Karen M. Mann

Sleeping Beauty mutagenesis reveals cooperating mutations and pathways in pancreatic adenocarcinoma

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.

Inaugural article: Sleeping Beauty mutagenesis reveals cooperating mutations and pathways in pancreatic adenocarcinoma

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.

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

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.

KRAS-related proteins in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a highly metastatic disease with a high mortality rate. Genetic and biochemical studies have shown that RAS signaling mediated by KRAS plays a pivotal role in disease initiation, progression and drug resistance. RAS signaling affects several cellular processes in PDAC, including cellular proliferation, migration, cellular metabolism and autophagy. 90% of pancreatic cancer patients harbor somatic oncogenic point mutations in KRAS, which lead to constitutive activation of the molecule. Pancreatic cancers lacking KRAS mutations show activation of RAS via upstream signaling through receptor mediated tyrosine kinases, like EGFR, and in a small fraction of patients, oncogenic activation of the downstream B-RAF molecule is detected. RAS-stimulated signaling of RAF/MEK/ERK, PI3K/AKT/mTOR and RalA/B is active in human pancreatic cancers, cancer cell lines and mouse models of PDAC, although activation levels of each signaling arm appear to be variable across different tumors and perhaps within different subclones of single tumors. Recently, several targeted therapies directed towards MEK, ERK, PI3K and mTOR have been assayed in pancreatic cancer cell lines and in mouse models of the disease with promising results for their ability to impede cellular growth or delay tumor formation, and several inhibitors are currently in clinical trials. However, therapy-induced cross activation of RAS effector molecules has elucidated the complexities of targeting RAS signaling. Combinatorial therapies are now being explored as an approach to overcome RAS-induced therapeutic resistance in pancreatic cancer.

Analyzing tumor heterogeneity and driver genes in single myeloid leukemia cells with SBCapSeq

A central challenge in oncology is how to kill tumors containing heterogeneous cell populations defined by different combinations of mutated genes. Identifying these mutated genes and understanding how they cooperate requires single-cell analysis, but current single-cell analytic methods, such as PCR-based strategies or whole-exome sequencing, are biased, lack sequencing depth or are cost prohibitive. Transposon-based mutagenesis allows the identification of early cancer drivers, but current sequencing methods have limitations that prevent single-cell analysis. We report a liquid-phase, capture-based sequencing and bioinformatics pipeline, Sleeping Beauty (SB) capture hybridization sequencing (SBCapSeq), that facilitates sequencing of transposon insertion sites from single tumor cells in a SB mouse model of myeloid leukemia (ML). SBCapSeq analysis of just 26 cells from one tumor revealed the tumors major clonal subpopulations, enabled detection of clonal insertion events not detected by other sequencing methods and led to the identification of dominant subclones, each containing a unique pair of interacting gene drivers along with three to six cooperating cancer genes with SB-driven expression changes.

Using targeted transgenic reporter mice to study promoter-specific p53 transcriptional activity

The p53 transcription factor modulates gene expression programs that induce cell cycle arrest, senescence, or apoptosis, thereby preventing tumorigenesis. However, the mechanisms by which these fates are selected are unclear. Our objective is to understand p53 target gene selection and, thus, enable its optimal manipulation for cancer therapy. We have generated targeted transgenic reporter mice in which EGFP expression is driven by p53 transcriptional activity at a response element from either the p21 or Puma promoter, which induces cell cycle arrest/senescence and apoptosis, respectively. We demonstrate that we could monitor p53 activity in vitro and in vivo and detect variations in p53 activity depending on the response element, tissue type, and stimulus, thereby validating our reporter system and illustrating its utility for preclinical drug studies. Our results also show that the sequence of the p53 response element itself is sufficient to strongly influence p53 target gene selection. Finally, we use our reporter system to provide evidence for p53 transcriptional activity during early embryogenesis, showing that p53 is active as early as embryonic day 3.5 and that p53 activity becomes restricted to embryonic tissue by embryonic day 6.5. The data from this study demonstrate that these reporter mice could serve as powerful tools to answer questions related to basic biology of the p53 pathway, as well as cancer therapy and drug discovery.

Transposon insertional mutagenesis models of cancer.

Transposon-based insertional mutagenesis in the mouse provides a powerful approach for identifying new cancer genes. Transposon insertions in cancer genes are selected during tumor development because of their positive effect on tumor growth, and the transposon insertion sites in tumors thus serve as tags for identifying new cancer genes. Direct comparisons of transposon-mutated genes in mouse tumors with mutated genes in human tumors can lend insight into the genes and signaling pathways that drive tumorigenesis. This is critical for prioritizing genes for further study, either for their efficacy as biomarkers or drug targets. In this article, we will introduce DNA transposon-based systems used for gene discovery in mice and discuss their application to identify candidate cancer genes in light of recently published tumor studies.

SBCDDB: Sleeping Beauty Cancer Driver Database for gene discovery in mouse models of human cancers

Abstract Large-scale oncogenomic studies have identified few frequently mutated cancer drivers and hundreds of infrequently mutated drivers. Defining the biological context for rare driving events is fundamentally important to increasing our understanding of the druggable pathways in cancer. Sleeping Beauty (SB) insertional mutagenesis is a powerful gene discovery tool used to model human cancers in mice. Our lab and others have published a number of studies that identify cancer drivers from these models using various statistical and computational approaches. Here, we have integrated SB data from primary tumor models into an analysis and reporting framework, the Sleeping Beauty Cancer Driver DataBase (SBCDDB, http://sbcddb.moffitt.org), which identifies drivers in individual tumors or tumor populations. Unique to this effort, the SBCDDB utilizes a single, scalable, statistical analysis method that enables data to be grouped by different biological properties. This allows for SB drivers to be evaluated (and re-evaluated) under different contexts. The SBCDDB provides visual representations highlighting the spatial attributes of transposon mutagenesis and couples this functionality with analysis of gene sets, enabling users to interrogate relationships between drivers. The SBCDDB is a powerful resource for comparative oncogenomic analyses with human cancer genomics datasets for driver prioritization.

SB Driver Analysis: a Sleeping Beauty cancer driver analysis framework for identifying and prioritizing experimentally actionable oncogenes and tumor suppressors

Abstract Cancer driver prioritization for functional analysis of potential actionable therapeutic targets is a significant challenge. Meta-analyses of mutated genes across different human cancer types for driver prioritization has reaffirmed the role of major players in cancer, including KRAS, TP53 and EGFR, but has had limited success in prioritizing genes with non-recurrent mutations in specific cancer types. Sleeping Beauty (SB) insertional mutagenesis is a powerful experimental gene discovery framework to define driver genes in mouse models of human cancers. Meta-analyses of SB datasets across multiple tumor types is a potentially informative approach to prioritize drivers, and complements efforts in human cancers. Here, we report the development of SB Driver Analysis, an in-silico method for defining cancer driver genes that positively contribute to tumor initiation and progression from population-level SB insertion data sets. We demonstrate that SB Driver Analysis computationally prioritizes drivers and defines distinct driver classes from end-stage tumors that predict their putative functions during tumorigenesis. SB Driver Analysis greatly enhances our ability to analyze, interpret and prioritize drivers from SB cancer datasets and will continue to substantially increase our understanding of the genetic basis of cancer.

Abstract 2893: Sleeping Beauty uncovers cooperating driver genes in a preclinical mouse model of myeloid leukemia

Myeloid leukemia is associated with few predominant mutations and translocations in humans. In order to uncover additional loci that drive the disease, we developed an aggressive, fully-penetrant mouse model of myeloid leukemia using Sleeping Beauty insertional mutagenesis. Animals developed distinct foci of expanded splenic red pulp as early as 30 days of age and had a median survival of 70 days. SB insertions sequenced from 168 spleens of mice with end-stage disease revealed more than 470 statistically defined candidate cancer genes (CCGs), 87 of which have orthologs mutated in human AML. Pathway enrichment analysis using the CCGs confirmed that MAP kinase and Jak-Stat signaling play an important role in the disease. Twenty-five CCGs were defined as trunk drivers based on high sequence read representation in multiple tumors from the population. Among the trunk drivers was a suite of transcription factors that act downstream of MAP kinase signaling, including Ets- family member genes Erg, Ets1 and Fli1. We sequenced SB insertions from single cells isolated from a single tumor to interrogate cooperating relationships among trunk drivers. Hierarchical clustering of SB insertions revealed intra-tumor heterogeneity and identified three distinct subclonal populations. Importantly, Erg and Ghr anchored one subpopulation that was distinct from a second subpopulation anchored by Ets1 and Notch1. These gene relationships observed in single cells confirmed the population-based statistical analysis of cooperativity between Erg and Ghr and Ets1 and Notch1 from the cohort of 168 tumors. Mutual-exclusivity for Erg and Ets1 in driving myeloid leukemia was supported by microarray expression analysis in a subset of the tumors. RNA-seq analysis confirmed that SB insertions drive expression of many CCGs including trunk divers Erg, Ghr, Ets1 and Notch1. Chimeric transcripts for the four driver genes contained the SB splice donor and downstream wild type exons encoding the functional domains of the translated proteins, suggesting that SB drives the mis-expression of these proteins to induce myeloid leukemia. We have identified new CCGs that contribute to myeloid disease progression and gained insight into both inter- and intra- tumor heterogeneity of this tumor type. Importantly, SB-driven myeloid leukemia is a robust, transplantable genetic model that offers a unique platform for preclinical testing. Transposon insertion profiles provide a powerful means to stratify tumors for targeted therapies. Finally, SB can be harnessed to uncover loci that confer therapy response or resistance. Citation Format: Karen M. Mann, Justin Newberg, Nicholas Navin, David J. Adams, Nancy Jenkins, Neal Copeland, Michael B. Mann. Sleeping Beauty uncovers cooperating driver genes in a preclinical mouse model of myeloid leukemia. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2893. doi:10.1158/1538-7445.AM2015-2893