miR‐205 mediates adaptive resistance to MET inhibition via ERRFI 1 targeting and raised EGFR signaling
2018; Springer Nature; Volume: 10; Issue: 9 Linguagem: Inglês
10.15252/emmm.201708746
ISSN1757-4684
AutoresCristina Migliore, Elena Morando, Elena Ghiso, Sergio Anastasi, Vera P. Leoni, Maria Apicella, Davide Corà, Anna Sapino, Filippo Pietrantonio, Filippo de Braud, Amedeo Columbano, Oreste Segatto, Silvia Giordano,
Tópico(s)PI3K/AKT/mTOR signaling in cancer
ResumoResearch Article24 July 2018Open Access Source DataTransparent process miR-205 mediates adaptive resistance to MET inhibition via ERRFI1 targeting and raised EGFR signaling Cristina Migliore Corresponding Author Cristina Migliore cristina.migl[email protected] orcid.org/0000-0003-3722-2814 Department of Oncology, University of Torino, Candiolo, Italy Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy Search for more papers by this author Elena Morando Elena Morando Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy Search for more papers by this author Elena Ghiso Elena Ghiso Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy Search for more papers by this author Sergio Anastasi Sergio Anastasi Unit of Oncogenomics and Epigenetics, IRCCS Regina Elena National Cancer Institute, Rome, Italy Search for more papers by this author Vera P Leoni Vera P Leoni Department of Biomedical Sciences, Unit of Oncology and Molecular Pathology, University of Cagliari, Cagliari, Italy Search for more papers by this author Maria Apicella Maria Apicella Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy Search for more papers by this author Davide Cora' Davide Cora' Department of Oncology, University of Torino, Candiolo, Italy Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy Department of Translational Medicine, Piemonte Orientale University "Amedeo Avogadro", Novara, Italy Search for more papers by this author Anna Sapino Anna Sapino Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy Department of Medical Science, University of Torino, Torino, Italy Search for more papers by this author Filippo Pietrantonio Filippo Pietrantonio Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy Department of Oncology and Hemato-oncology, University of Milano, Milan, Italy Search for more papers by this author Filippo De Braud Filippo De Braud Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy Department of Oncology and Hemato-oncology, University of Milano, Milan, Italy Search for more papers by this author Amedeo Columbano Amedeo Columbano Department of Biomedical Sciences, Unit of Oncology and Molecular Pathology, University of Cagliari, Cagliari, Italy Search for more papers by this author Oreste Segatto Corresponding Author Oreste Segatto [email protected] orcid.org/0000-0003-2561-1142 Unit of Oncogenomics and Epigenetics, IRCCS Regina Elena National Cancer Institute, Rome, Italy Search for more papers by this author Silvia Giordano Corresponding Author Silvia Giordano [email protected] orcid.org/0000-0003-1854-1086 Department of Oncology, University of Torino, Candiolo, Italy Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy Search for more papers by this author Cristina Migliore Corresponding Author Cristina Migliore [email protected] orcid.org/0000-0003-3722-2814 Department of Oncology, University of Torino, Candiolo, Italy Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy Search for more papers by this author Elena Morando Elena Morando Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy Search for more papers by this author Elena Ghiso Elena Ghiso Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy Search for more papers by this author Sergio Anastasi Sergio Anastasi Unit of Oncogenomics and Epigenetics, IRCCS Regina Elena National Cancer Institute, Rome, Italy Search for more papers by this author Vera P Leoni Vera P Leoni Department of Biomedical Sciences, Unit of Oncology and Molecular Pathology, University of Cagliari, Cagliari, Italy Search for more papers by this author Maria Apicella Maria Apicella Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy Search for more papers by this author Davide Cora' Davide Cora' Department of Oncology, University of Torino, Candiolo, Italy Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy Department of Translational Medicine, Piemonte Orientale University "Amedeo Avogadro", Novara, Italy Search for more papers by this author Anna Sapino Anna Sapino Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy Department of Medical Science, University of Torino, Torino, Italy Search for more papers by this author Filippo Pietrantonio Filippo Pietrantonio Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy Department of Oncology and Hemato-oncology, University of Milano, Milan, Italy Search for more papers by this author Filippo De Braud Filippo De Braud Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy Department of Oncology and Hemato-oncology, University of Milano, Milan, Italy Search for more papers by this author Amedeo Columbano Amedeo Columbano Department of Biomedical Sciences, Unit of Oncology and Molecular Pathology, University of Cagliari, Cagliari, Italy Search for more papers by this author Oreste Segatto Corresponding Author Oreste Segatto [email protected] orcid.org/0000-0003-2561-1142 Unit of Oncogenomics and Epigenetics, IRCCS Regina Elena National Cancer Institute, Rome, Italy Search for more papers by this author Silvia Giordano Corresponding Author Silvia Giordano [email protected] orcid.org/0000-0003-1854-1086 Department of Oncology, University of Torino, Candiolo, Italy Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy Search for more papers by this author Author Information Cristina Migliore *,1,2, Elena Morando2, Elena Ghiso2, Sergio Anastasi3, Vera P Leoni4, Maria Apicella2, Davide Cora'1,2,5, Anna Sapino2,6, Filippo Pietrantonio7,8, Filippo De Braud7,8, Amedeo Columbano4, Oreste Segatto *,3 and Silvia Giordano *,1,2 1Department of Oncology, University of Torino, Candiolo, Italy 2Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy 3Unit of Oncogenomics and Epigenetics, IRCCS Regina Elena National Cancer Institute, Rome, Italy 4Department of Biomedical Sciences, Unit of Oncology and Molecular Pathology, University of Cagliari, Cagliari, Italy 5Department of Translational Medicine, Piemonte Orientale University "Amedeo Avogadro", Novara, Italy 6Department of Medical Science, University of Torino, Torino, Italy 7Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy 8Department of Oncology and Hemato-oncology, University of Milano, Milan, Italy *Corresponding author. Tel: +39 011993221; Fax: +39 011993225; E-mail: [email protected] *Corresponding author. Tel: +39 0652662551; Fax: +39 0652662600; E-mail: [email protected] *Corresponding author. Tel: +39 011993233; Fax: +39 011993225; E-mail: [email protected] EMBO Mol Med (2018)10:e8746https://doi.org/10.15252/emmm.201708746 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract The onset of secondary resistance represents a major limitation to long-term efficacy of target therapies in cancer patients. Thus, the identification of mechanisms mediating secondary resistance is the key to the rational design of therapeutic strategies for resistant patients. MiRNA profiling combined with RNA-Seq in MET-addicted cancer cell lines led us to identify the miR-205/ERRFI1 (ERBB receptor feedback inhibitor-1) axis as a novel mediator of resistance to MET tyrosine kinase inhibitors (TKIs). In cells resistant to MET-TKIs, epigenetically induced miR-205 expression determined the downregulation of ERRFI1 which, in turn, caused EGFR activation, sustaining resistance to MET-TKIs. Anti-miR-205 transduction reverted crizotinib resistance in vivo, while miR-205 over-expression rendered wt cells refractory to TKI treatment. Importantly, in the absence of EGFR genetic alterations, miR-205/ERRFI1-driven EGFR activation rendered MET-TKI-resistant cells sensitive to combined MET/EGFR inhibition. As a proof of concept of the clinical relevance of this new mechanism of adaptive resistance, we report that a patient with a MET-amplified lung adenocarcinoma displayed deregulation of the miR-205/ERRFI1 axis in concomitance with onset of clinical resistance to anti-MET therapy. Synopsis Currently, one of the main challenges associated to targeted therapies is the almost inevitable occurrence of resistance. Thus, the identification of predictive biomarkers of resistance and the understanding of the resistance mechanisms are mandatory to improve the efficacy of these therapies. An actionable mechanism of resistance not relying on genomic alterations was identified. EGFR activation in MET-addicted tumors is due to decreased ERRFI1 expression, caused by miR-205 up-regulation. Adaptive resistance can be overcome by combined blockade of MET and EGFR. Introduction MET is the receptor tyrosine kinase (RTK) for hepatocyte growth factor (HGF). MET triggering by HGF engagement activates a complex cellular program termed invasive growth, which protects from apoptosis and drives cells to proliferate and invade the surrounding tissues (Gherardi et al, 2012; De Silva et al, 2017). While physiologically required for tissue patterning during embryonic development and tissue homeostasis in post-natal life, the MET-driven invasive growth program can be also exploited by cancer cells in their quest to acquire growth autonomy and metastatic capabilities (Gherardi et al, 2012; De Silva et al, 2017). MET over-expression linked to constitutive signaling is observed in several tumors, including gastric, lung, ovarian, renal, thyroid, liver, and esophageal cancers (Ghiso & Giordano, 2013). It may be caused by diverse mechanisms (hypoxia, activation of upstream genes, miRNA deregulation or MET gene amplification/exon 14 skipping mutations) and can confer to cancer cells a state of oncogene addiction. Accordingly, MET is categorized as a driver oncogene. Among the different therapeutic approaches being exploited for suppressing MET oncogenic activity, selective (capmatinib, tepotinib) or non-selective (cabozantinib, crizotinib) small molecule tyrosine kinase inhibitors (TKIs) are in advanced clinical testing. Achieving a therapeutic success with MET-TKIs depends on patients' molecular selection, because only tumors truly addicted to MET signaling may respond to MET blockade. In addition, pre-clinical and clinical studies point to resistance as a vexing hurdle to the therapeutic success of MET-TKIs (Ghiso & Giordano, 2013). It follows that a detailed understanding of the signaling circuitries underpinning resistance must be viewed as an integral component of the clinical development of MET-TKIs. Several mechanisms of resistance to MET-TKIs have been already reported and include (i) MET amplification (Cepero et al, 2010), point mutations (Bahcall et al, 2016), and over-expression (Martin et al, 2014); (ii) KRAS amplification (Cepero et al, 2010); (iii) bypass activation of downstream pathways by RTKs acting in parallel to MET (Corso et al, 2010). Concerning the latter mechanism, altered miRNA expression is increasingly recognized as a strategy through which cancer cells reprogram their signaling circuitries in order to escape from pharmacological suppression of driver oncogenes (Migliore & Giordano, 2013). Herein, we report that miR-205 upregulation is sufficient to render MET-addicted tumors resistant to structurally different MET-TKIs (non-selective such as crizotinib, or selective such as PHA-665752 and JNJ-38877605) via ERRFI1 targeting and consequent EGFR (epidermal growth factor receptor) activation. Accordingly, combined MET and EGFR pharmacological blockade reverts the adaptive resistance to MET-TKIs imposed by miR-205 upregulation. Results Generation and characterization of MET-TKI-resistant cell lines GTL16 (gastric carcinoma cells), SG16 (primary gastric carcinoma cells), and EBC-1 (lung squamous cell carcinoma cells) are addicted to MET (Corso et al, 2010; Apicella et al, 2016). This phenotype is caused by MET amplification leading to MET over-expression and constitutive activation (Cepero et al, 2010; Apicella et al, 2016). In line, treatment of EBC-1, GTL16 and SG16 cells with crizotinib (non-selective MET-TKI) or PHA-665752 and JNJ-38877605 (selective MET-TKIs), strongly impaired their viability (Fig 1A–C). Figure 1. Characterization of MET-addicted cells rendered resistant to MET tyrosine kinase inhibitors A–C. EBC-1 (A), GTL16 (B), and SG16 (C), either parental (wt) or resistant (-R) to the indicated MET-TKIs, were exposed to escalating concentrations of the indicated drugs. Cell viability was measured after 72 h (CellTiterGlo) to derive IC50 values. Data are the mean of three independent experiments, each performed in quadruplicate wells. PHA = PHA-665752; CRIZ = crizotinib; JNJ = JNJ-38877605; R-PHA = cells resistant to PHA-665752; R-CRIZ = cells resistant to crizotinib; R-JNJ = cells resistant to JNJ-38877605. D. SG16 and EBC-1 genomic DNA from resistant and wt cells, untreated or treated with 5-Aza-2′-deoxycytidine (5-AZA), was extracted and subjected to bisulfite conversion. The miR-205 genomic region was amplified by PCR and pyrosequenced. The scatter dot plot represents the percentage of DNA methylation of miR-205 genomic region; each dot exemplifies the average of the methylation status of six CpGs (shown in Appendix Fig S2) analyzed in two technical replicates (Squares = wt untreated cells; triangles = wt cells treated with 5-AZA (used as control); circles = resistant cells). E. MiR-205 expression was evaluated by RT–qPCR in SG16 and EBC-1 wt treated or not with 5-Aza-2′-deoxycytidine (5-AZA). As shown, miR-205 level significantly increased upon 5-AZA treatment. n = 3 per condition. Data information: (A–C) Data are presented as mean ± SD. Two-way ANOVA, Bonferroni's multiple comparisons test was significant (P < 0.001) in wt versus resistant cells. (D) Data are presented as median and 95% CI. ***P < 0.001; one-way-ANOVA, Bonferroni's multiple comparisons test. (E) Data are presented as mean ± SD. ***P < 0.001, two-tailed t-test. Download figure Download PowerPoint We generated EBC-1, GTL16, and SG16 cells resistant to crizotinib, PHA-665752, or JNJ-38877605 by a stepwise dose escalation protocol, eventually obtaining derivatives capable of normal growth at drug concentrations roughly ten times higher than the IC50 calculated for parental cells (Fig 1A–C). Notably, all the resistant cells were cross-resistant to the other MET-targeted drugs (Fig 1A–C). To investigate a potential role for miRNAs in resistance to MET-TKIs, pairs of sensitive and resistant GTL16, EBC-1, and SG16 cells were profiled for miRNA expression. Out of 375 miRNAs examined, we found that 201, 98, and 140 miRNAs were expressed by GTL16, EBC-1, and SG16 cells, respectively. This compendium of expressed miRNAs was used for further analyses. Unsupervised hierarchical clustering of expressed miRNAs discriminated the three cell lines (Fig EV1A). When comparing relative levels of expressed miRNAs in each pair of sensitive/resistant cell lines, a single miRNA, namely miR-205, was concordantly and abundantly upregulated in all of the MET-TKI-resistant cells (Table 1). Real-time PCR analyses confirmed miR-205 upregulation (up to 130-fold) in resistant cells (Fig EV1B–D). This datum was consolidated by the observation that two additional MET-addicted cells lines, namely KATO II and SNU-5, showed increased miR-205 expression upon becoming resistant to MET-TKIs (Fig EV1E and F; and Appendix Fig S1A and B). Click here to expand this figure. Figure EV1. MiR-205 expression is increased in MET-TKI-resistant cells A. Hierarchical clustering of miRNA expression profile reveals a cell line-specific pattern of expression. MiRNAs were clustered on the basis of the cell line of origin. Each row represents a miRNA, expressed as ΔCt calculated respect to the internal control (RNU48); each column represents a cell line. For each miRNA, units (−1 to +1) represent the ΔCt values against the median of the values in all the samples. Red and green colors denote higher or lower expression levels of the miRNA (median-centered), respectively. B–F. MiR-205 expression was evaluated by RT–qPCR in EBC-1 (B), GTL16 (C), SG16 (D), KATO II (E), and SNU-5 (F) wt and resistant (R-) cells. As shown, miR-205 was over-expressed in resistant versus wt cells. n = 3 per condition. Data information: Average ± SD. Asterisks: ***P < 0.001; **P < 0.01; one-way ANOVA (B, C) and two-tailed t-test (D–F). Download figure Download PowerPoint Table 1. Global miRNA expression measured by TaqMan low density array EBC1 R150 CRIZ EBC1 R250 PHA GTL16 R200 CRIZ GTL16 R300 PHA SG16 R250JNJ miRNA Fold change miRNA Fold change miRNA Fold change miRNA Fold change miRNA Fold change TOP 10 miRNAsUPREGULATED miR-205 53.1 miR-146a 1301.5 miR-205 1256.9 miR-205 1368.7 miR-339-3p 8.1 miR-27b 1.8 miR-205 115.4 miR-328 528.7 miR-125b 305.6 miR-339-5p 7.7 miR-27a 1.5 miR-27b 7.7 miR-133a 330.0 miR-501-3p 260.6 miR-148a 7.5 miR-193b 1.4 let-7a 4.6 miR-146a 245.8 miR-184 104.1 miR-375 6.9 miR-141 1.4 miR-422a 3.0 miR-125b 179.5 miR-146a 81.5 miR-205 5.8 miR-24 1.4 miR-146b-5p 2.7 miR-642 94.6 miR-99a 58.1 miR-328 5.4 let-7a 1.2 miR-24 2.7 miR-423-5p 93.9 miR-495 46.9 miR-9 5.3 miR-19a 1.2 miR-27a 2.3 miR-579 91.8 miR-133a 41.0 miR-10a 5.2 miR-9 1.1 miR-598 2.3 miR-125a-3p 50.4 miR-616 37.3 miR-139-5p 4.9 miR-365 1.1 miR-200a 2.3 miR-142-3p 48.8 miR-15a 27.6 miR-23b 4.7 TOP 10 miRNAsDOWNREGULATED miR-196b 0.1 miR-100 0.1 miR-363 0.0 miR-450b-5p 0.0 miR-106b 0.3 miR-125b 0.1 miR-99a 0.1 miR-379 0.2 miR-363 0.0 miR-155 0.4 miR-146a 0.1 miR-125b 0.1 miR-517c 0.2 miR-22 0.1 miR-93 0.4 miR-100 0.2 miR-886-5p 0.1 miR-410 0.2 miR-190 0.1 miR-25 0.5 miR-99a 0.2 miR-139-5p 0.2 miR-518f 0.4 miR-517c 0.1 miR-100 0.6 miR-345 0.2 miR-138 0.2 miR-190 0.4 miR-150 0.2 miR-99a 0.7 miR-886-3p 0.2 miR-886-3p 0.3 miR-20b 0.4 miR-452 0.2 miR-345 0.7 miR-339-5p 0.3 miR-222 0.4 miR-505 0.5 miR-449b 0.3 miR-10b 0.7 miR-212 0.3 has-miR-155 0.4 miR-9 0.5 miR-362-3p 0.3 miR-320 0.7 miR-138 0.3 miR-484 0.4 miR-376a 0.5 miR-338-3p 0.4 miR-18a 0.7 Since epigenetic regulation can modify miR-205 level (Hulf et al, 2013), we explored the possibility that differential methylation of miR-205 genomic locus could contribute to the observed differences of miR-205 expression in resistant versus wt cells. To this aim, we investigated the methylation status of the miR-205 genomic region (Appendix Fig S2A and B). As shown in Fig 1D, we observed that the level of methylation of the CpG enriched region mapping to the miR-205 locus was significantly lower in resistant cells when compared to their wt counterpart. In fact, the CpG methylation level at the miR-205 locus in resistant cells was comparable to that observed in parental cells upon treatment with 5-Aza-2′-deoxycytidine, with CpG de-methylation leading to increased miR-205 expression (Fig 1E). MiR-205 upregulation mediates resistance to MET-TKIs, which is linked to reduced expression of the putative miR-205 target ERRFI1 As shown in Fig 2A–C and Appendix Fig S3A–C, miR-205 silencing significantly reduced cell viability in all drug-resistant derivatives. Conversely, ectopic expression of miR-205 in wt cells was capable of increasing their viability at TKIs concentrations in the IC50 range (Fig 2D–F and Appendix Fig S3D–F). Figure 2. miR-205 modulates tumor cell sensitivity to MET-TKIs A–C. MiR-205 expression was silenced by transfection of either anti-miR-205 or control antagomiR (Ctrl) in EBC-1 (A), GTL16 (B), and SG16 (C) resistant cells. Resistant cells were grown in the presence of the TKI to which they are resistant; viability was assessed after 72 h by CellTiterGlo. n = 4 per condition. D–F. MiR-205 or a control miRNA (Ctrl miR, a random miRNA sequence) was over-expressed in parental (wt) EBC-1 (D), GTL16 (E), and SG16 (F) cells. Cells were grown for 72 h in the presence of MET-TKIs at the indicated doses. Viability was evaluated as above. n = 4 per condition. G. GTL16 R-CRIZ cells transduced with pCDH-anti-miR-205 or pCDH (control vector) were subcutaneously injected in NOD/SCID mice. Mice were treated with crizotinib (25 mg/kg), and tumor volume was monitored for 18 days as indicated. n = 6 per condition. H. GTL16 wt cells transduced with pCDH-miR-205 or with the control vector (pCDH) were injected in NOD/SCID mice. When tumors reached an average volume of around 150 mm3, treatment with either crizotinib (12.5 mg/kg) or vehicle was started. Tumor volume was monitored for 18 days as indicated. n = 6 per condition. I. Western blot analysis of EBC-1, GTL16, and SG16 cells, either parental (wt, untreated or treated for 2 h with the indicated TKIs) or TKI-resistant (either in the presence or absence of the TKIs to which they are resistant). Cell lysates were probed with the indicated antibodies. Actin was used as loading control (one actin panel for each WB performed/cell line). Drug abbreviation is as shown in Fig 1. J. The transcriptome of crizotinib-sensitive/resistant pairs of GTL16 and EBC-1 cells was determined by RNA-Seq. The mRNA heatmap shows the expression levels of the predicted miR-205 targets (TargetScan 7.1) downregulated in resistant versus wt cells. For each gene, the log2 of the RSEM expected counts was converted to the log2 ratio against the global median expression of the gene in all samples. Log2 ratio values were loaded in GEDAS software to perform hierarchical clustering analysis and represent data in a heatmap. Units (−1.5 to +1.5) represent the log2 ratio against the median. Red and blue colors represent the highest and lowest ends of mRNA expression levels, respectively. Data information: (A–H) Data are presented as mean ± SD. Asterisks: ***P < 0.001; **P < 0.01; *P < 0.05. Two-tailed t-test was used for panels (A–C, F); two-way ANOVA, Bonferroni's multiple comparisons test was used for panels (D, E, G, H). Source data are available online for this figure. Source Data for Figure 2 [emmm201708746-sup-0004-SDataFig2.pdf] Download figure Download PowerPoint To further validate these in vitro results, we performed in vivo experiments. To this end, GTL16 R-CRIZ cells transduced with either control or anti-miR-205 lentivirus (Appendix Fig S4A) were injected s.c. in NOD-SCID mice. Tumor-bearing mice were subjected to treatment with crizotinib. As shown in Fig 2G, tumors generated by anti-miR-205 transduced cells were highly sensitive to crizotinib treatment, while controls remained resistant. In mirror experiments, tumors generated by wt GTL16 cells engineered to over-express miR-205 (Appendix Fig S4B) were refractory to crizotinib treatment, while control tumors were highly sensitive (Fig 2H). The above dataset is compatible with the hypothesis that miR-205 upregulation can cause MET-addicted cancer cells to acquire resistance to MET-TKIs. This, in turn, raises the question of which among miR-205 targets may determine the observed resistance phenotype. In broad terms, resistance to TKIs may be caused by mutations of the target kinase, resulting in reduced/abolished TKI binding, or bypass activation of downstream pathway(s) despite enduring target blockade (Lackner et al, 2012). In general, MET kinase retained sensitivity to inhibition by MET-TKIs in resistant cells even if this was not complete. Treatment with MET-TKIs did not translate into significant suppression of ERK and AKT activation in resistant cells, although in CRIZ-resistant GTL16 cells AKT activation was not as high as in the wt control (Fig 2I). Of note, activation of ERK and AKT was affected marginally, or not at all, by drug withdrawal (Fig 2I), suggesting that preservation of oncogenic signaling downstream to MET was not a direct effect of MET-TKI administration. In searching for mechanisms responsible for bypass signal activation, we found that expression of PTEN, a bona fide miR-205 target gene (Cai et al, 2013), was not altered in resistant cells (Appendix Fig S5), while EGFR, a well-known MET partner (Haura & Smith, 2013), retained or even increased its expression/activity in resistant cells (Fig 2I). No EGFR mutation/amplification was detected in resistant cells. These data suggest a potential role for non-mutational EGFR activation in fueling vicarious ERK and AKT activation in MET-TKI-resistant derivatives subjected to MET blockade. A potential link between miR-205 upregulation and increased EGFR activity emerged from the comparative analysis of RNA-Seq data obtained from wt and crizotinib-resistant pairs of both EBC-1 and GTL16 cells. Figure 2J and Appendix Fig S6 show that ERRFI1 (ERBB receptor feedback inhibitor 1) was one of the few putative miR-205 targets, as predicted by the TargetScan algorithm (Agarwal et al, 2015), to be downregulated in both EBC-1- and GTL16-resistant cells (Cora' et al, 2017). The ERRFI1 product (also named MIG6) is an inducible feedback inhibitor of the EGFR/HER receptor family (Anastasi et al, 2016). Genetic studies in the mouse have pointed to an essential role of Errfi1 in restraining Egfr-dependent cell proliferation in normal tissues as well as suppressing Egfr-driven tumor formation (Anastasi et al, 2016). Mechanistically, ERRFI1 binds to the EGFR activated kinase domain, thus suppressing its catalytic activity. In addition, ERRFI1 instigates endocytosis/degradation of the kinase-inactive EGFR molecules to which it binds (Frosi et al, 2010). Hence, we surmised that ERRFI1 downregulation consequent to miR-205 uprise could mediate an increase in EGFR expression/activity (see Fig 2I) sufficient to fuel resistance to MET blockade. In line, ERRFI1 expression was clearly lower in resistant cells compared to their parental counterpart (Fig EV2A–C). GTL16 R-PHA cells stood out as the single exception, most likely because KRAS amplification mediates the resistance of these cells to PHA-665752 (Cepero et al, 2010). Ectopic ERRFI1 sufficed to re-sensitize resistant cells to MET-TKIs both in vitro (Fig 3A and B; and Appendix Fig S7A and B) and in vivo (Fig EV3A and B), with the predictable exception of GTL16 R-PHA cells (Appendix Fig S7B). Critically, ERRFI1 knockdown attenuated growth suppression of parental EBC-1 and SG16 cells mediated by either crizotinib or JNJ-38877605 (Fig 3C). We noted that ectopic ERRFI1 was not as effective as anti-miR-205 in restoring sensitivity to crizotinib in tumor xenotransplants (compare Fig 2G with Fig EV3B). The most parsimonious explanation for this discrepancy is that while ERRFI1 appears to be the most critical miR-205 target in this context, other miRNA targets may contribute to the development of the resistant phenotype. The above data provide a causal link between loss of ERRFI expression and the development of resistance to MET-TKIs in MET-addicted cells. Interestingly, we observed that the miR-205/ERRFI1/EGFR regulatory axis can operate also in not-addicted cells, as ectopic miR-205 expression in A549 lung cancer cells resulted in ERRFI1 downregulation and increased EGFR expression/activation (Appendix Fig S8A and B). Click here to expand this figure. Figure EV2. ERRFI1 expression is decreased in MET-TKI-resistant cells A. ERRFI1 expression was evaluated by WB in EBC-1, GTL16, and SG16 wt and resistant (R-) cells. ERRFI1 expression was strongly reduced in resistant cells compared to wt, with the exception of GTL16 R-PHA. Actin was used as loading control. Column chart shows the ERRFI1/Actin band quantification obtained by ImageJ software. B, C. ERRFI1 expression was evaluated by RT–qPCR in KATO II (B) and SNU-5 wt and resistant (R-) cells. As shown, ERRFI1 was downregulated in resistant versus wt cells. n = 3 per condition. Data information: (B, C) Average ± SD. ***P < 0.001; **P < 0.01, two-tailed t-test. Source data are available online for this figure. Download figure Download PowerPoint Figure 3. ERRFI1 is the main miR-205 target responsible for resistance to MET-TKIs A, B. EBC-1 (A) and SG16 (B) resistant cells were transduced with either empty (pCDH) or ERRFI1-encoding (pCDH ERRFI1) recombinant lentivirus stocks. Resistant cells (R-CRIZ, R-PHA, R-JNJ) were grown in the presence of the TKIs to which they are resistant. As control, wt cells were grown in the absence (NT) or in the presence of the indicated MET-TKIs. Viability was assessed by CellTiterGlo 72 h after plating. n = 4 per condition. C. ERRFI1 expression was silenced in EBC-1 and SG16 wt cells by transfection of either control (siCtrl) or ERRFI1-targeting siRNA pools. Cells were treated with MET-TKIs at concentrations averaging the respective IC50; viability was evaluated 72 h later. n = 4 per condition. D. Immunoblot analysis of ERRFI1 in cell lines expressing ectopic miR-205. Actin was used as total protein loading control. The column chart shows the ratio between the ERRFI1 and actin band intensities as quantified by ImageJ software. E. GTL16 wt cells were co-transfected with the Luc-ERRFI1 reporter plasmid along with the indicated miRNAs. Data are computed from six independent experiments and expressed as arbitrary units (RLU), one being the control value obtained in cells expressing a non-targeting miRNA. n = 6 per condition. F. GTL16 wt cells were co-transfected with miR-205 and the Luc-ERFFI1 reporter, either in wt or delta, or mutated configuration. Data are computed from three independent experiments and expressed as in (E). n = 3 per condition. G. Western blot analysis of EGFR expression and activation (pEGFR) in wt EBC-1, GTL16, and SG16 cells transfected with a control miRNA (Ctrl) or miR-
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