Báo cáo khoa học: Mutation of epidermal growth factor receptor is associated with MIG6 expression docx

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
1 Cellular Systems Modeling Team, Computational Systems Biology Research Group, Advanced Computational Sciences Department, RIKEN
Advanced Science Institute, Yokohama, Kanagawa, Japan
2 High-Performance Molecular Simulation Team, Computational Systems Biology Research Group, Advanced Computational Sciences
Department, RIKEN Advanced Science Institute, Yokohama, Kanagawa, Japan
3 School of Biomedical Science, Tokyo Medical and Dental University, Tokyo, Japan
4 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:
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 2 domain containing.

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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-

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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. 1.
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

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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. 1. 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

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A

B

C

Fig. 2. 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
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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).

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A

C

B

Fig. 3. 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

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Table 1. 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
5
4
21
2
5
3
13

24
5
7
19
14
15
4
11

26
5
14
28
10
17
1
13

41
7
18
40
19
23
4
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 ( 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
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(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

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EGFR mutation and MIG6 expression

A

B

Fig. 4. 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
2 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

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A

Fig. 5. 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 = 0 and SD = 1. 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
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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

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EGFR mutation and MIG6 expression

B

Fig. 5. 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 1 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 min
prior to growth hormone administration. For the transcriptional analysis, cells were incubated with 10 nm EGF
for 0.5, 1, 2, 4, 6 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

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A

T. Nagashima et al.

B

Fig. 6. 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 0. (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.

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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 ⁄ 2 (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 ( with
accession number GSE11729. The reviewer link for the
dataset is: />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 2 fWT, EGFR - WT,L858Rg;

log2
xE;t
t

t 2 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 3. 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 0 and 1, respectively. The number of

clusters for parental cells, EGFR-WT and L858R was set to
2, 3 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 0 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 > 3 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

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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,
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should be addressed to the authors.

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5251