EGFR mutations in human lung adenocarcinoma give rise to a
consistent gene expression pattern
Katsuhiko Naoki1, Stefano Monti4, Tzu-Hsiu Chen1, Jeffrey C. Lee1, Pasi A. Jänne1, 2,
Christine Ladd-Acosta4, D. Neil Hayes1, Sean Tracy1, Hidefumi Sasaki5, Yoshitaka
Fujii5, William G. Richards2, David J. Sugarbaker2, William R. Sellers1, 2, 4, Bruce E.
Johnson1, Todd R. Golub1, 4, and Matthew Meyerson1, 3, 4
1
Departments of Medical Oncology and Pediatric Oncology, Dana-Farber Cancer Institute; 2Departments
of Medicine and Surgery, Brigham and Women’s Hospital; and 3Department of Pathology, Harvard
Medical School, Boston, Massachusetts; 4The Broad Institute at MIT and Harvard, Cambridge,
Massachusetts; 5Department of Surgery, Nagoya City University, Nagoya, Japan
(Running Title)
mRNA expression and EGFR mutation in lung adenocarcinoma
(Key Words)
receptor, epidermal growth factor; carcinoma, non-small-cell lung;
adenocarcinoma; oligonucleotide array sequence analysis
(Footnotes)
Grant Support: American Cancer Society, Flight Attendant Medical Research Institute,
Uehara Memorial Foundation, National Cancer Institute, the Thoracic Foundation,
Novartis Pharmaceuticals, and Dana Farber/Harvard Cancer Center Lung Cancer SPORE
P20CA90578-02.
Note: K. Naoki. and S. Monti. contributed equally to this work.
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Requests for reprints: Matthew Meyerson, Department of Medical Oncology, DanaFarber Cancer Institute, room M430, 44 Binney Street, Boston, MA 02115. Phone: (617)
632-4768; Fax (617) 582-7880; E-mail: matthew_meyerson@dfci.harvard.edu.
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Abstract
Mutations of the epidermal growth factor receptor (EGFR) gene have recently
been identified in human lung cancers and are associated with clinical response to
gefitinib treatment. We sought to determine the pattern of EGFR mutations in lung
adenocarcinomas from the United States and the relationship between EGFR mutations
and mRNA expression profiles. Analyses of EGFR kinase domain sequence from 127
lung adenocarcinomas from U.S. patients revealed mutations in 13 tumor specimens
(10%). A majority of the specimens with EGFR mutations fall into several distinct
adenocarcinoma sub-groups as previously defined by unsupervised clustering of gene
expression data. Supervised analysis of gene expression data revealed higher expression
of several downstream targets of the EGFR pathway, as well as of EGFR itself, in tumors
with EGFR mutation. The genes associated with EGFR mutation in the original data set
were likewise enriched in the 14/29 specimens with mutant EGFR in an independent set
of lung adenocarcinomas from Japan. The consistency of gene expression profiles in
both populations suggests that lung adenocarcinomas with EGFR mutation form a
biologically distinct subset.
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Introduction
Recent studies have revealed somatic missense mutations in the kinase domain of
the epidermal growth factor receptor (EGFR) gene in a significant fraction of primary
non-small cell lung cancers (1, 2). EGFR mutations were reported in 3 / 86 (3%)
unselected non-small cell lung cancers from patients in the United States and in 15 / 58
(26%) unselected non-small cell lung cancers from patients in Japan.
Treatment of patients with non-small cell lung cancer with the EGFR kinase
inhibitor gefitinib (IressaTM) has led to clinical responses in approximately 10-20% of
patients. Clinical characteristics associated with response and longer survival after
gefitinib treatment include female gender, a non-smoking history, diagnosis of
adenocarcinoma especially with bronchioloalveolar features, and Japanese nationality (36). Mutations in the kinase domain of the EGFR gene appear to account for the
increased sensitivity of some non-small cell lung cancers to treatment with gefitinib.
Specifically, EGFR kinase domain mutations were reported in a total of 13 of 14 lung
adenocarcinoma specimens from gefitinib-responsive patients, but in none of 10 nonresponsive lung adenocarcinomas from gefitinib-insensitive patients (1, 2).
To determine the frequency of EGFR mutations in a large unselected population
of resectable lung adenocarcinomas from U.S. patients and to analyze the gene
expression characteristics of lung adenocarcinomas with EGFR mutations, we have
analyzed EGFR mutations in a set of lung adenocarcinomas from patients treated in the
United States that were previously characterized according to gene expression profiling
and unsupervised clustering (7) as well as sequencing for somatic mutations of KRAS and
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BRAF (8). The profiles of lung adenocarcinomas with EGFR mutations were compared
with an independent data set of lung adenocarcinomas from Japan characterized by
expression profiling and EGFR sequencing. This allowed us to compare the mRNA
expression profiles in two different ethnic populations with different frequencies of
EGFR mutations.
.
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Materials and Methods
Tumor and RNA specimens
Lung adenocarcinoma samples from the Brigham and Women’s Hospital
Thoracic Surgery tumor bank and RNA preparation methods have been previously
described (7). The lung adenocarcinoma samples from Nagoya City University in Japan
have also been previously described (1). We performed expression microarray analysis of
22 adenocarcinoma samples from this previous report (1) for which RNA samples were
available. We also performed both EGFR and KRAS sequencing as well as RNA
expression profiling on 7 additional lung adenocarcinoma samples from Nagoya City
University that have not been previously reported. . All tumor samples were obtained
and experiments were performed under protocols approved by the institutional review
board of each institute. The age, gender, stage, smoking status, and outcome of the
patients were collected as previously described (1) (7). We also analyzed mRNA
expression data from 10 lung cancer cell lines (NCI-H1650, NCI-H1666, NCI-H1781,
NCI-H23, NCI-H2347, NCI-H3122, NCI-H3255, NCI-H358, NCI-H441, and A549) that
have been studied for EGFR mutation and their sensitivity to gefitinib (9). NCI-H1650
and NCI-H3255 have been previously shown to have mutations in the tyrosine kinase
domain of EGFR (1, 10).
RT-PCR, Genomic DNA PCR, and DNA Sequencing
Total RNA from lung cancer specimens was used to generate complementary
RNA (cRNA) by in vitro transcription (7). The cRNA and/or total RNA was used as
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template for reverse transcription-PCR (RT-PCR) amplifications of kinase domain of
EGFR (exon 18 to exon 24). The primer sequences have been described (1).
RT-PCR was performed using Superscript One-Step RT-PCR with a Platinum Taq kit
(Life Technologies, Gaithersburg, MD). A single 50 l RT-PCR reaction mix contained
1 g of cRNA, 3mM MgSO4, 100 pmol of each primer, and 1 l RT / Platinum Taq mix.
RT-PCR was carried out as described (1).
We also analyzed KRAS codon 12, 13, and 61 mutations using cRNA or total
RNA samples with RT-PCR. Amplification was done using specific primers (forward:
CGGGAGAGAGGCCTGCTGA; reverse: CCACTTGTACTAGTATGCCTTAAGAA).
Conditions were the same as above. The BRAF gene was sequenced as previously
described and reported (8).
For samples with mutations detected at the cDNA level, we isolated genomic
DNA from frozen specimens of uninvolved normal lung controls, using the QIAamp
DNA Mini Kit (QIAGEN, Chatsworth, CA) according to the manufacturer’s instructions.
EGFR genomic DNA was amplified and sequenced as previously described (1).
RT-PCR and PCR products were purified using the QIA quick purification kit (QIAGEN,
Chatsworth, CA) or SPRI (Solid phase reversible immobilization) chemistry. Purified
products were subjected to primer extension sequencing (11) in both forward and reverse
directions, by either the Molecular Biology Core Facility at Dana-Farber Cancer Institute
or Agencourt Bioscience Corporation (Beverly MA). Forward and reverse
chromatograms were analyzed by Sequencher software (Gene Codes Corporation, Ann
Arbor, MI) or by Mutation Surveyor 2.03 (SoftGenetics, State College, PA).
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Expression profiling
Processing of expression data of Brigham and Women’s Hospital samples have
been previously described (7). These data were obtained from HGU95Av2 arrays
(Affymetrix, Santa Clara, CA). RNA samples from the Nagoya tumors were processed
and hybridized to HGU133A 2.0 arrays (Affymetrix, Santa Clara, CA), RNA samples
from lung cancer cell lines were processed and hybridized to HGU95Av2 arrays
(Affymetrix, Santa Clara, CA) according to the manufacturer’s instructions.
Normalization and scaling of all three sets of samples was carried out as described in (7)
based on the dChip program (12).
Differential analysis
In both datasets, gene expression values were compared between the EGFR
mutant lung adenocarcinomas and the lung adenocarcinomas documented to be wild type
for EGFR. In particular, a variation filter was first applied so as to select genes with
median expression (in arbitrary Affymetrix expression units) greater than 20 and with
median absolute standard deviation of gene expression units greater than the 50th
percentile for all genes. From within this gene pool, genes correlating with the class
distinction of interest (wild type = 1 vs. mutant = 2) were identified by ranking them
according to their signal-to-noise ratio (SNR). For a given gene g, SNR (g) = ( x 1 –
x 2)/(s1 + s2), where x i and si denote, respectively, gene g's sample mean (or median) and
sample standard deviation within class i = 1, 2. A Monte Carlo simulation of the
permutation distribution of the signal-to-noise ratios was performed by permuting the
sample labels indicating class membership (n = 1000); thereafter, the observed values in
the data were compared with the 99th percentile of the permutation distribution.
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Cross-platform analysis
The EGFR gene expression signatures identified in the two datasets were
compared based on a Kolmogorov-Smirnoff (KS) rank test as described (13). In
particular, the total list of genes present in one dataset (e.g., the Nagoya data) was ranked
according to the signal-to-noise ratio with respect to the “mutant vs. wild type” signature;
the top n up-regulated EGFR genes in the second dataset (e.g., the Brigham and
Women’s Hospital data) were located within the ranked list and their proximity to the
genes with higher levels of expression in the mutant class was measured by a KS score
(with a higher score corresponding to a higher proximity). An empirical p-value for the
observed KS score was computed by comparing it to the distribution of KS scores
obtained by permutation of the class labels and corresponding re-ranking of the list of
genes. A similar procedure was used to place the set of down-regulated mutant genes in
one dataset within the list of genes ranked according to the “wild type vs. mutant”
signature in the other dataset. We selected n=300 as the number of up- (down-) regulated
genes defining the gene set to test for enrichment. Selection of a different number n of
genes did not change the significance of the results (data not shown).
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Results and Discussion
Mutation frequency of EGFR in primary lung adenocarcinomas from the United States
To determine the frequency of mutations in the EGFR gene in unselected U.S.
lung adenocarcinomas, we sequenced the kinase domain from the 127 lung
adenocarcinomas of U.S. origin characterized by gene expression analysis (7). Among
these tumors, we detected a higher frequency of EGFR mutations (13/127, 10.2%; Table
1 and Supplementary Fig.1) (1). This frequency is consistent with the rate of clinical
response to gefitinib treatment in U.S.-based clinical trials (3, 6, 14).
EGFR mutations are summarized in Table 1A. Five human lung adenocarcinomas have
the missense mutation L858R or L861Q in exon 21 and six samples have in-frame
deletion mutations in exon 19. We have also found several novel mutations. Two tumor
samples contain in-frame insertions in exon 20, and one sample has a missense mutation
(G779S) in exon 20 as well as the L861Q mutation. All mutations were shown to be
somatic, except for one specimen in which the sequencing of normal control tissue failed.
Clinical characteristics of patients with EGFR mutations
In this sample set of lung adenocarcinomas of U.S. source, the relationship
between EGFR mutations, gender, and smoking status is striking (Table 1B). Only one
adenocarcinoma from 46 men (2%) had a mutation of EGFR, compared to 10 of 69
(14%) of adenocarcinomas from women with mutations in EGFR (the information
regarding gender as well as smoking status was anonymized in 12 out of the 127
patients). One half of the 12 adenocarcinomas (6/12) from women who did not smoke
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have mutations of EGFR while the rest are wild type. Two of two adenocarcinomas from
men who did not smoke have wild type EGFR. In contrast, KRAS mutations are found in
a similar rate both in adenocarcinomas from female patients (30%, 21/69) and from male
patients (41%, 19/46). All of the 12 adenocarcinomas from non-smoking women were
wild-type for KRAS.
We compared the outcome of patients whose lung adenocarcinomas contained
wild type or mutant EGFR (Fig. 1A and Supplementary Fig.2). The median survival time
of patients with EGFR mutant adenocarcinomas (n=11) was 57.8 months and that of
patients with wild type EGFR (n= 103) was 41.2 months (Fig 1A; no outcome
information available for 13 of 127 anonymized patients). The difference in survival is
not statistically significant (p = 0.41, log rank test). Patients with KRAS mutant
adenocarcinomas (n=40) had a median survival of 41 months while patients with KRAS
wild-typed adenocarcinomas (n=74) had a median survival of 48 months (Fig 1B).
Again, this difference was not statistically significant (p=0.67, log rank test). These
results are similar when only stage I tumors are considered (Supplementary Fig.2). There
are also confounding factors of age, gender, and variations in treatment; as patients and
samples were anonymized in 2000 and 2001, further follow-up information (including
possible gefitinib treatment) is not available.
EGFR mRNA expression
To analyze the expression of multiple genes as a consequence of EGFR mutation,
we examined the data obtained by hybridizing the RNA from each tumor to Affymetrix
oligonucleotide arrays. As a first effort to determine the genes affected by EGFR
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mutation, we compared the mean expression of EGFR mRNA between the mutant and
wild-type tumors. On average, EGFR expression was significantly higher in
adenocarcinomas with EGFR mutations compared to the wild type EGFR as measured by
2 probe-sets on the Affymetrix U95Av2 expression arrays (Fig. 2; p=0.0065 and
p=0.0014, two-sided t test). However, the ranges are overlapping and the highest
individual levels of EGFR mRNA expression were noted in adenocarcinoma specimens
with wild type EGFR.
EGFR copy number and EGFR mutations may be related to the mRNA
expression. Hirsch et al (15) reported that EGFR gene copy number correlated with
EGFR protein expression, but not with prognosis in a cohort of patients not treated with
gefitinib. Some of our samples with wild type EGFR may have high copy number as
well because elevated levels of EGFR mRNA. Further study will be needed to delineate
the relationships among EGFR mutation, EGFR gene copy number, EGFR mRNA
expression, and EGFR protein expression.
EGFR, KRAS and BRAF mutations are mutually exclusive in lung adenocarcinoma
As KRAS and BRAF are downstream targets of EGFR and are mutated in lung
adenocarcinoma, we checked the mutation status of EGFR, KRAS and BRAF in all lung
adenocarcinomas with gene expression data (7) (Table 1C and Fig. 2). The frequency of
KRAS mutations was 34.6% (44 of 127), similar to that reported in the literature (16-20).
In these lung adenocarcinomas, EGFR, BRAF and KRAS mutations were mutually
exclusive and were not present in the same tumor specimen. This does not exclude the
possibility that additional lung adenocarcinomas may show mutation of more than one of
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these genes, but the mutually exclusive relationship suggests that they may be
functioning within the same oncogenic pathway, consistent with known signal
transduction mechanisms.
Relationship between EGFR, KRAS and BRAF mutation status and gene expression
Unsupervised clustering of gene expression data revealed that the majority of
EGFR mutants were clustered in three adenocarcinoma sub-groups (Figure 2A). There
are four major clusters (C1 through C4) and three groups with weaker association (Group
I, II and III) defined in our previous study (7). The 13 samples with EGFR mutation
were mainly clustered in either one small cluster adjacent to Group I, with high relative
EGFR expression, or in the closely-related Group III and C4 (Fig. 2A). As previously
described for the C4 class (7), which comprises many of the EGFR mutant
adenocarcinomas, EGFR mutation is associated with bronchioloalveolar carcinoma
(BAC) features in this data set; 7/13 samples with EGFR mutation have BAC features
according to at least one of three pathologists who examined the tumors.
With unsupervised and supervised analysis of microarray data, there is a cluster of
adenocarcinomas with both wild type EGFR and wild type KRAS (Fig. 2A and B),
closely related to the formerly defined C1 class (7). Large scale sequencing efforts (1,
21) may reveal additional targets of therapy in this and other groups.
Gene Expression analysis – genes correlated with EGFR mutations
Differential analysis of transcriptional profiles was carried out to identify gene
expression correlates of EGFR mutation. The genes were sorted by their degree of
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correlation with the “mutant vs. wild type” distinction according to a median-based
signal-to-noise metric (see "Materials and methods"). Permutation of the sample labels
indicated that mutant samples had significantly lower expression of more than 30 genes
and significantly higher expression of more than 1000 additional genes, compared with
wild type samples (P < .01) (Supplementary Fig. 3). Among the significantly upregulated genes were several known downstream targets of EGFR pathway in EGFR
mutants (Supplementary Table 1), such as RSK (ribosomal protein S6 kinase, p90,
polypeptides 1 and 3) (22-27), JAK1 (28), filamin A (27), SOS (26, 29), SLC9A1
(NHE1) (30, 31), calpain-2 (32, 33), and MAPK8IP3 (JSAP1) (34). Among the top
genes on our list are the genes encoding RSK subunits, which are located downstream of
the RAF-MEK-ERK pathway. RSK has many substrates, which include filamin A (27),
SOS (26), and SLC9A1 (NHE1) (31) among the top 50 genes in terms of correlation to
EGFR mutation. RSK is worth investigating for its role in EGFR signaling, as a potential
therapeutic target in lung adenocarcinoma and as a candidate for gene mutation in tumors
with wild-type EGFR. Another interesting gene is calpain-2, which is shown to be
activated by EGFR (32) and to contribute to alterations in cell motility, proliferation, and
apoptosis (33).
To determine the statistical significance of the similarity of the “mutant vs. wild
type” signatures in the two datasets we performed enrichment tests based on the
Kolmogorov-Smirnoff rank statistic. We selected the top 300 up-regulated genes in the
U.S. source EGFR mutants using data from U95A human gene expression arrays,
mapped the probes for these genes to the U133A array platform, and tested for their
enrichment in the Japan source mutant signature (see “Materials and methods”, Fig. 3 and
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Supplementary Table 2). When compared with repeated random class labelings
(N=1000), the Kolmogorov-Smirnov (KS) score indicated that the observed U.S.-Japan
similarity was highly significant (mean-based analysis: p=0.004, median-based analysis:
p=0.007, Figure 3A). Performing a similar procedure in the opposite direction (selecting
the set of top 300 up-regulated genes in the Japan source EGFR mutants and testing this
set in the U.S. data) also yielded a highly significant KS score (mean-based analysis:
p=0.003, median-based analysis: p=0.006, Figure 3B). In the Japanese patients, there are
two patients with KRAS mutations in an EGFR wild type background but none in the
EGFR mutant background, showing that EGFR and KRAS mutations are mutually
exclusive as in US patients.
One of our initial goals was to develop an expression-based classifier that would
distinguish lung adenocarcinomas with mutant from those with wild-type EGFR. Our
efforts to date to accomplish cross-platform clustering between U95 and U133 arrays,
using several different approaches including k-nearest neighbors, weighted voting, and
KS analysis, have all maintained error rates in the 15% to 25% range. As one goal of
such a classifier would be to develop a pre-selection for tumors that should be sequenced
for EGFR, this error rate appears to be too high for practical utility.
We also sought to delineate the statistical significance of the similarity of the
“mutant vs. wild type” signatures in the cell line datasets. We obtained U95Av2 data
from a panel of 10 cell lines, two of which have EGFR mutations (NCI-H1650 has
del746-750 deletion and NCI-H3255 has the L858R missense mutation) while the
remainder are EGFR wild type. We performed enrichment tests based on the
Kolmogorov-Smirnoff rank statistic. We selected the top 300 up-regulated genes in the
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U.S. source patients with EGFR mutation and tested for their enrichment in the cell line
mutant signature. Although not statistically significant, the tendency of the enrichment
was similar (Figure 3C).
In summary, microarray analysis of different populations (U.S. vs. Japanese
source) and different specimen types (tumor vs. cell lines) has revealed characteristic
gene expression correlates of gefitinib–responsive EGFR mutations within human lung
adenocarcinomas. A subset of the genes that are up-regulated in the adenocarcinomas
with EGFR mutations are themselves down-stream targets of EGFR, suggesting a
significant biological role for these expression alterations.
In addition, this work has defined the frequency of EGFR mutations in unselected
U.S. lung adenocarcinoma patients as approximately 10%, consistent with the rate of
response to gefitinib in this population.
Acknowledgments
We thank Dr. Heidi Greulich for the critical review of the manuscript and helpful
discussion.
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Figure Legends
Fig. 1. Kaplan-Meier curves of survival by mutation status for lung adenocarcinoma
patients.
A. Survival of patients with EGFR mutations versus patients with a normal EGFR
sequence. B. Survival of patients with KRAS mutations versus patients with a normal
KRAS sequence. The circles represent censored patients.
Fig. 2. EGFR expression vs. EGFR mutation status in lung adenocarcinoma.
Mean expression of EGFR mRNA was significantly higher in adenocarcinomas
with EGFR mutations compared to the wild-type EGFR as measured by 2 probe sets on
the Affymetrix U95Av2 expression arrays. A, 1537_at (EGFR): p=0.0065, B, 37327_at
(EGFR): p=0.0014 (two-sided t test).
Fig. 3. Dendrogram of lung adenocarcinoma with reference to EGFR mutation.
A, Unsupervised expression-defined adenocarcinoma subclasses (7). There are four
major clusters (C1 through C4) and three groups with weaker association (Group I, II and
III). Samples of normal lung (NL) were not included in this EGFR mutation search. The
13 samples with EGFR mutation were mainly clustered in either one cluster in left side or
in a cluster closely related to Group III and C4. Please note that this figure is adapted
from ref. (7).
B, Supervised clustering of U.S. lung adenocarcinoma samples in space of top 200
markers (median base) / class (mutant vs. wild type). Notably there is a cluster both
EGFR wild type and KRAS wild type.
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Fig. 4. Correlation of mRNA expression with EGFR mutation in U.S. and Japan lung
adenocarcinoma patients and lung cancer cell lines (Enrichment test).
U.S. vs. Japan. A. Mean-based, B. Median-based signature. Selected the top 300 EGFR
mutation markers in U.S. source data (U95Av2 Affymetrix Genechip). Mapped to
U133A Affymetrix Genechip, 252 remain (258 for median). Tested on Japan source data
(U133A) by KS score.
Japan vs. U.S. C. Mean-based, D. Median-based signature. Selected the top 300 EGFR
mutation markers in Japan source data (U133A). Mapped to U95Av2, 155 remain (139
for median). Tested on U.S. source data (U95Av2) by KS score.
U.S. primary tumors vs. cell line. E. Mean-based, F. Median-based signature. Selected the
top 300 EGFR mutation markers in U.S. source data (U95Av2). Tested on cell line data
(U95Av2) by KS score.
Supplementary Fig. 1. Mutations in the EGFR gene.
A, A point mutation was identified in exon 20 of one human lung adenocarcinoma
sample, AD356T (GG2234-35TT / G779S), which is not present in a normal lung tissue
sample from the same patient, AD356N. B, An insertion mutation was identified in exon
20 of sample AD261T (2311-12insGCGTGGACA / D770_N771insSVD).
Supplementary Fig. 2. Survival curve of the stage I patients with EGFR mutations (U.S.
source).
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A, Survival by EGFR mutation. EGFR mutant (n=8): median survival 66.8 months,
EGFR wild type (n=60): median survival 71.5 months (p= 0.43; Logrank test).
B, Survival by KRAS mutation. KRAS mutant (n= 26): median survival 71.5 months,
KRAS wild type (n= 42): median survival 86.3 months (p = 0.47; Logrank test).
Supplementary Fig. 3. Differential analysis of transcriptional profiles
A, Mean-based analysis. B, Median-based analysis. Permutation of the sample labels
indicated that EGFR mutant samples had significantly lower expression of more than 30
genes and significantly higher expression of more than 1000 additional genes, compared
with wild type samples (P < .01) in median based analysis.
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References
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
Paez, J. G., Janne, P. A., Lee, J. C., et al. EGFR mutations in lung cancer:
correlation with clinical response to gefitinib therapy. Science, 304: 1497-1500,
2004.
Lynch, T. J., Bell, D. W., Sordella, R., et al. Activating mutations in the epidermal
growth factor receptor underlying responsiveness of non-small-cell lung cancer to
gefitinib. N Engl J Med, 350: 2129-2139, 2004.
Miller, V. A., Kris, M. G., Shah, N., et al. Bronchioloalveolar pathologic subtype
and smoking history predict sensitivity to gefitinib in advanced non-small-cell
lung cancer. J Clin Oncol, 22: 1103-1109, 2004.
Fukuoka, M., Yano, S., Giaccone, G., et al. Multi-institutional randomized phase
II trial of gefitinib for previously treated patients with advanced non-small-cell
lung cancer. J Clin Oncol, 21: 2237-2246, 2003.
Cohen, M. H., Williams, G. A., Sridhara, R., et al. United States Food and Drug
Administration Drug Approval summary: Gefitinib (ZD1839; Iressa) tablets. Clin
Cancer Res, 10: 1212-1218, 2004.
Janne, P. A., Gurubhagavatula, S., Yeap, B. Y., et al. Outcomes of patients with
advanced non-small cell lung cancer treated with gefitinib (ZD1839, "Iressa") on
an expanded access study. Lung Cancer, 44: 221-230, 2004.
Bhattacharjee, A., Richards, W. G., Staunton, J., et al. Classification of human
lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma
subclasses. Proc Natl Acad Sci U S A, 98: 13790-13795, 2001.
Naoki, K., Chen, T. H., Richards, W. G., Sugarbaker, D. J., and Meyerson, M.
Missense mutations of the BRAF gene in human lung adenocarcinoma. Cancer
Res, 62: 7001-7003, 2002.
Tracy, S., Mukohara, T., Hansen, M., Meyerson, M., Johnson, B. E., and Jänne, P.
A. Gefitinib induces apoptosis in the EGFR L858R Non-Small Cell Lung Cancer
Cell line H3255. Cancer Res, 2004 (Submitted).
Sordella, R., Bell, D. W., Haber, D. A., and Settleman, J. Gefitinib-Sensitizing
EGFR Mutations in Lung Cancer Activate Anti-Apoptotic Pathways. Science,
2004.
Sanger, F. and Coulson, A. R. A rapid method for determining sequences in DNA
by primed synthesis with DNA polymerase. J Mol Biol, 94: 441-448, 1975.
Li, C. and Wong, W. H. Model-based analysis of oligonucleotide arrays:
expression index computation and outlier detection. Proc Natl Acad Sci U S A,
98: 31-36, 2001.
Mootha, V. K., Lindgren, C. M., Eriksson, K.-F., et al. PGC-1-responsive genes
involved in oxidative phosphorylation are coordinately downregulated in human
diabetes. Nature Genetics, 34: 267-273, 2003.
Kris, M. G., Natale, R. B., Herbst, R. S., et al. Efficacy of gefitinib, an inhibitor of
the epidermal growth factor receptor tyrosine kinase, in symptomatic patients
with non-small cell lung cancer: a randomized trial. Jama, 290: 2149-2158, 2003.
Hirsch, F. R., Varella-Garcia, M., Bunn, P. A., Jr., et al. Epidermal growth factor
receptor in non-small-cell lung carcinomas: correlation between gene copy
Naoki et al.
-20-
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
number and protein expression and impact on prognosis. J Clin Oncol, 21: 37983807, 2003.
Kwiatkowski, D. J., Harpole, D. H., Jr., Godleski, J., et al. Molecular pathologic
substaging in 244 stage I non-small-cell lung cancer patients: clinical
implications. J Clin Oncol, 16: 2468-2477, 1998.
Rodenhuis, S., van de Wetering, M. L., Mooi, W. J., Evers, S. G., van Zandwijk,
N., and Bos, J. L. Mutational activation of the K-ras oncogene. A possible
pathogenetic factor in adenocarcinoma of the lung. N Engl J Med, 317: 929-935,
1987.
Mitsudomi, T., Steinberg, S. M., Oie, H. K., et al. ras gene mutations in non-small
cell lung cancers are associated with shortened survival irrespective of treatment
intent. Cancer Res, 51: 4999-5002, 1991.
Slebos, R. J., Kibbelaar, R. E., Dalesio, O., et al. K-ras oncogene activation as a
prognostic marker in adenocarcinoma of the lung. N Engl J Med, 323: 561-565,
1990.
Johnson, B. E. Biologic and molecular prognostic factors--impact on treatment of
patients with non-small cell lung cancer. Chest, 107: 287S-290S, 1995.
Davies, H., Bignell, G. R., Cox, C., et al. Mutations of the BRAF gene in human
cancer. Nature, 417: 949-954, 2002.
Zhao, Y., Bjorbaek, C., and Moller, D. E. Regulation and interaction of pp90(rsk)
isoforms with mitogen-activated protein kinases. J Biol Chem, 271: 29773-29779,
1996.
Nebreda, A. R. and Gavin, A. C. Perspectives: signal transduction. Cell survival
demands some Rsk. Science, 286: 1309-1310, 1999.
Bonni, A., Brunet, A., West, A. E., Datta, S. R., Takasu, M. A., and Greenberg,
M. E. Cell survival promoted by the Ras-MAPK signaling pathway by
transcription-dependent and -independent mechanisms. Science, 286: 1358-1362,
1999.
Blenis, J. Signal transduction via the MAP kinases: proceed at your own RSK.
Proc Natl Acad Sci U S A, 90: 5889-5892, 1993.
Frodin, M. and Gammeltoft, S. Role and regulation of 90 kDa ribosomal S6
kinase (RSK) in signal transduction. Mol Cell Endocrinol, 151: 65-77, 1999.
Woo, M. S., Ohta, Y., Rabinovitz, I., Stossel, T. P., and Blenis, J. Ribosomal S6
kinase (RSK) regulates phosphorylation of filamin A on an important regulatory
site. Mol Cell Biol, 24: 3025-3035, 2004.
Shuai, K., Ziemiecki, A., Wilks, A. F., et al. Polypeptide signalling to the nucleus
through tyrosine phosphorylation of Jak and Stat proteins. Nature, 366: 580-583,
1993.
Buday, L. and Downward, J. Epidermal growth factor regulates p21ras through
the formation of a complex of receptor, Grb2 adapter protein, and Sos nucleotide
exchange factor. Cell, 73: 611-620, 1993.
Maly, K., Strese, K., Kampfer, S., et al. Critical role of protein kinase C alpha and
calcium in growth factor induced activation of the Na(+)/H(+) exchanger NHE1.
FEBS Lett, 521: 205-210, 2002.
Naoki et al.
-21-
31.
32.
33.
34.
Takahashi, E., Abe, J., Gallis, B., et al. p90(RSK) is a serum-stimulated Na+/H+
exchanger isoform-1 kinase. Regulatory phosphorylation of serine 703 of Na+/H+
exchanger isoform-1. J Biol Chem, 274: 20206-20214, 1999.
Glading, A., Chang, P., Lauffenburger, D. A., and Wells, A. Epidermal growth
factor receptor activation of calpain is required for fibroblast motility and occurs
via an ERK/MAP kinase signaling pathway. J Biol Chem, 275: 2390-2398, 2000.
Mamoune, A., Luo, J. H., Lauffenburger, D. A., and Wells, A. Calpain-2 as a
target for limiting prostate cancer invasion. Cancer Res, 63: 4632-4640, 2003.
Matsuura, H., Nishitoh, H., Takeda, K., et al. Phosphorylation-dependent
scaffolding role of JSAP1/JIP3 in the ASK1-JNK signaling pathway. A new mode
of regulation of the MAP kinase cascade. J Biol Chem, 277: 40703-40709, 2002.
Naoki et al.
-22-