Mutation of epidermal growth factor receptor is
associated with MIG6 expression
Takeshi Nagashima
1
, Ryoko Ushikoshi-Nakayama
1
, Atsushi Suenaga
2
, Kaori Ide
1
, Noriko Yumoto
1
,
Yoshimi Naruo
3
, Kaoru Takahashi
1
, Yuko Saeki
1
, Makoto Taiji
2
, Hiroshi Tanaka
3
, Shih-Feng Tsai
4
and Mariko Hatakeyama
1
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

tions in EGFR kinase activity may reflect changes in gene expression asso-
ciated 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 (EGFR-
WT) 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 expres-
sion was correlated with basal EGFR phosphorylation and inversely corre-
lated 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 tis-
sue 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.
FEBS Journal 276 (2009) 5239–5251 ª 2009 The Authors Journal compilation ª 2009 FEBS 5239
Introduction
Epidermal growth factor receptor (EGFR) is a mem-
brane tyrosine kinase that is involved in the regulation

substituted with Arg (mutant: L858R), in addition to
the parental cell line, was used. Various point muta-
tions at L858R, L861, S768, E709 and G719 in the
EGFR kinase domain, insertions in exon 20 and dele-
tion 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 predic-
tive marker of Iressa (Gefitinib) responsiveness [11–13].
Therefore, delineating the transcriptional regulation of
this mutant is of clinical importance in terms of con-
tributing towards our understanding of patient sensi-
tivity to Iressa, as well as side effects and drug
resistance. The results obtained demonstrate differ-
ences 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-
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 feed-
back 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

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
EGFR mutation and MIG6 expression T. Nagashima et al.
5240 FEBS Journal 276 (2009) 5239–5251 ª 2009 The Authors Journal compilation ª 2009 FEBS
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

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 func-
tions 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 bind-
ing and receptor activity. Thus, a difference in EGFR
activity can result in the distinct transcriptional regula-
tion of important biological processes that may con-
tribute 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
Fig. 1. Workflow of gene expression data analysis. The workflow
of microarray data analysis applied in the present study is shown.
T. Nagashima et al. EGFR mutation and MIG6 expression
FEBS Journal 276 (2009) 5239–5251 ª 2009 The Authors Journal compilation ª 2009 FEBS 5241
these differences were observed even in those genes

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. How-
ever, 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
AC
B
Fig. 3. Iressa-induced differences in gene expression. Iressa-induced differences in gene expression amplitude (A) and time course (B). Dif-
ferences in Iressa-induced gene expression were calculated using two indexes: (a) the I
c
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 I
c
and correlation coefficient in three cell lines are shown in (A) and
(B), respectively. Larger (I
c
> 10) and smaller (I
c
< )10) I
c
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.
T. Nagashima et al. EGFR mutation and MIG6 expression

database ( accession
number = GSE5720). The results of the analysis sur-
prisingly 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
(Fig. 5B). The results indicate that MIG6 could be uti-
lized as a molecular marker for indicating the func-
tional 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 muta-

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 expres-
sion 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 suppres-
sion by Iressa. Time-course microarray analysis
enabled us to identify differentially expressed genes
and obtain insight into the dynamic behavior of coor-
dinated transcription associated with their upstream
signaling pathways and functions.

performed twice independently.
T. Nagashima et al. EGFR mutation and MIG6 expression
FEBS Journal 276 (2009) 5239–5251 ª 2009 The Authors Journal compilation ª 2009 FEBS 5245
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, ERR-
FI1 demonstrated an anti-correlated expression pattern
with ERBB2 and ERBB3 in various tumors and cancer
cell lines [15,16], and higher expression levels of recep-
tor 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 expres-
sion 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 phosphory-
lated ERK levels in H1299 cell lines. The presence of
high levels of MIG6 expression might ensure, in the
eight cell lines examined in the present study, that sig-
nal transduction downstream of EGFR is disturbed.
However, further investigations are required to eluci-
date the regulatory mechanism of MIG6 in relation to
the EGFR mutation.
The present study is the first to show the association

[28,29]. Thus, cells with distinct EGFR mutations have
their total signaling activity modulated at the molecu-
lar (kinase activity) and transcriptional levels, and
these modulations might compensate each other to
control the final cellular output at the systems level.
Experimental procedures
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%
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 tran-
scriptional 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 lgÆmL
)1
cycloheximide. Total
RNA was isolated using TRIzol reagent (Life Technolo-
gies 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).
Western blot analysis
Cells were treated with EGF in the absence or presence of
Iressa for the indicated time period, washed three times

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 Flu-
idics 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 just-
RMA 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 I
c
, which

, respectively. Genes satisfying I
c
> a and
I
c
< )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 unbi-
ased (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 stron-
ger 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 I
c

ries, including the KEGG pathway database, PubMed
abstracts and the Entrez Gene database, including Gene-
RIF. ErbB and MAPK signaling pathway-related genes
T. Nagashima et al. EGFR mutation and MIG6 expression
FEBS Journal 276 (2009) 5239–5251 ª 2009 The Authors Journal compilation ª 2009 FEBS 5249
were identified using the PubMed identifier from PubMed
abstracts and GeneRIF.
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 Horiu-
chi of NTT Software Corporation (Yokohama, Japan)
for the transcriptional network analysis. We are grate-
ful for the computational resources of the RIKEN
Super Combined Cluster (RSCC) (Saitama, Japan).
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Supporting information