Oncogenic mutations in the small GTPase Ras contribute to ~30% of human cancers. However, Ras mutations alone are insufficient for tumorigenesis, therefore it is paramount to identify cooperating cancer-relevant signaling pathways. We devised an in vivo near genome-wide, functional screen in Drosophila and discovered multiple novel, evolutionarily-conserved pathways controlling Ras-driven epithelial tumorigenesis. Human gene orthologs of the fly hits were significantly downregulated in thousands of primary tumors, revealing novel prognostic markers for human epithelial tumors. Of the top 100 candidate tumor suppressor genes, 80 were validated in secondary Drosophila assays, identifying many known cancer genes and multiple novel candidate genes that cooperate with Ras-driven tumorigenesis. Low expression of the confirmed hits significantly correlated with the KRASG12 mutation status and poor prognosis in pancreatic cancer. Among the novel top 80 candidate cancer genes, we mechanistically characterized the function of the top hit, the Tetraspanin family member Tsp29Fb, revealing that Tsp29Fb regulates EGFR signaling, epithelial architecture and restrains tumor growth and invasion. Our functional Drosophila screen uncovers multiple novel and evolutionarily conserved epithelial cancer genes, and experimentally confirmed Tsp29Fb as a key regulator of EGFR/Ras induced epithelial tumor growth and invasion.
Mutations in Ha-RAS, N-RAS, and K-RAS genes are frequent in human tumors and represent oncogenic drivers of cell proliferation and survival. However, tissue growth induced by oncogenic Ras is restrained by the induction of cellular senescence [1,2] and additional mutations are needed to promote tumorigenesis and metastasis [3]. Metastatic dissemination represents a complex process [4,5] that appears to be regulated via multiple biological pathways [6,7]. Considering millions of cancer patients, in particular in an aging population, it is, therefore, paramount to identify novel pathways and genes involved in cancer growth and metastasis. Many developmental pathways and signaling cascades were originally identified in Drosophila melanogaster [8,9]. Among the most studied conserved signal transduction pathways involved in tumorigenesis are Ras and Notch [8,10–12]. In landmark studies, invasion-metastasis has also been successfully modeled in fly larvae [13]. Using the Drosophila system, cell polarity genes were identified as suppressors of Ras-driven tumor growth and invasion [14,15] implicating high inter-species conservation of fundamental genetic networks controlling transformation and metastasis [16]. Given the accessibility of Drosophila to in vivo RNAi screening approaches and conservation of major developmental pathways including Ras, the fly offers a valuable opportunity to genetically dissect cancer-relevant signaling networks in the context of a whole animal, which could lead to the identification of novel biomarkers and new drug targets for a diverse range of epithelial tumors. Moreover, whereas mammals have three RAS genes, K-, N- and Ha-Ras, the Drosophila has only two: D-Ras1 or Ras85D and D-Ras2 or Ras64B [17]. Ras85D has been shown to be the authentic human ortholog [18,19], making the fly a suitable model system to genetically dissect Ras-regulated networks. One of the most frequent Ras mutations is RasV12, an activating point mutation in codon 12 [20,21]. To identify novel conserved genes involved in RasV12-driven tumor progression and metastases, we performed a dRas (Ras85DV12) -driven tumorigenesis screen using Drosophila genetics, stratified the mammalian orthologs of the fly hits in thousands of human cancers, and further validated the top 100 candidate tumor suppressor genes in secondary Drosophila assays. Our screen identified many known cancer genes and multiple novel, evolutionary conserved candidate genes that cooperate with Ras in tumorigenesis. We also validated and analyzed the mechanism of action of one of these novel tumor suppressors, the Tetraspanin Tsp29Fb, showing that it inhibits Ras signaling and acts to maintain epithelial tissue architecture.
To identify genes involved specifically in Ras-driven epithelial cancer development, we designed a high-throughput in vivo RNAi screening system in Drosophila that enabled us to assay a large number of previously unexplored genes in tumorigenesis in vivo, and to monitor tumor formation, invasion, and formation of distant site metastases. dRasV12 (Ras85DV12), GFP and RNAi expression were targeted to the larval eye disc epithelium using the eyeless (ey) -FLP; actin flip-out system [22,23]. This enables monitoring of stable GFP-labelled tumors in transparent third instar larvae. Overexpression of Drosophila dRasV12 in developing eye discs resulted in GFP-labelled epithelial hyperplasia (Fig 1A and 1B) and ultimately death at the pupal stage. To exclude the possibility that defective larval-pupal transition was due to tumor formation in the ecdysone-producing larval ring gland, we showed that the ey driver was not expressed in the ring gland (S1A Fig). To rule out the possibility that the observed phenotypes might be due to genetic load of the experimental line, we showed that expression of a dominant negative dRaf transgene was able to rescue pupal lethality in both male and female dRasV12 bearing flies (S1B Fig), indicating that larval delay is a consequence of dRasV12-induced overgrowth of the larval eye disc. To test whether RNAi-mediated knockdown can identify suppressors of epithelial tumor progression, we assayed Drosophila genes previously reported to have this function [14,15,24]; knockdown of Discs-large (Dlg) and Bazooka (Baz) resulted in large tumor formation with ectopic GFP+ foci (Fig 1A) and ventral nerve cord invasion (Fig 1B). To establish the cut-off criteria for selecting valid hits from the screen we analysed multiple tests of control and dRasV12 larvae (n = 600 each), which revealed complete pupariation on day 8 after egg laying (AEL), a 2–3 day delay relative to the control, with a mean pupal progeny percentage of 100% ± 0. 0 SD for control and 98. 8% ± 5 SD for dRasV12 flies (Fig 1C). In contrast, co-expression of dlg-RNAi or baz-RNAi with dRasV12 (n = 600 each) led to defective pupariation (with larval progeny present at 66. 64% ± 7. 58 SD and 62. 5% ± 5. 98 SD, respectively) on day 8 AEL (Fig 1C). Based on these data, we set our primary screen cut-off at the 80-percentile probability of pupariation corresponding to a Z-score of > 1. 65. At this threshold, we consistently identified baz and dlg RNAi lines in multiple blinded tests (Fig 1D). Thus, our experimental set-up can be used to identify genes that modulate progression of dRasV12-induced tumors. Using this Drosophila set-up, we screened for novel modulators of dRasV12 -driven epithelial tumor progression. Female Drosophila carrying the dRasV12 oncogene were crossed to male flies containing UAS-RNAi transgenes (from the GD RNAi library from the Vienna Drosophila Resource Center) resulting in progeny that express dRasV12 and the respective RNAi transgenes specifically in the developing eye-antenna disc epithelium (Fig 1E). We choose to study the eye-antenna disc because it constitutes an epithelial monolayer where proliferation, differentiation and tissue structure can be easily analyzed [20,25]. This allowed us to screen for enhanced growth of the dRasV12-driven epithelial tumors using delayed pupariation on day 8 AEL as a high throughput read-out. In addition to larval arrest, we recorded metastatic events, namely invasion of the tumors anteriorly to the larval mouth hooks and ectopic GFP signals for all UAS-RNAi lines assayed. We excluded RNAi hairpins with insufficient targeting specificity, i. e. , with more than six Can repeats and/or S19 score <0. 8 leaving 10,720 UAS-RNAi lines corresponding to 6,675 evolutionarily conserved genes for the screen (S1 Table). Using our selection criteria, we identified 951 Drosophila candidate genes that when down-regulated resulted in larval arrest and/or local invasion of the tumor anteriorly to the larval mouth hook and distant foci of GFP-labelled eye disc epithelial cells (S2 Table). Thus, our screen identified multiple Drosophila candidate genes that when downregulated via RNAi potentially promote dRasV12-induced in vivo epithelial tumorigenesis. To determine whether our Drosophila tumor screen results are translatable to human cancers, we performed differential mRNA expression analysis of the corresponding human orthologs in multiple tumors and matched normal tissues of 3351 patients (S3 Table). Among 637 predicted human orthologs, we could derive data for 619 genes, which were present on the respective gene expression arrays. A large number of genes identified exhibited reduced expression in primary human cancers compared to the respective matching normal tissue (S3 Table). As a control, we also analyzed the mRNA expression profiles of one million randomly selected sets of 619 genes. Compared to these expression profiles, our experimentally-obtained candidate list was significantly enriched for conserved genes with lower expression in all human malignancies tested (p<0. 000007; Fig 2A). The strongest enrichment for low tumor expression was observed in tumors with frequent Ras activating mutations, such as in colon, lung and pancreatic cancer (Fig 2B). In line with our screen set-up, significant enrichment of hits with low tumor expression was observed almost exclusively in solid malignancies, indicating their conserved function in control of epithelial transformation (Fig 2B and S2 Fig). Using public mRNA sequencing data from human tumors and normal tissues (TCGA and GTEx), we were able to retrieve expression data for all 637 candidate genes. Analysis of TCGA and GTEx again showed that the identified fly genes were in toto significantly downregulated in human cancer (Fig 2A and 2B), confirming the microarray data. Of note, although the 637 gene set was significantly downregulated in colon and lung TCGA and GTEx datasets (Fig 2B), we did not observe significant enrichment for genes downregulated in the TCGA pancreatic ductal carcinoma data set, which might be explained by the low tumor sample number (n = 179). Thus, the mammalian orthologs of our fly candidate genes are strongly under-expressed in multiple human cancers, in particular in tumors with the highest frequency of Ras activating mutations. We next selected the top one hundred candidate genes with the lowest expression scores in all human cancers assayed as compared to their respective normal tissue controls (Fig 3A, S4 Table). The respective fly orthologs (using several RNAi lines to each gene, where possible) were then retested in a secondary Drosophila screen using the ey-GAL4 UAS-dRasV12 system. In this system, expression of oncogenic dRasV12 in the eye epithelium results in a rough adult eye phenotype due to epithelial hyperplasia that can be scored for the effects of the dRasV12 cooperating genes [26]. Enhanced rough eye phenotypes were scored on a scale ranging from 0–3, where a value of 0 indicates no and 3 shows maximum phenotype enhancement. In these confirmation assays, downregulation of eighty out of the 100 retested genes resulted in increased hyperplasia of the eye epithelium, using an arbitrary cut off of an average score of 0. 75 or above, specifically in the presence of constitutively activated dRasV12 (Fig 3B and 3C; S5 Table). It should be noted that although some of the RNAi lines tested were KK RNAi lines (indicated by KK in S5 Table), some of which may overexpress the tiptop (tio) gene and result in potentially aberrant effects [27], we have previously shown that tio overexpression does not modify the ey>dRasV12 phenotype [28]. Thus, the results from the KK lines tested, which mostly confirmed the results of GD lines, are valid. Interestingly, knockdown of tio was identified as cooperating with dRasV12 in our primary screen and was validated as a dRasV12 cooperating tumor suppressor in our secondary screen, consistent with its interaction with the Hippo pathway [27] and the known interaction of the Hippo pathway with Ras signaling [29]. Altogether, the results from our secondary screen indicate that eighty of the top 100 genes are evolutionarily conserved tumor suppressors that control epithelial tissue growth. To characterize the molecular networks collaborating with RasV12-driven tumorigenesis we identified first degree binding partners for both the primary fly hits (S6 Table) and their conserved human orthologs (S7 Table). We only included binding partners if they interacted with at least two hits/orthologs. Gene Ontology (GO) analysis on the primary hits and their first degree binding partners revealed statistically significant enrichment of genes involved in cell adhesion, polarity, differentiation, vesicular transport, growth and apoptosis regulation in Drosophila (S6 Table). In humans, we observed enrichment of many biological processes linked with tumorigenesis including cell migration, morphogenesis, MAPK activation, mitochondrial functions, phosphorylation and signaling, growth, metabolism, lipid metabolism, and cell adhesion (S7 Table). Among known human tumor suppressor genes, we, for instance, identified DCC, NF1, TSC2, FCLN, as well as genes known to be involved in suppression of tumor cell motility and invasion, such as Chordin, Stardust, TIMP2 and Mdyn-D7 (encoding a nucleoside diphosphate kinase related to nm23/Awd, a metastasis tumor suppressor in both flies and mammals) [3,30–33], or genes involved in cell polarity (e. g. MDPZ/patj, MPP5/sdt, NCAM2/fas2) [34,35]. Other identified genes [e. g. TMEM47 (claudin homolog), MYH10/zip (MyoII), CTNNA1, A2 (α-Catenin) ROBO1-3, or VAV1,2] have functions in cell adhesion, cell morphology, and have been implicated in the epithelial-to-mesenchymal transition (EMT) and invasion and metastasis [30,36]. Enrichment analysis of KEGG pathways and MsigDB C2 gene sets [37,38] on human orthologs and their first degree binding partners (S7 Table) identified MAPK signaling, Wnt, VEGF, TGFβ, or EGFR signaling pathways, all strongly implicated in cancer [39,40]. Moreover, genes annotated to leukocyte transendothelial migration, cell adhesion, adherence or tight junctions, and, most importantly Ras signaling, were markedly enriched in our KEGG and C2 pathway maps (S3 Fig, S8 Table). Finally, we analysed the 80 human orthologs that were confirmed in our secondary Drosophila screen, along with their 40 first degree binding partners, which interacted directly with at least 2 of the 80 validated hits (S5 Table). Using this approach, 55 out of the 80 validated hits could be assigned to pathways associated with tumorigenesis and invasion, such as Ras, Wnt, Notch, or EGFR signaling, as well as DNA damage response and repair, cell cycle checkpoint control, immune responses, metabolism, autophagy or apoptosis (Fig 4, S9 Table). Further, we found that key nodes of regulation by our confirmed hits in this interaction map were known molecules that modulate survival, EMT and drug resistance in Ras mutated pancreatic cancers, such as YAP1 [41,42], SIRT1 [43,44], BAG3 [45,46], ILK [47–49], CDK5 [50–52], HDACs or GSK3β [53,54]. Overall, our functional screen in Drosophila has identified many known mammalian cancer genes. Importantly, 25 of the 80 validated hits could not be assigned to known signaling networks, indicating that this approach identified novel genes with putative tumor suppressor functions. Our experimental approach aimed to identify evolutionarily conserved tumor suppressors of RAS codon 12 mutant-driven carcinomas. Based on the observed cooperative interaction, putative tumor suppressor genes identified in our screen should show low mRNA expression in human KRASG12 mutated tumors. We first tested whether expression of the human orthologs of the 80 confirmed Drosophila hits correlate with the survival of patients with pancreatic adenocarcinomas, a tumor-type with frequent KRAS activating mutations. Tumor mRNA expression levels (stratified by high and low expression levels) for the 80 human gene orthologs showed that this gene set indeed predicts survival of pancreatic cancer patients (TCGA cohort), with low expression significantly correlating with poor survival (Fig 5A). In addition to poor survival, tumors exhibiting low mRNA expression of the 80 genes were markedly enriched for KRASG12 mutations (Fig 5B). Analysis of the TCGA cohort of lung and colon adenocarcinomas, cancers in which RasG12 mutations are known to be commonly involved, showed that whilst low expression of the 80 genes correlated with poor prognosis in lung cancer at high significance, this was not observed in colon cancer samples (Data File S1). Moreover, in lung and colon adenocarcinomas, there was no correlation with low expression of the 80 genes and the G12 activating mutation in KRAS, HaRas or NRas mutations, or with other Ras activating mutations, G13 or Q61 [55] (Data File S1). Considering that we did not observe high frequency of genetic mutations for the 80 gene-set in these pancreatic tumors (CBioportal), we next asked whether these genes may be epigenetically regulated in KRASG12 mutant tumors. In line with mRNA survival analysis, CpG methylation analysis of pancreatic adenocarcinoma tumors (TCGA) revealed that high methylation of the 80 genes in pancreatic tumors is significantly associated with worse overall survival (Fig 5C). Moreover, using enrichment analysis, high methylation was associated with the KRASG12 mutational status (Fig 5D). Finally, we stratified our validated gene set into two groups: 1) the 55 known cancer genes and 2) the 25 novel, unassigned genes. Low expression of the set of 55 known cancer genes was associated with poorer patient survival in pancreatic cancer patients from the GSE21501 cohort, although this was just above statistical significance of p = 0. 05 (Fig 5E). Importantly, low expression of the set of 25 novel unassigned genes showed a significant association with poor survival in these pancreatic cancer patients (Fig 5F). These results show that our screen has uncovered evolutionarily conserved cancer genes that, collectively, correlate with the KRAS mutation status of tumors and predict survival of patients diagnosed with pancreatic cancer. Among the 25 novel hits, the strongest phenotypes in the secondary screen was observed for Tsp29Fb, a member of the Tetraspanin family coding for four-pass transmembrane proteins [56]. Therefore, we focused on the functional characterization of Tsp29Fb. During development, Tsp29Fb mRNA expression occurs in various larval tissues reaching the highest level at the third instar larval stage and in adults (http: //flybase. org/reports/FBgn0032075. html). ey-Flp-mediated knockdown of Tsp29Fb (using a RNAi transgene (v2824), subsequently labelled tspi or Tsp29Fb-RNAi in the Figures), had no overt effect on untransformed eye discs (Fig 6A), but reduced adult eye and head size. However, in a dRasV12 background, RNAi-mediated knock-down of Tsp29Fb resulted in larval arrest and massive overgrowth with invasion of GFP-labeled tumor cells into the ventral nerve cord, ectopic foci formation, and invasion into the hemolymph (Fig 6A–6C). In the absence of dRasV12 expression, knockdown of Tsp29Fb driven by different eye or wing specific drivers with distinct temporal and tissue expression (GMR-GAL4, en-GAL4 and MS1096-GAL4) resulted in reduced eye or wing sizes (S4A–S4D Fig), indicating that knockdown of Tsp29Fb itself can negatively affect tissue growth. Thus, Tsp29Fb knockdown results in reduced tissue growth, but in dRasV12 expressing tissue it enhances dRasV12-driven tumor growth and invasion. We next explored the molecular mechanism by which Tsp29Fb affects tissue growth using the power of fly genetics to specifically interfere with cell death pathways or defined signaling pathways. The eye and wing growth defects were not caused by changes in cell death, since no organ size rescue was observed when blocking apoptosis with BskDN (kinase-dead JNK) or the caspase inhibitors DIAP1 and p35 (S4B Fig and S5A Fig), and no proliferation changes were detected in the larval epithelium by EdU and phosphorylation of Histone 3 (PH3) staining. However, knockdown of Tsp29Fb in the developing eye affected baso-lateral junction morphology with aberrant localization of the polarity protein Dlg and actin polymerization in the pupal retina (S5B Fig). Importantly, Dlg localization defects were also observed in MARCM Tsp29Fb-RNAi clones in the pupal retinal epithelium (GFP+) (Fig 6D), giving rise to smaller rough adult eyes (Fig 6E). Dlg localization appeared diffuse (fuzzy) at the junctions in the MARCM Tsp29Fb-RNAi clones relative to wild-type clones, whilst the baso-lateral junction cell polarity protein Scribbled (Scrib) and the adherens junction protein E-cadherin (E-Cad), abundance or localization were not affected (S6A Fig), suggesting that the Tsp29Fb specifically affects Dlg localization. Although Dlg protein junctional localization was abnormal in Tsp29Fb depleted tissue, Dlg protein abundance in the Tsp29Fb-RNAi clones was not significantly affected (S6A Fig, S10 Table), nor was Dlg protein abundance in eyFLP-out larval eye tissue as determined by Western blot quantification (S6B Fig, S11 Table). Thus, Tsp29Fb specifically affects Dlg junctional localization. To assess whether the effect of Tsp29Fb knock-down on Dlg localization is functionally important, we examined genetic interactions with the cell polarity gene, scrib, which functions with dlg at baso-lateral junctions. We used a UAS-scrib-RNAi (previously shown to be effective in knocking down Scrib protein levels and specific for scrib [57]), expressed in the central region of the developing wing epithelium via dppBLK-GAL4 (S6C Fig), which resulted in a small wing phenotype with reduced area between wing veins L3 and L4, and breaks in the L3 wing vein. Co-knockdown of Tsp29Fb with scrib, strongly enhanced the loss of L3 vein defect (S6C Fig, S12 Table). Thus, Tsp29Fb affects Dlg junctional localization and genetically interacts with the Scrib/Dlg module. We finally investigated which signaling pathway (s) could be modulated by Tsp29Fb. We failed to find any interactions or effects of Tsp29Fb down-regulation on Notch, Jak-Stat, JNK, Hippo, Wingless (Wnt) or Dpp (BMP/TGFβ) signaling as assessed by phenotypic modification or target gene expression (S5C Fig). However, Tsp29Fb knockdown in the eye showed a genetic interaction with a Drosophila constitutively active EGFR allele [58] (Fig 7A). In addition to functional interaction with fly EGFR, we again independently confirmed that simultaneous down-regulation of Tsp29Fb (using v2824) with dRasV12 expression via ey resulted in an enhanced overgrowth eye phenotype (Fig 7B and 7C, S13 Table). A second Tsp29Fb RNAi line (v2823) also showed enhancement of the dRasV12-driven eye tissue overgrowth (S7A Fig). Conversely, overexpression of Tsp29Fb, using the UAS-Tsp29Fb-HA transgene, showed suppression of the ey>dRasV12 hyperplastic phenotype (S7B Fig, S14 Table), consistent with Tsp29Fb acting to reduce dRas signaling. As expected from a functional association, knocking down Tsp29Fb expression in dRasV12 transformed eye-antenna discs enhanced phosphorylation of key EGFR-Ras-downstream target ERK (pERK) (Fig 7D, S15 Table). Tsp29Fb knockdown alone also led to increased pERK without effecting total ERK relative to the control (S7C Fig, S16 Table). These genetic and biochemical results in Drosophila indicate that knock-down of Tsp29Fb affects epithelial architecture and enhances EGFR-Ras signaling.
We have performed a large-scale screen in Drosophila melanogaster to identify genes involved in suppression of RasV12-induced epithelial tumors and tested 10,777 UAS-RNAi lines corresponding to evolutionarily conserved 6675 genes. For this screen, we expressed dRasV12 and UAS-RNAi constructs in the eye imaginal disc of fly larvae, which allowed us to monitor tumor formation, invasion, and formation of distant site “metastases”. Moreover, RNAi-mediated knockdown allowed us to assay a large number of previously unexplored genes in epithelial tumorigenesis in vivo. Using this experimental set-up, we identified multiple known tumor suppressor pathways as well as many novel genes and pathways potentially involved in the suppression of RasV12-driven epithelial tumorigenesis. Importantly, human genes orthologous to the primary fly hits that, as an entire data set, were massively downregulated when we assayed gene expression in thousands of human cancers, supporting the concept that genes controlling epithelial growth, fate and integrity are highly conserved across phyla. The top 100 candidate genes stratified in human cancers were then further validated in secondary Drosophila Ras-driven eye hyperplasia assays, confirming that 80 of these genes were cooperating tumor suppressors with dRasV12 in epithelial tissues. This screen allowed us to construct an evolutionarily conserved network of genes and pathways predicted to cooperate with Ras in tumor growth and invasion-metastasis. This data set should provide a starting point for the identification of novel genes and pathways involved in tumor suppression and metastasis of Ras-driven epithelial cancers. Loss of function screens have previously been performed in Drosophila, identifying genes that modify Ras function [59,60]. However, only 20–30% of genes in Drosophila genome can be mutated to give a detectable phenotype [61], leaving the function of a majority of expressed gene unexplored. In addition, these screens have been largely conducted in differentiating eye cells of the Drosophila larvae/pupae that display limited or no mitotic potential and undergo final steps of differentiation [60]. Moreover, in previous studies, Drosophila has been established as a tool to screen for invasive tumor growth [14,15]; but these screens relied on genetic mutants and required 3 crosses to analyze the effects due to homozygous lethality of the mutants and pupal stage lethality of dRasV12 expression alone. We therefore established a model that circumvents dRasV12-induced lethality by using a CyO, tub-GAL80 balancer [62], offering a system that can be used for rapid screening and to model hyperplastic tumor growth, and formation of distant site “metastases” in a pool of epithelial progenitor cells. Using this experimental system, we tested genes that are conserved between Drosophila and human using RNAi-mediated knockdown, which allowed us to assay the role of a large number of previously unexplored genes in Ras-driven epithelial tumorigenesis in vivo. Since hallmarks of cancer such as migration and proliferation are regulated by evolutionarily conserved pathways, e. g. Ras/MAPK, Wnt or Hedgehog signaling, our high primary hit rate can be explained by the selected set of conserved genes [63]. C2 and KEGG analysis of our hit list revealed that our screen had markedly enriched for known genes and pathways implicated in mammalian tumorigenesis including cell adhesion, cell polarity, phosphoinositide signaling, metabolism, or Wnt, Hedgehog, and, as one must expect, Ras signaling. Interestingly, several genes and pathways identified in our primary screen have an effect on oxidative stress levels or proper mitochondrial function: detoxifying enzymes such as Gutathione S Transferases or Wwox [64], and many mitochondrial genes, the loss of which can potentially impede proper mitochondrial function and generate oxidative stress. Interestingly, we have recently shown that increased oxidative stress levels induced by autophagy impairment can indeed cooperate with oncogenic Ras by triggering the JNK stress response pathway [28]. Perhaps not surprisingly, giving the central role of Ras in conveying intracellular signals, we find that oncogenic Ras cooperates with a wide range of cellular processes scattered across all the different cellular compartments. However, we can attempt to group the Ras-cooperating tumor suppressors identified and validated in our screen into a few pivotal mechanisms that influence Ras tumorigenic outcome: i) modification of the DNA structure, replication or regulation (eg chromatin remodelling, cell cycle regulators, DNA repair enzymes); ii) regulation of Ras and other signalling pathways (eg Wnt, Notch, Hippo, Jak/Stat, TGFβ, PI3K); iii) regulation of apoptosis; iv) disruption of metabolic homeostasis and increase of oxidative stress (eg glycogen breakdown, glucose transport, TCA cycle, autophagy); iv) disruption of plasma membrane organisation or homeostasis (eg cell junction organization, endocytosis). Indeed in this study, in line with recent data suggesting that Tetraspanins are enriched in membrane micro-domains and regulate receptor signaling [65–67], we found that Tsp29Fb genetically interacts with EGFR and dRas and regulates EGFR-Ras signaling. Whilst we have concentrated here on the validated top 80 genes, future analysis of the remaining hits from our screen, may reveal new signaling pathways, or reveal further novel tumor suppressors, involved in Ras-driven tumorigenesis. Of the 80 confirmed genes, 55 belonged to known signaling networks, whereas 25 could not be assigned to any known functional module. Expression as well as methylation analysis of these genes accurately stratified high and low risk survival groups of patients diagnosed with pancreatic cancer, and also correlated with the mutational status for RASG12 in the pancreas tumors of these patients. Given our findings, it will be important to assess whether low expression of the 80 confirmed genes is correlated with poor patient survival in other cancers where RAS signaling is elevated, such as EGFR or RAS-GAP (NF1) tumors. The top hit among the novel 25 tumor suppressor genes was the Tetraspanin family member, Tsp29Fb. Mechanistically, our genetic and biochemical experiments show that Drosophila Tsp29Fb modulates Ras activation and in genetic epistasis experiments affects EGFR-Ras signaling. Whether Tsp29Fb regulates other signaling pathways, alone or together with dRasV12, needs to be further explored. Interestingly, depletion of Tsp29Fb resulted in mislocalized Dlg at cell junctions, and ommatidial patterning defects in terminally-differentiating photoreceptor cells, implicating this protein in epithelial apico-basal polarity regulation and the control of epithelial architecture. It is possible that these effects of Tsp29Fb depletion on epithelial architecture directly affect EGFR/Ras activation, as Scrib depletion does in mammalian cells [68,69] and in Drosophila epithelial tissue [70]. Indeed, our results showing that Tsp29Fb genetically interacts with scrib support that notion that Tsp29Fb regulates, or functions with, Scrib/Dlg in cell polarity regulation in epithelial development and in the control of signaling pathways. Further analysis of how Tsp29Fb, as well as the other novel tumor suppressor genes identified in our screen, cooperate with dRasV12 in tumorigenesis, will potentially reveal the importance of cell polarity regulation, as well as other novel pathways, in Ras-driven cancer.
All RNAi transgenic fly lines used in the primary screen were obtained from the VDRC RNAi stocks [71]. The following lines were used for generating the driver line for the screen: the P (UAS-2xEGFP), P (ey3. 1-FLP) 1, y (1) w (1118) and P (Act5C (FRT. y[+]) GAL4) 2 insertion lines and UAS-scrib-RNAi C2V (VDRC 11663,2nd Chromosome, viable) were obtained from Barry Dickson (IMP, Vienna). The fly stocks carrying insertion P (UAS-Ras85DV12) TL1 and P (UAS-Ras85DN17) TL1 were obtained from the Bloomington Drosophila Stock Centre (BDSC) (BL4847 and BL4846, respectively). The CyO, P (Tub-Gal80) line was a kind gift from Olaf Vef, Institute for Genetics, Mainz [62], UAS-RafDN was a kind gift from C. Samakovlis, Wenner-Gren Institute, Stockholm [72]. w1118 wild type control flies were obtained from the VDRC and used for controls since the Drosophila melanogaster w1118 background is isogenic to the VDRC RNAi library. Other fly stocks were: UAS-Dicer2 (VDRC; 60009), UAS-luciferase (BL31603, BDRC), ey-GAL4 (J. Treisman [73,74]), GMR-GAL4 (B. Hay; [75]), MS1096-GAL4 (BDRC; BL8860), en-GAL4 UAS-GFP (L. Johnston), C96-GAL4 (BDRC; BL43343), UAS-lacZ (G. Baeg), UAS-bskDN (M. Mlodzik [76]), UAS-Diap1 (BDRC; BL6657), UAS-p35 (B. Hay [77]), UAS-mamH (BL26672) and EGFR-ElpB1/CyO (EGFRE3; [58]) ), ey>dRasV12 [26], ey-FLP; Actin>CD2>GAL4, UAS-GFP/TM6B or Actin>CD2>GAL4, UAS-RFP/TM6B (ey-FLP-out, this study), UAS-GFP; dppBLK-GAL4 Tub-GAL80ts/TM6B (this study), UAS-scrib-RNAi UAS-GFP; dppBLK-GAL4 Tub-GAL80ts/TM6B (this study), ey-FLP1, UAS-mCD8-GFP; Tub-GAL4, FRT82B, Tub-GAL80 (MARCM3, J. Treisman), UAS-Tsp29Fb-HA (NCBS Drosophila Facility, Bangalore), and the Tsp29Fb deficiency stock was a kind gift from R. Le Borgne (Université de Rennes, Rennes, France). Transgenic UAS-RNAi males were crossed to driver virgin females and the GFP positive progeny were analyzed at the indicated days after egg laying. The numbers of progeny arrested at the larval stage were counted. A Z score cut-off of 1. 65 (Mean control-test) /SD was used to select for positive RNAi hits. Larval mouth hook invasion and occurrences of GFP-labelled foci at distinct sites were determined using a Leica fluorescence stereomicroscope. eyeless-GAL4, UAS-dRasV12/CyO (ey>RasV12) female flies were crossed to either UAS-lacZ males for control or to UAS-RNAi males. Crosses were raised at 29°C for 11 days before scoring. Enhancement of RasV12-driven overgrowth was assessed visually in at least 20 F1 ey-GAL4, UAS-dRasV12/UAS-RNAi females by comparing them to ey-GAL4, UAS-dRasV12/UAS-lacZ females. Overgrowth was scored as: 0 = no enhancement, 1 = enhancement, 2 = strong enhancement, 3 = very strong enhancement. Intermediate values are representative of variable penetrance in the phenotype. Scores of each RNAi line tested were plotted on a graph. When several independent RNAi lines were scored for a particular gene, the mean was calculated and plotted on the graph as a red line. An RNAi line was considered validated when it showed enhancement of 1 or above. For the analysis of cephalic complexes, eye/antennal and wing discs, larvae were picked at the third instar stage and appropriate age. Dissection was performed by pulling out larval mouth-hooks. Tissues were fixed in 3. 7% buffered formalin solution for 20 min, blocked with 5% Normal Goat Serum in 1XPBST (0,1% Tween-20 in phosphate buffer saline) for 1hour at room temperature. Primary antibodies used were anti-Dlg (Mouse monoclonal 4F3,1: 50, Developmental Studies Hydridoma Bank (DSHB) ), anti-Elav (Mouse monoclonal 1: 50, DSHB), anti-E-Cad (Rat monoclonal, 1: 50, DSHB), anti-Scrib (Rabbit polyclonal, 1: 1000, C. Doe), anti-HA (rat 1: 100, Roche). Secondary antibodies were anti-mouse Alexa 488,568,633, or anti-rat Alexa 488,568 (Molecular probes). F-actin was detected using phalloidin–tetramethylrhodamine isothioblueate (TRITC; Sigma, 0. 3 μM, 1/1000), and DNA using 2- (4-amidinophenyl) -1H-indole-6-carboxamidine (DAPI, 1mM). The samples were mounted with Vectashield DAPI containing medium (H-1200, Vector Laboratories, Inc. , 30 Ingold Road, Burlingame, CA or in 80% (v/v) glycerol/PBS. Images were taken using Carl Zeiss LSM 410 UV, Bio-Rad MRC1000, Olympus FV1000, LEICA TCS SP5 or Zeiss LSM780 PicoQuant FLIM confocal laser scanning microscopes. Images were processed using Confocal AssistantR, Fluorview, Leica LAS AF Lite, Zeiss Zen or Image J software. Images were assembled using Adobe Photoshop CS5. 1. Adult eyes were imaged with a Scitec Infinity1 camera. For quantification of Dlg staining in pupal mosaic epithelial tissue, maximum intensity projection images were generated from a subset of stacks from each z-stack image. Large areas of GFP-positive or GFP-negative tissue were, roughly equal in size and adjacent to one another, were selected. Using the histogram tool in Adobe Photoshop CC, the mean pixel intensity for Dlg1 staining was determined for each GFP-positive and GFP-negative area, and the ratio between the two areas was determined. Statistical analyses were performed using GraphPad Prism 7. For imaging of Drosophila adult eyes and wings, adult flies were frozen at -20°C before imaging in order to facilitate positioning them under the microscope. Images were captured on a Lumenera or Scitec Infinity1 camera attached to Olympus SZX7 dissection microscope and processed using Adobe Photoshop CS3. Flies were prepared for scanning electron microscopy by progressive dehydration in concentrations of ethanol ranging from 25% to 100% over the course of 4 days on a nutator. Flies were then desiccated by critical point drying in a Leica critical point dryer, mounted on steel stubs and coated with 20nm of gold particles in a sputter coater. Representative images of each phenotype were taken on a JEOL JCM-6000 NeoScope scanning electron microscope at 80x and 300x magnifications. Images were cropped and aligned in Adobe Photoshop. The area of adult eyes was measured in Photoshop CS5. 1 and statistically analyzed using Microsoft Excel and GraphPad Prism. Drosophila wings were prepared from female flies and mounted in Canadian Balsam. Wing size was measured in Photoshop CS5. 1 and statistically analyzed using Microsoft Excel and GraphPad Prism. For quantification of the wing vein defects in the dpp>scrib-RNAi experiment, the total length of the L3 vein was measured in all samples using ImageJ, with a straight line drawn between any gaps (covering the shortest possible distance). The length of the gaps in the L3 vein were measured using the shortest possible distance between vein sections. The length of L3 vein loss to total L3 vein length was then expressed as a ratio, and analysed statistically using a Student’s t-test. Eye-antennal discs (from 11 larvae per sample) were dissected from the ey-FLP-out dRasV12, ey-FLP-out dRasV12 tspi, ey-FLP-out lacZ, or ey-FLP-out tspi larvae, homogenized in 0. 1 M Tris-HCl pH 6. 8,2% SDS, 5 mM EDTA, 5 mM DTT buffer containing Complete protease inhibitor cocktail (Roche, Basel, Switzerland), 1 mM Na3 (VO) 4,5 mM NaF2. Protein concentration was determined by Lowry or DC assays. Samples containing 20 mg of protein were electrophoresed on a 10% SDS-PAGE gel and transferred to Immobilon-FL polyvinylidene difluoride membranes. Antibodies used were monoclonal mouse anti-pERK (diphosphorylated ERK1/2/MAPK1/2,1: 10. 000, #M8159 Sigma), anti-ERK (rabbit polyclonal, 1: 1000), anti-Dlg (mouse monoclonal 4F3, DHSB, 1: 1000), and monoclonal mouse anti-α-Tubulin (1: 10,000 Cell Biochem). Western band intensity was measured using ImageJ or Adobe Photoshop, and then normalized to the intensity value of the α-tubulin control using Microsoft Excel and statistical analyses were carried out using GraphPad Prism. To identify orthologs between Drosophila and mouse and between Drosophila and human, we obtained pre-computed one-to-one and one-to-many orthology predictions from Inparanoid (version 6. 1) to Ensembl databases [78]. In case of one-to-many mappings, all the ortholog targets for a given gene were considered for downstream analysis. 3351 samples of tumor and normal control tissues were hybridized using an Affymetrix HG-U133ab chipset. If a defined gene showed a significantly higher expression in the tumor tissue compared to normal tissue (t-test, different variances, p-value < 0. 01), it was scored +1. If the gene is significantly higher expressed in normal tissue, it was assigned a score of -1. If one gene has more than one probe set on the Affymetrix chip, the score was divided by the number of probe sets for this gene, e. g. if 2 of 3 probe sets of gene X showed a significantly higher expression in normal tissue and no significant differences in the 3rd probe set, expression was scored -2/3 for gene X in this specific tumor/normal tissue pair. The human orthologs of our putative tumor suppressor gene list were used to calculate the tumor-suppressor-oncogene-score (TSOS). For 619 of the 637 genes we found at least one probe set on our Affymetrix chip. Moreover, we assessed the TSOS scores using TCGA and GTEx RNAseq datasets. The TSOS is the sum of the described score for all genes in our list and all tumor/normal tissue pairs divided by the number of genes. It is a measure for an overall significantly higher expression in tumor tissue (positive values) or normal tissue (negative values). As a control, we randomly sampled 613 human genes from the Drosophila ortholog gene list one million times. Only 7 random lists had a lower TSOS than our gene list, which indicate a significantly low TSOS (p-value 7x10-6) for our gene list. The following normal tissue/tumor tissue pairs were analyzed using arrays: We performed a functional analysis separately for the Drosophila hits and their corresponding human orthologs for Gene Ontology, KEGG and C2 gene sets from the MsigDB database. In order to identify indirectly associated gene sets and pathways, we performed additional analysis considering also direct binding partners of the Drosophila hits and corresponding human orthologs. We defined protein interactions from the protein-protein interaction database BioGRID (version 2. 0. 50) in order to extract binding partners to the Drosophila candidate gene set and to the corresponding human orthologs. Only first degree binding partners that interact with at least two direct Drosophila hits or their human seed and inparalog orthologs were considered for the analysis. Gene Ontology (GO) analysis was performed using the Biomart database [79] for Drosophila melanogaster and Ensembl (http: //www. ensembl. org/) for Homo sapiens. For the Gene Ontology enrichment analysis we used the”topGO” package [80] from Bioconductor [81] version R-2. 7. 1. GO analysis was performed with fly hits and human orthologs whose RNAi hits had a Z-score >1. 65. For each Gene Ontology term of the class”biological process”," cellular component" , and" molecular function" a hypergeometric test (one-sided Fisher exact test) was performed. A GO enrichment analysis was also performed for the Drosophila candidate gene set with and without binding partners and for human seed and inparalog orthologs with and without binding partners, where the reference gene set is defined by all screened Drosophila genes and for the corresponding human seed and inparalog orthologs. GO terms with a nominal p-value ≤ 0. 05 were selected as significantly enriched. Since terms that occur at a deeper level in the GO tree hierarchy, therefore containing lesser numbers of genes, are considered more biologically informative, we discarded terms containing more than 500 genes from further analysis. KEGG (Kyoto Encyclopedia of Genes and Genomes) annotation of genes for Drosophila melanogaster and Homo sapiens were retrieved from the KEGG database (http: //www. genome. jp/kegg/). The C2 gene set assignments to human genes were obtained from the Molecular Signature Database (http: //www. broadinstitute. org/gsea/msigdb). The C2 dataset defines a collection of curated gene sets derived from KEGG, BioCarta (http: //www. biocarta. com/), GenMAPP (http: //www. genmapp. org/), chemical and genetic perturbations gene sets and canonical pathway gene sets. All screened Drosophila genes and the corresponding human seed or inparalog orthologs were used as reference gene sets. A hypergeometric test (one-sided Fisher' s Exact Test), similar to the test used for GO enrichment analysis, was used to identify over-represented gene lists (C2) and pathways (KEGG) amongst the hits. This analysis was performed on the gene list identified as human orthologs corresponding to the Drosophila cancer hits (Z-score > 1. 65). Significantly enriched terms were arranged in a GO graph structure that was extracted using the bioconductor packages GOstat and GO. db. We implemented a procedure in R that creates a condensed GO graph structure for the set of significant terms by removing iteratively all non-significant parental terms from the graph. The child terms of non-significant parental terms that were removed were reconnected by an edge to the corresponding grandparental terms. The resulting graph layout was performed in Cytoscape (http: //www. cytoscape. org/). The GO terms were subsequently manually assigned to" functional groups" based on their shared roles in a" biological process" ," cellular component" ," molecular function" , for visual representation of the GO data. We associated a total of 40 proteins that directly interact with at least two of the 80 candidate genes from experimental protein interactions defined from BioGrid (version 3. 2. 11) present in the set of human orthologs from all screened Drosophila genes. The analysis of the 80 selected candidate genes and their direct interactors was performed by an enrichment analysis using a hypergeometric test separately for the MsigDB database (v4. 0), Gene Ontology gene sets (org. Hs. eg. db, Bioconductor package: Biological Process, Molecular Function, Cellular Component), Consensus pathway database (cpdb, http: //consensuspathdb. org/), Pathway Commons (pcom, http: //www. pathwaycommons. org/), gene sets from the graphite Bioconductor package including KEGG, Biocarta, NCI, Spike and Reactome. The MsigDB database (v4. 0) includes gene sets for positional gene sets, curated gene sets (pathways), motif gene sets, computational gene sets, gene ontology (GO) gene sets, oncogenic signatures gene sets, and immunologic signatures gene sets (http: //www. broadinstitute. org/gsea/msigdb). We retrieved the RNAseq, methylation, KRAS mutation profiles and clinical data on patient survival from the TCGA database (version 2016_01_28, https: //gdac. broadinstitute. org/). For mutational profiles we used MutSig [82] variant calls (TP. MutSigNozzleReport2. 0. Level_4. 2016012800). For the RNAseq and methylation profile survival analysis we considered only patient samples that were present in the MutSig report" TP. final_analysis_set. maf" and had clinical overall survival information. Using these criteria, for PAAD, 132/177 (74%) RNAseq patient samples with KRAS mutations were available; retrieving normalized illumina hiseq rnaseqv2 RSEM Level 3 data, the Clinical Level_1 and the methylation profiles of preprocessed mean aggregated gene level methylation beta values (Methylation_Preprocess. Level_3. 2016012800, meth. by_mean. data). Patient samples were stratified in RAS-G12 mutation carriers (" G12" )," other" for samples with KRAS mutations assigned but not G12 mutations, and" none" for no RAS mutations. For the enrichments, the category “none” and “other” were aggregated. For survival analysis, patients were split into two groups using k-means clustering. The patient cohort with a larger average RNAseq expression or methylation beta values were denoted as" high" and the cohorts with lower average expression as" low" . The k-means clustering procedure was repeated 100 times on all samples and a patient sample was designated to the most frequently assigned group (low or high). For the clustering procedure we log_e (1+x) transformed the normalized RSEM gene RNAseq samples and scaled the data (z-transformed and centered) for each patient sample. Enrichment and underrepresentation of KRAS-G12 mutations in the low group compared to the high group was assessed using a one-sided Fisher’s exact test. The processing and analysis of the data was performed in R. For the survival analysis we used the survplot function of the survival R package (http: //www. cbs. dtu. dk/~eklund/survplot/). Survival analysis using PROGgeneV2 web-based tool was conducted using the following genes represented by probes on the respective arrays: Set of known cancer genes: ADHFE1, ARL6IP5, AUH, BLVRB, BNIP2, CCND2, CDA, CDO1, CHRD, CRY2, CTSF, CYB5A, CYB5R3, DAB2, DCUN1D4, DPYD, DYNLRB2, ECH1, EIF1, ELMO1, EPB41L3, EPC1, FAM122A, FDX1, FUZ, GABARAP, H3F3B, HMGCL, HSD17B4, ITPR1, MAF, MAP2K4, MCC, MKNK2, MPP5, NR4A2, PGM1, PTPN4, RPS3A, RXRA, SACM1L, SELENBP1, SIK2, SLC7A3, SSBP3, SUCLG2, TK2, UBE4A, UBR2. Set of novel cancer genes: ALG9, EFHD1, NEGR1, PCF11, RBM5, RGN, RSBN1, SNRK, PQLC3, VPS13D, WDR91, YPEL5, ZDHHC3, TSPAN6, HECA, MTMR10, PEX11G, RAB4A, TRAPPC6B, SLC25A25, COLEC11, RASSF9, SMG6, TSHZ3. The analysis was conducted using GSE21501 pancreatic cancer patient cohort in PROGgeneV2 prognostic database (http: //watson. compbio. iupui. edu/chirayu/proggene/database/index. php). Statistical analysis using gene expression data was performed with R version 2. 5. 1 and BioConductor version 1. 8. The remaining statistical analyzes were performed on SPSS 18. 0. Normally distributed data was statistically analyzed using unpaired two-tailed Student’s t-test for single comparisons, and one-way or two-way analysis of variance–ANOVA- for multiple comparisons. ANOVA analyzes were followed by Bonferroni’s post hoc tests. Data that was ordinary was analyzed using unpaired two-tailed Mann-Whitney test. The statistical test used and p values are indicated in each figure legend. p ≤ 0. 05 was considered to indicate statistical significance. Data are shown as mean values ± standard error of the mean (SEM). Supplemental data includes 7 supplemental figures, a supplemental data file and 16 supplemental tables.
Cancer involves the cooperative interaction of many gene mutations. The Ras signaling pathway is upregulated in many human cancers, but upregulated Ras signaling alone is not sufficient to induce malignant tumors. We have undertaken a genome-wide genetic screen using a transgenic RNAi library in the vinegar fly, Drosophila melanogaster, to identify tumor suppressor genes that cooperate with the Ras oncogene (RasV12) in conferring overgrown invasive tumors. We stratified the hits by analyzing the expression of human orthologs of these genes in human epithelial cancers, revealing genes that were strongly downregulated in human cancer. By conducting secondary genetic interaction tests, we validated 80 of the top 100 genes. Pathway analysis of these genes revealed that 55 fell into known pathways involved in human cancer, whereas 25 were unique genes. We then confirmed the tumor suppressor properties of one of these genes, Tsp29Fb, encoding a Tetraspanin membrane protein, and showed that Tsp29Fb functions as a tumor suppressor by inhibiting Ras signaling and by maintaining epithelial cell polarity. Altogether, our study has revealed novel Ras-cooperating tumor suppressors in Drosophila and suggests that these genes may also be involved in human cancer.
invertebrates
medicine and health sciences
rna interference
animals
animal models
oncology
drosophila melanogaster
model organisms
experimental organism systems
genome analysis
gene types
epigenetics
eyes
drosophila
research and analysis methods
genomics
tumor suppressor genes
animal studies
genetic interference
gene expression
head
gene ontologies
insects
arthropoda
carcinogenesis
biochemistry
rna
eukaryota
anatomy
nucleic acids
genetic screens
gene identification and analysis
genetics
biology and life sciences
ocular system
computational biology
suppressor genes
organisms
A genome-wide Drosophila epithelial tumorigenesis screen identifies Tetraspanin 29Fb as an evolutionarily conserved suppressor of Ras-driven cancer