Transposon insertional mutagenesis models of cancer

Transposon mutagenesis identifies cooperating genetic drivers during keratinocyte transformation and cutaneous squamous cell carcinoma progression

The systematic identification of genetic events driving cellular transformation and tumor progression in the absence of a highly recurrent oncogenic driver mutation is a challenge in cutaneous oncology. In cutaneous squamous cell carcinoma (cuSCC), the high UV-induced mutational burden poses a hurdle to achieve a complete molecular landscape of this disease. Here, we utilized the Sleeping Beauty transposon mutagenesis system to statistically define drivers of keratinocyte transformation and cuSCC progression in vivo in the absence of UV-IR, and identified both known tumor suppressor genes and novel oncogenic drivers of cuSCC. Functional analysis confirms an oncogenic role for the ZMIZ genes, and tumor suppressive roles for KMT2C, CREBBP and NCOA2, in the initiation or progression of human cuSCC. Taken together, our in vivo screen demonstrates an extremely heterogeneous genetic landscape of cuSCC initiation and progression, which can be harnessed to better understand skin oncogenic etiology and prioritize therapeutic candidates.

Non-melanoma skin cancers, the most common cancers in the US, are caused by UV skin exposure. Nearly 1 million cases of cutaneous squamous cell carcinoma (cuSCC) are diagnosed in the US each year. While most cuSCCs are highly treatable, more than twice as many individuals die from this disease as from melanoma. The high burden of UV-induced DNA damage in human skin poses a challenge for identifying initiating and cooperating mutations that promote cuSCC development and for defining potential therapeutic targets. Here, we describe a genetic screen in mice using a DNA transposon system to mutagenize the genome of keratinocytes and drive squamous cell carcinoma in the absence of UV. By sequencing where the transposons selectively integrated in the genomes of normal skin, skin with pre-cancerous lesions and skin with fully developed cuSCCs from our mouse model, we were able to identify frequently mutated genes likely important for this disease. Our analysis also defined cooperation between sets of genes not previously appreciated in cuSCC. Our mouse model and ensuing data provide a framework for understanding the genetics of cuSCC and for defining the molecular changes that may lead to the future therapies for patients.

Promoterless Transposon Mutagenesis Drives Solid Cancers via Tumor Suppressor Inactivation

A central challenge in cancer genomics is the systematic identification of single and cooperating tumor suppressor gene mutations driving cellular transformation and tumor progression in the absence of oncogenic driver mutation(s). Multiple in vitro and in vivo gene inactivation screens have enhanced our understanding of the tumor suppressor gene landscape in various cancers. However, these studies are limited to single or combination gene effects, specific organs, or require sensitizing mutations. In this study, we developed and utilized a Sleeping Beauty transposon mutagenesis system that functions only as a gene trap to exclusively inactivate tumor suppressor genes. Using whole body transposon mobilization in wild type mice, we observed that cumulative gene inactivation can drive tumorigenesis of solid cancers. We provide a quantitative landscape of the tumor suppressor genes inactivated in these cancers and show that, despite the absence of oncogenic drivers, these genes converge on key biological pathways and processes associated with cancer hallmarks.

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

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.

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

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.

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 tumor's 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.

Use of DNA transposons for functional genetic screens in mouse models of cancer

Cancer is a very heterogeneous disease with complex genetic interactions. In recent years, the systematic sequencing of cancer genomes has provided information to design personalized therapeutic interventions. However, the complexity of cancer genomes commonly makes it difficult to identify specific genes involved in tumour development or therapeutic responsiveness. The generation of mouse models of cancer using transposon-mediated approaches has provided a powerful tool to unveil the role of key genes during cancer development. Here we will discuss how the use of forward and reverse genetic approaches mediated by DNA transposons can support the investigation of cancer pathogenesis, including the identification of cancer promoting mechanisms and potential therapeutic targets.