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Review
. 2010 Aug;20(4):261-8.
doi: 10.1016/j.semcancer.2010.05.003. Epub 2010 May 15.

Transposon-based screens for cancer gene discovery in mouse models

Affiliations
Review

Transposon-based screens for cancer gene discovery in mouse models

Adam J Dupuy. Semin Cancer Biol. 2010 Aug.

Abstract

Significant emphasis has recently been placed on the characterization of the human cancer genome. This effort has been assisted by the development of new DNA sequencing technologies that allow the genomes of individual tumors to be analyzed in much greater detail. However, the genetic complexity of human cancer has complicated the identification of driver mutations among the more abundant passenger mutations found in tumors. Recently, the Sleeping Beauty (SB) transposon system has been engineered to model cancer in mice. SB-induced tumors are produced by transposon insertional mutagenesis, thus the tagged mutations facilitate the identification of novel cancer genes. This review provides a brief summary of the SB system and its use in modeling cancer in mice.

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Figures

Figure 1
Figure 1
The Sleeping Beauty (SB) transposon system. Like all cut-and-paste transposons, the SB system requires two functional parts: the transposase enzyme (SBase) and the transposon vector. When these two elements are found within the same host cell nucleus, the SBase can bind to the inverted repeats (IRL and IRR) at the ends of the transposon, mediate excision of the transposon from the donor site and insertion into a new TA dinucleotide site. The TA site is duplicated and flanks each end of the transposon at the insertion site. The DNA breaks generated by the SBase at the donor site are repaired by the host cell. The repair often leaves behind a footprint (TACTGTA) at the donor site.
Figure 2
Figure 2
Structure and function of mutagenic transposon vectors used in SB-induced models of cancer. (a) Two different transposon vectors have been generated to induce tumors when mobilized in the somatic cells of mice. The promoter (MSCV or CAG) together with the splice donor (SD) can cause overexpression of downstream oncogenes. The splice acceptors (SA) and polyadenylation sites (pA) included on both strands of the transposons allow it to function as a gene trap to disrupt expression of tumor suppressor genes. (b) The mutagenic transposons are capable of driving oncogene overexpression in SB-induced tumors via two main mechanisms in which the T2/Onc transposon expresses a near full-length (above) or truncated (below) transcript. These mechanisms employ the MSCV or CAG promoter along with the splice donor within the transposon. (c) Transposon-induced tumor suppressor gene disruption can be achieved through the action of the gene trap elements (e.g. splice acceptor, polyA) on the plus strand (above) or minus strand (below), depending on the orientation of the transposon relative to the mutated gene.
Figure 3
Figure 3
Identification of common insertion sites in SB-induced tumors. High throughput PCR-based methods have recently been developed to identify a large number of transposon-induced mutations in individual SB-induced tumors. The driver mutations causally linked to transformation are identified as common insertion sites (CISs) — regions of the genome that harbor transposon insertions in multiple independent tumors. Currently, a Monte Carlo simulation is used to model random transposon insertion within the mouse genome to mimic the number of tumors and insertion events observed within each experiment. The cumulative results of thousands of interations of the Monte Carlo simulation are used to identify non-random clusters of transposon insertions in the tumor data set. These regions are then defined as CISs. The figure graphically depicts this analysis to identify CISs within a region of the genome containing three hypothetical genes (geneA-C) shown below. Each circle represents an independent transposon insertion in this 200 kilobase interval. The results of three iterations of the Monte Carlo simulation are shown, and these results are used to identify CISs in the tumor data set (shown in red).

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