Pancreatic cancer is one of the most deadly cancers affecting the Western world. Because the disease is highly metastatic and difficult to diagnosis until late stages, the 5-y survival rate is around 5%. The identification of molecular cancer drivers is critical for furthering our understanding of the disease and development of improved diagnostic tools and therapeutics. We have conducted a mutagenic screen using Sleeping Beauty (SB) in mice to identify new candidate cancer genes in pancreatic cancer. By combining SB with an oncogenic Kras allele, we observed highly metastatic pancreatic adenocarcinomas. Using two independent statistical methods to identify loci commonly mutated by SB in these tumors, we identified 681 loci that comprise 543 candidate cancer genes (CCGs); 75 of these CCGs, including Mll3 and Ptk2, have known mutations in human pancreatic cancer. We identified point mutations in human pancreatic patient samples for another 11 CCGs, including Acvr2a and Map2k4. Importantly, 10% of the CCGs are involved in chromatin remodeling, including Arid4b, Kdm6a, and Nsd3, and all SB tumors have at least one mutated gene involved in this process; 20 CCGs, including Ctnnd1, Fbxo11, and Vgll4, are also significantly associated with poor patient survival. SB mutagenesis provides a rich resource of mutations in potential cancer drivers for cross-comparative analyses with ongoing sequencing efforts in human pancreatic adenocarcinoma. P ancreatic ductal adenocarcinoma is one of the most deadly forms of cancer. In the United States, the rate of mortality is nearly equal to the rate of new diagnoses (1). Early disease detection is rare, and of those patients diagnosed with early-stage disease, only 20% are candidates for surgical resection. Approximately 50% of patients develop highly metastatic disease, for which current treatment regimens provide little increase in longevity (2). Clearly, there is a need for better biomarkers of disease and the identification of new therapeutic targets, particularly for metastatic disease.Human pancreatic cancer develops from preinvasive neoplasias, typically intraepithelial neoplasias (PanINs), although intraductal papillary mucinous neoplasia and mucinous cystic neoplasia can also give rise to adenocarcinoma (3). The majority of pancreatic adenocarcinoma is of ductal origin and contains a large desmoplastic component thought to promote tumorigenesis by modulating the tumor microenvironment. Oncogenic KRAS mutations are found in 90% of human tumors, and they appear in early PanIN (4). The accumulation of additional inactivating driver mutations in P16/CDKN2A, TP53, and SMAD4 occurs with high frequency in later-stage PanIN, and these mutations are likely required for tumor progression to invasive adenocarcinoma.Recent high-throughput sequencing efforts have shown that the majority of somatic point mutations in primary pancreatic adenocarcinoma are missense mutations and that most occur at low frequency (5). Pancreatic cancers exhibit a very unstable genome, and through whole-genome...

The recent development of the Sleeping Beauty (SB) system has led to the development of novel mouse models of cancer. Unlike spontaneous models, SB causes cancer through the action of mutagenic transposons that are mobilized in the genomes of somatic cells to induce mutations in cancer genes. While previous methods have successfully identified many transposon-tagged mutations in SB-induced tumors, limitations in DNA sequencing technology have prevented a comprehensive analysis of large tumor cohorts. Here we describe a novel method for producing genetic profiles of SB-induced tumors using Illumina sequencing. This method has dramatically increased the number of transposon-induced mutations identified in each tumor sample to reveal a level of genetic complexity much greater than previously appreciated. In addition, Illumina sequencing has allowed us to more precisely determine the depth of sequencing required to obtain a reproducible signature of transposon-induced mutations within tumor samples. The use of Illumina sequencing to characterize SB-induced tumors should significantly reduce sampling error that undoubtedly occurs using previous sequencing methods. As a consequence, the improved accuracy and precision provided by this method will allow candidate cancer genes to be identified with greater confidence. Overall, this method will facilitate ongoing efforts to decipher the genetic complexity of the human cancer genome by providing more accurate comparative information from Sleeping Beauty models of cancer.

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Nonviral vector systems are used increasingly in gene targeting and gene transfer applications. The piggyBac transposon represents an alternative integrating vector for in vivo gene transfer. We hypothesized that this system could achieve persistent gene transfer to the liver when administered systemically. We report that a novel hyperactive transposase generated higher transposition efficiency than a codon-optimized transposase in a human liver cell line. Hyperactive transposase-mediated reporter gene expression persisted at levels twice that of codon-optimized transposase in the livers of mice for the 6-month study. Of note, expression persisted in mice following partial hepatectomy, consistent with expression from an integrated transgene. We also used the hyperactive transposase to deliver the human α1-antitrypsin gene and achieved stable expression in serum. To determine the integration pattern of insertions, we performed large-scale mapping in human cells and recovered 60,685 unique hyperactive transposase-mediated insertions. We found that a hyperactive piggyBac transposase conferred an altered pattern of integration from that of insect piggyBac transposase, with a decreased frequency of integration near transcription start sites than previously reported. Our results support that the piggyBac transposon combined with the hyperactive transposase is an efficient integrating vector system for in vitro and in vivo applications.

BackgroundAnimal models of cancer are useful to generate complementary datasets for comparison to human tumor data. Insertional mutagenesis screens, such as those utilizing the Sleeping Beauty (SB) transposon system, provide a model that recapitulates the spontaneous development and progression of human disease. This approach has been widely used to model a variety of cancers in mice. Comprehensive mutation profiles are generated for individual tumors through amplification of transposon insertion sites followed by high-throughput sequencing. Subsequent statistical analyses identify common insertion sites (CISs), which are predicted to be functionally involved in tumorigenesis. Current methods utilized for SB insertion site analysis have some significant limitations. For one, they do not account for transposon footprints – a class of mutation generated following transposon remobilization. Existing methods also discard quantitative sequence data due to uncertainty regarding the extent to which it accurately reflects mutation abundance within a heterogeneous tumor. Additionally, computational analyses generally assume that all potential insertion sites have an equal probability of being detected under non-selective conditions, an assumption without sufficient relevant data. The goal of our study was to address these potential confounding factors in order to enhance functional interpretation of insertion site data from tumors.ResultsWe describe here a novel method to detect footprints generated by transposon remobilization, which revealed minimal evidence of positive selection in tumors. We also present extensive characterization data demonstrating an ability to reproducibly assign semi-quantitative information to individual insertion sites within a tumor sample. Finally, we identify apparent biases for detection of inserted transposons in several genomic regions that may lead to the identification of false positive CISs.ConclusionThe information we provide can be used to refine analyses of data from insertional mutagenesis screens, improving functional interpretation of results and facilitating the identification of genes important in cancer development and progression.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-1150) contains supplementary material, which is available to authorized users.