Sleeping Beauty mutagenesis: exploiting forward genetic screens for cancer gene discovery

Sleeping Beauty mutagenesis: exploiting forward genetic screensfor cancer gene discoveryMichael B Mann, Nancy A Jenkins, Neal G Copeland and Karen M Mann

Available online at www.sciencedirect.com

ScienceDirect

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

IntroductionSleeping 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 SBhave identified hundreds of candidate cancer genes. SB

mutagenesis proved to be advantageous over classic retro-

viral 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.


Sleeping Beauty models of cancerInsertional 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 expres-

sion (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 transposi-

tion [1,2,4]. An inducible allele of the transposase tar-

geted 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 double-

strand 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-reinte-

gration 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

As SB data sets grew in size and complexity due to

advances in next generation sequencing (NGS) plat-

forms over the past few years, so too have the methods

used to define candidate cancer genes from SB muta-

genized cancer genomes. The field has moved away from

simply listing genes that contain two or more SB inser-

tions 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 inser-

tions, termed common insertion sites (CISs). The most

frequently used methods to identify CISs are Monte

Carlo (MC) simulation [5], Gaussian Kernel Convolu-

tions (GKC) [7��], gene-centric CIS (gCIS) analysis [8�]and Poisson Regression Insertion Modeling (PRIM) [9]

algorithms (see [6] for review). Two recent studies inte-

grated the various analysis platforms for comprehensive

candidate cancer gene detection [10��,11]. Future efforts

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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 relevantto human cancersHematopoietic and skin cancers

Hematopoietic malignancies and squamous cell carci-

noma (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 hemato-

poietic malignancies, including Trp53 [12], Cadm1 [13]

and Runx2 [14] in B-cell lymphoma, Jak2 in myeloproli-

ferative disease [15] and NFKB in chronic lymphoblastic

leukemia [16]. Berquam-Vrieze and colleagues demon-

strated that cell-of-origin and timing of mutagenesis

initiation influences transposon selection and disease

outcome [17]. Quintana et al. identified loci that partici-

pate 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 sup-

pressors that cooperate with Myc to induce HCC. Riordan

et al. [23] confirmed that Rtl1, which maps to the Rianlocus, is a causative oncogene in HCC. Keng and col-

leagues 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 Krasmodel 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. Impor-

tantly, both studies identified known cancer genes

previously implicated in pancreatic cancer, including


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 pan-

creatic SB screens is the significant concordance between

the CCGs and the human orthologs with non-synon-

ymous mutations or copy number variation in human

colorectal and pancreatic cancers. Many of the mutations

identified in human PDAC exomes occur at low fre-

quency [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 concor-

dance 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 modu-

lators 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 can-

cer sequencing efforts and may serve to help prioritize

human mutation data for further validation.

Nervous system cancersRecently, SB has been successfully used to model tumors

of the nervous system. Koso et al. reported an in vitromutagenesis strategy for identifying candidate cancer

genes that contribute to glioma-initiating cells. Mobiliz-

ation 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), in-

cluding 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


18 Cancer genomics

Table 1

Tumor types modeled by Sleeping Beauty mutagenesis

Tumor SB model system cohort and allele summary Study

Hematopoietic cancers

Rosa26SBase/+; T2/Onc2(TG.6070 or TG.6113) [1]

Vav1-cre; Rosa26SBase/+; T2/Onc2(TG.6070 or TG.6113) [17]

Lck-cre; Rosa26SBase/+; T2/Onc2(TG.6070 or TG.6113) [17]

CD4-cre; Rosa26SBase/+; T2/Onc2(TG.6070 or TG.6113) [17]

Etv6+/RUNX1::HSB5; Rosa26SB11/+; T2/Onc(TG.76); [33]

Rosa26SB11/+; T2/Onc(TG.76); Rassf1a�/� [14]

Rosa26SB11/+; T2/Onc(TG.76); Cadm1�/� [13]

Rosa26SB11/+; T2/Onc(TG.76); Trp53�/� [12]

Vav1-cre; Rosa26LSL�SB11/+; T2/Onc2(TG.6113); Jak2V617F [15]

Em-TCL1; CD19-cre; Rosa26LSL�SB11/+; T2/Onc2(TG.6070 or TG.6113) [16]

Squamous cell carcinoma/keratoacanthoma

Rosa26SBase/+; T2/Onc3(TG.12740 or TG.12775) [4]

Rosa26SBase/+; T2/Onc2(TG.12740 or TG.12775); Rag2�/� [36]

K5-SB11; T2/Onc2(TG.6070); Tg.AC [18]

Hepatocellular adenoma/hepatocellular carcinoma

Alb-cre; Rosa26LSL�SB11/+; T2/Onc(TG.68); �Trp53R270H/+ [21]

Rosa26SB11/+; T2/Onc3(TG.12740 or TG.12775) [4]

Rosa26SBase/+; T2/Onc2(TG.12740 or TG.12775); Rag2�/� [36]

Rosa26SB11/+; T2/Onc(TG.68); tet-O-MYC; LAPtTA [22]

Colorectal cancer

Villin-cre; Rosa26LSL�SB11/+; T2/Onc(TG.68) [5]

Ah-cre; Rosa26Lox66�SB11�Lox71/+; T2/Onc(TG.76); ApcFloxed/+ [7��]

Ah-cre; Rosa26Lox66�SB11�Lox71/+; T2/Onc(TG.76); ApcMin/+ [7��]

Pancreatic ductal adenocarcinoma

Pdx1-cre; Rosa26LSL�SB11/+; T2/Onc2(TG.6113); KrasLSL�G12D/+ [10��]

Pdx1-cre; Rosa26LSL�SB11/+; T2/Onc3(TG.12740); KrasLSL�G12D/+ [10��]

Pdx1-cre; Rosa26LSL�SB13/+; T2/Onc(TG.76); KrasLSL�G12D/+ [24��]

Medulloblastoma

Math1-SB11; T2/Onc(TG.68); Ptch1+/� [28��]

Math1-SB11; T2/Onc(TG.68); Trp53Mut [28��]

Glioblastoma multiforme

Nestin-cre; Rosa26LSL�SB11/+; T2/Onc2(TG.6113); �Trp53R172H/+ [27]

Nestin-cre; Rosa26LSL�SB11/+; T2/Onc3(TG.12740); �Trp53R172H/+ [27]

Malignant peripheral nerve sheath tumor

Cnp-cre; Rosa26LSL�SB11/+; T2/Onc(TG.68); �Trp53R270H/+ [11]

Cnp-cre; Rosa26LSL�SB11/+; T2/Onc(TG.68); Cnp-EGFR [11]

Cnp-cre; Rosa26LSL�SB11/+; T2/Onc(TG.68); Cnp-EGFR; Trp53R270H/+ [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 SBinsertion events might have arisen in a rare subclone in

the primary tumor or were de novo events in the metas-

tases. 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


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, accom-

panied 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 metas-

tasis expansion after seeding.

Validation of candidate cancer genesDemonstrating biological relevance of identified CCGs,

particularly with respect to human cancers, is an integral

part of cancer gene discovery in both mouse models and



Table 2

Validation studies of candidate cancer genes identified by Sleeping Beauty mutagenesis screens

Candidate cancer gene(s) Tumor Summary Validation platform(s) and Summary Study

Bnip2, Esp8, Ncoa5, Tcf12 CRC Four new positive regulators

(punitive oncogenes) of Wnt

signaling in colorectal cancer

Knock-down of candidate CISs with

shRNAs in SW480 cancer cell line

using LEF/TCF-bla Wnt reporter

system

[7��]

Bcl11b, Btbd3, Crkl, Csnk2a1,

Mbnl2, Nedd4, Numb,

Onecut2, Rbm9, Rcor1,

Rlbp1, Rrbp1, Sema4b,

Tcf7l2, Ywhae, Zfpm1

CRC 16 new negative regulators (punitive

tumor suppressors) of Wnt signaling

in colorectal cancer

Knock-down of candidate CISs with

shRNAs in SW480 cancer cell line

using LEF/TCF-bla Wnt reporter

system

[7��]

Ctnnd1, Gnaq PDAC 2 new tumor suppressors in

pancreatic cancer

Absent CTNND1 and reduced

GNAQ protein levels correlated with

poor survival outcomes in advance

human PDAC patients sample tissue

array

[10��]

Acvr2a, Aff4, Ap1g1,

Map2k4, Meis2,

Mkln1, Thsd7a

PDAC 7 new cancer genes in pancreatic

cancer

Damaging mutations in ACVR2A,

AFF4, AP1G1, MAP2K4, MEIS2,

MKLN1, and THSD7A discovered by

deep resequencing of human PDAC

genomes

[10��]

Usp9x PDAC 1 new tumor suppressor in

pancreatic cancer

Low expression of USP9X

correlated with poor survival after

surgery; Usp9x-deficiency

enhances transformation of

KrasG12D-sensitized pancreatic

ductal cells into mPanIN

[24��]

Dtnb, Ncoa2, Zfx HCC 3 new tumor suppressors in liver

cancer

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

[22]

Rtl1 HCC 1 new oncogene in liver cancer Hydrodynamic gene delivery of Rtl1

overexpression constructs into the

livers of adult mice drives HCC

[23]

Zmiz1 KA 1 new oncogene in skin cancer Transgenic overexpression of N-

tuncated Zmiz1 in skin produced

invasive keratoacanthoma

[19�]

Ccrk, Eras, Lhx1 MB 3 new metastasis-inducing

oncogenes in Shh-driven malignant

medulloblastoma

Overexpression of candidate CISs

with RCAS retroviral vectors in Shh-

expressing, Nestin-positive in

mouse cerebellar nerual progenitor

cells and RCAS/tv-a system

promoted leptomeningeal

dissemination

[29]

Foxr2 MPNST 1 new oncogene in malignant

peripheral nerve sheath cancer

FOXR2 overexpression in

immortalized human Schwann cells

permitted xenograft tumor growth;

shRNA knock-down in STS26T

human MPNST cell line prevented

xenograft tumor formation

[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 suppres-

sors based on the transposon insertion orientation. Perez-

Mancera et al. [24��] demonstrated that in the absence of

mutation, low expression of USP9X, the human ortholog


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


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 onco-

genes, Ccrk, Eras, and Lhx1 using retroviruses in the

mouse cerebellum [29]. In combination with Shh acti-

vation, these genes promoted leptomeningeal dissemina-

tion. 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 suppres-

sor genes function to drive cancer, will undoubtedly

involve both in vitro and in vivo strategies using mouse

models.

Integration of candidate cancer genes acrossSB models and beyondFrom 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 sur-

veyed 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 Rianlocus) 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 contrib-

ute 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 thera-

peutic 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 frequen-

cies, might represent new cancer genes that have a

general role in promoting cancer initiation and/or pro-

gression. Another possibility is that these loci represent

SB hotspots and are commonly found because SB inser-

tions occur at those sites more often than predicted by

chance. However, the latter seems unlikely since March


et al. showed that unselected SB insertion sites from pre-

malignant cells of the intestinal epithelium do not

identify many CISs [7��].

The rapid success of adapting Sleeping Beauty mutagen-

esis 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 discov-

ery 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]. Impor-

tantly, PB has significantly fewer local hopping events

than SB, allowing for greater genomic mutational cover-

age 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 valu-

able 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 func-

tion broadly as cancer susceptibility loci from CCGs that

have a profound role in a particular tumor type.

Concluding remarksSB 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 can-

cers 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 gener-

ated by The Cancer Gene Atlas (TCGA) and Inter-

national 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



identified by SB are not mutated in human cancers but

do exhibit expression changes with prognostic implica-

tions. 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.

AcknowledgementsWe apologize to those research groups whose work was not discussed due tospace limitations. NGC and NAJ are Cancer Prevention Research Instituteof Texas (CPRIT) Scholars in Cancer Research.

References and recommended readingPapers of particular interest, published within the period of review,have been highlighted as:

� of special interest�� of outstanding interest

1. Dupuy AJ, Akagi K, Largaespada DA, Copeland NG, Jenkins NA:Mammalian mutagenesis using a highly mobile somaticSleeping Beauty transposon system. Nature 2005, 436:221-226.

2. Collier LS, Carlson CM, Ravimohan S, Dupuy AJ, Largaespada DA:Cancer gene discovery in solid tumours using transposon-based somatic mutagenesis in the mouse. Nature 2005,436:272-276.

3. Copeland NG, Jenkins NA: Harnessing transposons for cancergene discovery. Nat Rev Cancer 2010, 10:696-706.

4. Dupuy AJ, Rogers LM, Kim J, Nannapaneni K, Starr TK, Liu P,Largaespada DA, Scheetz TE, Jenkins NA, Copeland NG: Amodified Sleeping Beauty transposon system that can beused to model a wide variety of human cancers in mice. CancerRes 2009, 69:8150-8156.

5. Starr TK, Allaei R, Silverstein KA, Staggs RA, Sarver AL,Bergemann TL, Gupta M, O’Sullivan MG, Matise I, Dupuy AJ et al.:A transposon-based genetic screen in mice identifies genesaltered in colorectal cancer. Science 2009, 323:1747-1750.

6. Mann KM, Jenkins NA, Copeland NG, Mann MB: TransposonInsertional Mutagenesis Models of Cancer. Cold Spring HarbProtoc 2013.

7.��

March HN, Rust AG, Wright NA, ten Hoeve J, de Ridder J,Eldridge M, van der Weyden L, Berns A, Gadiot J, Uren A et al.:Insertional mutagenesis identifies multiple networks ofcooperating genes driving intestinal tumorigenesis. Nat Genet2011, 43:1202-1209.

This article reported the largest SB forward genetic screen to date andintegrated comparative oncogenomic approaches to identify candidatecancer genes in CRC, including 20 novel regulators of Wnt signaling. Alsosee annotation of Ref [8]

8.�

Brett BT, Berquam-Vrieze KE, Nannapaneni K, Huang J,Scheetz TE, Dupuy AJ: Novel molecular and computationalmethods improve the accuracy of insertion site analysis inSleeping Beauty-induced tumors. PLoS One 2011,6:e24668.

This manuscript and Refs [7,9] provided major advances to statisticalframeworks used to define loci and/or genes mutated by Sleeping Beautythat contribute to cancer.

9. Bergemann TL, Starr TK, Yu H, Steinbach M, Erdmann J, Chen Y,Cormier RT, Largaespada DA, Silverstein KA: New methods forfinding common insertion sites and co-occurring common


insertion sites in transposon- and virus-based geneticscreens. Nucleic Acids Res 2012, 40:3822-3833.

10.��

Mann KM, Ward JM, Yew CC, Kovochich A, Dawson DW,Black MA, Brett BT, Sheetz TE, Dupuy AJ, Chang DK et al.:Sleeping Beauty mutagenesis reveals cooperating mutationsand pathways in pancreatic adenocarcinoma. Proc Natl AcadSci U S A 2012, 109:5934-5941.

This manuscript reported the first SB screen to achieve genome-wideCCG discovery by employing transposons on different chromosomes;this model was among the first reports of metastatic disease driven by SBmutagenesis and validated 21 CCGs with prognostic implications forPDAC.

11. Rahrmann EP, Watson AL, Keng VW, Choi K, Moriarity BS,Beckmann DA, Wolf NK, Sarver A, Collins MH, Moertel CL et al.:Forward genetic screen for malignant peripheral nerve sheathtumor formation identifies new genes and pathways drivingtumorigenesis. Nat Genet 2013, 45:756-766.

12. van der Weyden L, Rust AG, McIntyre RE, Robles-Espinoza CD,del Castillo Velasco-Herrera M, Strogantsev R, Ferguson-Smith AC, McCarthy S, Keane TM, Arends MJ et al.: Jdp2downregulates Trp53 transcription to promoteleukaemogenesis in the context of Trp53 heterozygosity.Oncogene 2013, 32:397-402.

13. van der Weyden L, Arends MJ, Rust AG, Poulogiannis G,McIntyre RE, Adams DJ: Increased tumorigenesis associatedwith loss of the tumor suppressor gene Cadm1. Mol Cancer2012, 11:29.

14. van der Weyden L, Papaspyropoulos A, Poulogiannis G, Rust AG,Rashid M, Adams DJ, Arends MJ, O’Neill E: Loss of RASSF1Asynergizes with deregulated RUNX2 signaling intumorigenesis. Cancer Res 2012, 72:3817-3827.

15. Tang JZ, Carmichael CL, Shi W, Metcalf D, Ng AP, Hyland CD,Jenkins NA, Copeland NG, Howell VM, Zhao ZJ et al.: Transposonmutagenesis reveals cooperation of ETS family transcriptionfactors with signaling pathways in erythro-megakaryocyticleukemia. Proc Natl Acad Sci U S A 2013, 110:6091-6096.

16. Zanesi N, Balatti V, Riordan J, Burch A, Rizzotto L, Palamarchuk A,Cascione L, Lagana A, Dupuy AJ, Croce CM et al.: A SleepingBeauty screen reveals NF-kB activation in CLL mouse model.Blood 2013, 121:4355-4358.

17. Berquam-Vrieze KE, Nannapaneni K, Brett BT, Holmfeldt L, Ma J,Zagorodna O, Jenkins NA, Copeland NG, Meyerholz DK,Knudson CM et al.: Cell of origin strongly influences geneticselection in a mouse model of T-ALL. Blood 2011,118:4646-4656.

18. Quintana RM, Dupuy AJ, Bravo A, Casanova ML, Alameda JP,Page A, Sanchez-Viera M, Ramirez A, Navarro M: A transposon-based analysis of gene mutations related to skin cancerdevelopment. J Invest Dermatol 2013, 133:239-248.

19.�

Rogers LM, Riordan JD, Swick BL, Meyerholz DK, Dupuy AJ:Ectopic expression of Zmiz1 induces cutaneous squamouscell malignancies in a mouse model of cancer. J Invest Dermat2013, 133:1863-1869.

This study provided in vivo validation of a top-ranked CCG in SCC thatwas first reported in Ref. [4].

20. Keng VW, Sia D, Sarver AL, Tschida BR, Fan D, Alsinet C, Sole M,Lee WL, Kuka TP, Moriarity BS et al.: Sex bias occurrence ofhepatocellular carcinoma in Poly7 molecular subclass isassociated with EGFR. Hepatology 2013, 57:120-130.

21. Keng VW, Villanueva A, Chiang DY, Dupuy AJ, Ryan BJ, Matise I,Silverstein KA, Sarver A, Starr TK, Akagi K et al.: A conditionaltransposon-based insertional mutagenesis screen for genesassociated with mouse hepatocellular carcinoma. NatBiotechnol 2009, 27:264-274.

22. O’Donnell KA, Keng VW, York B, Reineke EL, Seo D, Fan D,Silverstein KA, Schrum CT, Xie WR, Mularoni L et al.: A SleepingBeauty mutagenesis screen reveals a tumor suppressor rolefor Ncoa2/Src-2 in liver cancer. Proc Natl Acad Sci U S A 2012,109:E1377-E1386.

23. Riordan JD, Keng VW, Tschida BR, Scheetz TE, Bell JB, Podetz-Pedersen KM, Moser CD, Copeland NG, Jenkins NA, Roberts LRet al.: Identification of rtl1, a retrotransposon-derived


http://refhub.elsevier.com/S0959-437X(13)00153-6/sbref0005




































































































22 Cancer genomics

imprinted gene, as a novel driver of hepatocarcinogenesis.PLoS Genet 2013, 9:e1003441.

24.��

Perez-Mancera PA, Rust AG, van der Weyden L, Kristiansen G,Li A, Sarver AL, Silverstein KA, Grutzmann R, Aust D, Rummele Pet al.: The deubiquitinase USP9X suppresses pancreatic ductaladenocarcinoma. Nature 2012, 486:266-270.

This letter was among the first reports of metastatic disease driven by SBmutagenesis and validated Usp9x as a new tumor suppressor gene inPDAC.

25. Biankin AV, Waddell N, Kassahn KS, Gingras MC,Muthuswamy LB, Johns AL, Miller DK, Wilson PJ, Patch AM, Wu Jet al.: Pancreatic cancer genomes reveal aberrations in axonguidance pathway genes. Nature 2012, 491:399-405.

26. Jones S, Zhang X, Parsons DW, Lin JC, Leary RJ, Angenendt P,Mankoo P, Carter H, Kamiyama H, Jimeno A et al.: Core signalingpathways in human pancreatic cancers revealed by globalgenomic analyses. Science 2008, 321:1801-1806.

27. Koso H, Takeda H, Yew CC, Ward JM, Nariai N, Ueno K,Nagasaki M, Watanabe S, Rust AG, Adams DJ et al.: Transposonmutagenesis identifies genes that transform neural stem cellsinto glioma-initiating cells. Proc Natl Acad Sci U S A 2012,109:E2998-E3007.

28.��

Wu X, Northcott PA, Dubuc A, Dupuy AJ, Shih DJ, Witt H, Croul S,Bouffet E, Fults DW, Eberhart CG et al.: Clonal selection drivesgenetic divergence of metastatic medulloblastoma. Nature2012, 482:529-533.

This letter was among the first reports of metastatic disease driven by SBmutagenesis and the first in vivo model of nervous system malignancy.

29. Mumert M, Dubuc A, Wu X, Northcott PA, Chin SS, Pedone CA,Taylor MD, Fults DW: Functional genomics identifies drivers of


medulloblastoma dissemination. Cancer Res 2012,72:4944-4953.

30. Keng VW, Ryan BJ, Wangensteen KJ, Balciunas D, Schmedt C,Ekker SC, Largaespada DA: Efficient transposition of Tol2 in themouse germline. Genetics 2009, 183:1565-1573.

31. Ivics Z, Hackett PB, Plasterk RH, Izsvak Z: Molecularreconstruction of Sleeping Beauty, a Tc1-like transposon fromfish, and its transposition in human cells. Cell 1997, 91:501-510.

32. Landrette SF, Cornett JC, Ni TK, Bosenberg MW: Xu T: piggyBactransposon somatic mutagenesis with an activated reporterand tracker (PB-SMART) for genetic screens in mice. PLoSOne 2011, 6:e26650.

33. Rad R, Rad L, Wang W, Cadinanos J, Vassiliou G, Rice S,Campos LS, Yusa K, Banerjee R, Li MA et al.: PiggyBactransposon mutagenesis: a tool for cancer gene discovery inmice. Science 2010, 330:1104-1107.

34. de Ridder J, Uren A, Kool J, Reinders M, Wessels L: Detectingstatistically significant common insertion sites in retroviralinsertional mutagenesis screens. PLoS Comput Biol 2006,2:e166.

35. Sarver AL, Erdman J, Starr T, Largaespada DA, Silverstein KA:TAPDANCE: an automated tool to identify and annotatetransposon insertion CISs and associations between CISsfrom next generation sequence data. BMC Bioinform 2012,13:154.

36. Rogers LM, Olivier AK, Meyerholz DK, Dupuy AJ: Adaptiveimmunity does not strongly suppress spontaneous tumors in aSleeping Beauty model of cancer. J Immunol 2013,190:4393-4399.
























































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Sleeping Beauty mutagenesis: exploiting forward genetic screens for cancer gene discovery