximal diameter Hydrodynamic gene delivery of Rtl1 overexpression constructs into the livers of adult mice drives HCC Transgenic overexpression of Ntuncated Zmiz1 in skin produced invasive keratoacanthoma Overexpression of candidate CISs with RCAS retroviral vectors in Shhexpressing, Nestin-positive in mouse cerebellar nerual progenitor cells and RCAS/tv-a system promoted leptomeningeal dissemination FOXR2 overexpression in immortalized human Schwann cells permitted xenograft tumor growth; shRNA knock-down in STS26T human MPNST cell line prevented xenograft tumor formation
[10]
[7]
[10]
[24]
[22]
[23]
[19]
[29]
[11]
CRC, colorectal cancer; MB, medulloblastoma; MPNST, malignant peripheral nerve sheath tumor; PDAC, pancreatic ductal adenocarcinoma; HCC, hepatocellular carcinoma; KA, keratoacanthoma; RCAS, Replication-Competent ASLV long terminal repeat (LTR) with a Splice acceptor; mPanIN, mouse pancreatic intraepithelial neoplasia.
human patients. A major finding from the SB PDAC screens was the large number of statistically defined CCGs with no known mutations in human pancreatic cancer. Most of these genes are predicted tumor suppressors based on the transposon insertion orientation. PerezMancera et al. [24] demonstrated that in the absence of mutation, low expression of USP9X, the human ortholog www.sciencedirect.com
of the top SB candidate cancer gene Usp9x, in pancreatic cancer patients correlated with poor survival after surgery in one cohort and inversely correlated with widespread metastases in a second independent cohort. Mann et al. [10] performed targeted deep sequencing to confirm damaging mutations in seven new CCGs (see Table 2). They also used expression data from human PDAC Current Opinion in Genetics & Development 2014, 24:16–22
20 Cancer genomics
patients to identify orthologs of SB candidate cancer genes that significantly associated with patient survival, only two of which had identified mutations in human pancreatic cancer. CTNND1 and GNAQ protein levels were decreased or absent in advanced adenocarcinoma by immunohistochemical analysis of a human PDAC tissue microarray and were associated with decreased patient survival in these patients. Mumert and colleagues provided convincing evidence for a role of SB metastasis-specific genes in medulloblastoma tumor progression in a follow-up manuscript where they overexpressed three predicted metastasis-specific oncogenes, Ccrk, Eras, and Lhx1 using retroviruses in the mouse cerebellum [29]. In combination with Shh activation, these genes promoted leptomeningeal dissemination. These examples highlight a few of the current approaches to characterize roles for CCGs; others are listed in Table 2. Functional studies, particularly to understand how the plethora of predicted tumor suppressor genes function to drive cancer, will undoubtedly involve both in vitro and in vivo strategies using mouse models.
Integration of candidate cancer genes across SB models and beyond From the SB screens that have been published thus far, it is clear that the major ‘drivers,’ statistically defined to be the top-ranked CCGs, are unique to individual tumor types. Among top-ranked CCGs from each study surveyed here (Table 1), Usp9x appears to be uniquely and specifically associated with PDAC [10,24], Apc with CRC [5,7], Nf1 with nervous system tumors [11,27], Zmiz1 with KA/SCC [4,19], and Rtl3 (within the Rian locus) with HCC [4,23,30]. Recurrently identifying CCGs in independent SB screens for a particular tumor type lends increased confidence for their role in driving tumorigenesis, especially when different statistical algorithms are employed. Often, SB infrequently mutates top drivers from one cancer in other tumor types, where they are not likely to significantly contribute to tumorigenesis on their own. This raises the possibility that these so-called ‘private’ CCGs, and the biological pathways and processes in which they participate, may hold clues to identify important therapeutic intervention points. It is notable that some CCGs (including Crebbp, Dmd, Jmy, Myst2 and Ppp6r3) are recurrently identified in SB tumor screens. These so-called ‘public’ CCGs, found across various SB cancer models at appreciable frequencies, might represent new cancer genes that have a general role in promoting cancer initiation and/or progression. Another possibility is that these loci represent SB hotspots and are commonly found because SB insertions occur at those sites more often than predicted by chance. However, the latter seems unlikely since March Current Opinion in Genetics & Development 2014, 24:16–22
et al. showed that unselected SB insertion sites from premalignant cells of the intestinal epithelium do not identify many CISs [7]. The rapid success of adapting Sleeping Beauty mutagenesis to cancer gene discovery since its reawakening in 1997 [31] has sparked intense interest into identifying additional insertional mutagenesis platforms. The three other transposons able to mobilize in mouse cells are piggyBac (PB), Tol2 and Minos (see Copeland and Jenkins, 2010 for review). Recently, PB has emerged as a potent alternative mutagen for cancer gene discovery in the mouse [32,33]. PB transposase activity is more efficient than SB, and excision of the transposon from genomic DNA leaves no major mutagenic footprint in contrast to SB, which leaves behind the 5 bp tag TACTG. One useful attribute of PB is its ability to carry up to 9.1 kb of cloned DNA sequence between the inverted repeats of the transposon (SB is constrained at 2.1 kb of sequence for optimal transposition efficiency). The larger cargo capacity of PB allows flexibility to add reporters or other DNA elements of interest [33]. Importantly, PB has significantly fewer local hopping events than SB, allowing for greater genomic mutational coverage with a single transposon donor. However, PB prefers the sequence TTAA to insert in the mouse genome, which is 11-fold less frequent in the mouse genome than the TA dinucleotide used by SB. Therefore, the number of possible mutational events driven by PB is less than SB. Nevertheless, PB insertional mutagenesis is a valuable complement to SB insertional mutagenesis. As more SB and PB screens are published from new tumor types, we will be able to differentiate CCGs that function broadly as cancer susceptibility loci from CCGs that have a profound role in a particular tumor type.
Concluding remarks SB mutagenesis has proven to be a more powerful system for cancer gene discovery than anyone could have predicted. SB models of cancer recapitulate the anatomical and histological features of the human cancers they model, including metastases in SB models of pancreatic cancer and medulloblastoma. Sophisticated statistical pipelines have been developed by multiple groups to annotate and prioritize the plethora of data gleaned from mapping transposon insertions in SB tumors [7,8,34,35]. Most importantly, comparisons of SB candidate cancer genes to sequencing data generated by The Cancer Gene Atlas (TCGA) and International Cancer Gene Consortium (ICGC), two publically funded efforts to characterize human cancer genomes, has revealed striking overlap of mutated genes and perturbed signaling pathways between human cancers and their SB-driven mouse models. New associations of genes and signaling pathways with particular cancers have been simultaneously discovered in both human cancer and SB models, and many genes www.sciencedirect.com
Sleeping Beauty mouse models for cancer gene discovery Mann et al. 21
identified by SB are not mutated in human cancers but do exhibit expression changes with prognostic implications. SB mutagenesis has the potential to provide great insight into clonal evolution of primary tumors and their metastases, potentially distinguishing gatekeeper genes, present in both primary and metastatic lesions, from metastasis-promoting genes. Identifying both classes of genes are of particular interest for broadening our knowledge of targeted therapy design. This is an exciting time in the SB mutagenesis field, where the potential to find new cancer genes is being realized and the data from the mouse models is influencing the validation priorities for human cancer genes.
Acknowledgements We apologize to those research groups whose work was not discussed due to space limitations. NGC and NAJ are Cancer Prevention Research Institute of Texas (CPRIT) Scholars in Cancer Research.
References and recommended reading Papers of particular interest, published within the period of review, have been highlighted as: of special interest of outstanding interest 1.
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Dupuy AJ, Rogers LM, Kim J, Nannapaneni K, Starr TK, Liu P, Largaespada DA, Scheetz TE, Jenkins NA, Copeland NG: A modified Sleeping Beauty transposon system that can be used to model a wide variety of human cancers in mice. Cancer Res 2009, 69:8150-8156.
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