Sleeping Beauty mutagenesis reveals cooperating mutations and pathways in pancreatic adenocarcinoma

Abstract

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...

Transposon mutagenesis identifies genes that cooperate with mutant Pten in breast cancer progression

“…In addition, in many cases, only a single transposon insertion was present at a CCG in tumor cells, suggesting that many CCGs are functioning as haploinsufficient TSGs. This is similar to what has been observed in other SB mutagenesis screens performed in solid tumors (18)(19)(20)(21)(22)(23)(24)(25)(26).…”

“…New technologies that would provide a more complete understanding of the genetics of TNBC are still needed to deconvolute the complexity of this deadly cancer. Our laboratory and others have pioneered the use of transposon mutagenesis in mice as a tool for cancer gene discovery (18)(19)(20)(21)(22)(23)(24)(25)(26). Transposons induce cancer by randomly inserting into the mouse genome, mutating, and disrupting potential cancer genes.…”

MaxMIF: A New Method for Identifying Cancer Driver Genes through Effective Data Integration

“…We compared the 12 prioritizing results with those obtained by DNmax and DNsum (two algorithms in MUFFINN)21 using the same data and the same five reference cancer gene sets, that is, CGC (Cancer Genome Census),26 CGCpointMut, Rule2020,5 HCD,27 and MouseMut28, 29 (see the Supporting Information for details), with CGC being the most well‐known and confident cancer gene set. Both ROC curves ( Figure 2 a) and AUC (area under the ROC curve) scores (Figure 2b) show that MaxMIF outperforms DNmax and DNsum in the AWG Pan‐Cancer dataset, using either the HumanNet or STRINGv10 networks validated on the CGC reference cancer gene set.…”

“…Unfortunately, such a gold‐standard set of cancer genes is currently unavailable. Alternatively, five different cancer gene sets were collected to reduce the bias caused by using a single reference cancer gene set: (i) 616 cancer genes from the CGC,26 currently the most popular cancer gene set; (ii) a subset of 245 CGC cancer genes that mainly undergo somatic point mutations in various cancers (CGCpointMut); (iii) 125 cancer genes screened by the “20/20 rule” (Rule2020);5 (iv) 291 high‐confidence candidate genes concentrated by a rule‐based method (HCD);27 (v) 797 candidate cancer genes were identified as human ortholog of mouse cancer genes (MouseMut)28, 29 (see details in the Supporting Information and the overlaps of the five reference gene sets are shown in Figure S18, Supporting Information). In spite of the fact that each reference cancer gene set has a different trade‐off for accuracy, credibility, comprehensiveness, and unbiasedness, a more effective method should consistently outperform the other methods evaluated on the five reference gene sets.…”

Transposon mutagenesis identifies genes that transform neural stem cells into glioma-initiating cells

“…To identify genes specific to GBM further, we compared our CIS genes with those identified in large-scale mutagenesis screens, such as intestinal tumors (1,010 genes) (22), pancreatic tumors (136 genes) (45), and medulloblastomas (64 genes) (46). This comparison showed that 70 tumor CIS genes (47%) and 66 immortalization CIS genes (47%) are unique to our screen (Datasets S3 and S4).…”