Novel Molecular and Computational Methods Improve the Accuracy of Insertion Site Analysis in Sleeping Beauty-Induced Tumors

et al. (2011) Novel Molecular and Computational Methods Improve the Accuracy of Insertion Site Analysis in Sleeping Beauty-Induced Tumors. PLoS ONE 6(9): e24668. doi:10.1371/journal.pone.0024668 Novel Molecular and Computational Methods Improve the Accuracy of Insertion Site Analysis in Sleeping Beauty-Induced Tumors Benjamin T. Brett 0 Katherine E. Berquam-Vrieze 0 Kishore Nannapaneni 0 Jian Huang 0 Todd E. 0 Scheetz 0 Adam J. Dupuy 0 Andrew C. Wilber, Southern Illinois University School of Medicine, United States of America 0 1 Center for Bioinformatics and Computational Biology , Roy J. and Lucille A. Carver College of Medicine, University of Iowa , Iowa City , Iowa, United States of America, 2 Department of Anatomy and Cell Biology , Roy J. and Lucille A. Carver College of Medicine, University of Iowa , Iowa City , Iowa, United States of America, 3 Department of Biomedical Engineering , Roy J. and Lucille A. Carver College of Medicine, University of Iowa , Iowa City , Iowa, United States of America, 4 Department of Statistics and Actuarial Science , Roy J. and Lucille A. Carver College of Medicine, University of Iowa , Iowa City , Iowa, United States of America, 5 Department of Biostatistics , Roy J. and Lucille A. Carver College of Medicine, University of Iowa , Iowa City , Iowa, United States of America, 6 Department of Ophthalmology and Visual Sciences , Roy J. and Lucille A. Carver College of Medicine, University of Iowa , Iowa City , Iowa, United States of America, 7 Department of Pathology, Roy J. and Lucille A. Carver College of Medicine, University of Iowa , Iowa City, Iowa , United States of America 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 SBinduced 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. - Funding: This work was funded by grant number 5R01CA130867-03 from the National Cancer Institute (NCI) at the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. Recent work has indicated that the human cancer genome is complex, consisting of many somatically acquired genetic and epigenetic changes [1]. A key challenge faced by the cancer genetics community is deciphering the role that this complexity plays in the etiology of human cancer. Unfortunately, we still have a limited ability to specifically identify those mutations that drive cancer initiation and progression among the larger number of passenger mutations found in an individual tumor. A subsequent goal would then be to determine how individual driver mutations cooperate to generate and maintain a tumor. Armed with this knowledge, it is thought that more effective cancer therapies can be generated to specifically target tumors. Mouse models of cancer have become a useful tool in modeling the genetics of human cancer, allowing the investigator to test the role of mutant forms of specific candidate genes in vivo. Moreover, insertional mutagenesis models of cancer have shown great promise not only in the identification of novel candidate cancer genes, but also in providing insight into how specific combinations of gene mutations produce cancer [2]. Retroviral, and more recently, transposon mutagenesis models have been described that model a wide variety of tumor types in the mouse [2,3]. The great advantage of these models is that the driver mutations are tagged by proviral or transposon sequences that facilitate their rapid identification. Advances in DNA sequencing technology have greatly facilitated the characterization of mouse tumors induced by insertional mutagenesis. Several independent methods have been produced that utilize a ligation-mediated PCR approach to amplify proviral or transposon junction fragments from the tumor genome [4,5]. Incorporation of barcodes in the PCR allows the products from independent samples to be mixed and directly sequenced [6,7,8]. This approach has dramatically increased the amount of data generated from insertional mutagenesis screens in mouse cancer models. However, the bioinformatic analysis of data derived from tumors induced by insertional mutagenesis has also been complicated by the increased scale of DNA sequencing. For example, tumors that develop in existing mouse models continue to acquire retroviral or transposon integration events. Although ongoing insertional mutagenesis increases likely drives tumor progression in these models, the resulting genetic complexity also makes it difficult to accurately identify the collection of insertional mutations that were acquired during the early steps of transformation. Past analysis has assumed that early integration events present in the tumor-initiating cell will be present in all tumor cells. As a consequence, these early events will be clonally expanded to a greater extent compared to integration events acquired in tumor subclones. By extension, initiating integration events should be recurrently PCR-amplified and sequenced. While this is a reasonable assumption, it is also likely that PCR bias contributes to the frequency at which specific integration events are amplified and sequenced. Finally, recent work has shown that many hundreds of independent insertion events can be identified in an individual tumor sample [7,8]. Given this complexity, suboptimal sequence depth is also likely to introduce sampling error and confound efforts to identify the clonally expanded integration events associated with early s (...truncated)