Transposon mediated functional genomic screening for BRAF inhibitor resistance reveals convergent Hippo and MAPK pathway activation events

Transposon mediated functional genomic screening for BRAF inhibitor resistance reveals convergent Hippo and MAPK pathway activation events | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Transposon mediated functional genomic screening for BRAF inhibitor resistance reveals convergent Hippo and MAPK pathway activation events Li Chen, Iulian Pruteanu-Malinici, Anahita Dastur, Xunqin Yin, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5320870/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Genotype-informed anticancer therapies such as BRAF inhibitors can show remarkable clinical efficacy in BRAF-mutant melanoma; however, drug resistance poses a major hurdle to successful cancer treatment. Many resistance events to targeted therapies have been identified, suggesting a complex path to improve therapeutics. Here, we showed the utility of a piggyBac transposon activation mutagenesis screen for the efficient identification of genes that are resistant to BRAF inhibition in melanoma. Although several forward genetic screens performed in the same context have identified a broad range of resistance genes that poorly overlap, an integrative analysis revealed a much smaller functional diversity of resistance mechanisms, including reactivation of the MAPK pathway, PI3K-AKT pathway, and Hippo pathway, suggesting that a relatively small number of therapeutic strategies might overcome resistance manifested by a large gene set. Moreover, we illustrated the pivotal role of the Hippo pathway effector WWTR1 (TAZ ) in mediating BRAF inhibition resistance through transcriptional regulation of receptor tyrosine kinases and through interactions with the E3 ubiquitin ligase NEDD4L. Biological sciences/Cancer/Cancer genomics Biological sciences/Cancer/Cancer therapy Biological sciences/Cancer/Skin cancer Biological sciences/Computational biology and bioinformatics/Genome informatics Biological sciences/Computational biology and bioinformatics/High throughput screening Biological sciences/Molecular biology/Transposition Biological sciences/Genetics/Cancer genomics Biological sciences/Genetics/Functional genomics Biological sciences/Genetics/Gene regulation Biological sciences/Genetics/Genomics Biological sciences/Systems biology/Genomic engineering Biological sciences/Systems biology/Regulatory networks Biological sciences/Systems biology/Systems analysis Health sciences/Oncology/Cancer/Cancer genomics Health sciences/Oncology/Cancer/Cancer therapy Health sciences/Oncology/Cancer/Skin cancer Biological sciences/Molecular biology targeted cancer therapy BRAF inhibitor resistance transposon mutagenesis screening Hippo pathway ubiquitination. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION A vast array of genetic and nongenetic events, such as point mutations, overexpression, translocations, and alternative splicing, occurring across many genes have been shown to cause resistance to BRAF inhibitors in melanoma and other tumor types 1–9 . Although the analysis of clinical samples provides unique insight into therapeutically relevant resistance tumor heterogeneity, limitations in the availability of clinical samples and functional characterization of the human genome combined with a high number of passenger mutations limit the potential to identify low occurrence resistance events 10–13 . To prospectively identify genes that can promote resistance to BRAF and MEK inhibition several landmark studies using genetic screens in the context of BRAF V600E mutant melanoma have been performed. These include gain-of-function (GoF) open reading frame (ORF) and aptamer-mediated dCAS9-activator screens, and loss-of-function (LoF) CRISPR knockout and shRNA screens 14–17 . These screens leverage collections of individually made elements, are rather labor-intensive and are associated with relatively high costs. Even the most advanced of these screening approaches have limitations leading to incomplete saturation; for example, an ORF library is typically limited to one isoform per gene, and noncoding GoF events are poorly covered by most screens. Interestingly, these screens and several transposon mutagenesis screens that have been previously performed in the BRAF V600E melanoma cell lines offer insight into the complexity and diversity of the genes that can mediate resistance to a given therapeutic agent in a specific genomic context. Comparison of the results of these screens revealed different gene sets identified even by screens that were functionally analogous (gain- or loss-of-function) and well validated 18–20 . This raises several interesting questions such as whether screens that are genome-wide by design have indeed reached a substantial level of saturation, how many genes in total can mediate resistance and whether they fall into common resistance pathways. We previously developed an in vitro piggyBac (PB) transposon activation mutagenesis screen with unbiased genome-wide insertions that activated, and in some cases disrupted, endogenous genes and demonstrated its applications in cancer cell tolerance to the cytotoxic reagent paclitaxel 21 and in host cell resistance to Ebola or coronaviruses 22 . Here, we used this approach to further study resistance to BRAF inhibition in BRAF V600E melanoma. RESULTS A transposon screen to identify BRAF inhibitor resistance genes To identify genes conferring resistance to BRAF inhibition in melanoma, ten individual PB mutagenesis libraries were constructed by transfecting independently cultured PLX4720-sensitive BRAF V600E A375 melanoma cells with the PB and transposase (PBase) plasmids (Fig. 1 A, B). Each library was treated with PLX4720, a potent and selective BRAF inhibitor (Fig. 1 C), and insertion sites were identified from each resistant pool by targeted deep sequencing. To first gain insight into the breadth of target drug resistance events that can be captured with this approach, the overall distribution of insertions across the genome was analyzed. This analysis revealed that inserts were evenly distributed across the genome without major local preference, highlighting the genome-wide coverage ability of this approach (see also a previous report 21 ). Supporting the mechanistic specificity of the insertion events, a strong enrichment of sequencing reads for a small number of loci was observed. The top 100 inserts represented most (> 99.5%) of the sequencing reads ( Fig. S1 A ) of each pool and corresponded to 902 genomic loci associated with 767 genes ( Table S1 ) based on most proximal genes (see below). We considered the top 132 genes of this aggregated list constituting 95% of sequencing reads, and including all 128 genes with multiple inserts, as high-confidence hits (Fig. 1 D). We note however that some pathologically relevant genes (such as PTEN , ranked 134) might have been omitted by this prioritization strategy. To confirm that the analytical approach identified driver rather than passenger events, 224 colonies, including 23 isolated directly from the primary screening plates and 201 derived by limiting dilution of the multipassaged individual resistant pools, were sequenced ( Table S2 ). On average, 2.2 inserts (ranging from 1 to 12) were found per clone ( Fig. S1 B ), indicating that co-enrichment of passenger inserts in resistant cells was infrequent and thus indicating that candidate resistance-driving genes should be readily identified by considering high read count genes and proximity to redundant insertion events. To characterize the functional consequence of the insertions, we performed RNA-Seq on 10 clones and plotted the differential gene expression according to the insertion orientation and proximity ( Fig. S1 C, D, Table S3 ). This analysis indicated that, as predicted and consistent with our previous report 21 , expression variations mostly affected the nearest gene (13 of 16 inserts, including LPP , the second nearest gene after an undetectable antisense isoform LPP-AS2 , p < 0.05, q < 0.05) and that upstream/sense-strand inserts result in activation of the host gene transcription. We also noted that an insertion as far as 48 kb robustly upregulated the expression of the nearest gene ( LPP) (20.8-fold, p = 0). While genome structural and regulatory complexity are likely to impact how far a PB insertion can activate a gene, alternative splicing involving splice donor (SD) and acceptor (SA) site selection is affected by promoter activity and the transcription elongation rate 23,24 , and several human genes, such as dystrophin ( DMD ), indeed contain introns well over 100 kb. Thus, the capacity for distal activation demonstrated here is likely due to CMV-initiated strong transcriptional readthrough and the coupling of the exogenous SD to an endogenous SA and may therefore substantially reduce the number of clones needed for genome-wide coverage compared to the proximal effect alone. In addition to upstream sense-strand events, intragenic sense-strand inserts at the 5’ end of the gene or the protein coding sequence, effectively analogous to 5’ alternative splicing isoforms, were also observed to activate transcription in clones containing NEDD4L, BRAF , or MCF2 . Of the 132 high-confidence hits, 52 were associated with multiple incidences of nonidentical sense-strand, upstream or proximal, insertion events and thus most likely resulted in host gene overexpression (Fig. 1 D, Table S1 ). Interestingly, the PB approach might also be capable of identifying resistance through LoF in addition to GOF events. Conceptually, LoF could occur either through insertion in the gene body or through insertion downstream of a gene in the opposite direction to transcription, leading to gene disruption or antisense RNA expression, among other mechanisms. In line with this possibility and with the previous identification of disruption of the NPC1 gene in a similar transposon-mediated screen for viral invasion 22 , several potentially disruptive insertions were found in genes such as GPC6 and PLS3 , and one was discovered in the well-characterized resistance gene PTEN , a lipid phosphatase that acts as a tumor suppressor to counteract the activity of phosphoinositide 3-kinases (PI3K) with PI3K activity promoting proliferation and survival ( Table S1 -S3 ). However, robust identification of such LoF driven resistance events is complicated by the broad range of insertion positions and disruption of gene structures that could underlie LoF. In addition, in the context of a diploid genome, loss of function might only involve haploinsufficient genes, although these could actually constitute a large fraction of human genes 25 . Furthermore, cancer cell genomes are frequently not simply diploid due to whole genome duplication or other complex genomic rearrangements. For these different reasons, below, we focus on GoF events. Confirming the ability of the PB approach to identify functionally relevant and drug target-related resistance events, BRAF ( BRAF V600E , in which A375 is homozygous for that mutation), the direct target of inhibition, was among the top hit genes with 8 insertion events (Fig. 1 D, Table S1 ). Interestingly, although not the most prominent target in the primary screening, the majority (140/201) of subclones isolated by limiting dilution of the pools contained BRAF insertions ( Table S2 ), implying that BRAF V600E overexpression itself promotes a strong proliferative advantage, an observation in line with a previously published study 18 . Two insertion events were mapped to RAF1 ( CRAF ), a paralog of BRAF, and a predicted activating insert was also found 2.7 kb upstream of the KRAS gene, the upstream activator of RAF kinases in the mitogen-activated protein kinase (MAPK) signaling cascade ( Table S1 ). Interestingly, among the high-confidence hits, the only other kinase predicted to be upregulated was the SRC family tyrosine kinase YES1 . SRC kinases, particularly SRC, FYN and YES (encoded by YES1 ), are indeed positive regulators of RAF kinases and are known to be upstream activators of the MAPK pathway in many contexts, including BRAF inhibitor resistance 26 . Other candidate resistance genes belong to a broad variety of functional classes. Multiple guanine exchange factors, including three RAP exchange factors ( RAPGEF2 , RAPGEF4 , and RAPGEF6 ) and two Rho exchange factors ( VAV1 and ARHGEF28 ), were among the high-confidence hits, and another Rho exchange factor ( MCF2 ) ranked 540 from the pools ( Table S1 ), whose expression was also induced 33-fold by an intragenic sense-strand insert in a clone ( Fig. S1 D, Table S3 ). Several transcriptional regulators were found among the most redundant hit genes such as EYA1 (11 inserts), POU2F1 (9 inserts), and the zinc finger proteins ZFHX3 and ZFHX4 (7 inserts each). Chromatin binding proteins ( ARID1A , SMC2 , ACIN1 , and PDS5A ) and other gene family members, such as sorting nexins ( SNX6 and SNX14 ), semaphorins ( SEMA3D and SMA6A ) and potassium (Kv) channel interacting proteins ( KCNIP1 and KCNIP4 ), were also identified. Notably, these genes do not colocalize in the host genome, suggesting functionally meaningful enrichment. Validation of resistance genes To validate the results of the transposon screen we selected 15 gene candidates based on the predicted activation effect for single-gene ORF-mediated overexpression (circled and labeled in Fig. 1 D). The expression of these genes was mediated through lentiviral infection in A375 cells, and cellular proliferation measured in the presence of the BRAF inhibitor PLX4720, the pan-RAF inhibitor AZ628, and the MEK inhibitor AZD6244 (MEK is immediately downstream of RAF in the MAPK pathway) ( Fig. S2 A ). Seven candidates, NEDD4L , WWTR1 (also known as TAZ , a Hippo pathway component), ESRRG , RAPGEF6 , ARHGEF28 , YES1 , and POU2F1 were further confirmed to enhance viability in the presence of inhibitors using dose titration assays (Fig. 2 A), proliferation rate assays (Fig. 2 B) and long-term clonogenic assays (Fig. 2 C). As a frame of reference, the expression of these genes conferred a level of resistance to RAF and MEK inhibition comparable to that of the previously reported and well-validated resistance gene COT 27 . Supporting the specificity of the findings for BRAF inhibition, none of these genes induced resistance to the microtubule-disrupting agent paclitaxel while expression of the drug exporter ABCB1 gene did, as expected ( Fig. S2 B ) 21 . We note that among the genes that were not validated, some were likely due to the limitations of the ORF clones. For example AKAP13 was only modestly overexpressed (1.5- to 2.2-fold greater than basal expression, Fig. S2 C ) but did induce partial resistance to PLX4720; BRAF wild-type cDNA also did not induce resistance, consistent with previously reported ORF screening results that did not identify wild-type BRAF as a resistance gene 15 , likely because only the V600 mutation allele overexpression can drive resistance 2 , which is what occurs upon transposon insertion in A375 cells. Furthermore, the available NEDD4L ORF construct used in the ORF screen and initially tested in our validation studies corresponded to a WW2 domain deletion isoform that failed to induce resistance (named herein NEDD4L-dWW2 to avoid confusion and further described below) 28 , highlighting the possibility that specific ORF inactivity or particular isoforms can contribute to false negatives in ORF based genetic studies. Overall, the results show a high rate of validation for the top transposon screen hits. Concordance of multiple genetic screens The exact model of melanoma used here (the A375 cell line) was previously used to identify resistance genes via multiple screening modalities 14–17 . Surprisingly, the overlap between the lists of genes identified previously and the top 132 genes we identified was limited to 6 genes in the case of a previously reported ORF screen ( ESRRG , FOXP2 , RAF1 , RAPGEF4 , VAV1 , and WWTR1 ) and 6 different genes in the case of a dCAS9-activator screen ( ATP10A , BCAS3 , GLIS3 , MECOM , PCDH7 , and ZFHX4 ). Although this could be explained at least in part by incomplete genome coverage of each library with some events functionally tested in only one of the screens, this very limited overlap across likely true positive hits (based on validation rates in the different studies) was still somewhat unexpected. For example, the genome-wide ORF screen should, in theory, capture the majority of predicted activated genes mapped to coding sequences in our screen. Interestingly, among the seven genes that we confirmed to induce resistance by overexpression experiments, WWTR1 and ESRRG were successfully identified in the ORF resistance screen (z score ≥ the threshold level of 2.5 in that report); RAPGEF6 narrowly missed the hit status (z score = 2.43), but YES1 (z score = -0.13) was not a hit. Finally, three ( NEDD4L , POU2F1 , and ARHGEF28 ) were among the genes that did not pass quality-control filters prior to screening. This illustrates the challenge of large genetic screens both in terms of functional coverage due to genomic bias or technical factors as well as inherently somewhat arbitrary choices of thresholds for hit calling. To better understand whether the lack of overlap points to a higher-than-expected rate of false positive discovery or to low functional saturation and thus corresponding more to lack of screen sensitivity, we analyzed the functional relationship between the hit gene lists from the various GoF and LoF screens performed to uncover resistance events to BRAF inhibition in A375 cells. Genes identified by GoF ORF 15 and dCAS9 activator 14 screens and LoF CRISPR knockout 16 and shRNA knockdown 17 screens were mapped to a global functional cellular network using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) (Fig. 3 A, Table S4 ) 29 . Between the transposon and ORF sets, we found 139 STRING connections, which was significantly greater than random (p < 10 − 6 ) and more significant than other gene set pairs tested ( Fig. S3 A ). The overall network constituted by genes from both screens was highly interconnected, and some of the genes identified in one screen were connected only to genes from the other screen but not to genes found within their own screen (circled in red in Fig. 3 B). Similarly, 66 STRING database gene-gene connections were found between the transposon and dCAS9 activator hit gene lists (p = 0.046). The STRING connections between the LoF CRISPR and shRNA screens were also significant (p < 10 − 5 ), but neither matched the GoF screens except for the CRISPR-to-ORF pair, albeit presenting no actual gene overlap. We further sought to leverage the results of all the screens together to identify core programs contributing to resistance. We identified a core set of 133 genes by globally searching for all RefSeq genes with at least 10 connections to each of the screened gene sets (excluding the small gene set of the shRNA screen) ( Table S5 ) with the top functional clusters of MAPK and PI3K pathways, RAS-related genes, and receptor tyrosine kinases (RTKs) (Fig. 3 C). We further specifically analyzed the STRING connections between the screened gene sets and major MAPK pathway components and found significant functional connections from the GoF gene sets (p-values ranging from 0.01 to 10 − 5 ) ( Fig. S3 B ). Consistent with our STRING network analysis results and with other studies indicating that reactivation of MAPK activity is a major route for resistance in BRAF driven models 3,27,30–33 , among the 7 genes validated through ORF overexpression, 5 ( NEDD4L , WWTR1 , RAPGEF6 , ARHGEF28 and YES1 ) led to maintenance of the MAPK signaling pathway upon BRAF inhibition (Fig. 3 D). Overall, these results show that there is much better concordance at the functional network level across genes discovered by different screens than suggested by the lack of actual overlap of the gene lists. Moreover, many of the genes found to induce resistance appear to coalesce on a relatively narrow set of mechanisms, suggesting that a few core programs of resistance are repeatedly reached through different paths and could potentially be targeted to counter resistance. The Hippo pathway modulator WWTR1 as a global effector The Hippo pathway effector WWTR1 as a transcriptional activator was by far the most prevalent hit for resistance gain among all genes tested in long-term drug treatment assays (Fig. 2 C). Activation of YAP, the paralog of WWTR1, has previously been shown to promote resistance to BRAF inhibition 34–36 . Hippo pathway inhibition (inhibition of MST1/2 kinases corresponding to Hippo in drosophila), leads to YAP/WWTR1 transcriptional program activation by nuclear translocation. Hippo pathway inactivation was previously shown to effectively resensitize cells to RTK inhibition 37 . Although the baseline level of WWTR1 did not predict the response to BRAF inhibitors in a panel of BRAF V600E melanoma cell lines, exogenous overexpression increased resistance in several BRAF V600E melanoma cell lines ( Fig. S4A, B ). Similarly, knocking down WWTR1 induced further sensitization of A375 cells and antagonized their resistance to PLX4720 (Fig. 4 A, B). This is also reminiscent of the sensitization of BRAF and other MAPK pathway activation tumor models to MAPK pathway inhibitors via the suppression of YAP1 34,35,38 . WWTR1 was identified as a candidate resistance gene in the previous ORF screen along with YAP1 15 , and interestingly, as stated above, the SRC family tyrosine kinase YES1 validated in our screen was previously shown to regulate YAP1, which indeed was named after its association with YES1 (Yes Associated Protein 1) 39 . Consistent with these findings, we observed that the SRC family kinase inhibitor dasatinib strongly sensitized WWTR1-overexpressing cells to PLX4720 (Fig. 4 C). We also observed that YAP1 knockdown increased PLX4720 sensitivity in A375 parental cells but did not affect WWTR1 overexpressing cells (Fig. 4 D, E), consistent with the redundancy between the paralogs YAP1 and WWTR1. Furthermore, knockdown of TEADs (TEAD1-4) the transcriptional partners that mediate the DNA binding of YAP1 and WWTR1 40,41 , sensitized both parental and WWTR1 overexpressing A375 cells to PLX4720 (Fig. 4 F, G). To elucidate the transcriptional changes induced by WWTR1 and their relevance across melanoma models, we divided 22 BRAF V600 mutant melanoma cell lines in our collection into two groups based on their sensitivity to PLX4720, analyzed the differences in global gene expression between the two groups and identified 508 differentially expressed genes (DEGs) ( Table S6 ). We also performed RNA-Seq on parental and WWTR1 ORF-expressing A375 cells and found 315 DEGs with 4-fold up- or downregulation ( Table S7 ). Comparison of these two DEG lists revealed a significant overlap of 50 genes out of 269 WWTR1 target DEGs with measurements available in the cell line panel (chi-square = 284, p < 0.0001, Fig. 4 H). This subset included several genes previously implicated in melanoma or tumor malignancy more broadly, such as SOX10 , whose downregulation leads to elevated EGFR and PDGF receptor signals and increased resistance to BRAF inhibition 9 ; MIA , which is involved in melanocyte lineage and melanoma development 42,43 ; and ERBB3 , which can reactivate MAPK upon MEK inhibition through a feedback mechanism 44,45 . Importantly, for almost all (46/50) genes, the DEG effect of the WWTR1 ORF was predicted by PLX4720 sensitivity − 39 genes downregulated by WWTR1 were highly expressed in the sensitive cell line group, while 7 upregulated genes were expressed in the resistant group (Fig. 4 I). Furthermore, unsupervised clustering of the 18 most diversely expressed genes divided the melanoma lines according to their response to PLX4720 (Fig. 4 J). WWTR1 and NEDD4L interconnect The E3 ubiquitin-protein ligase NEDD4L , was a prominent hit in our study with 32 nonidentical insertion events across 20 sites and 12.5% of all sequence reads in the pools (Fig. 1 D, Table S1 ). Interestingly, NEDD4L was also a hit in a previous BRAF inhibitor resistance study 20 . It was also implicated in EGFR-mTOR signaling in lung adenocarcinoma 46,47 although in a somewhat functionally opposite way to our expectation. At least 8 NEDD4L protein isoforms have been reported ( https://www.uniprot.org/uniprotkb/Q96PU5 ) 48,49 and we noticed that two more isoforms at 230 kDa and 180 kDa might be present in A375 ( Fig. S4A ). To understand whether a specific isoform of NEDD4L was involved in resistance to BRAF inhibition, we initially investigated a prominent A375 cell isoform (PB clone B181 in Fig. S1 D, S5A ). However, cDNA-mediated (ORF) expression of a canonical NEDD4L isoform (110 kDa) was readily able to induce resistance in A375 and in other cell lines ( Fig. S4B ). Moreover, PLX4720 sensitivity across cell lines was not correlated with the expression of any specific isoform ( Fig. S4A ). The NEDD4L protein contains an N-terminal C2 domain, four WW domains, and a HECT domain with E3 ubiquitin ligase catalytic activity (Fig. 5 A) 28,50 . Previous studies have indicated that NEDD4L mediates the degradation of AMOT proteins, which are known regulators of the Hippo pathway 51–53 . According to these studies, AMOT proteins repress WWTR1 and YAP1 activity at least in part via cytoplasm retention of WWTR1 and YAP. We therefore studied the mechanistic underlying of NEDD4L mediated resistance by expressing a catalytically inactive C962A mutant of the HECT domain (DD) or a WW2-domain deletion mutant (dWW2), which was previously shown to be critical for the recognition of a number of NEDD4L substrates. In both cases, we found that resistance was indeed impaired compared to that obtained with wild-type NEDD4L (Fig. 5 B, S5A-C). NEDD4L has also been reported to regulate SMAD2/3, which are downstream effectors of the TGFβ pathway 50 . However, the projected effects of such possible role in BRAF melanoma are contradictory: NEDD4L-mediated SMAD2/3 degradation would lead to the termination of TGFβ signaling rather than its activation 28 , while previous reports have shown that TGFβ signaling upregulates the EGFR pathway to confer resistance to BRAF inhibition in melanoma 9 . Nonetheless, we tested this potential involvement by expressing missense mutations (S382A, S468A, or both (SASA)) targeting phosphorylation sites for SGK1, which is critical for SMAD2/3 regulation, and found that while protein expression of the S468A mutant failed despite decent mRNA expression ( Fig. S5A ), both S382A and SASA were comparable to those of the wild type in inducing resistance (Fig. 5 B, S5A-C). Furthermore, knockdown of SMAD2/3 modestly reduced rather than increasing the resistance mediated by both parental A375 and NEDD4L cells ( Fig. S5D, E ), and the SMAD2/3 protein level did not increase upon NEDD4L knockdown ( Fig. S5F ), suggesting the independent regulation of sensitivity to BRAF inhibition by NEDD4L and by TGFβ. WWTR1 expression led to moderate NEDD4L upregulation at the protein (Fig. 5 C) and mRNA levels (1.5-fold, p = 3.19E-06, FDR = 3.03E-05; Table S7 ); conversely, WWTR1 knockdown led to reduced NEDD4L expression (Fig. 4 B). NEDD4L expression was also positively correlated with WWTR1 expression in a panel of cancer cell lines (Fig. 5 D, Table S8 ), and both were correlated with melanoma treatment outcome (Fig. 5 E, S6A, B). These findings suggested that NEDD4L might be involved in WWTR1-mediated resistance. Consistent with a role of NEDD4L in the regulation of other signaling pathways, NEDD4L overexpression led to a strong increase in AKT phosphorylation upon BRAF inhibition (Fig. 3 D), and correspondingly, the AKT inhibitor MK2206 that strongly reduced AKT phosphorylation (Fig. 5 F) effectively resensitized NEDD4L-overexpressing A375 cells to PLX4720 (Fig. 5 G). NEDD4L also led to the phosphorylation of EGFR and another RTK, IGF1R, upon BRAF inhibition, while WWTR1 upregulated EGFR protein expression more strongly than it promoted EGFR phosphorylation (Fig. 5 H,I). The regulation of EGFR expression by WWTR1 is also in line with the positive correlation between EGFR and WWTR1 expression in a cell line panel (Fig. 5 J, Table S8 ) and the elevated EGFR mRNA expression in WWTR1 ORF-overexpressing cells (2.0-fold, p = 3.31E-12, FDR = 8.08E-11; Table S7 ). Therefore, WWTR1 plays an important role in the maintenance of MAPK pathway activation both through transcriptional regulation of some pathway component genes and likely other signaling mechanisms (Fig. 5 K). In line with this, both the EGFR inhibitor pelitinib (EKB569) and the IGF1R inhibitor BMS754807 countered resistance induced by NEDD4L, WWTR1, and all other resistance genes validated in this study ( Fig. S7 ), suggesting that RTK inhibitors might resensitize cells to MAPK pathway inhibition more broadly than previously described 3,6,56 . DISCUSSION As an alternative to genetic screening platforms consisting of complex mutagen libraries (such as ORFs), transposon mutagenesis has the potential to tackle some of the complexity of host genome and transcriptome with unidentified components, non-coding elements, and alternative isoforms. Moreover, activation of endogenous elements might provide more context specific results than exogenously expressed transcription activators. While transposon mutagenesis has been widely used in vivo 19,54 , its application as an in vitro cell line screening platform results in expandable off-the-shelf libraries that are ideal for applications involving complex treatment conditions, such as comparing resistance to multiple drugs. The low cost and simplicity of this approach, requiring only transient transfection of two plasmids, enables straightforward high-throughput platform deployment 20 . While not utilized in this study, additional features, such as tagging transposition plasmids with degenerate nucleotide barcodes, should further improve platform scalability. Such barcode diversity can be readily sequenced by next generation sequencing (NGS) to reveal nonidentical insertions even at the same genomic location, essentially eliminating the need to generate library replicates. While our study provided further support for therapies targeting the Hippo pathway 37 or protein ubiquitination 57,58 , we also demonstrated that many different genes with a broad range of functions implicated in resistance to target inhibition therapies appear to coalesce to the pathway of primary target inhibition (here the MAPK pathway) and a restricted number of additional pathways, suggesting that the staggering complexity of the resistance landscape may not need to be addressed by an equally complex array of therapeutic strategies. METHODS Cell culture reagents. All cell lines were obtained from the collection of the Genomics of Drug Sensitivity in Cancer (GDSC) project 13 , cultured in either DMEM/F12 or RPMI supplemented with 5% FBS and 1% penicillin/streptavidin, and maintained in a 37°C/5% CO 2 cell culture incubator. Therapeutic compounds were purchased from Selleckchem and ChemieTek and dissolved in dimethyl sulfoxide. Transposon mutagenesis library construction and screening. The piggyBac activation transposon plasmid pPB-SB-CMV-puro-SD was described previously 21 . A derivative plasmid containing the degenerate nucleotide barcodes mentioned in the Discussion section was verified by NGS and the plasmid map and complete sequence are provided in the supplementary information ( Fig. S8, Supplementary Text ). Cell line libraries were constructed by transfecting A375 cells with the transposon plasmid and the transposase plasmid pCMV-hyPBase 59 . After puromycin selection, the cells were treated with PLX4720, and surviving cells were pooled or isolated. Insertion site sequencing libraries were prepared using ligation-mediated PCR and identified with NGS as described previously 21 . Gene annotation for insert sites. Illumina sequencing data in FASTQ files were demultiplexed and trimmed to retain only the genomic DNA sequences. Reads of 7 bp or longer were aligned to the human reference genome (hg19) using Bowtie and unique reads were recorded. For the resistant pools, the top sequences by read count were reported based on our estimate of the survivor colony numbers from each library, with the top 100 inserts representing more than 99.5% of the total sequencing reads. For each insert, the three most proximal genes were annotated. Melanoma patient cohort. Patients with metastatic melanoma harboring the BRAF V600E mutation (confirmed by genotyping) were enrolled in clinical trials for treatment with a BRAF inhibitor (vemurafenib) or combined BRAF + MEK inhibitor (dabrafenib + trametinib or LGX818 + MEK162) and consented to tissue acquisition per the IRB-approved protocol. Lentiviral cDNA plasmid construction and cell assays. Open reading frame (ORF) entry clones were subcloned and inserted into two sets of lentiviral expression vectors (blasticidine and puromycin) for cell-based assays. Cells viability, apoptosis, and proliferation were assayed. All assays were performed at least three times. The results from representative experiments are presented with the number of technical replicates (n) indicated in each figure. RNA-Seq. Total RNA was prepared using an RNeasy Mini Kit (Qiagen) and processed using a TruSeq RNA Sample Preparation Kit (Illumina) to generate libraries of sequencing molecules. For samples derived from transposon-inserted clones, equal amounts of 12 indexed subsamples were pooled and sequenced using an Illumina HiSeq2500 instrument. STAR aligner 60 was used to map sequencing reads to transcripts. Read counts for individual transcripts were produced with HTSeq-count 61 , followed by the estimation of expression values as RPKM (reads per kilobase per million) and the detection of differentially expressed transcripts using EdgeR 62 . For WWTR1-overexpressing cDNA clones, samples were prepared from four biological replicates of viral infections, indexed, pooled, and sequenced. The Benjamini-Hochberg false discovery rate (FDR) was used to estimate the statistical significance of differences in gene expression. STRING analysis. Known and predicted protein-protein associations were tested using STRING analysis ( http://string-db.org , Homo sapiens: 9606.protein.links.detailed.v9.1.txt.gz). STRING analyses were first performed between pairs of screened gene sets ( Table S4 ). To identify a core set of genes responsible for resistance, all RefSeq genes were queried for their connections to components of every screened gene set ( Table S5 ). The list of genes with at least 10 connections to each of four screens (excluding the small shRNA screen gene set) was used as input for the Database for Annotation, Visualization and Integrated Discovery (DAVID) analyses 63 . Clustering of gene expression values. The genes most differentially expressed between BRAF inhibitor-sensitive and -resistant cell lines were selected for unsupervised clustering using Pearson’s correlation across genes and cell lines. Clustering and heatmap generation were performed using Gene-E ( http://www.broadinstitute.org/cancer/software/GENE-E/index.html ). Declarations ACKNOWLEDGMENTS We thank Pentao Liu for providing the piggyBac transposase plasmid pCMV-hyPBase, Joan Massague for the NEDD4L cDNA plasmids, and Bristol-Myers Squibb for the U219 expression data for the cancer cell lines. We also thank Wilhelm Haas, Keith T. Flaherty, and Adam Lacy-Hulbert for useful discussions. This work was supported by grants from the Wellcome Trust (086357 and 102696). AUTHOR CONTRIBUTIONS L.C.: project coordination, experiments, data analysis, and manuscript preparation. C.H.B.: project design and supervision, data analysis, and manuscript preparation. I.P-M.: STRING and clustering analyses. A.D.: Drug response assays in melanoma cell lines and discussion of the project. X.Y.: contributed to cell culture and immunoblotting. D.F.: provision of patient RNA-Seq and treatment data. R.I.S.: NGS data processing. DATA AVAILABILITY Melanoma patient RNA-Seq data were deposited in the NCBI GEO under accession number GSE73470. Cell line data are available through the Genomics of Drug Sensitivity in Cancer (https://www.cancerrxgene.org/) or DepMap (https://depmap.org/portal/) portals. ADDITIONAL INFORMATION All authors declare no competing interests for this work. Additional protocol details can be found in the Supplementary Methods . References Bollag, G. et al. Clinical efficacy of a RAF inhibitor needs broad target blockade in BRAF-mutant melanoma. Nature 467 , 596-599 (2010). https://doi.org/10.1038/nature09454 Corcoran, R. B. et al. BRAF gene amplification can promote acquired resistance to MEK inhibitors in cancer cells harboring the BRAF V600E mutation. Science signaling 3 , ra84 (2010). https://doi.org/10.1126/scisignal.2001148 Corcoran, R. B. et al. EGFR-mediated re-activation of MAPK signaling contributes to insensitivity of BRAF mutant colorectal cancers to RAF inhibition with vemurafenib. Cancer discovery 2 , 227-235 (2012). https://doi.org/10.1158/2159-8290.CD-11-0341 Flaherty, K. T. et al. 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Supplementary Files SupFigures20241014.pptx SupTables20241014.xlsx SupplementaryMethodsandText.docx Cite Share Download PDF Status: Published Journal Publication published 24 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 04 Dec, 2024 Reviews received at journal 30 Nov, 2024 Reviews received at journal 25 Nov, 2024 Reviewers agreed at journal 08 Nov, 2024 Reviewers agreed at journal 08 Nov, 2024 Reviewers invited by journal 07 Nov, 2024 Editor assigned by journal 07 Nov, 2024 Editor invited by journal 07 Nov, 2024 Submission checks completed at journal 06 Nov, 2024 First submitted to journal 23 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5320870","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":375931044,"identity":"43740f60-34a7-4913-a325-256a9c1e9987","order_by":0,"name":"Li Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYBACPmYGNiB1AML7AMRs7AS0sMG1ACnGGRARAloYkLQw84CECGphZ3724OOOO3Ly85uPPbb5tU0e6FTGDx9z8DmMzdxw5plnxgbH2NKNc/tuG7YxMzBLztyGTwsPmzRv2+HEDWw8ZtK5PbcZgVrYmHkJafkL1DK/DajFsue2PXFaGIFaGo4BtTD8uJ1IhBY2M8neNpBf0tIkextuJ7cxMzbj9Qs//+FnEj/bgCHWfPiYxI8/t23ntzcf/PARjxZUwNgGJhuIVQ8Cf0hRPApGwSgYBSMFAAClHEceVzYAEQAAAABJRU5ErkJggg==","orcid":"","institution":"Massachusetts General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Chen","suffix":""},{"id":375931045,"identity":"ef93e264-f935-4e6a-a0ac-dd4834f4218c","order_by":1,"name":"Iulian Pruteanu-Malinici","email":"","orcid":"","institution":"Massachusetts General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Iulian","middleName":"","lastName":"Pruteanu-Malinici","suffix":""},{"id":375931046,"identity":"d1a54dba-b824-4eaf-85b1-f48639db6d85","order_by":2,"name":"Anahita Dastur","email":"","orcid":"","institution":"Massachusetts General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Anahita","middleName":"","lastName":"Dastur","suffix":""},{"id":375931047,"identity":"ebf0b9bb-7405-415a-bfa5-2edd91aa1a94","order_by":3,"name":"Xunqin Yin","email":"","orcid":"","institution":"Massachusetts General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xunqin","middleName":"","lastName":"Yin","suffix":""},{"id":375931048,"identity":"85a68a11-7c6c-40c8-ab2d-613ae28ff554","order_by":4,"name":"Dennie Frederick","email":"","orcid":"","institution":"Massachusetts General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dennie","middleName":"","lastName":"Frederick","suffix":""},{"id":375931049,"identity":"f4031958-6e0f-43b0-bd4e-4f612a2d2c11","order_by":5,"name":"Ruslan Sadreyev","email":"","orcid":"","institution":"Massachusetts General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ruslan","middleName":"","lastName":"Sadreyev","suffix":""},{"id":375931050,"identity":"7c1e71fb-6b4f-463b-bcc4-392b008aba93","order_by":6,"name":"Cyril Benes","email":"","orcid":"","institution":"Massachusetts General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Cyril","middleName":"","lastName":"Benes","suffix":""}],"badges":[],"createdAt":"2024-10-23 17:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5320870/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5320870/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-86694-5","type":"published","date":"2025-01-24T15:57:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69549279,"identity":"7d3cab44-cd71-4940-b1ae-49248088b246","added_by":"auto","created_at":"2024-11-21 14:18:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":301977,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePB transposon mutagenesis screen.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Scheme of the PB mutagenesis screening process. Parental cancer cells were transfected with transposon (PB) and transposase (PBase) plasmids, passaged with puromycin to generate mutagenized libraries, and treated with PLX4720 to generate resistant colonies or pools. Insert sites were detected using linker-mediated PCR and NGS, with the shown amplicon structure of the transposon (PB), host genomic DNA (gDNA), linker, and PCR primers (arrows).\u003c/p\u003e\n\u003cp\u003e(B) The PB transposon plasmid included a cassette with an antibiotic selection marker (puroR) for transposon-mutagenized cell maintenance, a CMV promoter (CMV) and a splice donor (SD) for host gene transcription and alternative splicing, and two inverted repeats (IRs) for transposition.\u003c/p\u003e\n\u003cp\u003e(C) Clonogenic assays showing the sensitive parental cells and PLX4720-resistant cells from transposon screen.\u003c/p\u003e\n\u003cp\u003e(D) High confidence hits identified from resistant pools are displayed by genomic location (chrs.1 to 22 to x). The inserts for each gene were summarized, read numbers of each gene normalized to the total number of reads were plotted as y-coordinates, and the dot surfaces represent the number of insertion events. Genes with a predicted activation effect are shown as orange bubbles, and candidates for follow-up validation assays are circled and labeled.\u003c/p\u003e","description":"","filename":"Figures202410221.png","url":"https://assets-eu.researchsquare.com/files/rs-5320870/v1/badcf7170b1251ae9f84195e.png"},{"id":69549280,"identity":"4b22f0da-4b19-4321-ab15-f8dab1cf3ba5","added_by":"auto","created_at":"2024-11-21 14:18:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1181007,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of candidate resistance genes on proliferation evaluated by cDNA overexpression.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Half-maximal inhibition concentration (IC50) values of cells expressing the vector controls, candidate resistance genes, and a previously known resistance gene, COT. Experiments were performed on two sets of lentiviral vectors (left: blasticidine; right: puromycin). The values are the means ± 95% CIs (confidence intervals).\u003c/p\u003e\n\u003cp\u003e(B) Proliferation rate of cells transfected with candidate genes, either untreated or treated with 0.5 µM PLX4720. CellTiter-Glo measurements 4 days after drug treatment were normalized to the Day-0 values. The data are presented as the mean ± SEM (standard error of the mean), n=3.\u003c/p\u003e\n\u003cp\u003e(C) Clonogenic assay with cells treated with the corresponding drugs at 2-fold serial dilutions.\u003c/p\u003e","description":"","filename":"Figures202410222.png","url":"https://assets-eu.researchsquare.com/files/rs-5320870/v1/c17c58eb0d88e46503c2b0a1.png"},{"id":69549281,"identity":"91426a8b-38ad-455e-aeb1-bebb220bbe2a","added_by":"auto","created_at":"2024-11-21 14:18:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1828070,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInterconnections between different screens.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Summary of STRING connections between different genetic screens. The gene set sizes are indicated within parentheses, and each arrow points from the query gene set to a reference gene set, with the numbers of overlapping genes, total STRING connections, and p values denoted along the arrows.\u003c/p\u003e\n\u003cp\u003e(B) STRING network connections between the transposon and ORF gene sets. Nodes are color-coded according to their gene list of origin and edges. Nodes with red circles (interlinked hits) are those genes from a given list that only have connections to the other list. Edges are color-coded according to whether the connection is between genes from the same list or not. Edges connecting genes found in both screens to other genes (inter-and intra-list connections incident to solid pink nodes) are distinguished from those connecting nonoverlapping genes to the other list (only inter-list connections).\u003c/p\u003e\n\u003cp\u003e(C) Top functional groups identified by DAVID Gene Functional Classification analysis of the core gene set highly connected to all screens. Enrichment scores are indicated within parentheses.\u003c/p\u003e\n\u003cp\u003e(D) MAPK and PI3K signaling. Cells were treated with the indicated doses of PLX4720 for 24 hours, and the levels of the indicated phosphorylated (pGENE) and total (tGENE) proteins were measured by Western blotting.\u003c/p\u003e","description":"","filename":"Figures202410223.png","url":"https://assets-eu.researchsquare.com/files/rs-5320870/v1/8e685725d3d05a4a75f9f0ba.png"},{"id":69549283,"identity":"9f572a81-a4a0-4c71-8801-87e1e767d065","added_by":"auto","created_at":"2024-11-21 14:18:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":688989,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWWTR1 regulates gene expression.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) PLX4720 IC50 values of control or WWTR1-overexpressing cells following siRNA mediated knockdown of \u003cem\u003eNEDD4L\u003c/em\u003e or \u003cem\u003eWWTR1\u003c/em\u003e. The values are the means ± 95% CIs, n=3.\u003c/p\u003e\n\u003cp\u003e(B) NEDD4L and WWTR1 protein expression in cells treated with siRNA for 48 hours and then treated with 0.5 µM PLX4720 for 24 hours.\u003c/p\u003e\n\u003cp\u003e(C) Cell viability of the indicated cells treated with 300 nM PLX4720 (PLX) and 12.5 nM dasatinib (Das). Error bars denote the SEM, n=3.\u003c/p\u003e\n\u003cp\u003e(D) Effect of \u003cem\u003eYAP1\u003c/em\u003e knockdown on protein expression and phosphorylation status in NEDD4L and WWTR1 overexpressing cells.\u003c/p\u003e\n\u003cp\u003e(E) PLX4720 IC50 values of control, WWTR1-, or NEDD4L-overexpressing cells with \u003cem\u003eYAP1\u003c/em\u003eknockdown (siYAP) or scramble (SCR) vectors. The values are the means ± 95% CIs, n=5.\u003c/p\u003e\n\u003cp\u003e(F) Effect of \u003cem\u003eTEAD1\u003c/em\u003e knockdown on protein expression in WWTR- and NEDD4L-overexpressing cells.\u003c/p\u003e\n\u003cp\u003e(G) PLX4720 IC50 values of control, WWTR1-, or NEDD4L-overexpressing cells with \u003cem\u003eTEAD1\u003c/em\u003eknockdown. The values are means ± 95% CIs, n=3.\u003c/p\u003e\n\u003cp\u003e(H) Intersects of WWTR1-regulated targets and DEGs between the PLX4720-sensitive and PLX4720-resistant melanoma cell lines.\u003c/p\u003e\n\u003cp\u003e(I) Expression heatmap of the 50 overlapping genes. In the first column, the regulatory effects of WWTR1 are denoted either as repression (blue) or activation (red). In the second column, the cell lines were divided into two groups based on the PLX4720 IC50 and apoptosis, and the gene expression ratios between the two groups were calculated, with blue indicating high expression in the sensitive group and red indicating high expression in the resistant group.\u003c/p\u003e\n\u003cp\u003e(J) Unsupervised clustering of the expression values of the 18 overlapping genes that most varied between the PLX4720-sensitive and PLX4720-resistant melanoma cell lines. The bars on the bottom panel indicate the PLX4720 IC50 values.\u003c/p\u003e","description":"","filename":"Figures202410224.png","url":"https://assets-eu.researchsquare.com/files/rs-5320870/v1/23f2f1f02a2c2bd74893a632.png"},{"id":69550893,"identity":"37dbdba5-0755-4da8-a80c-7b23d5778e98","added_by":"auto","created_at":"2024-11-21 14:26:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":800123,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional connection between NEDD4L and WWTR1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Diagram of NEDD4L domains and mutation locations.\u003c/p\u003e\n\u003cp\u003e(B) Effect of increasing doses of PLX4720 on the viability of cells expressing the indicated forms of NEDD4L.\u003c/p\u003e\n\u003cp\u003e(C) Endogenous and exogenous NEDD4L and WWTR1 protein expression in control, NEDD4L-, and WWTR1-overexpressing cells after treatment with the indicated doses of PLX4720 for 24 hours.\u003c/p\u003e\n\u003cp\u003e(D) Correlations between \u003cem\u003eWWTR1\u003c/em\u003e and \u003cem\u003eNEDD4L\u003c/em\u003e mRNA expression across cancer cell lines of diverse origins. Each dot denotes one cell line. Red lines indicate the mean ± SEM, n=737.\u003c/p\u003e\n\u003cp\u003e(E) \u003cem\u003eNEDD4L\u003c/em\u003e and \u003cem\u003eWWTR1\u003c/em\u003e mRNA expression in pre-treatment, on-treatment, and progression clinical samples. The values are presented as the means ± SEMs.\u003c/p\u003e\n\u003cp\u003e(F) AKT phosphorylation in NEDD4L-overexpressing A375 cells treated with PLX4720 and MK2206. Cells were treated with 0.5 µM PLX4720 and the indicated concentrations of MK2206 for 24 hours.\u003c/p\u003e\n\u003cp\u003e(G) Viabilities of the indicated cells treated with 300 nM PLX4720 (PLX) and 1 µM MK2206 (MK) for 5 days. The values are presented as the means ± SEMs, n=10. Cellular activities were measured by the CellTiter-Glo assay.\u003c/p\u003e\n\u003cp\u003e(H) Kinase signaling in vector control (bsd-VEC), NEDD4L-, or WWTR1-overexpressing cells. Cells were treated for 24 hours and phosphorylated (pGENE) or total (tGENE) protein levels were measured by Western blotting.\u003c/p\u003e\n\u003cp\u003e(I) Kinase signaling in the scramble control, \u003cem\u003eNEDD4L\u003c/em\u003e, and \u003cem\u003eWWTR1\u003c/em\u003e knockdown groups. Cells were transfected with siRNA for 48 hours, followed by treatment with 0.5 µM PLX4720 for 24 hours.\u003c/p\u003e\n\u003cp\u003e(J) Correlation between \u003cem\u003eWWTR1\u003c/em\u003e and \u003cem\u003eEGFR\u003c/em\u003e mRNA expression across a panel of cancer cell lines of diverse origins. Each dot denotes one cell line. Red lines indicate the mean ± SEM, n=737.\u003c/p\u003e\n\u003cp\u003e(K) Diagram summarizing the roles of WWTR1 in mediating resistance to BRAF inhibition. Solid arrows and solid blocked lines indicate activation and repression respectively. Dashed arrows indicate an indirect phosphorylation effect with a question mark denoting an unidentified component.\u003c/p\u003e","description":"","filename":"Figures202410225.png","url":"https://assets-eu.researchsquare.com/files/rs-5320870/v1/0ebbb2f22ca2765ec0d9d1bb.png"},{"id":74858575,"identity":"5159b976-4aad-4acc-b196-20e11e8c4f4b","added_by":"auto","created_at":"2025-01-27 16:11:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6828823,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5320870/v1/5c5f7d1e-cc3c-42b6-a974-28bcc59986bf.pdf"},{"id":69549287,"identity":"b0f83533-5e0d-4da5-8abe-dcf16cf7a9c6","added_by":"auto","created_at":"2024-11-21 14:18:18","extension":"pptx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":6208296,"visible":true,"origin":"","legend":"","description":"","filename":"SupFigures20241014.pptx","url":"https://assets-eu.researchsquare.com/files/rs-5320870/v1/7bafff478828d13bd53a12db.pptx"},{"id":69549285,"identity":"4332cfd3-e53f-4fbf-a74f-3a5c3290e3f5","added_by":"auto","created_at":"2024-11-21 14:18:18","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":400578,"visible":true,"origin":"","legend":"","description":"","filename":"SupTables20241014.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5320870/v1/9efd2c329a26d96d957e6c44.xlsx"},{"id":69550892,"identity":"476614a4-4c80-4927-a595-6de8965b749d","added_by":"auto","created_at":"2024-11-21 14:26:17","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":39832,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMethodsandText.docx","url":"https://assets-eu.researchsquare.com/files/rs-5320870/v1/0f936ed4d77789304ebab1a1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transposon mediated functional genomic screening for BRAF inhibitor resistance reveals convergent Hippo and MAPK pathway activation events","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eA vast array of genetic and nongenetic events, such as point mutations, overexpression, translocations, and alternative splicing, occurring across many genes have been shown to cause resistance to BRAF inhibitors in melanoma and other tumor types \u003csup\u003e1\u0026ndash;9\u003c/sup\u003e. Although the analysis of clinical samples provides unique insight into therapeutically relevant resistance tumor heterogeneity, limitations in the availability of clinical samples and functional characterization of the human genome combined with a high number of passenger mutations limit the potential to identify low occurrence resistance events \u003csup\u003e10\u0026ndash;13\u003c/sup\u003e. To prospectively identify genes that can promote resistance to BRAF and MEK inhibition several landmark studies using genetic screens in the context of \u003cem\u003eBRAF\u003c/em\u003e\u003csup\u003eV600E\u003c/sup\u003e mutant melanoma have been performed. These include gain-of-function (GoF) open reading frame (ORF) and aptamer-mediated dCAS9-activator screens, and loss-of-function (LoF) CRISPR knockout and shRNA screens\u003csup\u003e14\u0026ndash;17\u003c/sup\u003e. These screens leverage collections of individually made elements, are rather labor-intensive and are associated with relatively high costs. Even the most advanced of these screening approaches have limitations leading to incomplete saturation; for example, an ORF library is typically limited to one isoform per gene, and noncoding GoF events are poorly covered by most screens. Interestingly, these screens and several transposon mutagenesis screens that have been previously performed in the \u003cem\u003eBRAF\u003c/em\u003e\u003csup\u003eV600E\u003c/sup\u003e melanoma cell lines offer insight into the complexity and diversity of the genes that can mediate resistance to a given therapeutic agent in a specific genomic context. Comparison of the results of these screens revealed different gene sets identified even by screens that were functionally analogous (gain- or loss-of-function) and well validated\u003csup\u003e18\u0026ndash;20\u003c/sup\u003e. This raises several interesting questions such as whether screens that are genome-wide by design have indeed reached a substantial level of saturation, how many genes in total can mediate resistance and whether they fall into common resistance pathways.\u003c/p\u003e \u003cp\u003eWe previously developed an \u003cem\u003ein vitro piggyBac\u003c/em\u003e (PB) transposon activation mutagenesis screen with unbiased genome-wide insertions that activated, and in some cases disrupted, endogenous genes and demonstrated its applications in cancer cell tolerance to the cytotoxic reagent paclitaxel \u003csup\u003e21\u003c/sup\u003e and in host cell resistance to Ebola or coronaviruses \u003csup\u003e22\u003c/sup\u003e. Here, we used this approach to further study resistance to BRAF inhibition in \u003cem\u003eBRAF\u003c/em\u003e\u003csup\u003eV600E\u003c/sup\u003e melanoma.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eA transposon screen to identify BRAF inhibitor resistance genes\u003c/h2\u003e \u003cp\u003eTo identify genes conferring resistance to BRAF inhibition in melanoma, ten individual PB mutagenesis libraries were constructed by transfecting independently cultured PLX4720-sensitive \u003cem\u003eBRAF\u003c/em\u003e\u003csup\u003eV600E\u003c/sup\u003e A375 melanoma cells with the PB and transposase (PBase) plasmids (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). Each library was treated with PLX4720, a potent and selective BRAF inhibitor (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), and insertion sites were identified from each resistant pool by targeted deep sequencing.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo first gain insight into the breadth of target drug resistance events that can be captured with this approach, the overall distribution of insertions across the genome was analyzed. This analysis revealed that inserts were evenly distributed across the genome without major local preference, highlighting the genome-wide coverage ability of this approach (see also a previous report\u003csup\u003e21\u003c/sup\u003e). Supporting the mechanistic specificity of the insertion events, a strong enrichment of sequencing reads for a small number of loci was observed. The top 100 inserts represented most (\u0026gt;\u0026thinsp;99.5%) of the sequencing reads (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA\u003c/b\u003e) of each pool and corresponded to 902 genomic loci associated with 767 genes (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e) based on most proximal genes (see below). We considered the top 132 genes of this aggregated list constituting 95% of sequencing reads, and including all 128 genes with multiple inserts, as high-confidence hits (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). We note however that some pathologically relevant genes (such as \u003cem\u003ePTEN\u003c/em\u003e, ranked 134) might have been omitted by this prioritization strategy.\u003c/p\u003e \u003cp\u003eTo confirm that the analytical approach identified driver rather than passenger events, 224 colonies, including 23 isolated directly from the primary screening plates and 201 derived by limiting dilution of the multipassaged individual resistant pools, were sequenced (\u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). On average, 2.2 inserts (ranging from 1 to 12) were found per clone (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB\u003c/b\u003e), indicating that co-enrichment of passenger inserts in resistant cells was infrequent and thus indicating that candidate resistance-driving genes should be readily identified by considering high read count genes and proximity to redundant insertion events.\u003c/p\u003e \u003cp\u003eTo characterize the functional consequence of the insertions, we performed RNA-Seq on 10 clones and plotted the differential gene expression according to the insertion orientation and proximity (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC, D, Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e). This analysis indicated that, as predicted and consistent with our previous report\u003csup\u003e21\u003c/sup\u003e, expression variations mostly affected the nearest gene (13 of 16 inserts, including \u003cem\u003eLPP\u003c/em\u003e, the second nearest gene after an undetectable antisense isoform \u003cem\u003eLPP-AS2\u003c/em\u003e, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, q\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and that upstream/sense-strand inserts result in activation of the host gene transcription. We also noted that an insertion as far as 48 kb robustly upregulated the expression of the nearest gene (\u003cem\u003eLPP)\u003c/em\u003e (20.8-fold, p\u0026thinsp;=\u0026thinsp;0). While genome structural and regulatory complexity are likely to impact how far a PB insertion can activate a gene, alternative splicing involving splice donor (SD) and acceptor (SA) site selection is affected by promoter activity and the transcription elongation rate \u003csup\u003e23,24\u003c/sup\u003e, and several human genes, such as dystrophin (\u003cem\u003eDMD\u003c/em\u003e), indeed contain introns well over 100 kb. Thus, the capacity for distal activation demonstrated here is likely due to CMV-initiated strong transcriptional readthrough and the coupling of the exogenous SD to an endogenous SA and may therefore substantially reduce the number of clones needed for genome-wide coverage compared to the proximal effect alone. In addition to upstream sense-strand events, intragenic sense-strand inserts at the 5\u0026rsquo; end of the gene or the protein coding sequence, effectively analogous to 5\u0026rsquo; alternative splicing isoforms, were also observed to activate transcription in clones containing \u003cem\u003eNEDD4L, BRAF\u003c/em\u003e, or \u003cem\u003eMCF2\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eOf the 132 high-confidence hits, 52 were associated with multiple incidences of nonidentical sense-strand, upstream or proximal, insertion events and thus most likely resulted in host gene overexpression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Interestingly, the PB approach might also be capable of identifying resistance through LoF in addition to GOF events. Conceptually, LoF could occur either through insertion in the gene body or through insertion downstream of a gene in the opposite direction to transcription, leading to gene disruption or antisense RNA expression, among other mechanisms. In line with this possibility and with the previous identification of disruption of the \u003cem\u003eNPC1\u003c/em\u003e gene in a similar transposon-mediated screen for viral invasion\u003csup\u003e22\u003c/sup\u003e, several potentially disruptive insertions were found in genes such as \u003cem\u003eGPC6 and PLS3\u003c/em\u003e, and one was discovered in the well-characterized resistance gene \u003cem\u003ePTEN\u003c/em\u003e, a lipid phosphatase that acts as a tumor suppressor to counteract the activity of phosphoinositide 3-kinases (PI3K) with PI3K activity promoting proliferation and survival (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S3\u003c/b\u003e). However, robust identification of such LoF driven resistance events is complicated by the broad range of insertion positions and disruption of gene structures that could underlie LoF. In addition, in the context of a diploid genome, loss of function might only involve haploinsufficient genes, although these could actually constitute a large fraction of human genes \u003csup\u003e25\u003c/sup\u003e. Furthermore, cancer cell genomes are frequently not simply diploid due to whole genome duplication or other complex genomic rearrangements. For these different reasons, below, we focus on GoF events.\u003c/p\u003e \u003cp\u003eConfirming the ability of the PB approach to identify functionally relevant and drug target-related resistance events, \u003cem\u003eBRAF\u003c/em\u003e (\u003cem\u003eBRAF\u003c/em\u003e\u003csup\u003eV600E\u003c/sup\u003e, in which A375 is homozygous for that mutation), the direct target of inhibition, was among the top hit genes with 8 insertion events (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Interestingly, although not the most prominent target in the primary screening, the majority (140/201) of subclones isolated by limiting dilution of the pools contained \u003cem\u003eBRAF\u003c/em\u003e insertions (\u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e), implying that \u003cem\u003eBRAF\u003c/em\u003e\u003csup\u003eV600E\u003c/sup\u003e overexpression itself promotes a strong proliferative advantage, an observation in line with a previously published study\u003csup\u003e18\u003c/sup\u003e. Two insertion events were mapped to \u003cem\u003eRAF1\u003c/em\u003e (\u003cem\u003eCRAF\u003c/em\u003e), a paralog of BRAF, and a predicted activating insert was also found 2.7 kb upstream of the \u003cem\u003eKRAS\u003c/em\u003e gene, the upstream activator of RAF kinases in the mitogen-activated protein kinase (MAPK) signaling cascade (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Interestingly, among the high-confidence hits, the only other kinase predicted to be upregulated was the SRC family tyrosine kinase \u003cem\u003eYES1\u003c/em\u003e. SRC kinases, particularly SRC, FYN and YES (encoded by \u003cem\u003eYES1\u003c/em\u003e), are indeed positive regulators of RAF kinases and are known to be upstream activators of the MAPK pathway in many contexts, including BRAF inhibitor resistance\u003csup\u003e26\u003c/sup\u003e. Other candidate resistance genes belong to a broad variety of functional classes. Multiple guanine exchange factors, including three RAP exchange factors (\u003cem\u003eRAPGEF2\u003c/em\u003e, \u003cem\u003eRAPGEF4\u003c/em\u003e, and \u003cem\u003eRAPGEF6\u003c/em\u003e) and two Rho exchange factors (\u003cem\u003eVAV1\u003c/em\u003e and \u003cem\u003eARHGEF28\u003c/em\u003e), were among the high-confidence hits, and another Rho exchange factor (\u003cem\u003eMCF2\u003c/em\u003e) ranked 540 from the pools (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e), whose expression was also induced 33-fold by an intragenic sense-strand insert in a clone (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eD, Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e). Several transcriptional regulators were found among the most redundant hit genes such as \u003cem\u003eEYA1\u003c/em\u003e (11 inserts), \u003cem\u003ePOU2F1\u003c/em\u003e (9 inserts), and the zinc finger proteins \u003cem\u003eZFHX3\u003c/em\u003e and \u003cem\u003eZFHX4\u003c/em\u003e (7 inserts each). Chromatin binding proteins (\u003cem\u003eARID1A\u003c/em\u003e, \u003cem\u003eSMC2\u003c/em\u003e, \u003cem\u003eACIN1\u003c/em\u003e, and \u003cem\u003ePDS5A\u003c/em\u003e) and other gene family members, such as sorting nexins (\u003cem\u003eSNX6\u003c/em\u003e and \u003cem\u003eSNX14\u003c/em\u003e), semaphorins (\u003cem\u003eSEMA3D\u003c/em\u003e and \u003cem\u003eSMA6A\u003c/em\u003e) and potassium (Kv) channel interacting proteins (\u003cem\u003eKCNIP1\u003c/em\u003e and \u003cem\u003eKCNIP4\u003c/em\u003e), were also identified. Notably, these genes do not colocalize in the host genome, suggesting functionally meaningful enrichment.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eValidation of resistance genes\u003c/h3\u003e\n\u003cp\u003eTo validate the results of the transposon screen we selected 15 gene candidates based on the predicted activation effect for single-gene ORF-mediated overexpression (circled and labeled in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). The expression of these genes was mediated through lentiviral infection in A375 cells, and cellular proliferation measured in the presence of the BRAF inhibitor PLX4720, the pan-RAF inhibitor AZ628, and the MEK inhibitor AZD6244 (MEK is immediately downstream of RAF in the MAPK pathway) (\u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA\u003c/b\u003e). Seven candidates, \u003cem\u003eNEDD4L\u003c/em\u003e, \u003cem\u003eWWTR1\u003c/em\u003e (also known as \u003cem\u003eTAZ\u003c/em\u003e, a Hippo pathway component), \u003cem\u003eESRRG\u003c/em\u003e, \u003cem\u003eRAPGEF6\u003c/em\u003e, \u003cem\u003eARHGEF28\u003c/em\u003e, \u003cem\u003eYES1\u003c/em\u003e, and \u003cem\u003ePOU2F1\u003c/em\u003e were further confirmed to enhance viability in the presence of inhibitors using dose titration assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), proliferation rate assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) and long-term clonogenic assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). As a frame of reference, the expression of these genes conferred a level of resistance to RAF and MEK inhibition comparable to that of the previously reported and well-validated resistance gene \u003cem\u003eCOT\u003c/em\u003e \u003csup\u003e\u003cem\u003e27\u003c/em\u003e\u003c/sup\u003e. Supporting the specificity of the findings for BRAF inhibition, none of these genes induced resistance to the microtubule-disrupting agent paclitaxel while expression of the drug exporter \u003cem\u003eABCB1\u003c/em\u003e gene did, as expected (\u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB\u003c/b\u003e) \u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe note that among the genes that were not validated, some were likely due to the limitations of the ORF clones. For example \u003cem\u003eAKAP13\u003c/em\u003e was only modestly overexpressed (1.5- to 2.2-fold greater than basal expression, \u003cb\u003eFig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC\u003c/b\u003e) but did induce partial resistance to PLX4720; \u003cem\u003eBRAF\u003c/em\u003e wild-type cDNA also did not induce resistance, consistent with previously reported ORF screening results that did not identify wild-type \u003cem\u003eBRAF\u003c/em\u003e as a resistance gene \u003csup\u003e15\u003c/sup\u003e, likely because only the V600 mutation allele overexpression can drive resistance \u003csup\u003e2\u003c/sup\u003e, which is what occurs upon transposon insertion in A375 cells. Furthermore, the available \u003cem\u003eNEDD4L\u003c/em\u003e ORF construct used in the ORF screen and initially tested in our validation studies corresponded to a WW2 domain deletion isoform that failed to induce resistance (named herein \u003cem\u003eNEDD4L-dWW2\u003c/em\u003e to avoid confusion and further described below) \u003csup\u003e28\u003c/sup\u003e, highlighting the possibility that specific ORF inactivity or particular isoforms can contribute to false negatives in ORF based genetic studies. Overall, the results show a high rate of validation for the top transposon screen hits.\u003c/p\u003e\n\u003ch3\u003eConcordance of multiple genetic screens\u003c/h3\u003e\n\u003cp\u003eThe exact model of melanoma used here (the A375 cell line) was previously used to identify resistance genes via multiple screening modalities\u003csup\u003e14\u0026ndash;17\u003c/sup\u003e. Surprisingly, the overlap between the lists of genes identified previously and the top 132 genes we identified was limited to 6 genes in the case of a previously reported ORF screen (\u003cem\u003eESRRG\u003c/em\u003e, \u003cem\u003eFOXP2\u003c/em\u003e, \u003cem\u003eRAF1\u003c/em\u003e, \u003cem\u003eRAPGEF4\u003c/em\u003e, \u003cem\u003eVAV1\u003c/em\u003e, and \u003cem\u003eWWTR1\u003c/em\u003e) and 6 different genes in the case of a dCAS9-activator screen (\u003cem\u003eATP10A\u003c/em\u003e, \u003cem\u003eBCAS3\u003c/em\u003e, \u003cem\u003eGLIS3\u003c/em\u003e, \u003cem\u003eMECOM\u003c/em\u003e, \u003cem\u003ePCDH7\u003c/em\u003e, and \u003cem\u003eZFHX4\u003c/em\u003e). Although this could be explained at least in part by incomplete genome coverage of each library with some events functionally tested in only one of the screens, this very limited overlap across likely true positive hits (based on validation rates in the different studies) was still somewhat unexpected. For example, the genome-wide ORF screen should, in theory, capture the majority of predicted activated genes mapped to coding sequences in our screen. Interestingly, among the seven genes that we confirmed to induce resistance by overexpression experiments, \u003cem\u003eWWTR1\u003c/em\u003e and \u003cem\u003eESRRG\u003c/em\u003e were successfully identified in the ORF resistance screen (z score\u0026thinsp;\u0026ge;\u0026thinsp;the threshold level of 2.5 in that report); \u003cem\u003eRAPGEF6\u003c/em\u003e narrowly missed the hit status (z score\u0026thinsp;=\u0026thinsp;2.43), but \u003cem\u003eYES1\u003c/em\u003e (z score = -0.13) was not a hit. Finally, three (\u003cem\u003eNEDD4L\u003c/em\u003e, \u003cem\u003ePOU2F1\u003c/em\u003e, and \u003cem\u003eARHGEF28\u003c/em\u003e) were among the genes that did not pass quality-control filters prior to screening. This illustrates the challenge of large genetic screens both in terms of functional coverage due to genomic bias or technical factors as well as inherently somewhat arbitrary choices of thresholds for hit calling.\u003c/p\u003e \u003cp\u003eTo better understand whether the lack of overlap points to a higher-than-expected rate of false positive discovery or to low functional saturation and thus corresponding more to lack of screen sensitivity, we analyzed the functional relationship between the hit gene lists from the various GoF and LoF screens performed to uncover resistance events to BRAF inhibition in A375 cells. Genes identified by GoF ORF \u003csup\u003e15\u003c/sup\u003e and dCAS9 activator \u003csup\u003e14\u003c/sup\u003e screens and LoF CRISPR knockout \u003csup\u003e16\u003c/sup\u003e and shRNA knockdown \u003csup\u003e17\u003c/sup\u003e screens were mapped to a global functional cellular network using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cb\u003eTable S4\u003c/b\u003e)\u003csup\u003e29\u003c/sup\u003e. Between the transposon and ORF sets, we found 139 STRING connections, which was significantly greater than random (p\u0026thinsp;\u0026lt;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e) and more significant than other gene set pairs tested (\u003cb\u003eFig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA\u003c/b\u003e). The overall network constituted by genes from both screens was highly interconnected, and some of the genes identified in one screen were connected only to genes from the other screen but not to genes found within their own screen (circled in red in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Similarly, 66 STRING database gene-gene connections were found between the transposon and dCAS9 activator hit gene lists (p\u0026thinsp;=\u0026thinsp;0.046). The STRING connections between the LoF CRISPR and shRNA screens were also significant (p\u0026thinsp;\u0026lt;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), but neither matched the GoF screens except for the CRISPR-to-ORF pair, albeit presenting no actual gene overlap. We further sought to leverage the results of all the screens together to identify core programs contributing to resistance. We identified a core set of 133 genes by globally searching for all RefSeq genes with at least 10 connections to each of the screened gene sets (excluding the small gene set of the shRNA screen) (\u003cb\u003eTable S5\u003c/b\u003e) with the top functional clusters of MAPK and PI3K pathways, RAS-related genes, and receptor tyrosine kinases (RTKs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). We further specifically analyzed the STRING connections between the screened gene sets and major MAPK pathway components and found significant functional connections from the GoF gene sets (p-values ranging from 0.01 to 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) (\u003cb\u003eFig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eB\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConsistent with our STRING network analysis results and with other studies indicating that reactivation of MAPK activity is a major route for resistance in \u003cem\u003eBRAF\u003c/em\u003e driven models\u003csup\u003e3,27,30\u0026ndash;33\u003c/sup\u003e, among the 7 genes validated through ORF overexpression, 5 (\u003cem\u003eNEDD4L\u003c/em\u003e, \u003cem\u003eWWTR1\u003c/em\u003e, \u003cem\u003eRAPGEF6\u003c/em\u003e, \u003cem\u003eARHGEF28\u003c/em\u003e and \u003cem\u003eYES1\u003c/em\u003e) led to maintenance of the MAPK signaling pathway upon BRAF inhibition (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Overall, these results show that there is much better concordance at the functional network level across genes discovered by different screens than suggested by the lack of actual overlap of the gene lists. Moreover, many of the genes found to induce resistance appear to coalesce on a relatively narrow set of mechanisms, suggesting that a few core programs of resistance are repeatedly reached through different paths and could potentially be targeted to counter resistance.\u003c/p\u003e\n\u003ch3\u003eThe Hippo pathway modulator WWTR1 as a global effector\u003c/h3\u003e\n\u003cp\u003eThe Hippo pathway effector WWTR1 as a transcriptional activator was by far the most prevalent hit for resistance gain among all genes tested in long-term drug treatment assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Activation of YAP, the paralog of WWTR1, has previously been shown to promote resistance to BRAF inhibition \u003csup\u003e34\u0026ndash;36\u003c/sup\u003e. Hippo pathway inhibition (inhibition of MST1/2 kinases corresponding to Hippo in drosophila), leads to YAP/WWTR1 transcriptional program activation by nuclear translocation. Hippo pathway inactivation was previously shown to effectively resensitize cells to RTK inhibition \u003csup\u003e37\u003c/sup\u003e. Although the baseline level of WWTR1 did not predict the response to BRAF inhibitors in a panel of \u003cem\u003eBRAF\u003c/em\u003e\u003csup\u003eV600E\u003c/sup\u003e melanoma cell lines, exogenous overexpression increased resistance in several \u003cem\u003eBRAF\u003c/em\u003e\u003csup\u003eV600E\u003c/sup\u003e melanoma cell lines (\u003cb\u003eFig. S4A, B\u003c/b\u003e). Similarly, knocking down WWTR1 induced further sensitization of A375 cells and antagonized their resistance to PLX4720 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). This is also reminiscent of the sensitization of \u003cem\u003eBRAF\u003c/em\u003e and other MAPK pathway activation tumor models to MAPK pathway inhibitors via the suppression of YAP1 \u003csup\u003e34,35,38\u003c/sup\u003e. WWTR1 was identified as a candidate resistance gene in the previous ORF screen along with YAP1 \u003csup\u003e15\u003c/sup\u003e, and interestingly, as stated above, the SRC family tyrosine kinase YES1 validated in our screen was previously shown to regulate YAP1, which indeed was named after its association with YES1 (Yes Associated Protein 1)\u003csup\u003e39\u003c/sup\u003e. Consistent with these findings, we observed that the SRC family kinase inhibitor dasatinib strongly sensitized WWTR1-overexpressing cells to PLX4720 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). We also observed that \u003cem\u003eYAP1\u003c/em\u003e knockdown increased PLX4720 sensitivity in A375 parental cells but did not affect WWTR1 overexpressing cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, E), consistent with the redundancy between the paralogs YAP1 and WWTR1. Furthermore, knockdown of TEADs (TEAD1-4) the transcriptional partners that mediate the DNA binding of YAP1 and WWTR1 \u003csup\u003e40,41\u003c/sup\u003e, sensitized both parental and WWTR1 overexpressing A375 cells to PLX4720 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF, G).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo elucidate the transcriptional changes induced by WWTR1 and their relevance across melanoma models, we divided 22 \u003cem\u003eBRAF\u003c/em\u003e\u003csup\u003eV600\u003c/sup\u003e mutant melanoma cell lines in our collection into two groups based on their sensitivity to PLX4720, analyzed the differences in global gene expression between the two groups and identified 508 differentially expressed genes (DEGs) (\u003cb\u003eTable S6\u003c/b\u003e). We also performed RNA-Seq on parental and \u003cem\u003eWWTR1\u003c/em\u003e ORF-expressing A375 cells and found 315 DEGs with 4-fold up- or downregulation (\u003cb\u003eTable S7\u003c/b\u003e). Comparison of these two DEG lists revealed a significant overlap of 50 genes out of 269 WWTR1 target DEGs with measurements available in the cell line panel (chi-square\u0026thinsp;=\u0026thinsp;284, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH). This subset included several genes previously implicated in melanoma or tumor malignancy more broadly, such as \u003cem\u003eSOX10\u003c/em\u003e, whose downregulation leads to elevated EGFR and PDGF receptor signals and increased resistance to BRAF inhibition \u003csup\u003e9\u003c/sup\u003e; \u003cem\u003eMIA\u003c/em\u003e, which is involved in melanocyte lineage and melanoma development \u003csup\u003e42,43\u003c/sup\u003e; and \u003cem\u003eERBB3\u003c/em\u003e, which can reactivate MAPK upon MEK inhibition through a feedback mechanism \u003csup\u003e44,45\u003c/sup\u003e. Importantly, for almost all (46/50) genes, the DEG effect of the \u003cem\u003eWWTR1\u003c/em\u003e ORF was predicted by PLX4720 sensitivity \u0026minus;\u0026thinsp;39 genes downregulated by WWTR1 were highly expressed in the sensitive cell line group, while 7 upregulated genes were expressed in the resistant group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI). Furthermore, unsupervised clustering of the 18 most diversely expressed genes divided the melanoma lines according to their response to PLX4720 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eJ).\u003c/p\u003e\n\u003ch3\u003eWWTR1 and NEDD4L interconnect\u003c/h3\u003e\n\u003cp\u003eThe E3 ubiquitin-protein ligase \u003cem\u003eNEDD4L\u003c/em\u003e, was a prominent hit in our study with 32 nonidentical insertion events across 20 sites and 12.5% of all sequence reads in the pools (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Interestingly, \u003cem\u003eNEDD4L\u003c/em\u003e was also a hit in a previous BRAF inhibitor resistance study\u003csup\u003e20\u003c/sup\u003e. It was also implicated in EGFR-mTOR signaling in lung adenocarcinoma\u003csup\u003e46,47\u003c/sup\u003e although in a somewhat functionally opposite way to our expectation. At least 8 NEDD4L protein isoforms have been reported (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/uniprotkb/Q96PU5\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/uniprotkb/Q96PU5\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e48,49\u003c/sup\u003e and we noticed that two more isoforms at 230 kDa and 180 kDa might be present in A375 (\u003cb\u003eFig. S4A\u003c/b\u003e). To understand whether a specific isoform of NEDD4L was involved in resistance to BRAF inhibition, we initially investigated a prominent A375 cell isoform (PB clone B181 in \u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eD, S5A\u003c/b\u003e). However, cDNA-mediated (ORF) expression of a canonical NEDD4L isoform (110 kDa) was readily able to induce resistance in A375 and in other cell lines (\u003cb\u003eFig. S4B\u003c/b\u003e). Moreover, PLX4720 sensitivity across cell lines was not correlated with the expression of any specific isoform (\u003cb\u003eFig. S4A\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eThe NEDD4L protein contains an N-terminal C2 domain, four WW domains, and a HECT domain with E3 ubiquitin ligase catalytic activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) \u003csup\u003e28,50\u003c/sup\u003e. Previous studies have indicated that NEDD4L mediates the degradation of AMOT proteins, which are known regulators of the Hippo pathway \u003csup\u003e51\u0026ndash;53\u003c/sup\u003e. According to these studies, AMOT proteins repress WWTR1 and YAP1 activity at least in part via cytoplasm retention of WWTR1 and YAP. We therefore studied the mechanistic underlying of NEDD4L mediated resistance by expressing a catalytically inactive C962A mutant of the HECT domain (DD) or a WW2-domain deletion mutant (dWW2), which was previously shown to be critical for the recognition of a number of NEDD4L substrates. In both cases, we found that resistance was indeed impaired compared to that obtained with wild-type NEDD4L (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, S5A-C).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNEDD4L has also been reported to regulate SMAD2/3, which are downstream effectors of the TGFβ pathway \u003csup\u003e50\u003c/sup\u003e. However, the projected effects of such possible role in \u003cem\u003eBRAF\u003c/em\u003e melanoma are contradictory: NEDD4L-mediated SMAD2/3 degradation would lead to the termination of TGFβ signaling rather than its activation \u003csup\u003e28\u003c/sup\u003e, while previous reports have shown that TGFβ signaling upregulates the EGFR pathway to confer resistance to BRAF inhibition in melanoma\u003csup\u003e9\u003c/sup\u003e. Nonetheless, we tested this potential involvement by expressing missense mutations (S382A, S468A, or both (SASA)) targeting phosphorylation sites for SGK1, which is critical for SMAD2/3 regulation, and found that while protein expression of the S468A mutant failed despite decent mRNA expression (\u003cb\u003eFig. S5A\u003c/b\u003e), both S382A and SASA were comparable to those of the wild type in inducing resistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, S5A-C). Furthermore, knockdown of \u003cem\u003eSMAD2/3\u003c/em\u003e modestly reduced rather than increasing the resistance mediated by both parental A375 and NEDD4L cells (\u003cb\u003eFig. S5D, E\u003c/b\u003e), and the SMAD2/3 protein level did not increase upon \u003cem\u003eNEDD4L\u003c/em\u003e knockdown (\u003cb\u003eFig. S5F\u003c/b\u003e), suggesting the independent regulation of sensitivity to BRAF inhibition by NEDD4L and by TGFβ.\u003c/p\u003e \u003cp\u003eWWTR1 expression led to moderate NEDD4L upregulation at the protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC) and mRNA levels (1.5-fold, p\u0026thinsp;=\u0026thinsp;3.19E-06, FDR\u0026thinsp;=\u0026thinsp;3.03E-05; \u003cb\u003eTable S7\u003c/b\u003e); conversely, WWTR1 knockdown led to reduced NEDD4L expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). NEDD4L expression was also positively correlated with WWTR1 expression in a panel of cancer cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, \u003cb\u003eTable S8\u003c/b\u003e), and both were correlated with melanoma treatment outcome (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, S6A, B). These findings suggested that NEDD4L might be involved in WWTR1-mediated resistance.\u003c/p\u003e \u003cp\u003eConsistent with a role of NEDD4L in the regulation of other signaling pathways, NEDD4L overexpression led to a strong increase in AKT phosphorylation upon BRAF inhibition (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), and correspondingly, the AKT inhibitor MK2206 that strongly reduced AKT phosphorylation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF) effectively resensitized NEDD4L-overexpressing A375 cells to PLX4720 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). NEDD4L also led to the phosphorylation of EGFR and another RTK, IGF1R, upon BRAF inhibition, while WWTR1 upregulated EGFR protein expression more strongly than it promoted EGFR phosphorylation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH,I). The regulation of EGFR expression by WWTR1 is also in line with the positive correlation between EGFR and WWTR1 expression in a cell line panel (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ, \u003cb\u003eTable S8\u003c/b\u003e) and the elevated EGFR mRNA expression in WWTR1 ORF-overexpressing cells (2.0-fold, p\u0026thinsp;=\u0026thinsp;3.31E-12, FDR\u0026thinsp;=\u0026thinsp;8.08E-11; \u003cb\u003eTable S7\u003c/b\u003e). Therefore, WWTR1 plays an important role in the maintenance of MAPK pathway activation both through transcriptional regulation of some pathway component genes and likely other signaling mechanisms (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eK). In line with this, both the EGFR inhibitor pelitinib (EKB569) and the IGF1R inhibitor BMS754807 countered resistance induced by NEDD4L, WWTR1, and all other resistance genes validated in this study (\u003cb\u003eFig. S7\u003c/b\u003e), suggesting that RTK inhibitors might resensitize cells to MAPK pathway inhibition more broadly than previously described \u003csup\u003e3,6,56\u003c/sup\u003e.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eAs an alternative to genetic screening platforms consisting of complex mutagen libraries (such as ORFs), transposon mutagenesis has the potential to tackle some of the complexity of host genome and transcriptome with unidentified components, non-coding elements, and alternative isoforms. Moreover, activation of endogenous elements might provide more context specific results than exogenously expressed transcription activators. While transposon mutagenesis has been widely used \u003cem\u003ein vivo\u003c/em\u003e \u003csup\u003e19,54\u003c/sup\u003e, its application as an \u003cem\u003ein vitro\u003c/em\u003e cell line screening platform results in expandable off-the-shelf libraries that are ideal for applications involving complex treatment conditions, such as comparing resistance to multiple drugs. The low cost and simplicity of this approach, requiring only transient transfection of two plasmids, enables straightforward high-throughput platform deployment \u003csup\u003e20\u003c/sup\u003e. While not utilized in this study, additional features, such as tagging transposition plasmids with degenerate nucleotide barcodes, should further improve platform scalability. Such barcode diversity can be readily sequenced by next generation sequencing (NGS) to reveal nonidentical insertions even at the same genomic location, essentially eliminating the need to generate library replicates.\u003c/p\u003e \u003cp\u003eWhile our study provided further support for therapies targeting the Hippo pathway \u003csup\u003e37\u003c/sup\u003e or protein ubiquitination \u003csup\u003e57,58\u003c/sup\u003e, we also demonstrated that many different genes with a broad range of functions implicated in resistance to target inhibition therapies appear to coalesce to the pathway of primary target inhibition (here the MAPK pathway) and a restricted number of additional pathways, suggesting that the staggering complexity of the resistance landscape may not need to be addressed by an equally complex array of therapeutic strategies.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e \u003cb\u003eCell culture reagents.\u003c/b\u003e All cell lines were obtained from the collection of the Genomics of Drug Sensitivity in Cancer (GDSC) project\u003csup\u003e13\u003c/sup\u003e, cultured in either DMEM/F12 or RPMI supplemented with 5% FBS and 1% penicillin/streptavidin, and maintained in a 37\u0026deg;C/5% CO\u003csub\u003e2\u003c/sub\u003e cell culture incubator. Therapeutic compounds were purchased from Selleckchem and ChemieTek and dissolved in dimethyl sulfoxide.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTransposon mutagenesis library construction and screening.\u003c/b\u003e The \u003cem\u003epiggyBac\u003c/em\u003e activation transposon plasmid pPB-SB-CMV-puro-SD was described previously \u003csup\u003e21\u003c/sup\u003e. A derivative plasmid containing the degenerate nucleotide barcodes mentioned in the \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003eDiscussion\u003c/span\u003e section was verified by NGS and the plasmid map and complete sequence are provided in the supplementary information (\u003cb\u003eFig. S8, Supplementary Text\u003c/b\u003e). Cell line libraries were constructed by transfecting A375 cells with the transposon plasmid and the transposase plasmid pCMV-hyPBase \u003csup\u003e59\u003c/sup\u003e. After puromycin selection, the cells were treated with PLX4720, and surviving cells were pooled or isolated. Insertion site sequencing libraries were prepared using ligation-mediated PCR and identified with NGS as described previously \u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGene annotation for insert sites.\u003c/b\u003e Illumina sequencing data in FASTQ files were demultiplexed and trimmed to retain only the genomic DNA sequences. Reads of 7 bp or longer were aligned to the human reference genome (hg19) using Bowtie and unique reads were recorded. For the resistant pools, the top sequences by read count were reported based on our estimate of the survivor colony numbers from each library, with the top 100 inserts representing more than 99.5% of the total sequencing reads. For each insert, the three most proximal genes were annotated.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMelanoma patient cohort.\u003c/b\u003e Patients with metastatic melanoma harboring the BRAF\u003csup\u003eV600E\u003c/sup\u003e mutation (confirmed by genotyping) were enrolled in clinical trials for treatment with a BRAF inhibitor (vemurafenib) or combined BRAF\u0026thinsp;+\u0026thinsp;MEK inhibitor (dabrafenib\u0026thinsp;+\u0026thinsp;trametinib or LGX818\u0026thinsp;+\u0026thinsp;MEK162) and consented to tissue acquisition per the IRB-approved protocol.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLentiviral cDNA plasmid construction and cell assays.\u003c/b\u003e Open reading frame (ORF) entry clones were subcloned and inserted into two sets of lentiviral expression vectors (blasticidine and puromycin) for cell-based assays. Cells viability, apoptosis, and proliferation were assayed. All assays were performed at least three times. The results from representative experiments are presented with the number of technical replicates (n) indicated in each figure.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRNA-Seq.\u003c/b\u003e\u0026nbsp;Total RNA was prepared using an RNeasy Mini Kit (Qiagen) and processed using a TruSeq RNA Sample Preparation Kit (Illumina) to generate libraries of sequencing molecules. For samples derived from transposon-inserted clones, equal amounts of 12 indexed subsamples were pooled and sequenced using an Illumina HiSeq2500 instrument. STAR aligner\u003csup\u003e60\u003c/sup\u003e was used to map sequencing reads to transcripts. Read counts for individual transcripts were produced with HTSeq-count\u003csup\u003e61\u003c/sup\u003e, followed by the estimation of expression values as RPKM (reads per kilobase per million) and the detection of differentially expressed transcripts using EdgeR \u003csup\u003e62\u003c/sup\u003e. For WWTR1-overexpressing cDNA clones, samples were prepared from four biological replicates of viral infections, indexed, pooled, and sequenced. The Benjamini-Hochberg false discovery rate (FDR) was used to estimate the statistical significance of differences in gene expression.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSTRING analysis.\u003c/b\u003e Known and predicted protein-protein associations were tested using STRING analysis (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org\u003c/span\u003e\u003cspan address=\"http://string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Homo sapiens: 9606.protein.links.detailed.v9.1.txt.gz). STRING analyses were first performed between pairs of screened gene sets (\u003cb\u003eTable S4\u003c/b\u003e). To identify a core set of genes responsible for resistance, all RefSeq genes were queried for their connections to components of every screened gene set (\u003cb\u003eTable S5\u003c/b\u003e). The list of genes with at least 10 connections to each of four screens (excluding the small shRNA screen gene set) was used as input for the Database for Annotation, Visualization and Integrated Discovery (DAVID) analyses \u003csup\u003e63\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eClustering of gene expression values.\u003c/b\u003e The genes most differentially expressed between BRAF inhibitor-sensitive and -resistant cell lines were selected for unsupervised clustering using Pearson\u0026rsquo;s correlation across genes and cell lines. Clustering and heatmap generation were performed using Gene-E (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.broadinstitute.org/cancer/software/GENE-E/index.html\u003c/span\u003e\u003cspan address=\"http://www.broadinstitute.org/cancer/software/GENE-E/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Pentao Liu for providing the \u003cem\u003epiggyBac\u003c/em\u003e transposase plasmid pCMV-hyPBase, Joan Massague for the \u003cem\u003eNEDD4L\u003c/em\u003e cDNA plasmids, and Bristol-Myers Squibb for the U219 expression data for the cancer cell lines. We also thank Wilhelm Haas, Keith T. Flaherty, and Adam Lacy-Hulbert for useful discussions. This work was supported by grants from the Wellcome Trust (086357 and 102696).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL.C.: project coordination, experiments, data analysis, and manuscript preparation. C.H.B.: \u0026nbsp;project design and supervision, data analysis, and manuscript preparation. I.P-M.: STRING and clustering analyses. A.D.: Drug response assays in melanoma cell lines and discussion of the project. X.Y.: contributed to cell culture and immunoblotting. D.F.: provision of patient RNA-Seq and treatment data. R.I.S.: NGS data processing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMelanoma patient RNA-Seq data were deposited in the NCBI GEO under accession number GSE73470. Cell line data are available through the Genomics of Drug Sensitivity in Cancer (https://www.cancerrxgene.org/) or DepMap (https://depmap.org/portal/) portals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eADDITIONAL INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no competing interests for this work.\u003c/p\u003e\u003cp\u003eAdditional protocol details can be found in the \u003cb\u003eSupplementary Methods\u003c/b\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBollag, G.\u003cem\u003e et al.\u003c/em\u003e Clinical efficacy of a RAF inhibitor needs broad target blockade in BRAF-mutant melanoma. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e467\u003c/strong\u003e, 596-599 (2010). https://doi.org/10.1038/nature09454\u003c/li\u003e\n\u003cli\u003eCorcoran, R. 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Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. \u003cem\u003eNat Protoc\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 44-57 (2009). https://doi.org/10.1038/nprot.2008.211\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"targeted cancer therapy, BRAF inhibitor resistance, transposon mutagenesis screening, Hippo pathway, ubiquitination.","lastPublishedDoi":"10.21203/rs.3.rs-5320870/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5320870/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGenotype-informed anticancer therapies such as BRAF inhibitors can show remarkable clinical efficacy in BRAF-mutant melanoma; however, drug resistance poses a major hurdle to successful cancer treatment. Many resistance events to targeted therapies have been identified, suggesting a complex path to improve therapeutics. Here, we showed the utility of a\u003cem\u003e piggyBac\u003c/em\u003e transposon activation mutagenesis screen for the efficient identification of genes that are resistant to BRAF inhibition in melanoma. Although several forward genetic screens performed in the same context have identified a broad range of resistance genes that poorly overlap, an integrative analysis revealed a much smaller functional diversity of resistance mechanisms, including reactivation of the MAPK pathway, PI3K-AKT pathway, and Hippo pathway, suggesting that a relatively small number of therapeutic strategies might overcome resistance manifested by a large gene set. Moreover, we illustrated the pivotal role of the Hippo\u003cem\u003e \u003c/em\u003epathway effector WWTR1 (TAZ\u003cem\u003e)\u003c/em\u003e in mediating BRAF inhibition resistance through transcriptional regulation of receptor tyrosine kinases and through interactions with the E3 ubiquitin ligase NEDD4L.\u003c/p\u003e","manuscriptTitle":"Transposon mediated functional genomic screening for BRAF inhibitor resistance reveals convergent Hippo and MAPK pathway activation events","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-21 14:18:13","doi":"10.21203/rs.3.rs-5320870/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-04T07:18:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-30T20:20:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-25T16:31:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112951870827049613390449947029843306532","date":"2024-11-08T22:08:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"190177897212046627070157751435302620907","date":"2024-11-08T12:47:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-08T00:40:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-07T10:41:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-07T08:39:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-06T11:34:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-10-23T17:51:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"976fb9ae-5785-4e50-b222-0993e9566b67","owner":[],"postedDate":"November 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":40026786,"name":"Biological sciences/Cancer/Cancer genomics"},{"id":40026787,"name":"Biological sciences/Cancer/Cancer therapy"},{"id":40026788,"name":"Biological sciences/Cancer/Skin cancer"},{"id":40026789,"name":"Biological sciences/Computational biology and bioinformatics/Genome informatics"},{"id":40026790,"name":"Biological sciences/Computational biology and bioinformatics/High throughput screening"},{"id":40026791,"name":"Biological sciences/Molecular biology/Transposition"},{"id":40026792,"name":"Biological sciences/Genetics/Cancer genomics"},{"id":40026793,"name":"Biological sciences/Genetics/Functional genomics"},{"id":40026794,"name":"Biological sciences/Genetics/Gene regulation"},{"id":40026795,"name":"Biological sciences/Genetics/Genomics"},{"id":40026796,"name":"Biological sciences/Systems biology/Genomic engineering"},{"id":40026797,"name":"Biological sciences/Systems biology/Regulatory networks"},{"id":40026798,"name":"Biological sciences/Systems biology/Systems analysis"},{"id":40026799,"name":"Health sciences/Oncology/Cancer/Cancer genomics"},{"id":40026800,"name":"Health sciences/Oncology/Cancer/Cancer therapy"},{"id":40026801,"name":"Health sciences/Oncology/Cancer/Skin cancer"},{"id":40026802,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2025-01-27T16:06:08+00:00","versionOfRecord":{"articleIdentity":"rs-5320870","link":"https://doi.org/10.1038/s41598-025-86694-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-01-24 15:57:37","publishedOnDateReadable":"January 24th, 2025"},"versionCreatedAt":"2024-11-21 14:18:13","video":"","vorDoi":"10.1038/s41598-025-86694-5","vorDoiUrl":"https://doi.org/10.1038/s41598-025-86694-5","workflowStages":[]},"version":"v1","identity":"rs-5320870","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5320870","identity":"rs-5320870","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}