RNA splicing is an important biological process associated with cancer initiation and progression. However, the contribution of alternative splicing to pancreatic cancer (PDAC) development is not well understood. Here, we identify an enrichment of RNA binding proteins (RBPs) involved in splicing regulation linked to PDAC progression from a forward genetic screen using Sleeping Beauty insertional mutagenesis in a mouse model of pancreatic cancer. We demonstrate downregulation of RBFOX2, an RBP of the FOX family, promotes pancreatic cancer progression and liver metastasis. Specifically, we show RBFOX2 regulates exon splicing events in transcripts encoding proteins involved in cytoskeletal remodeling programs. These exons are differentially spliced in PDAC patients, with enhanced exon skipping in the classical subtype for several RBFOX2 targets. RBFOX2 mediated splicing of ABI1, encoding the Abelson-interactor 1 adapter protein, controls the abundance and localization of ABI1 protein isoforms in pancreatic cancer cells and promotes the relocalization of ABI1 from the cytoplasm to the periphery of migrating cells. Using splice-switching antisense oligonucleotides (AONs) we demonstrate the ABI1 ∆Ex9 isoform enhances cell migration. Together, our data identify a role for RBFOX2 in promoting PDAC progression through alternative splicing regulation.

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.

Mutationally activated BRAF is detected in approximately 7% of human lung adenocarcinomas, with BRAFT1799A serving as a predictive biomarker for treatment of patients with FDA-approved inhibitors of BRAFV600E oncoprotein signaling. In genetically engineered mouse (GEM) models, expression of BRAFV600E in the lung epithelium initiates growth of benign lung tumors that, without additional genetic alterations, rarely progress to malignant lung adenocarcinoma. To identify genes that cooperate with BRAFV600E for malignant progression, we used Sleeping Beauty–mediated transposon mutagenesis, which dramatically accelerated the emergence of lethal lung cancers. Among the genes identified was Rbms3, which encodes an RNA-binding protein previously implicated as a putative tumor suppressor. Silencing of RBMS3 via CRISPR/Cas9 gene editing promoted growth of BRAFV600E lung organoids and promoted development of malignant lung cancers with a distinct micropapillary architecture in BRAFV600E and EGFRL858R GEM models. BRAFV600E/RBMS3Null lung tumors displayed elevated expression of Ctnnb1, Ccnd1, Axin2, Lgr5, and c-Myc mRNAs, suggesting that RBMS3 silencing elevates signaling through the WNT/β-catenin signaling axis. Although RBMS3 silencing rendered BRAFV600E-driven lung tumors resistant to the effects of dabrafenib plus trametinib, the tumors were sensitive to inhibition of porcupine, an acyltransferase of WNT ligands necessary for their secretion. Analysis of The Cancer Genome Atlas patient samples revealed that chromosome 3p24, which encompasses RBMS3, is frequently lost in non–small cell lung cancer and correlates with poor prognosis. Collectively, these data reveal the role of RBMS3 as a lung cancer suppressor and suggest that RBMS3 silencing may contribute to malignant NSCLC progression. Significance: Loss of RBMS3 cooperates with BRAFV600E to induce lung tumorigenesis, providing a deeper understanding of the molecular mechanisms underlying mutant BRAF-driven lung cancer and potential strategies to more effectively target this disease.

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.

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.

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 theSleeping Beautytransposon mutagenesis system to statistically define drivers of keratinocyte transformation and cuSCC progressionin vivoin 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 theZMIZgenes, and tumor suppressive roles forKMT2C, CREBBPandNCOA2, in the initiation or progression of human cuSCC. Taken together, ourin vivoscreen 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.