Mutation of epidermal growth factor receptor is associated with MIG6 expression Takeshi Nagashima1, Ryoko Ushikoshi-Nakayama1, Atsushi Suenaga2, Kaori Ide1, Noriko Yumoto1, Yoshimi Naruo3, Kaoru Takahashi1, Yuko Saeki1, Makoto Taiji2, Hiroshi Tanaka3, Shih-Feng Tsai4 and Mariko Hatakeyama1 Cellular Systems Modeling Team, Computational Systems Biology Research Group, Advanced Computational Sciences Department, RIKEN Advanced Science Institute, Yokohama, Kanagawa, Japan High-Performance Molecular Simulation Team, Computational Systems Biology Research Group, Advanced Computational Sciences Department, RIKEN Advanced Science Institute, Yokohama, Kanagawa, Japan School of Biomedical Science, Tokyo Medical and Dental University, Tokyo, Japan Division of Molecular and Genomic Medicine, National Health Research Institutes, Zhunan, Miaoli, Taiwan Keywords EGFR; gene expression; MIG6; mutation; signal transduction Correspondence M Hatakeyama, Cellular Systems Modeling Team, Computational Systems Biology Research Group, Advanced Computational Sciences Department, RIKEN Advanced Science Institute, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan Fax: +81 45 509 9613 Tel: +81 45 509 9302 E-mail: marikoh@gsc.riken.jp Database Microarray data used in the present study have been deposited in the Gene Expression Omnibus database (http://www ncbi.nlm.nih.gov/geo/) with accession number GSE11729 (Received 10 May 2009, revised 14 July 2009, accepted 16 July 2009) doi:10.1111/j.1742-4658.2009.07220.x Controlled activation of epidermal growth factor receptor (EGFR) is systematically guaranteed at the molecular level; however, aberrant activation of EGFR is frequently found in cancer Transcription induced by EGFR activation often involves the coordinated expression of genes that positively and negatively regulate the original signaling pathway; therefore, alterations in EGFR kinase activity may reflect changes in gene expression associated with the pathway In the present study, we investigated transcriptional changes after EGF stimulation with or without the EGFR kinase inhibitor Iressa in H1299 human non-small-cell lung cancer cells [parental H1299, H1299 cells that overexpress wild-type EGFR (EGFRWT) and mutant H1299 cells that overexpress EGFR where Leu858 is substituted with Arg (L858R)] The results obtained clearly demonstrate differences in transcriptional activity in the absence or presence of EGFR kinase activity, with genes sharing the same molecular functions showing distinct expression dynamics The results show the particular enrichment of EGFR ⁄ ErbB signaling-related genes in a differentially expressed gene set, and significant protein expression of MIG6 ⁄ RALT(ERRFI1), an EGFR negative regulator, was confirmed in L858R High MIG6 protein expression was correlated with basal EGFR phosphorylation and inversely correlated with EGF-induced extracellular signal-regulated protein kinase phosphorylation levels Investigation of the NCI-60 cell lines showed that ERRFI1 expression was correlated with EGFR expression, regardless of tissue type These results suggest that cells accumulate MIG6 as an inherent negative regulator to suppress excess EGFR activity when basal EGFR kinase activity is considerably high Taking all the above together, an EGFR mutation can cause transcriptional changes to accommodate the activation potency of the original signaling pathway at the cellular level Abbreviations AU, approximate unbiased; EGFR, epidermal growth factor receptor; ERK, extracellular signal-regulated protein kinase; GEO, gene expression omnibus; GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; MAPK, mitogen-activated protein kinase; MEK, mitogen-activated protein kinase kinase; NSCLC, human non-small-cell lung cancer; SHC, Src homology domain containing FEBS Journal 276 (2009) 5239–5251 ª 2009 The Authors Journal compilation ª 2009 FEBS 5239 EGFR mutation and MIG6 expression T Nagashima et al Introduction Epidermal growth factor receptor (EGFR) is a membrane tyrosine kinase that is involved in the regulation of a wide variety of biological processes [1] Controlled activation of EGFR is systemically and evolutionarily guaranteed by the presence of a variety of ligands, or by dimerization and trans-activation with other family member receptors of ErbB [2,3] Additionally, the potency and dynamics of EGFR signaling are adaptively tuned via biochemical parameters such as affinity constants, catalytic activities, the concentration of the signaling mediators and plastic pathway architectures [4–6], thereby ensuring that cells can produce the desired outputs in response to various cellular conditions Transcription induced shortly after ligand stimulation is quantitatively regulated by upstream signaling dynamics, which concomitantly regulate the dynamics of the original pathway Kinases and effector proteins in the EGFR-mitogen-activated protein kinase (MAPK) cascade are particular major targets [7–10] Therefore, quantitative transcriptional outcomes, in addition to qualitative ones, may be altered if EGFR kinase activity is modified by mutation, overexpression, or suppressed by inhibitors such as Iressa (Gefitinib, AstraZeneca, London, UK) or Tarceva (Erlotinib, Roche, Basel, Switzerland) In the present study, time-course genome-wide gene expression was investigated, aiming to delineate the transcriptional outcomes induced by EGFR activation under various conditions In brief, the human nonsmall-cell lung cancer (NSCLC) cell line H1299 with EGFR overexpression (wild-type: EGFR-WT) and with or without the mutation in which Leu858 is substituted with Arg (mutant: L858R), in addition to the parental cell line, was used Various point mutations at L858R, L861, S768, E709 and G719 in the EGFR kinase domain, insertions in exon 20 and deletion mutations in exon 19 of the gene for EGFR are often found in NSCLC patients Among these, the L858R mutation has been known to be a good predictive marker of Iressa (Gefitinib) responsiveness [11–13] Therefore, delineating the transcriptional regulation of this mutant is of clinical importance in terms of contributing towards our understanding of patient sensitivity to Iressa, as well as side effects and drug resistance The results obtained demonstrate differences in EGF-stimulated transcription in the absence or presence of Iressa in all cell lines tested, and show that the expression dynamics of the affected genes with overlapping molecular functions are distinct in each cell group Particular enrichment of cell-specific genes involved in the cell cycle and MAPK signaling path- 5240 way was found and, of these, we confirmed significant protein expression of the EGFR negative regulator MIG6 ⁄ RALT only in L858R cells MIG6 ⁄ RALT is known to be a transcriptional feedback regulator of the ErbB-MAPK signaling pathway [14,15] and its loss is associated with ErbB2 ⁄ HER-2 oncogenic potency leading to Herceptin resistance [16] Furthermore, its overexpression is associated with down-regulation of phosphorylated-ErbB2 [17] in breast cancer The present study, using lung cancer cell lines with various EGFR mutants, suggested that endogenous MIG6 may be directly associated with basal EGFR kinase activity Cells might accumulate MIG6 to suppress excess EGFR activity; therefore, MIG6 may be regarded as a molecular marker for indicating the intrinsic kinase activity of EGFR, regardless of tissue type Results Ligand-induced transcriptional signatures of EGFR-WT and L858R in the absence or presence of EGFR kinase activity The transcriptional activity that follows EGFR activation often involves the expression of genes that negatively and positively adjust the magnitude and duration of the original EGFR-MAPK signaling pathways [7–10,18] Therefore, quantitative transcriptional outcomes, in addition to qualitative ones, may be altered if EGFR kinase activity is modified by mutation and overexpression, or suppressed by kinase inhibitors such as Iressa Accordingly, time-course microarray analysis was performed to identify genes that functionally modulate the EGFR signaling pathway For this purpose, three derivatives of human NSCLC cell lines, comprising parental H1299, EGFRWT and L858R, were employed as cellular systems The overall transcriptional signatures after EGF administration in the absence or presence of the EGFR kinase inhibitor Iressa were investigated using Affymetrix GeneChip (U133Plus 2.0; Affymetrix, Santa Clara, CA, USA) The workflow of gene expression data analysis is shown in Fig Cell-specific differentially expressed genes and effect of EGFR kinase inhibitor on gene expression First, the overall gene expression profiles in the EGF- and Iressa-stimulated three cell lines were FEBS Journal 276 (2009) 5239–5251 ª 2009 The Authors Journal compilation ª 2009 FEBS T Nagashima et al EGFR mutation and MIG6 expression highlighted differences in the effect of Iressa on gene expression dynamics in the three cell lines (Fig 3) Consistent with the clustering results, Iressa induced only minor changes in gene expression of the parental cell line (Fig 3A) On the other hand, EGFR-WT exhibited larger changes in gene expression levels in response to Iressa compared to L858R, whereas a larger change in the expression time course was observed for L858R (Fig 3B) Furthermore, our analysis revealed a distinct time-dependent effect of Iressa among the cell lines examined (Fig 3C) In EGFRWT, the effect of Iressa on gene expression was rather temporal (4–6 h), whereas its effect was more persistent in L858R (> 10 h) Different transcriptional regulation of biological pathways induced by ligand and EGFR kinase inhibitor Fig Workflow of gene expression data analysis The workflow of microarray data analysis applied in the present study is shown examined A hierarchical clustering together with assessment of cluster uncertainty was carried out according to the expression levels of all probe sets on the array for each cell stimulated with EGF in the absence or presence of Iressa Cluster analysis clearly showed distinct transcriptional outcomes in the three cell lines Interestingly, the cellular response of L858R was similar to that of EGFR-WT in terms of the EGF response, and similar to the parental cell line in the presence of Iressa (Fig 2A) Cell-specific gene expression associated with EGFR activity was determined using two criteria: (a) where the expression level shifted relative to nonstimulated cells after stimulation by EGF or EGF + Iressa (ligand responsive genes) and (b) where the expression level was altered in the absence or presence of Iressa (kinase responsive genes) As a result, 746, 1034 and 1444 genes were identified for H1299, EGFR-WT and L858R, respectively (2234 genes in total) (Fig 2B) The list of induced genes obtained included DUSP6 (a MAPK phosphatase), ERBB2 and ERBB3, which modulate EGFR signaling Cluster analysis of selected genes clearly showed a discrepancy in the expression dynamics of each cell type (Fig 2C) Although H1299 only had two major clusters (simple ascending and descending), EGFR-WT and L858R cells showed multiple clusters for EGF or EGF + Iressa stimulation Additionally, comparison of EGF- and EGF + Iressa-induced time courses In an effort to assess the biological functions of the differentially expressed genes described above, functional enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway [19] and Gene Ontology (GO) [20] databases The results showed that EGF and Iressa altered the expression levels of genes involved in specific biological processes, such as the cell cycle, circadian rhythm, MAPK signaling pathway, small cell lung cancer and p53 signaling pathway (Table 1) Furthermore, GO term analysis for individual clusters highlighted the commonality and discrepancy of cellular responses to ligand stimulation in the absence or presence of EGFR kinase activity (Table S1) For example, genes involved in transcriptional regulation and protein binding were found to be enriched in the early response gene cluster of the three cell lines (clusters presented within a red bar in Fig 2C) Genes associated with cell cycle functions were also significantly selected for all cell lines; however, the time-course expression patterns differed for each Different expression time courses for the same molecular function were also observed, such as genes related to signal transduction via receptor binding and receptor activity Thus, a difference in EGFR activity can result in the distinct transcriptional regulation of important biological processes that may contribute to the sensitivity of the cells to Iressa or ligand Identification of direct EGFR regulators through functional annotation of Iressa-induced differentially expressed genes As described above, EGF and Iressa-induced overall expression dynamics differed between cell lines, and FEBS Journal 276 (2009) 5239–5251 ª 2009 The Authors Journal compilation ª 2009 FEBS 5241 EGFR mutation and MIG6 expression T Nagashima et al A B C Fig Gene expression profiles in H1299, EGFR-WT and L858R after EGF or Iressa stimulation (A) Overall similarity of gene expression profiles in EGF- and Iressa-stimulated cells Clusters with high AU values (> 95) are highlighted by red rectangles (B) The number of differentially expressed genes in each cell line The Venn diagram represents the number of differentially expressed genes obtained using two selection criteria (see Experimental procedures) in H1299, EGFR-WT and L858R cell lines Numbers in round and square brackets represent the number of probe sets and the number of probe sets without gene IDs, respectively Other numbers refer to the number of genes (C) Clustering of expression profiles of differentially expressed genes Representative genes were shown by number: 1, ERRFI1; 2, DUSP6; 3, SPRY4; 4, ERBB3; 5, ERBB2 Ctrl, control (no stimulation); I, Iressa; E, EGF; E+I, EGF and Iressa these differences were observed even in those genes associated with same molecular functions In an effort to further examine how a single amino acid mutation in the EGFR kinase domain affects downstream gene expression, those genes likely to be under the regulation of activated EGFR were examined Genes showing distinct time-course patterns in the absence or presence of Iressa were extracted (a correlation coefficient of < )0.5 between EGF and EGF + Iressa) As 5242 a result, 405 genes were selected (12, 117 and 299 genes for H1299, EGFR-WT and L858R, respectively) Of these, KEGG pathway analysis revealed that the ErbB signaling pathway is enriched in L858R (P = 0.02057; Bonferonni corrected) Furthermore, functional annotation using public databases identified 52 out of 405 genes (12.8%) as comprising regulators of EGFR ⁄ ErbB and MAPK signaling pathways (Fig 4A) FEBS Journal 276 (2009) 5239–5251 ª 2009 The Authors Journal compilation ª 2009 FEBS T Nagashima et al EGFR mutation and MIG6 expression A C B Fig Iressa-induced differences in gene expression Iressa-induced differences in gene expression amplitude (A) and time course (B) Differences in Iressa-induced gene expression were calculated using two indexes: (a) the Ic value that reflects differences in the expression level and (b) the correlation coefficient which represents differences in the expression pattern Two time-course profiles (EGF and EGF + Iressa) of selected genes were used for the analysis The distribution of Ic and correlation coefficient in three cell lines are shown in (A) and (B), respectively Larger (Ic > 10) and smaller (Ic < )10) Ic values were rounded to 10 and )10, respectively (C) The number of probe sets where the expression level was altered by Iressa at individual time points in H1299, EGFR-WT and L858R Among these, we noted cell-specific differential expression of two direct EGFR regulators: SPRY4 (Sprouty-4) and ERRFI1 (MIG6) Sprouty family member proteins are known to be negative and positive regulators of fibroblast growth factor and EGFRs, respectively [10,21–23] In our analysis, SPRY4 expression was stimulated by EGF and reduced by the addition of Iressa in EGFR-WT However, the induction of Sprouty-4 remained unchanged at the protein level in both cell types (data not shown) MIG6 (RALT or ERRFI1) is a cytoplasmic adapter protein that can inhibit EGFR kinase activity through FEBS Journal 276 (2009) 5239–5251 ª 2009 The Authors Journal compilation ª 2009 FEBS 5243 EGFR mutation and MIG6 expression T Nagashima et al Table Enriched KEGG pathways in differentially expressed genes P-value No of genes KEGG pathway H1299 EGFR-WT L858R H1299 EGFR-WT L858R Total Cell cycle Circadian rhythm ErbB signaling pathway MAPK signaling pathway Pyrimidine metabolism Small cell lung cancer Terpenoid biosynthesis p53 signaling pathway 0.00000* 0.00368* 1.00000 0.03066** 1.00000 1.00000 0.08717 0.00006* 0.00000* 0.02294** 1.00000 1.00000 0.00911* 0.00249* 0.01108** 0.05781 0.00000* 0.10724 0.29148 1.00000 1.00000 0.00907* 1.00000 0.08025 22 21 13 24 19 14 15 11 26 14 28 10 17 13 41 18 40 19 23 19 *P < 0.01, **P < 0.05 direct binding to the kinase domain [15,24–26] ERRFI1 expression was significantly induced in EGFR-WT and L858R cells Although ERRFI1 was reduced in response to EGF stimulation in H1299 cells, it was transiently expressed in EGFR-WT cells and constitutively expressed in L858R cells Western blot analysis identified higher basal levels of MIG6 protein in L858R compared to parental H1299 and EGFR-WT cells, and little change was observed in response to EGF stimulation (Fig 4B) The evolutionarily conserved ErbB-binding region (EBR) of MIG6 is known to bind the RYLVIQ sequence of EGFR (amino acids 953–958), which participates in the allosteric control of EGFR activity [26] Therefore, high expression of MIG6 may be able to suppress the effect of EGF for EGFR phosphorylation in EGFR-WT Indeed, our validation experiment confirmed that overexpression of MIG6 decreased the phosphorylation of EGFR in the presence of a high concentration of EGF in EGFR-WT cells (data not shown), as previously reported for breast [17] and other cell lines [24] Accordingly, the expression level of MIG6 should be associated with high EGFR expression levels because the overexpression of EGFR has often been linked to high EGFR kinase activity Therefore, ERRFI1 expression in various cancer cell lines was investigated using the publicly available NCI-60 dataset The dataset was downloaded from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/; accession number = GSE5720) The results of the analysis surprisingly showed that ERRFI1 expression levels varied among all cell lines and no tissue-specific trend was observed (Fig 5A), although a previous study reported tissue-specific expression of ERRFI1 in some cancers [24] ERRFI1 expression was most correlated with EGFR expression, regardless of cancer cell type, and was not correlated with other ERBB gene expression 5244 (Fig 5B) The results indicate that MIG6 could be utilized as a molecular marker for indicating the functional activity of EGFR in tissues, regardless of cancer type Indeed, our transcriptional analysis indicated that Iressa totally abolished the expression of ERRFI1 in EGFR-WT and L858R cells (Fig 2C) Accordingly, ERRFI1 may operate as a molecular sensor to monitor EGFR kinase activity Relationship between MIG6 expression and EGFR mutation Although a functional role of MIG6 in relation to EGFR kinase regulation has been reported, as described above, and was confirmed in the present study, its relationship to Iressa sensitivity, EGFR mutation and the MAPK signaling pathway has not been reported Earlier studies found that clinical responsiveness to Iressa was closely associated with EGFR mutations such as L858R and delL747-P753insS in the kinase domain, which also enhance EGF-dependent EGFR activation [11,12] Huang et al [13] performed mutational analysis of the EGFR gene from exons 18–21 in a series of surgically resected NSCLCs and found a high mutation rate for EGFR in Taiwanese patients In addition to major mutation types such as the L858R mutation and deletions in exon 19, various point mutations at residues L861, S768, E709, G719 and H835 and insertions in exon 20 of the EGFR gene have been observed [13] Accordingly, other H1299 derivatives that overexpress different types of EGFR mutants, including EGFR-Del (deletion of the kinase domain), S768I, L861Q, E709G and G719S [27], in addition to parental H1299, EGFR-WT and L858R, were assessed by quantitative western blot analysis in an effort to delineate the relationship between EGFR mutation, Iressa sensitivity FEBS Journal 276 (2009) 5239–5251 ª 2009 The Authors Journal compilation ª 2009 FEBS T Nagashima et al EGFR mutation and MIG6 expression A B Fig Direct EGFR regulator ERRFI1 (MIG6) is included in the Iressa responsive MAPK and ErbB signaling-related gene list, its protein expression (A) 52 MAPK and ErbB signaling-related genes in 405 Iressa responsive genes are shown Genes involved in the EGFR ⁄ ErbB signaling pathway are highlighted in blue Genes included in the 405 gene group, which were differentially expressed and where the expression pattern was reversed by Iressa, are shown in red Genes included in 2234 gene group, but not in the 405 gene group, are depicted in orange PG, PubMed and GeneRIF (B) MIG6 protein expression in EGF (10 nM) or EGF + Iressa (10 lM) stimulated H1299, EGFR-WT and L858R as determined by western blot analysis The experiment was performed twice independently and MIG6 expression Surprisingly, MIG6 expression was significantly high in L861Q and G719S cells Accordingly, no clear correlation was observed between MIG6 expression and Iressa sensitivity in the eight cell lines tested (Figs 6A and S1A) However, MIG6 expression levels were uniquely correlated or anti-correlated with the phosphorylation state of EGFR, Src homology domain containing (SHC), mitogen-activated protein kinase kinase (MEK) and extracellular signal-regulated protein kinase (ERK) in the absence or presence of EGF (Figs 6B and S1B) Interestingly, MIG6 expression showed good correlation with basal phosphorylation levels of EGFR (correlation coefficient = 0.61) and with its direct effector protein SHC (correlation coefficient = 0.75) in the absence of stimuli, and strong anti-correlation (correlation coefficient = )0.83) with ERK phosphorylation in the presence of stimuli These results imply that increased MIG6 expression effectively inhibits signal transduction to the downstream pathway when EGFR is irregularly activated Discussion In the present study, we investigated the property of biological networks under various conditions related to EGFR kinase activity, which was altered by single amino acid mutation, activation by EGF and suppression by Iressa Time-course microarray analysis enabled us to identify differentially expressed genes and obtain insight into the dynamic behavior of coordinated transcription associated with their upstream signaling pathways and functions The L858R mutation of EGFR has been shown to be a good predictive marker in terms of Iressa treatment [11,12] The data obtained in the present study showed that Iressa effectively suppressed EGF-induced expression of DUSP6 and ERRFI1 and, at the same time, increased the expression of ERBB2 and ERBB3 (i.e dimerization partners of EGFR) in L858R cells The regulatory pattern of these four genes suggests that the activation of EGFR or ErbB2-3 pathways is an FEBS Journal 276 (2009) 5239–5251 ª 2009 The Authors Journal compilation ª 2009 FEBS 5245 EGFR mutation and MIG6 expression T Nagashima et al A Fig ERRFI1 and ErbB receptor family member expression in NCI-60 (A) ERRFI1 gene expression level in various cancer cell lines The dataset comprises nine different types of cancer, presented in different colors Vertical and horizontal axes represent cell line names and the gene expression level, respectively (B) Cluster analysis of ERRFI1 and ERBB expression in the NCI-60 dataset Prior to cluster analysis, the expression level of a gene was normalized so that the mean = and SD = Red and blue represent high and low normalized expression levels, respectively Color bars at the top of the heatmap represent the cancer type, with the same colors being used in the upper panel immediate transcriptional effect of Iressa Given that L858R cells are more sensitive to Iressa [27], inhibition of EGFR kinase activity may lead to the activation of alternative pathways that compensate for the loss of EGFR pathway activity in L858R cells Indeed, ERRFI1 demonstrated an anti-correlated expression pattern with ERBB2 and ERBB3 in various tumors and cancer cell lines [15,16], and higher expression levels of receptor tyrosine kinase genes were observed in other NSCLC cell lines showing high Iressa sensitivity (data not shown) Thus, the inherited molecular fragility of L858R in terms of Iressa sensitivity appeared to be neutralized by transcriptional feedback Although we initially speculated that MIG6 expression was EGF-inducible, as was observed for ERRFI1 expression, this was not the case Rather, we found that MIG6 expression was static and correlated with basal phosphorylation levels of EGFR and SHC, and was negatively correlated with EGF-stimulated phosphorylated ERK levels in H1299 cell lines The presence of 5246 high levels of MIG6 expression might ensure, in the eight cell lines examined in the present study, that signal transduction downstream of EGFR is disturbed However, further investigations are required to elucidate the regulatory mechanism of MIG6 in relation to the EGFR mutation The present study is the first to show the association of MIG6 expression with the EGFR mutation in cancer MIG6 ⁄ RALT is known to be a transcriptional negative regulator of EGFR signaling [14] Ferby et al [24] also showed: (a) reduced expression of ERRFI1 in skin, breast, pancreatic and ovarian cancers, as well as psoriasis; (b) an inverse relationship between MIG6 expression and phosphorylated EGFR; and (c) an inverse correlation between ERRFI1 and ERBB3 mRNA levels in human melanoma cell lines Their results suggest that down-regulation of MIG6 in tumors and cancer cell lines leads to activation of ErbB signaling [24] Although the results obtained in the present study partially support those reported in FEBS Journal 276 (2009) 5239–5251 ª 2009 The Authors Journal compilation ª 2009 FEBS T Nagashima et al EGFR mutation and MIG6 expression B Fig Continued earlier studies [24], our data clearly showed that MIG6 expression is correlated with the phosphorylated active state of EGFR and that ERRFI1 expression is associated with basal EGFR kinase activity in the absence of ligand Furthermore, MIG6 expression may be indirectly (i.e not directly) associated with EGFR or other ERBB mRNA levels Among the ErbB receptor family, the ErbB2 receptor is the most preferred binding partner that leads to activation of EGFR kinase [28,29] Thus, cells with distinct EGFR mutations have their total signaling activity modulated at the molecular (kinase activity) and transcriptional levels, and these modulations might compensate each other to control the final cellular output at the systems level fetal bovine serum and mm sodium pyruvate Prior to growth hormone treatment, cells were serum-starved for 16–24 h For EGFR kinase inhibition, Iressa (a generous gift from Astra Zeneca, London, UK) was added 20 prior to growth hormone administration For the transcriptional analysis, cells were incubated with 10 nm EGF for 0.5, 1, 2, 4, or 10 h and then washed twice with NaCl ⁄ Pi Cells not treated with growth hormone were used as the control Cells were scraped using ice-cold NaCl ⁄ Pi containing 10 lgỈmL)1 cycloheximide Total RNA was isolated using TRIzol reagent (Life Technologies Corporation, Carlsbad, CA, USA) and then purified using the QIAGEN RNeasy Mini kit RNA quality was assessed using a Bioanalyzer (Agilent Technologies Inc., Santa Clara, CA, USA) Experimental procedures Western blot analysis Cell culture and RNA isolation EGFR-mutated H1299 human lung cancer derivatives were established as described previously [27] Cells were maintained in RPMI medium supplemented with 10% Cells were treated with EGF in the absence or presence of Iressa for the indicated time period, washed three times with NaCl ⁄ Pi, and then lysed using Bio-Plex lysis buffer (Bio-Rad laboratories, Hercules, CA, USA) The cell FEBS Journal 276 (2009) 5239–5251 ª 2009 The Authors Journal compilation ª 2009 FEBS 5247 EGFR mutation and MIG6 expression A T Nagashima et al B Fig Comparison of MIG6 expression and phosphorylation of signaling molecules (A) MIG6 expression levels in eight H1299 derivative cell lines was compared with the Iressa sensitivity reported in a previous study [22] IC50 values were extracted from fig 2b,c of the same study [22] and values < 0.02 were rounded to (B) Basal MIG6 protein expression levels were compared with phosphorylation levels of EGFR, SHC, MEK and ERK in eight H1299 derivative cell lines in the absence (left) or presence (right) of EGF stimulation (5 min, 10 nM) Protein expression and phosphorylation were measured by western blot analysis Western blot images are shown in Fig S1 Horizontal and vertical axes represent quantified signal intensities for MIG6 and signaling molecules (EGFR, SHC, MEK and ERK), respectively 5248 FEBS Journal 276 (2009) 5239–5251 ª 2009 The Authors Journal compilation ª 2009 FEBS T Nagashima et al lysate was cleared by centrifugation, and the total protein concentration of the supernatant was determined using a protein assay reagent SDS–PAGE and western blotting were then performed Antibodies against anti-phosphoEGFR (PY1068), doubly-phosphorylated p44 ⁄ 42 ERK, ERK, phospho-MEK1 ⁄ (Ser217 ⁄ 221), MEK, MIG6 and actin were purchased from Cell Signaling Technology, Inc (Beverly, MA, USA) Anti-phospho-Shc (Tyr317), anti-Shc and anti-EGFR sera were purchased from Upstate Biotechnology (Lake Placid, NY, USA) Protein band intensities were quantified using a densitometer (Fuji Film Corp., Tokyo, Japan) Gene expression analysis GeneChip (Affymetrix U133Plus 2.0 chip) experiments were carried out according to the manufacturer’s instructions The probe was hybridized to the array for 16 h at 45 °C The hybridized probe array was then washed and stained according to an automated protocol for the Affymetrix Fluidics station, and the raw data were processed using the genechip operating software (gcos, version 1.2) Scanned images were processed by RMA implemented as a justRMA function in the affy software package [30] to obtain gene expression levels Quantified expression levels were used in the subsequent analyses Annotation file (na23) was downloaded from the manufacturer’s web site (http://www affymetrix.com/products_services/arrays/specific/hgu133plus affx) and used in the subsequent analyses Microarray data used in the present study were deposited in the GEO database (http://www.ncbi.nlm.nih.gov/geo/) with accession number GSE11729 The reviewer link for the dataset is: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? token=fhmjzamcwsqaybq&acc=GSE11729 Identification of differentially expressed genes Ligand and inhibitor induced differential gene expression were selected using two methods: one for genes where the expression levels were altered relative to the control after ligand and inhibitor treatment and the other for genes where the expression levels were altered after treatment with Iressa The former was obtained using a multiplicative decomposition model according to a previous study [8], whereas the latter was obtained by calculating Ic, which was defined as; Ic ¼   X xEI;t c fWT, EGFR - WT,L858Rg; log2 xE;t t t f0:5; 1; 2; 4; 6; 10ðhourÞg where xE,t and xEI,t represent the expression level of gene x after t h exposure to EGF and EGF + Iressa, respectively Ic represents the summation of log2-transformed fold EGFR mutation and MIG6 expression changes caused by Iressa Therefore, decreased and increased expression was expected to be reflected by a smaller and larger Ic, respectively Genes satisfying Ic > a and Ic < )a were identified and regarded as being stimulated and repressed, respectively, by Iressa a was set to After gene selection, probe set ID lists obtained by the two methods were merged and converted to Entrez Gene IDs for further analysis Hierarchical clustering was performed based on the expression levels of all probe sets in EGF or EGF + Iressa stimulated cells The uncertainty of the clusters was assessed using pvclust [31] pvclust calculated approximate unbiased (AU) P-values (%), which indicate the extent to which strong clusters are supported by the data, and are shown in the cluster dendrogram Higher AU P-values indicate stronger support of the data (Fig 2A) Hierarchical clustering was applied to the expression profile of selected probe sets in H1299 (746 genes, 946 probe sets), EGFR-WT (1034 genes, 1395 probe sets) and L858R (1444 genes, 1903 probe sets) cells (Fig 2B) Prior to cluster analysis, the gene expression level was scaled so that the mean and standard deviation were set to and 1, respectively The number of clusters for parental cells, EGFR-WT and L858R was set to 2, and 3, respectively, and is depicted by different colors to the right of the cluster dendrogram (Fig 2C) Expression amplitude and time-course patterns were closely examined by calculating: (a) the Ic value, which was defined by summation of log2-transformed fold changes between EGF and EGF + Iressa over time, and (b) the correlation coefficient between the time-course profile of EGF and EGF + Iressa, respectively (Fig 3A, B) The sharp distribution of H1299 (red line) with a peak at represents the expression level where only a few genes were altered by Iressa EGFR-WT (green line) and L858R (blue line) showed a higher portion of genes than H1299 and, in particular, Ic > and Ic < )3 showed that Iressa caused large changes in expression levels in these cell lines (Fig 3A) The correlation coefficient between the timecourse profile of EGF and EGF + Iressa is shown in the right-hand panel of Fig 3B Functional annotation of selected genes The KEGG pathway and GO databases were utilized to evaluate the biological functions of selected genes Enrichment of pathways and GO terms was assessed by Fisher’s exact test followed by Bonferroni’s correction Prior to the analysis, probe set IDs were mapped to Entrez Gene IDs using the manufacturer’s annotation (na23) Those probe set IDs without gene ID association were discarded in the subsequent analyses For gene annotation, molecular functional information was compiled from public data repositories, including the KEGG pathway database, PubMed abstracts and the Entrez Gene database, including GeneRIF ErbB and MAPK signaling pathway-related genes FEBS Journal 276 (2009) 5239–5251 ª 2009 The Authors Journal compilation ª 2009 FEBS 5249 EGFR mutation and MIG6 expression T Nagashima et al were identified using the PubMed identifier from PubMed abstracts and GeneRIF 12 Acknowledgements The authors are grateful to Dr Yi-Rong Chen (NHRI, Taiwan) for providing H1299 derivative cell lines for use in the present study We also thank Mr Jun Horiuchi of NTT Software Corporation (Yokohama, Japan) for the transcriptional network analysis We are grateful for the computational resources of the RIKEN Super Combined Cluster (RSCC) (Saitama, Japan) 13 References 14 Yarden Y & Sliwkowski MX (2001) Untangling the ErbB signalling network Nat Rev Mol Cell Biol 2, 127–137 Citri A & Yarden Y (2006) EGF-ERBB signalling: towards the systems level Nat Rev Mol Cell Biol 7, 505–516 Oda K, Matsuoka Y, Funahashi A & Kitano H (2005) A comprehensive pathway map of epidermal growth factor 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SJ & Greene MI (1992) p185c-neu and epidermal growth factor receptor associate into a structure composed of activated kinases Proc Natl Acad Sci USA 89, 330–334 Graus-Porta D, Beerli RR, Daly JM & Hynes NE (1997) ErbB-2, the preferred heterodimerization partner EGFR mutation and MIG6 expression of all ErbB receptors, is a mediator of lateral signaling EMBO J 16, 1647–1655 30 Gautier L, Cope L, Bolstad BM & Irizarry RA (2004) affy – analysis of Affymetrix GeneChip data at the probe level Bioinformatics 20, 307–315 31 Suzuki R & Shimodaira H (2006) Pvclust: an R package for assessing the uncertainty in hierarchical clustering Bioinformatics 22, 540–542 Supporting information The following supplementary material is available: Fig S1 MIG6 protein levels and phosphorylation of signaling molecules in eight H1299 derivative cell lines Table S1 Enriched gene ontology terms in differentially expressed gene clusters This supplementary material can be found in the online version of this article Please note: As a service to our authors and readers, this journal provides supporting information supplied by the authors Such materials are peer-reviewed and may be re-organized for online delivery, but are not copy-edited or typeset Technical support issues arising from supporting information (other than missing files) should be addressed to the authors FEBS Journal 276 (2009) 5239–5251 ª 2009 The Authors Journal compilation ª 2009 FEBS 5251 ... regulatory mechanism of MIG6 in relation to the EGFR mutation The present study is the first to show the association of MIG6 expression with the EGFR mutation in cancer MIG6 ⁄ RALT is known to be...EGFR mutation and MIG6 expression T Nagashima et al Introduction Epidermal growth factor receptor (EGFR) is a membrane tyrosine kinase that is involved in the regulation of a wide variety of biological... compilation ª 2009 FEBS 5247 EGFR mutation and MIG6 expression A T Nagashima et al B Fig Comparison of MIG6 expression and phosphorylation of signaling molecules (A) MIG6 expression levels in eight