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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.SignificanceLoss 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.

Differential mRNA expression between ancestry groups can be explained by both genetic and environmental factors. We outline a computational workflow to determine the extent to which germline genetic variation explains cancer-specific molecular differences across ancestry groups. Using multi-omics datasets from The Cancer Genome Atlas (TCGA), we enumerate ancestry-informative markers colocalized with cancer-type-specific expression quantitative trait loci (e-QTLs) at ancestry-associated genes. This approach is generalizable to other settings with paired germline genotyping and mRNA expression data for a multi-ethnic cohort. For complete details on the use and execution of this protocol, please refer to Carrot-Zhang et al. (2020), Robertson et al. (2021), and Sayaman et al. (2021).

We evaluated ancestry effects on mutation rates, DNA methylation, and mRNA and miRNA expression among 10,678 patients across 33 cancer types from The Cancer Genome Atlas. We demonstrated that cancer subtypes and ancestry-related technical artifacts are important confounders that have been insufficiently accounted for. Once accounted for, ancestry-associated differences spanned all molecular features and hundreds of genes. Biologically significant differences were usually tissue specific but not specific to cancer. However, admixture and pathway analyses suggested some of these differences are causally related to cancer. Specific findings included increased FBXW7 mutations in patients of African origin, decreased VHL and PBRM1 mutations in renal cancer patients of African origin, and decreased immune activity in bladder cancer patients of East Asian origin.

Nonalcoholic fatty liver disease (NAFLD) is the fastest rising cause of hepatocellular carcinoma (HCC) in Western countries; however, the molecular mechanisms that cause NAFLD-HCC remain elusive. To identify molecular drivers of NAFLD-HCC, we performed Sleeping Beauty (SB) transposon mutagenesis screens in liverspecific Pten knockout and in high-fat diet-fed mice, which are murine models of NAFLD-HCC. SB mutagenesis accelerated liver tumor formation in both models and identified 588 and 376 candidate cancer genes (CCGs), respectively; 257 CCGs were common to both screens and were enriched in signaling pathways known to be important for human HCC. Comparison of these CCGs with those identified in a previous SB screen of hepatitis B virusinduced HCC identified a core set of 141 CCGs that were mutated in all screens. Forty-one CCGs appeared specific for NAFLD-HCC, including Sav1, a component of the Hippo signaling pathway and the most frequently mutated gene identified in both NAFLD-HCC screens. Liver-specific deletion of Sav1 was found to promote hepatic lipid accumulation, apoptosis, and fibrogenesis, leading to the acceleration of hepatocarcinogenesis in liver-specific Pten mutant mice. Sav1/Pten double-mutant livers also showed a striking up-regulation of markers of liver progenitor cells (LPCs), along with synergistic activation of Yap, which is a major downstream effector of Hippo signaling. Lastly, Yap activation, in combination with Pten inactivation, was found to accelerate cell growth and sphere formation of LPCs in vitro and induce their malignant transformation in allografts. Our forward genetic screens in mice have thus identified pathways and genes driving the development of NAFLD-HCC. [ABSTRACT FROM AUTHOR]

Epithelial ovarian cancer (EOC) is a deadly cancer, and its prognosis has not been changed significantly during several decades. To seek new therapeutic targets for EOC, we performed an in vivo dropout screen in human tumor xenografts using a pooled shRNA library targeting thousands of druggable genes. Then, in follow-up studies, we performed a second screen using a genome-wide CRISPR/Cas9 library. These screens identified 10 high-confidence drug targets that included well-known oncogenes such as ERBB2 and RAF1, and novel oncogenes, notably KPNB1, which we investigated further. Genetic and pharmacological inhibition showed that KPNB1 exerts its antitumor effects through multiphase cell cycle arrest and apoptosis induction. Mechanistically, proteomic studies revealed that KPNB1 acts as a master regulator of cell cycle-related proteins, including p21, p27, and APC/C. Clinically, EOC patients with higher expression levels of KPNB1 showed earlier recurrence and worse prognosis than those with lower expression levels of KPNB1. Interestingly, ivermectin, a Food and Drug Administration-approved antiparasitic drug, showed KPNB1-dependent antitumor effects on EOC, serving as an alternative therapeutic toward EOC patients through drug repositioning. Last, we found that the combination of ivermectin and paclitaxel produces a stronger antitumor effect on EOC both in vitro and in vivo than either drug alone. Our studies have thus identified a combinatorial therapy for EOC, in addition to a plethora of potential drug targets. [ABSTRACT FROM AUTHOR]

Epithelial-mesenchymal transition (EMT) is thought to contribute to metastasis and chemoresistance in patients with hepatocellular carcinoma (HCC), leading to their poor prognosis. The genes driving EMT in HCC are not yet fully understood, however. Here, we show that mobilization of Sleeping Beauty (SB) transposons in immortalized mouse hepatoblasts induces mesenchymal liver tumors on transplantation to nude mice. These tumors show significant down-regulation of epithelial markers, along with up-regulation of mesenchymal markers and EMT-related transcription factors (EMT-TFs). Sequencing of transposon insertion sites from tumors identified 233 candidate cancer genes (CCGs) that were enriched for genes and cellular processes driving EMT. Subsequent trunk driver analysis identified 23 CCGs that are predicted to function early in tumorigenesis and whose mutation or alteration in patients with HCC is correlated with poor patient survival. Validation of the top trunk drivers identified in the screen, including MET (MET proto-oncogene, receptor tyrosine kinase), GRB2-associated binding protein 1 (GAB1), HECT, UBA, and WWE domain containing 1 (HUWE1), lysine-specific demethylase 6A (KDM6A), and protein-tyrosine phosphatase, nonreceptor-type 12 (PTPN12), showed that deregulation of these genes activates an EMT program in human HCC cells that enhances tumor cell migration. Finally, deregulation of these genes in human HCC was found to confer sorafenib resistance through apoptotic tolerance and reduced proliferation, consistent with recent studies showing that EMT contributes to the chemoresistance of tumor cells. Our unique cell-based transposon mutagenesis screen appears to be an excellent resource for discovering genes involved in EMT in human HCC and potentially for identifying new drug targets. [ABSTRACT FROM AUTHOR]

The Human Protein Atlas contains immunofluorescence images showing subcellular locations for thousands of proteins. These are currently annotated by visual inspection. In this paper, we describe automated approaches to analyze the images and their use to improve annotation. We began by training classifiers to recognize the annotated patterns. By ranking proteins according to the confidence of the classifier, we generated a list of proteins that were strong candidates for reexamination. In parallel, we applied hierarchical clustering to group proteins and identified proteins whose annotations were inconsistent with the remainder of the proteins in their cluster. These proteins were reexamined by the original annotators, and a significant fraction had their annotations changed. The results demonstrate that automated approaches can provide an important complement to visual annotation. [ABSTRACT FROM AUTHOR]

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., (© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.)