Note: Descriptions are shown in the official language in which they were submitted.
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Predictive markers for EGFR inhibitor treatment
The present invention provides biomarkers that are predictive for the clinical
benefit of
EGFR inhibitor treatment in cancer patients.
A number of human malignancies are associated with aberrant or over-expression
of
the epidermal growth factor receptor (EGFR). EGF, transforming growth factor-a
(TGF-a),
and a number of other ligands bind to the EGFR, stimulating
autophosphorylation of the
intracellular tyrosine kinase domain of the receptor. A variety of
intracellular pathways are
subsequently activated, and these downstream events result in tumour cell
proliferation in
vitro. It has been postulated that stimulation of tumour cells via the EGFR
may be important
for both tumour growth and tumour survival in vivo.
Early clinical data with TarcevaTM (erlotinib), an inhibitor of the EGFR
tyrosine kinase,
indicate that the compound is safe and generally well tolerated at doses that
provide the
targeted effective concentration (as determined by preclinical data). Clinical
phase I and II
trials in patients with advanced disease have demonstrated that TarcevaTM has
promising
clinical activity in a range of epithelial tumours. Indeed, TarcevaTM has been
shown to be
capable of inducing durable partial remissions in previously treated patients
with head and
neck cancer, and NSCLC (Non small cell lung cancer) of a similar order to
established
second line chemotherapy, but with the added benefit of a better safety
profile than chemo
therapy and improved convenience (tablet instead of intravenous [i.v.]
administration). A
recently completed, randomised, double-blind, placebo-controlled trial (BR.2
1) has shown
that single agent TarcevaTM significantly prolongs and improves the survival
of NSCLC
patients for whom standard therapy for advanced disease has failed.
TarcevaTM (erlotinib) is a small chemical molecule; it is an orally active,
potent,
selective inhibitor of the EGFR tyrosine kinase (EGFR-TKI).
Lung cancer is the major cause of cancer-related death in North America and
Europe.
In the United States, the number of deaths secondary to lung cancer exceeds
the combined
total deaths from the second (colon), third (breast), and fourth (prostate)
leading causes of
cancer deaths combined. About 75% to 80% of all lung cancers are NSCLC, with
approximately 40% of patients presenting with locally advanced and/or
unresectable disease.
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This group typically includes those with bulky stage IIIA and IIIB disease,
excluding
malignant pleural effusions.
The crude incidence of lung cancer in the European Union is 52.5, the death
rate 48.7
cases/ 100000/year. Among men the rates are 79.3 and 78.3, among women 21.6
and 20.5,
respectively. NSCLC accounts for 80% of all lung cancer cases. About 90% of
lung cancer
mortality among men, and 80% among women, is attributable to smoking.
In the US, according to the American Cancer Society, during 2004, there were
approximately 173,800 new cases of lung cancer (93,100 in men and 80,700 in
women) and
were accounting for about 13% of all new cancers. Most patients die as a
consequence of
their disease within two years of diagnosis. For many NSCLC patients,
successful treatment
remains elusive. Advanced tumours often are not amenable to surgery and may
also be
resistant to tolerable doses of radiotherapy and chemotherapy. In randomized
trials the
currently most active combination chemotherapies achieved response rates of
approximately
30% to 40% and a 1-year survival rate between 35% and 40%. This is really an
advance over
the 10% 1-year survival rate seen with supportive care alone (Shepherd 1999).
Until recently therapeutic options for relapsed patients following relapse
were limited
to best supportive care or palliation. A recent trial comparing docetaxel
(Taxotere) with best
supportive care showed that patients with NSCLC could benefit from second line
chemotherapy after cisplatin-based first-line regimens had failed. Patients of
all ages and with
ECOG performance status of 0, 1, or 2 demonstrated improved survival with
docetaxel, as
did those who had been refractory to prior platinum-based treatment. Patients
who did not
benefit from therapy included those with weight loss of 10%, high lactate
dehydrogenase
levels, multi-organ involvement, or liver involvement. Additionally, the
benefit of docetaxel
monotherapy did not extend beyond the second line setting. Patients receiving
docetaxel as
third-line treatment or beyond showed no prolongation of survival. Single-
agent docetaxel
became a standard second-line therapy for NSCLC. Recently another randomized
phase III
trial in second line therapy of NSCLC compared pemetrexed (Alimta ) with
docetaxel.
Treatment with pemetrexed resulted in a clinically equivalent efficacy but
with significantly
fewer side effects compared with docetaxel.
It has long been acknowledged that there is a need to develop methods of
individualising cancer treatment. With the development of targeted cancer
treatments, there is
a particular interest in methodologies which could provide a molecular profile
of the tumour
target, (i.e. those that are predictive for clinical benefit). Proof of
principle for gene
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expression profiling in cancer has already been established with the molecular
classification
of tumour types which are not apparent on the basis of current morphological
and
immunohistochemical tests. Two separate disease entities were differentiated
with differing
prognoses from the single current classification of diffuse large B-cell
lymphoma using gene
expression profiling.
Therefore, it is an aim of the present invention to provide expression
biomarkers that
are predictive for the clinical benefit of EGFR inhibitor treatment in cancer
patients.
In a first object the present invention provides an in vitro method of
predicting the
clinical benefit of a cancer patient in response to treatment with an EGFR
inhibitor. Said
method comprises the steps: determining an expression level of at least one
gene selected
from table 3 in a tumour sample of a patient and comparing the expression
level of the at
least one gene to a value representative of an expression level of the at
least one gene in
tumours of a population of patients deriving no clinical benefit from the
treatment, wherein a
differential expression level of the at least one gene in the tumour sample of
the patient is
indicative for a patient who will derive clinical benefit from the treatment.
The term "a value representative of an expression level of the at least one
marker gene
in tumours of a population of patients deriving no clinical benefit from the
treatment" refers
to an estimate of the mean expression level of a marker gene in tumours of a
population of
patients who do not derive a clinical benefit from the treatment. Clinical
benefit was defined
as either having an objective response, or disease stabilization for _ 12
weeks.
In a further preferred embodiment, the expression level of at least two genes
is
determined.
In another preferred embodiment, the expression level of at least three genes
is
determined.
In a further preferred embodiment, the gene is selected from the group
consisting of
ATP6VOE1, MAPREI, PSMA5, ACSL3, RAP1A, SLC2A3, CHMP2B, RFK, CTGF,
HSPA8, AKAP12, LOX, SLMO2, NOMO3, APOO and said gene shows a lower expression
level in the tumour sample of the patient compared to the value representative
of the
expression level in tumours of the population of patients deriving no clinical
benefit from the
treatment.
In a further preferred embodiment, the the gene is selected from the group
consisting of
SDC1, CEBPA, ST6GALNAC2, PLA2G6, PMS2L11, Cl9orf7, DDX17, SFPQ, PMS2L3,
SLC35E2, PMSL2, URG4, PPP1R13B, NRCAM, FLJ10916, FLJ13197, GPR172B,
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ZNF506, ARHGAP8, CELSRI, LYK5 and said gene shows a higher expression level in
the
tumour sample of the patient compared to the value representative of the
expression level in
tumours of the population of patients deriving no clinical benefit from the
treatment.
In a preferred embodiment, the expression level of the at least one gene is
determined
by microarray technology. The construction and use of gene chips are well
known in the art.
see, U. S. Pat Nos. 5,202,231; 5,445,934; 5,525,464; 5,695,940; 5,744,305;
5,795, 716 and 1
5,800,992. See also, Johnston, M. Curr. Biol. 8:R171-174 (1998); Iyer VR et
al., Science
283:83-87 (1999). Of course, the gene expression level can be determined by
other methods
that are known to a person skilled in the art such as e.g. northern blots, RT-
PCR, real time
quantitative PCR, primer extension, RNase protection, RNA expression
profiling.
The genes of the present invention can be combined to biomarker sets.
Biomarker sets
can be built from any combination of biomarkers listed in Table 3 to make
predictions about
the effect of EGFR inhibitor treatment in cancer patients. The various
biomarkers and
biomarkers sets described herein can be used, for example, to predict how
patients with
cancer will respond to therapeutic intervention with an EGFR inhibitor.
The term "gene" as used herein comprises variants of the gene. The term
"variant"
relates to nucleic acid sequences which are substantially similar to the
nucleic acid sequences
given by the GenBank accession number. The term "substantially similar" is
well understood
by a person skilled in the art. In particular, a gene variant may be an allele
which shows
nucleotide exchanges compared to the nucleic acid sequence of the most
prevalent allele in
the human population. Preferably, such a substantially similar nucleic acid
sequence has a
sequence similarity to the most prevalent allele of at least 80%, preferably
at least 85%, more
preferably at least 90%, most preferably at least 95%. The term "variants" is
also meant to
relate to splice variants.
The EGFR inhibitor can be selected from the group consisting of gefitinib,
erlotinib,
PKI-166, EKB-569, GW2016, CI-1033 and an anti-erbB antibody such as
trastuzumab and
cetuximab.
In another embodiment, the EGFR inhibitor is erlotinib.
In yet another embodiment, the cancer is NSCLC.
Techniques for the detection of gene expression of the genes described by this
invention include, but are not limited to northern blots, RT-PCR, real time
quantitative PCR,
primer extension, RNase protection, RNA expression profiling and related
techniques. These
techniques are well known to those of skill in the art see e.g. Sambrook J et
al., Molecular
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Cloning: A Laboratory Manual, Third Edition (Cold Spring Harbor Press, Cold
Spring
Harbor, 2000).
Techniques for the detection of protein expression of the respective genes
described by
this invention include, but are not limited to immunohistochemistry (IHC).
In accordance with the invention, cells from a patient tissue sample, e.g., a
tumour or
cancer biopsy, can be assayed to determine the expression pattern of one or
more biomarkers.
Success or failure of a cancer treatment can be determined based on the
biomarker expression
pattern of the cells from the test tissue (test cells), e.g., tumour or cancer
biopsy, as being
relatively similar or different from the expression pattern of a control set
of the one or more
biomarkers. In the context of this invention, it was found that the genes
listed in table 3 are
differentially expressed i.e. show a higher or lower expression level, in
tumours of patients
who derived benefit from the EGFR inhibitor treatment compared to tumours of
patients who
did not derive clinical benefit from the EGFR inhibitor treatment. Thus, if
the test cells show
a biomarker expression profile which corresponds to that of a patient who
responded to
cancer treatment, it is highly likely or predicted that the individual's
cancer or tumour will
respond favorably to treatment with the EGFR inhibitor. By contrast, if the
test cells show a
biomarker expression pattern corresponding to that of a patient who did not
respond to cancer
treatment, it is highly likely or predicted that the individual's cancer or
tumour will not
respond to treatment with the EGFR inhibitor.
The biomarkers of the present invention i.e. the genes listed in table 3, are
a first step
towards an individualized therapy for patients with cancer, in particular
patients with
refractory NSCLC. This individualized therapy will allow treating physicians
to select the
most appropriate agent out of the existing drugs for cancer therapy, in
particular NSCLC. The
benefit of individualized therapy for each future patient are: response rates
/ number of
benefiting patients will increase and the risk of adverse side effects due to
ineffective
treatment will be reduced.
In a further object the present invention provides a therapeutic method of
treating a
cancer patient identified by the in vitro method of the present invention.
Said therapeutic
method comprises administering an EGFR inhibitor to the patient who has been
selected for
treatment based on the predictive expression pattern of at least one of the
genes listed in table
3. A preferred EGFR inhibitor is erlotinib and a preferred cancer to be
treated is NSCLC.
Short description of the figures
Figure 1 shows the study design and
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Figure 2 shows the scheme of sample processing.
Experimental part
Rationale for the Study and Study Design
Recently mutations within the EGFR gene in the tumour tissue of a subset of
NSCLC
patients and the association of these mutations with sensitivity to erlotinib
and gefitinib were
described (Pao W, et al. 2004; Lynch et al. 2004; Paez et al. 2004). For the
patients combined
from two studies, mutated EGFR was observed in 13 of 14 patients who responded
to
gefitinib and in none of the 11 gefitinib-treated patients who did not
respond. The reported
prevalence of these mutations was 8% (2 of 25) in unselected NSCLC patients.
These
mutations were found more frequently in adenocarcinomas (21 %), in tumours
from females
(20%), and in tumours from Japanese patients (26%). These mutations result in
increased in
vitro activity of EGFR and increased sensitivity to gefitinib. The
relationship of the mutations
to prolonged stable disease or survival duration has not been prospectively
evaluated.
Based on exploratory analyses from the BR.21 study, it appeared unlikely that
the
observed survival benefit is only due to the EGFR mutations, since a
significant survival
benefit is maintained even when patients with objective response are excluded
from analyses
(data on file). Other molecular mechanisms must also contribute to the effect.
Based on the assumption that there are changes in gene expression levels that
are
predictive of response / benefit to TarcevaTM treatment, microarray analysis
was used to
detect these changes
This required a clearly defined study population treated with TarcevaTM
monotherapy
after failure of lst line therapy. Based on the experience from the BR.21
study, benefiting
population was defined as either having objective response, or disease
stabilization for 12
weeks. Clinical and microarray datasets were analyzed according to a pre-
defined statistical
plan.
The application of this technique requires fresh frozen tissue (FFT).
Therefore a
mandatory biopsy had to be performed before start of treatment. The collected
material was
frozen in liquid nitrogen (N2).
A second tumour sample was collected at the same time and stored in paraffin
(formalin fixed paraffin embedded, FFPE). This sample was analysed for
alterations in the
EGFR signaling pathway.
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The ability to perform tumour biopsies via bronchoscopy was a prerequisite for
this
study. Bronchoscopy is a standard procedure to confirm the diagnosis of lung
cancer.
Although generally safe, there is a remaining risk of complications, e.g.
bleeding.
This study was a first step towards an individualized therapy for patients
with
refractory NSCLC. This individualized therapy will allow treating physicians
to select the
most appropriate agent out of the existing drugs for this indication.
Once individualized therapy will be available, the benefit for each future
patient will
outweigh the risk patients have to take in the present study:
response rates / number of benefiting patients will increase,
the risk of adverse side effects due to ineffective treatment will be reduced.
Rationale for Dosage Selection
TarcevaTM was given orally once per day at a dose of 150 mg until disease
progression,
intolerable toxicities or death. The selection of this dose was based on
pharmacokinetic
parameters, as well as the safety and tolerability profile of this dose
observed in Phase I, II
and III trials in heavily pre-treated patients with advanced cancer. Drug
levels seen in the
plasma of patients with cancer receiving the 150 mg/day dose were consistently
above the
average plasma concentration of 500 ng / ml targeted for clinical efficacy.
BR.21 showed a
survival benefit with this dose.
Objectives of the Study
The primary objective was the identification of differentially expressed genes
that are
predictive for clinical benefit (CR, PR or SD _12 weeks) of TarcevaTM
treatment.
Identification of differentially expressed genes predictive for "response"
(CR, PR) to
TarcevaTM treatment was an important additional objective.
The secondary objectives were to assess alterations in the EGFR signaling
pathways
with respect to benefit from treatment.
Study Design
Overview of Study Design and Dosing Regimen
This was an open-label, predictive marker identification Phase II study. The
study was
conducted in approximately 26 sites in about 12 countries. 264 patients with
advanced
NSCLC following failure of at least one prior chemotherapy regimen were
enrolled over a 12
month period. Continuous oral TarcevaTM was given at a dose of 150 mg/day.
Dose
reductions were permitted based on tolerability to drug therapy. Clinical and
laboratory
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parameters were assessed to evaluate disease control and toxicity. Treatment
continued until
disease progression, unacceptable toxicity or death. The study design is
depicted in figure 1.
Tumour tissue and blood samples were obtained for molecular analyses to
evaluate the
effects of TarcevaTM and to identify subgroups of patients benefiting from
therapy.
Predictive Marker Assessments
Biopsies of the tumour were taken within 2 weeks before start of treatment.
Two
different samples were collected:
The first sample was always frozen immediately in liquid N2
The second sample was fixed in formalin and embedded in paraffin
Snap frozen tissue had the highest priority in this study.
Figure 2 shows a scheme of the sample processing.
Microarray Analysis
The snap frozen samples were used for laser capture microdissection (LCM) of
tumour
cells to extract tumour RNA and RNA from tumour surrounding tissue. The RNA
was
analysed on Affymetrix microarray chips (HG-U 133A) to establish the patients'
tumour gene
expression profile. Quality Control of Affymetrix chips was used to select
those samples of
adequate quality for statistical comparison.
Single Biomarker Analyses on Formalin Fixed Paraffin Embedded Tissue
The second tumour biopsy, the FFPE sample, was used to perform DNA mutation,
IHC
and ISH analyses as described below. Similar analyses were performed on tissue
collected at
initial diagnosis.
The DNA mutation status of the genes encoding EGFR and other molecules
involved in
the EGFR signaling pathway were analysed by DNA sequencing. Gene amplification
of
EGFR and related genes were be studied by FISH.
Protein expression analyses included immunohistochemical [IHC] analyses of
EGFR
and other proteins within the EGFR signalling pathway.
Response Assessments
The RECIST (Uni-dimensional Tumour Measurement) criteria were used to evaluate
response. These criteria can be found under the following link:
http://www.eortc.be/recist/
Note that:To be assigned a status of CR or PR, changes in tumour measurements
must
be confirmed by repeated assessments at least 4 weeks apart at any time during
the treatment
period.
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In the case of SD, follow-up measurements must have met the SD criteria at
least once
after study entry at a minimum interval of 6 weeks.
In the case of maintained SD, follow-up measurements must have met the SD
criteria at
least once after study entry with maintenance duration of at least 12 weeks.
Survival Assessment
A regular status check every 3 months was performed either by a patient's
visit to the
clinic or by telephone. All deaths were recorded. At the end of the study a
definitive
confirmation of survival was required for each patient.
Methods
RNA sample preparation and quality control of RNA samples
All biopsy sample processing was handled by a pathology reference laboratory;
fresh
frozen tissue samples were shipped from investigator sites to the Clinical
Sample Operations
facility in Roche Basel and from there to the pathology laboratory for further
processing.
Laser capture microdissection was used to select tumour cells from surrounding
tissue.
After LCM, RNA was purified from the enriched tumour material. The pathology
laboratory then carried out a number of steps to make an estimate of the
concentration and
quality of the RNA.
RNases are RNA degrading enzymes and are found everywhere and so all
procedures
where RNA will be used must be strictly controlled to minimize RNA
degradation. Most
mRNA species themselves have rather short half-lives and so are considered
quite unstable.
Therefore it is important to perform RNA integrity checks and quantification
before any
assay.
RNA concentration and quality profile can be assessed using an instrument from
Agilent (Agilent Technologies, Inc., Palo Alto, CA) called a 2100 Bioanalyzer
. The
instrument software generates an RNA Integrity Number (RIN), a quantitation
estimate
(Schroeder, A., et al., The RIN: an RNA integrity number for assigning
integrity values to
RNA measurements. BMC Mol Biol, 2006. 7: p. 3), and calculates ribosomal
ratios of the
total RNA sample. The RIN is determined from the entire electrophoretic trace
of the RNA
sample, and so includes the presence or absence of degradation products.
The RNA quality was analysed by a 2100 Bioanalyzer . Only samples with at
least
one rRNA peak above the added poly-I noise and sufficient RNA were selected
for further
analysis on the Affymetrix platform. The purified RNA was forwarded to the
Roche Centre
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for Medical Genomics (RCMG; Basel, Switzerland) for analysis by microarray.
122 RNA
samples were received from the pathology lab for further processing.
Target Labeling of tissue RNA samples
Target labeling was carried out according to the Two-Cycle Target Labeling
Amplification Protocol from Affymetrix (Affymetrix, Santa Clara, California),
as per the
manufacturer's instructions.
The method is based on the standard Eberwine linear amplification procedure
[but uses
two cycles of this procedure to generate sufficient labeled cRNA for
hybridization to a
microarray.
Total RNA input used in the labeling reaction was lOng for those samples where
more
than lOng RNA was available; if less than this amount was available or if
there was no
quantity data available (due to very low RNA concentration), half of the total
sample was
used in the reaction. Yields from the labeling reactions ranged from 20-180ug
cRNA. A
normalization step was introduced at the level of hybridization where 15}zg
cRNA was used
for every sample.
Human Reference RNA (Stratagene, Carlsbad, CA, USA) was used as a control
sample
in the workflow with each batch of samples. lOng of this RNA was used as input
alongside
the test samples to verify that the labeling and hybridization reagents were
working as
expected.
Microarray hybridizations
Affymetrix HG-U 133A microarrays contain over 22,000 probe sets targeting
approximately 18,400 transcripts and variants which represent about 14,500
well-
characterized genes.
Hybridization for all samples was carried out according to Affymetrix
instructions
(Affymetrix Inc., Expression Analysis Technical Manual, 2004). Briefly, for
each sample,
15pg of biotin-labeled cRNA were fragmented in the presence of divalent
cations and heat
and hybridized overnight to Affymetrix HG-U133A full genome oligonucleotide
arrays. The
following day arrays were stained with streptavidin-phycoerythrin (Molecular
Probes;
Eugene, OR) according to the manufacturer's instructions. Arrays were then
scanned using a
GeneChip Scanner 3000 (Affymetrix), and signal intensities were automatically
calculated by
GeneChip Operating Software (GCOS) Version 1.4 (Affymetrix).
Statistical Analysis
Analysis of the AffymetrixTM data consisted of five main steps.
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Step 1 was quality control. The goal was to identify and exclude from analysis
array
data with a sub-standard quality profile.
Step 2 was pre-processing and normalization. The goal was to create a
normalized and
scaled "analysis data set", amenable to inter-chip comparison. It comprised
background noise
estimation and subtraction, probe summarization and scaling.
Step 3 was exploration and description. The goal was to identify potential
bias and
sources of variability. It consisted of applying multivariate and univariate
descriptive analysis
techniques to identify influential covariates.
Step 4 was modeling and testing. The goal was to identify a list of candidate
markers
based on statistical evaluation of the difference in mean expression level
between "clinical
benefit" and "no clinical benefit" patients. It consisted in fitting an
adequate statistical model
to each probe-set and deriving a measure of statistical significance.
Step 5 was a robustness analysis. The goal was to generate a qualified list of
candidate
markers that do not heavily depend on the pre-processing methods and
statistical
assumptions. It consisted in reiterating the analysis with different
methodological approaches
and intersecting the list of candidates.
All analyses were performed using the R software package.
Step 1: Quality Control
The assessment of data quality was based on checking several parameters. These
included standard Affymetrix GeneChipTM quality parameters, in particular:
Scaling Factor,
Percentage of Present Call and Average Background. This step also included
visual
inspection of virtual chip images for detecting localized hybridization
problems, and
comparison of each chip to a virtual median chip for detecting any unusual
departure from
median behavior. Inter-chip correlation analysis was also performed to detect
outlier samples.
In addition, ancillary measures of RNA quality obtained from analysis of RNA
samples with
the Agilent BioanalyzerTM 2100 were taken into consideration.
Based on these parameters, data from 20 arrays were excluded from analysis.
Thus data
from a total of 102 arrays representing 102 patients was included in the
analysis. The clinical
description of these 102 samples set is reported in table 1.
Table 1: Description of clinical characteristics of patients included in the
analysis
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Variable Value n=102
n (%)
Best Response N/A 16 (15.7%)
PD 49 (48.0%)
SD 31 (30.4%)
PR 6 (5.9%)
Clinical Benefit NO 81 (79.4%)
YES 21 (20.6%)
SEX FEMALE 25 (24.5%)
MALE 77 (74.5%)
ETHNICITY CAUCASIAN 65 (63.7%)
ORIENTAL 37 (36.3%)
Histology ADENOCARCINOMA 35 (34.3%)
SQUAMOUS 53 (52.0%)
OTHERS 14 (13.7%)
Ever-Smoking NO 20 (19.6%)
YES 82 (80.4%)
Step 2: Data pre-processing and normalization
The rma algorithm (Irizarry, R.A., et al., Summaries of Affymetrix GeneChip
probe
level data. Nucl. Acids Res., 2003. 31(4): p. e15) was used for pre-processing
and
normalization. The mas5 algorithm (AFFYMETRIX, GeneChip Expression: Data
Analysis
Fundamentals. 2004, AFFYMETRIX) was used to make detection calls for the
individual
probe-sets. Probe-sets called "absent" or "marginal" in all samples were
removed from
further analysis; 5930 probe-sets were removed from analysis based on this
criterion. The
analysis data set therefore consisted of a matrix with 16353 (out of 22283)
probe-sets
measured in 102 patients.
Step 3: Data description and exploration
Descriptive exploratory analysis was performed to identify potential bias and
major
sources of variability. A set of covariates with a potential impact on gene
expression profiles
was screened. It comprised both technical and clinical variables. Technical
covariates
included: date of RNA processing (later referred to as batch), RIN (Schroeder,
A., et al., The
RIN: an RNA integrity number for assigning integrity values to RNA
measurements. BMC
Mol Biol, 2006. 7: p. 3) (as a measure of RNA quality/integrity), Operator and
Center of
sample collection. Clinical covariates included: Histology type, smoking
status, tumour
grade, performance score (Oken, M.M., et al., Toxicity and response criteria
of the Eastern
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Cooperative Oncology Group. Am J Clin Oncol, 1982. 5(6): p. 649-55),
demographic data,
responder status and clinical benefit status.
The analysis tools included univariate ANOVA and principal component analysis.
For
each of these covariates, univariate ANOVA was applied independently to each
probe-set.
A significant effect of the batch variable was identified. In practice, the
batch variable
captured differences between dates of sample processing and Affymetrix chip
lot. After
checking that the batch variable was nearly independent from the variables of
interest, the
batch effect was corrected using the method described in Johnson, W.E., C. Li,
and A.
Rabinovic, Adjusting batch effects in microarray expression data using
empirical Bayes
methods. Biostat, 2007. 8(1): p. 118-127.
The normalized data set after batch effect correction served as the analysis
data set in
subsequent analyses.
Histology and RIN were two additional important variables highlighted by the
descriptive analysis.
Step 4: Data modeling and testing.
A linear model was fitted independently to each probe-set. Variables included
in the
model are reported in table 2. The model parameters were estimated by the
maximum
likelihood technique. The parameter corresponding to the "Clinical Benefit"
variable (X1)
was used to assess the difference in expression level between the group of
patients with
clinical benefit and the group with no clinical benefit.
Table 2: Description of the variables included in the linear model.
Variable Type Value
gene Dependent (Y;P) 1og2 intensity of probe-set i in
expression patient p.
Intercept Overall mean ( )
Clinical Predictor of interest (Xl) YES / NO
Benefit
Histology Adjustment Covariate (X2) ADENO. / SQUAM. / OTHERS
RACE Adj. Cov. (X3) ORIENT. / CAUCAS.
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SEX Adj. Cov. (X4) FEMALE / MALE
RIN Adj. Cov. (X5) [2,...,7.9]
SMOKER Adj. Cov. (X6) CURRENT/PAST/NEVER
Stage Adj. Cov. (X7) UNRESECT.III / IV
For each probe-set i, the aim of the statistical test was to reject the
hypothesis that the
mean expression levels in patients with clinical benefit and patients without
clinical benefit
are equal, taking into account the other adjustment covariates listed in table
2. Formally, the
null hypothesis of equality was tested against a two sided altemative.The
corresponding p-
values are reported in table 3.
The choice of linear model was motivated by two reasons. Firstly, linear
modeling is a
versatile, well-characterized and robust approach that allows for adjustment
of confounding
variables when estimating the effect of the variable of interest. Secondly,
given the sample
size of 102, and the normalization and scaling of the data set, the normal
distribution
assumption was reasonable and justified.
For each probe-set, the assumption of homogeneity of variance was evaluated
using
Fligner-Killeen tests based on the model residuals. The analysis consisted of
3 steps:
1. Test each categorical variables for homogeneity of residual variance
2. Note the variable V with the least p-value
3. If the least p-value is less than 0.001, re-fit the model allowing the
different
level of variables V to have a different variance.
Step 5 : Robustness
The goal of the robustness analysis was to reduce the risk that the results of
the analysis
might be artifactual and a result of the pre-processing steps or assumptions
underlying the
statistical analysis. The following three aspects were considered: a)
inclusion or exclusion of
a few extra chips at the quality control step; b) pre-processing and
normalization algorithm;
c) statistical assumptions and testing approach.
The list of candidate markers was defined as the subset of genes consistently
declared
as significant with different analysis settings. The different applied
analysis options were the
following:
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a) An additional subset of 8 chips was identified based on more stringent
quality
control criteria. A "reduced data set" was defmed by excluding these 8 chips.
b) MAS5 was identified as an alternative to rma for pre-processing and
normalization. MAS5 uses different method for background estimation, probe
summarization
and normalization.
c) Two additional statistical tests were employed. These two additional tests
rely
on a different set of underlying statistical assumptions.
a. A wilcoxon test for the difference between clinical and no clinical benefit
and
b. a likelihood ratio test (LRT) testing for the logistic regression model
where
Clinical benefit was taken as the response variable and gene expression as
covariates. For
each probe-set, the LRT was following a Chi-square with 1 degree of freedom.
In summary, two sets of samples (the "full" data-set and the "reduced" data-
set) , and 2
pre-processing algorithm (mas5 and rma) were considered; this resulted in four
different
analysis data sets. To each of these four data sets, three different
statistical tests were applied.
Therefore, for each probe-set, three p-values were calculated. In each
analysis data set, a
composite criterion was applied to identify the list of differentially
regulated genes. This
criterion was defined as: the maximum p-value is less than 0.05 and the
geometric mean of p-
values is less than 0.01.
The robustness analysis using criterion 2 for identifying markers resulted in
a list of 36
probe-sets, corresponding to 36 different genes. These markers are reported in
table 3.
Table 3: Gene markers of Clinical Benefit based on the robustness analysis
after
application of the composite Criterion.
Colunm 1 is the Affymetrix identifier of the probe-set. Column 2 is the
GenBank
accession number of the corresponding gene sequence. Column 3 is the
corresponding
official gene name. Column 4 is the corresponding adjusted mean fold change in
expression
level between clinical and no clinical benefit patient, as estimated with the
linear model.
Column 5 is the p-value for the test of difference in expression level between
clinical benefit
and no clinical benefit patients as derived from the linear model.
Affymetrix GenBank Gene Adjusted P-value
Probe Set ID Mean Fold
Change
200096 s at NM 003945 ATP6VOE1 -1.41 8.6E-3
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200712 s at NM 012325 MAPREI -1.16 2.4E-2
201274 at NM 002790 PSMA5 -1.33 3.2E-3
201286_at NM_001006946 SDC 1 1.84 8.4E-4
NM_002997
201661_s_at NM004457 ACSL3 -1.50 7.6E-3
NM 203372
202362_at NM_001010935 RAP1A -1.28 8.8E-3
NM 002884
202499 s at NM 006931 SLC2A3 -1.71 1.6E-2
202536 at NM 014043 CHMP2B -1.54 5.7E-3
203224 at NM 018339 RFK -1.52 2.5E-3
203225 s at NM 018339 RFK -1.27 7.9E-3
204039 at NM 004364 CEBPA 1.15 2.4E-3
204542 at NM 006456 ST6GALNAC2 1.58 2.3E-3
209101 at NM 001901 CTGF -1.25 7.9E-3
210338sat NM006597 HSPA8 -1.37 6.8E-3
NM 153201
210517_s_at NM_005100 AKAP12 -1.44 1.5E-2
NM 144497
210647xat NM001004426 PLA2G6 1.10 4.4E-3
NM 003560
210707 x at BC015750 PMS2L11 1.27 3.5E-3
213390_at XM028253 C 19orf7 1.20 1.8E-3
XM 942694
213998_s_at NM_006386 DDX17 1.45 9.2E-3
NM 030881
214016_s_at NM005066 SFPQ 1.43 7.2E-3
214473xat NM001003686 PMS2L3 1.25 4.3E-3
NM 005395
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215169 at NM 182838 SLC35E2 1.34 9.2E-3
215412 x at XM 001134437 PMS2L2 1.28 4.9E-3
215446 s at NM 002317 LOX -1.43 1.6E-2
216173_at AK025360 URG4 1.10 1.3E-3
NM 017920
216347 s at NM 015316 PPP1R13B 1.24 1.2E-3
216959xat NM001037132 NRCAM 1.11 1.0E-3
NM_001037133
NM 005010
217851 s at NM 016045 SLMO2 -1.25 3.5E-2
219044 at NM 018271 FLJ 10916 1.11 6.9E-3
219871_at NM024614 FLJ13197 1.27 8.9E-5
XM_001125952
XM 001132609
220756 s at NM 017986 GPR172B 1.14 7.OE-3
221620_s_at NM001004067 NOMO3 -1.13 4.8E-3
NM 024122 APOO
221625 at NM 021030 ZNF506 1.07 2.8E-3
37117_at NM001017526 ARHGAP8 1.32 4.5E-3
NM 181335
41660 at NM 014246 CELSR 1 1.46 1.1 E-3
52169_at NM001003786 LYK5 1.16 9.7E-4
NM_001003787
NM_001003788
NM 153335
Discussion
By analyzing tissue samples with high-density oligonucleotide microarray
technology,
and applying statistical modeling to the data, we have been able to identify
genes whose
expression levels may be predictive of patients deriving a clinical benefit
from treatment with
erlotinib.
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A composite significance criterion (defmed here above), was applied and
resulted in a
list (see table 3) of 36 probe sets representing 35 known genes. The function
of these genes is
complex and not always well characterized or fully understood.
The functional annotations of genes in table 3 was analyzed using the
Ingenuity
software (Ingenuity Systems, www.ingenuity.com). This software provides an
interface to a
proprietary knowledge base of gene annotation compiled and regularly updated
based on
scientific literature.
This global analysis shows that table 3 contains genes that are useful for
discriminating
different tumour categories, in particular with regard to response to the EGFR
inhibitor
Erlotinib.
Table 4: List of the marker genes of the present invention
Column 1 is the GenBank accession number of the human gene sequence; Column 2
is
the corresponding official gene name and Column 3 is the Sequence
Identification number of
the human nucleotide sequence as used in the present application. For certain
genes table 4
contains more than one sequence identification number since several variants
of the gene are
registered in the GeneBank.
GenBank Gene Sequence
Accession identification number
number Seq. Id. No.
NM_003945 ATP6VOE 1 = ATPase, H+ Seq. Id. No. 1
transporting, lysosomal 9kDa, VO
subunit e 1
NM_012325 MAPRE 1= microtubule-associated Seq. Id. No. 2
protein, RP/EB family, member 1
NM_002790 PSMA5 = proteasome (prosome, Seq. Id. No. 3
macropain) subunit, alpha type, 5
NM_001006946 SDC 1= syndecan 1 Seq. Id. No. 4
NM_002997 Seq. Id. No. 5
NM004457 ACSL3 = acyl-CoA synthetase Seq. Id. No. 6
NM_203372 long-chain family member 3 Seq. Id. No. 7
NM_001010935 RAP1A = member of RAS Seq. Id. No. 8
NM_002884 oncogene family Seq. Id. No. 9
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NM_006931 SLC2A3 = solute carrier family 2 Seq. Id. No. 10
(facilitated glucose transporter),
member 3
NM_014043 CHMP2B = chromatin modifying Seq. Id. No. 11
protein 2B
NM_018339 RFK = riboflavin kinase Seq. Id. No. 12
NM_004364 CEBPA = CCAAT/enhancer Seq. Id. No. 13
binding protein (C/EBP), alpha
NM_006456 ST6GALNAC2 = ST6 (alpha-N- Seq. Id. No. 14
acetyl-neuraminyl-2,3-beta-
galactosyl-1,3)-N-
acetylgalactosaminide alpha-2,6-
sialyltransferase 2
NM_001901 CTGF = connective tissue growth Seq. Id. No. 15
factor
NM_006597 HSPA8 = heat shock 70kDa protein Seq. Id. No. 16
NM_153201 8 Seq. Id. No. 17
NM_005100 AKAP 12 = A kinase (PRKA) Seq. Id. No. 18
NM_144497 anchor protein (gravin) 12 Seq. Id. No. 19
NM_001004426 PLA2G6 = phospholipase A2, Seq. Id. No. 20
NM_003560 group VI (cytosolic, calcium- Seq. Id. No. 21
independent)
BC015750 PMS2L11 Seq. Id. No. 22
XM_028253 C19orf7 = chromosome 19 open Seq. Id. No. 23
XM_942694 reading frame 7 Seq. Id. No. 24
NM_006386 DDX17 = DEAD (Asp-Glu-Ala- Seq. Id. No. 25
NM 030881 Asp) box polypeptide 17 Seq. Id. No. 26
NM_005066 SFPQ = splicing factor Seq. Id. No. 27
proline/glutamine-rich
(polypyrimidine tract binding
protein associated)
NM_001003686 PMS2L3 = postmeiotic segregation Seq. Id. No. 28
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NM 005395 increased 2-like 3 Seq. Id. No. 29
NM_182838 SLC35E2 = solute carrier family Seq. Id. No. 30
35, member E2
XM_001134437 PMS2L2 = postmeiotic segregation Seq. Id. No. 31
increased 2-like 2
NM_002317 LOX = lysyl oxidase Seq. Id. No. 32
AK025360 URG4 = up-regulated gene 4 Seq. Id. No. 33
NM 017920 Seq. Id. No. 34
NM_015316 PPP1R13B = protein phosphatase Seq. Id. No. 35
1, regulatory (inhibitor) subunit 13B
NM_001037132 NRCAM = neuronal cell adhesion Seq. Id. No. 36
NM_001037133 molecule Seq. Id. No. 37
NM_005010 Seq. Id. No. 38
NM_016045 SLMO2 = slowmo homolog 2 Seq. Id. No. 39
N1V1 018271 FLJ 10916 = threonine synthase-like Seq. Id. No. 40
2
NM_024614 FLJ13197 Seq. Id. No. 41
XM 001125952 Seq. Id. No. 42
XM_001132609 Seq. Id. No. 43
NM_017986 GPR172B = G protein-coupled Seq. Id. No. 44
receptor 172B
NM_001004067 NOMO3 = NODAL modulator 3 Seq. Id. No. 45
NM_024122 APOO = apolipoprotein 0 Seq. Id. No. 46
NM_021030 ZNF506 = zinc finger protein 14 Seq. Id. No. 47
NM_001017526 ARHGAP8 = Rho GTPase Seq. Id. No. 48
NM_181335 activating protein 8 Seq. Id. No. 49
NM_014246 CELSRI = cadherin, EGF LAG Seq. Id. No. 50
seven-pass G-type receptor 1
NM_001003786 LYK5 = protein kinase LYK5 Seq. Id. No. 51
NM_001003787 Seq. Id. No. 52
NM_001003788 Seq. Id. No. 53
NM_153335 Seq. Id. No. 54
