JP2010535517A - Predictive marker for EGFR inhibitor treatment - Google Patents

Abstract

  The present invention provides biomarkers that predict the clinical benefit of EGFR inhibitors in cancer patients.

Description

  The present invention provides biomarkers for predicting the clinical benefit of EGFR inhibitor treatment in cancer patients.

  Many human malignancies are associated with abnormal or overexpression of epidermal growth factor receptor (EGFR). EGF, transforming growth factor-α (TGF-α), and many other ligands bind to EGFR that stimulates autophosphorylation of the receptor's intracellular tyrosine kinase domain. Various intracellular pathways are then activated, and these downstream events result in tumor cell growth in vitro. It is believed that stimulation of tumor cells via EGFR is important for both tumor growth and tumor survival in vivo.

  Early clinical data for Tarceva ™ (erlotinib), an inhibitor of EGFR tyrosine kinase, shows that the compound has a target effective concentration (determined by preclinical data) (determined by preclinical data) Suggests that it is safe and generally well tolerated at doses that provide Phase I and II clinical trials in patients with advanced disease have demonstrated the potential for Tarceva ™ to have clinical activity in various epithelial tumors. In fact, Tarceva ™ is durable in a similar order to established second-line chemotherapy in previously treated patients with head and neck cancer and NSCLC (Non-Small Cell Lung Cancer). Although it can induce some partial remissions, it has been shown to have the added benefit of a better safety profile and convenience (tablets instead of intravenous [iv] administration) than chemotherapy. From a recently completed randomized, double-blind, placebo-controlled trial (BR.21), the survival of patients with NSCLC who failed standard therapy because of advanced disease was significantly prolonged and improved by Tarceva ™ alone It is shown.

  Tarceva ™ (erlotinib) is a small chemical molecule that is orally active and is a potent selective inhibitor of EGFR tyrosine kinase (EGFR-TKI).

  Lung cancer is a leading cause of cancer-related death in North America and Europe. In the United States, deaths from lung cancer exceed the combined total deaths from the combined causes of cancer death caused by the second (colon), third (breast), and fourth (prostate). About 75% to 80% of all lung cancers are NSCLC, and approximately 40% of patients present with locally advanced and / or unresectable disease symptoms. This group typically includes persons with bulky stage IIIA and IIIB without malignant pleural infiltration.

  The approximate prevalence of lung cancer in Europe is 52.5 per 100,000 people per year and the mortality rate is 48.7. The rates are 79.3 and 78.3 for men and 21.6 and 20.5 for women, respectively. NSCLC accounts for 80% of all lung cancer patients. Lung cancer mortality is about 90% for men and 80% for women, which is due to smoking.

  According to the American Cancer Society, there were approximately 173,800 new lung cancer patients in the United States in 2004 (93,100 for men and 807,000 for women), accounting for about 13% of all new cancers. Most patients died as a result of the disease within 2 years of diagnosis. For many NSCLC patients, a successful treatment remains unclear. Advanced tumors are often unsuitable for surgery and may become resistant to tolerable doses of radiation therapy and chemotherapy. In randomized trials, the most active combination chemotherapy currently has a response rate of approximately 30% to 40% and a one-year survival rate of 35% to 40%. This is a tremendous advance compared to the 10% 1-year survival rate observed with coping therapy alone (Shepherd 1999).

  To date, therapy options for recurrent patients who later relapse have been limited to best coping therapy or remission. Recent trials comparing docetaxel (Taxotere) with best treatment therapy have shown that patients with NSCLC can benefit from second-line chemotherapy after a cisplatin-based primary treatment plan failure. Patients of all ages with an ECOG performance status of 0, 1, or 2 demonstrated improved survival with docetaxel, as did those who had previously failed prior platinum-based treatment. Patients who did not benefit from therapy had 10% weight loss, high levels of lactate dehydrogenase, multi-organ involvement, or liver involvement. In addition, the benefits of docetaxel monotherapy did not extend beyond the secondary setting. Patients receiving docetaxel in tertiary or higher therapy did not show prolonged survival. Single agent docetaxel has become the standard second line therapy for NSCLC. Recently, another randomized Phase III trial in second line therapy for NSCLC compared pemetrexed (Alimta®) with docetaxel. The results of treatment with pemetrexed were clinically equivalent, but had significantly fewer side effects compared to docetaxel.

  The need for the development of individual cancer treatments has long been recognized. With regard to the development of targeted cancer therapies, methodologies that can provide molecular profiles of tumor targets (ie those that are predictive for clinical benefit) have received particular attention. The proof of principle of gene expression profiling in cancer has already been established in the molecular classification of tumor types that are unknown based on current morphological and immunohistochemical studies. The two distinct diseases are distinguished by a prognosis that differs from the current only classification of extensive giant B-cell lymphoma using gene expression profiling.

  Accordingly, it is an object of the present invention to provide an expression biomarker that predicts the clinical benefit of EGFR inhibitors in cancer patients.

  In a first object, the present invention provides an in vitro method for predicting the clinical benefit of cancer patients in response to treatment with an EGFR inhibitor. The method includes determining an expression level of at least one gene selected from Table 3 in a patient tumor sample, and determining the expression level of the at least one gene from a tumor of a group of patients not receiving clinical benefit from treatment Comparing the expression level of said at least one gene in said patient with a representative value, wherein said differential expression level of said at least one gene in said patient's tumor sample receives clinical benefit from said treatment It becomes an indicator for patients who become.

FIG. 1 shows the test plan. FIG. 2 shows a sample process scheme.

  The term “representative value of the expression level of at least one gene in a tumor of a patient group that does not receive clinical benefit from treatment” refers to the average expression level of a marker gene in a tumor of a patient group that does not receive clinical benefit from the treatment The estimated value. Clinical benefit was defined as having an objective response or ≧ 12 weeks of disease stability.

According to a further preferred embodiment, the expression levels of at least two genes are determined.
According to another preferred embodiment, the expression levels of at least three genes are determined.

  According to a further preferred embodiment, the gene is selected from the group consisting of ATP6V0E1, MAPRE1, PSMA5, ACSL3, RAP1A, SLC2A3, CHMP2B, RFK, CTGF, HSPA8, AKAP12, LOX, SLMO2, NOMO3, APOO, and the gene Shows lower expression levels in patient tumor samples compared to a representative value of the expression level of at least one gene in tumors of a group of patients not receiving clinical benefit from EGFR inhibitor treatment.

  According to a further preferred embodiment, the genes are SDC1, CEBPA, ST6GALNAC2, PLA2G6, PMS2L11, C19orf7, DDX17, SFPQ, PMS2L3, SLC35E2, PMSL2, URG4, PPP1R13B, NRCAM, FLJ1316G, PRJ13G, PR17 Compared to a representative value of the expression level of at least one gene in a tumor of a group of patients selected from the group consisting of LYK5 and the gene in the patient's tumor sample does not receive clinical benefit from EGFR inhibitor treatment, Higher expression levels are shown.

  According to a preferred embodiment, the expression level of at least one gene is determined by microarray technology. The construction and use of gene chips is well known in the art, see U.S. Patent Nos. 5,202,231, 5,445,934, 5,525,464, 5,695,940, 5,744,305, 5,795,716 and 15,800,992. I want. See also Johnston, M. Curr. Biol. 8: R171-174 (1998); Iyer VR et al., Science 283: 83-87 (1999). Of course, this gene expression level can be determined by other methods known in the art such as, for example, Northern blot, RT-PCR, real-time quantitative PCR, primer extension, RNase protection, RNA expression profiling.

  The gene of the present invention can be combined with a biomarker set. The biomarker set can be constructed from any combination of the biomarkers listed in Table 3, allowing predictions about the effectiveness of EGFR inhibitors in cancer patients. Various biomarkers and biomarker sets described herein can be used, for example, to predict how patients suffering from cancer will respond to therapeutic intervention with EGFR inhibitors.

  The term “gene” as used herein comprises variants of that gene. The term “variant” refers to a nucleic acid sequence that is substantially homologous to the nucleic acid sequence given the GenBank accession number. The term “substantially homologous” is well understood by those skilled in the art. In particular, genetic variants may be alleles that exhibit nucleotide exchange compared to the nucleic acid sequence of the most common allele in the human group. Preferably, such substantially homologous nucleic acid sequences have at least 80%, preferably at least 85%, more preferably at least 90%, most preferably at least 95% sequence homology with the most common alleles. .

  The EGFR inhibitor can be selected from the group consisting of gefitinib, erlotinib, PKI-166, EKB-569, GW2016, CI-1033 and anti-erbB antibodies such as trastuzumab and cetuximab.

  According to another embodiment, the EGFR inhibitor is erlotinib.

  According to yet another embodiment, the cancer is NSCLC.

  Techniques for detection of gene expression of genes described by the present invention include, but are not limited to, Northern blot, RT-PCR, real-time quantitative PCR, primer extension, RNase protection, RNA expression profiling and related techniques. is there. Such techniques are well known in the art, see, for example, Sambrook J et al., Molecular Cloning: A Laboratory Manual, Third Edition (Cold Spring Harbor Press, Cold Spring Harbor, 2000).

  Techniques for detecting protein expression of each gene described by the present invention include, but are not limited to, immunohistochemistry (IHC).

  According to the present invention, cells from patient tissue samples, such as tumor or cancer biopsies, can be evaluated to determine the expression pattern of one or more biomarkers. As the success or failure of cancer treatment is similar or different compared to the expression pattern of a control set of one or more biomarkers, a cell from a test tissue (test cell), eg, a biopsy of a tumor or cancer It can be determined based on the biomarker expression pattern. In the present invention, the genes listed in Table 3 are differentially expressed in the tumors of patients who benefited from EGFR inhibitor treatment compared to patients who did not benefit clinically from EGFR inhibitor treatment, It was found to show higher or lower expression levels. That is, if this test cell exhibits a biomarker expression profile corresponding to that of a patient who responded to cancer treatment, the individual's cancer or tumor is likely to respond smoothly to treatment with an EGFR inhibitor. Or is expected to be. Conversely, if the test cell exhibits a biomarker expression pattern that corresponds to that of a patient who did not respond to cancer treatment, the individual's cancer or tumor may not respond to treatment with an EGFR inhibitor. High or expected to be so.

  The biomarkers of the present invention, ie the genes listed in Table 3, are the first step towards individualized therapy for patients suffering from cancer, especially those suffering from refractory NSCLC. This individual therapy will allow the treating physician to select the most appropriate agent from among the drugs that exist for cancer therapy, particularly in NSCLC. The benefit of individual therapy for each patient in the future will increase the response speed / number of patients benefiting and reduce the risk of side effects from ineffective treatment.

  According to a further object, the present invention provides a therapeutic method for treating a patient identified by the in vitro method of the present invention. The method of treatment comprises administering an EGFR inhibitor to a patient whose treatment has been selected based on an expression pattern prediction of at least one gene listed in Table 3. A preferred EGFR inhibitor is erlotinib and a preferred cancer to be treated is NSCLC.

Principles of experimental studies and study design Recently, mutations in the EGFR gene in tumor tissue of a subset of NSCLC patients and the susceptibility to erlotinib and gefitinib and their associations have been reported (Pao W, et al. 2004; Lynch et al. al. 2004; Paez et al. 2004). For patients combined from the two trials, mutant EGFR was observed in 13 out of 14 responders to gefitinib and none in 11 gefitinib treated patients who did not respond. The prevalence of the mutation reported was 8% (2/25) in unselected NSCLC patients. The mutation was found more frequently in adenocarcinoma (21%), female tumors (20%), and Japanese patients (26%). The mutation increases the in vitro activity of EGFR and increases sensitivity to gefitinib. The association of this mutation with stable disease duration or prolonged survival has not been previously assessed.

  Based on the diagnostic analysis in the BR.21 study, significant survival benefit is maintained even if patients with objective responses are excluded from the analysis (in-house data), so the observed survival benefit is only due to EGFR mutations. It seems unlikely. Other molecular mechanisms will also contribute to the effect.

  Based on the assumption that there are changes in gene expression levels that are predictive of response / benefit to Tarceva ™ treatment, microarray analysis was used to detect these changes.

  To this end, a well-defined target population treated with Tarceva ™ monotherapy after failure of primary therapy was needed. Based on experience in the BR.21 study, the benefit group was defined as having an objective response or being ≥12 weeks disease stable. Clinical and microarray data sets were analyzed according to a predefined statistical plan.

  Application of this technology requires fresh frozen tissue (FFT). Therefore, an essential biopsy had to be performed before the start of treatment. The collected material was frozen in liquid nitrogen (N2).

  A second tumor sample was simultaneously collected and stored in paraffin (formalin fixed paraffin embedded, FFPE), which was analyzed for changes in the EGFR signaling pathway.

  For this test, it must be possible to perform a biopsy of the tumor using bronchoscopy. Bronchoscopy is a standard method for confirming the diagnosis of lung cancer. Although generally safe, there is still a risk of complications, ie bleeding.

  This trial was the first step in individual therapy for patients with refractory NSCLC. This individual therapy will allow the treating physician to select the most appropriate agent from among the drugs that exist for this indication.

  When individual therapy becomes available, the benefit for each future patient outweighs the risk that the patient must take in the current trial, increases the response speed / number of patients benefiting, and is due to ineffective treatment Will reduce the risk of side effects.

Principle of Dosage Selection Tarceva ™ was administered orally once daily at a dose of 150 mg until disease progression, intolerable toxicity, or death. The choice of this dose is based on pharmacokinetic parameters, and the safety and tolerance profile of this dose is based on Phase I, II and III trials in patients with advanced cancer who have been previously treated severely Observed in. Drug levels found in the plasma of cancer patients administered at 150 mg / day consistently exceeded the mean plasma concentration of 500 ng / ml, which is the goal of clinical efficacy. BR.21 showed a survival benefit at this dose.

Study Objectives The primary objective was the confirmation of differential gene expression, predicting the clinical benefit (CR, PR or SD ≧ 12 weeks) of Tarceva ™ treatment. Confirmation of differential expression of genes that predict “response” (CR, PR) to Tarceva ™ treatment was another important objective.
The secondary objective was to assess changes in the EGFR signaling pathway for benefits from treatment.

Study Design Study Design and Dose Design Overview This was an open-label, predictive marker specific phase II study. Tests were conducted at approximately 26 locations in approximately 12 countries. After failing at least one prior chemotherapy regimen, 264 patients with advanced NSCLC were enrolled for 12 months. An oral continuous dose of Tarceva ™ was administered at a dose of 150 mg / day. Clinical and experimental parameters were judged as disease control and toxicity assessments. Treatment continued until disease progression, unacceptable toxicity or death. This test plan is shown in FIG.

  Tumor tissue and blood samples were obtained for molecular analysis to assess the efficacy of Tarceva ™ and to identify subgroups of patients that would benefit from therapy.

Predictive marker evaluation Tumor biopsies were taken within 2 weeks of the start of treatment. Two different samples were collected.
The first sample was always frozen quickly in liquid N2.
The second sample was fixed in formalin and embedded in paraffin.
In this study, snap frozen tissues have the highest priority.
FIG. 2 shows a sample processing scheme.

Microarray analysis Rapid frozen samples were used for laser capture microdigestion (LCM) of tumor samples to extract tumor RNA and RNA from surrounding tumor tissue. RNA was analyzed with Affymetrix microarray chips (HG-U133A) to establish an expression profile of the patient's tumor gene. Affymetrix chips quality control was used to select samples of sufficient quality for statistical comparison.

Single biomarker analysis in formalin fixed paraffin embedded tissue A second tumor biopsy, FFPE sample, was used to perform DNA mutation, IHC and ISH analysis as described below. A similar analysis was performed on the tissues collected at the first diagnosis.
The DNA mutation status of the gene encoding EGFR and other molecules involved in the EGFR signaling pathway were analyzed by DNA sequence analysis. Gene amplification of EGFR and related genes was examined by FISH.
Protein expression analysis includes immunohistochemistry (IHC) analysis of EGFR and other proteins within the EGFR signaling pathway.

Response Evaluation The RECIST (Uni-dimensional Tumor Measurement (Uni-dimensional)) criterion was used to evaluate the response. The standards can be found at the following link (http://www.eortc.be/recist/).
In order to achieve a CR or PR status, the measurement of changes in the tumor should be confirmed by repeating the assessment at any time point at least 4 weeks apart during the treatment period.
For SD, follow-up measurements should meet SD criteria at least once after the start of the study at intervals of at least 6 weeks.
For maintenance SD, follow-up measurements should meet SD criteria at least once after the start of the study for a maintenance period of at least 12 weeks.

Survival assessment Normal status was confirmed every 3 months by patient visit or telephone. All deaths were recorded. At the end of the study, patients were asked for final confirmation of survival.

Methods RNA sample preparation and RNA sample quality control All biopsy sample processing was processed at the Pathology Reference Laboratory. Fresh frozen tissue samples were transported from the research department to the Clinical Sample Operations facility at Roche Basel and from there to the pathology laboratory for further processing. Laser capture microdissection was used to select tumor cells from the surrounding tissue.

After LCM, RNA was purified from enriched tumor material. Subsequently, the pathology laboratory performed a number of steps to create estimates of RNA concentration and quality.
Since RNase is an RNase and is present everywhere, when RNA is used, it should be strictly controlled to minimize RNA degradation. Many mRNA species themselves are considered to be very unstable due to their rather short half-life. It is therefore important to perform RNA integrity checks and quantification prior to any assay.

  RNA concentration and quality profile can be assessed using a device called 2100 Bioanalyzer® from Agilent (Agilent Technologies, Inc., Palo Alto, Calif.). This instrument software is based on RNA complete numbers (RIN), quantitative estimates (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 calculate the ribosome ratio of the total RNA sample. RIN is determined from all electrophoretic bands of the RNA sample, including the presence or absence of degradation products.

  RNA quality was analyzed by 2100 Bioanalyzer®. Only samples with added poly-I noise and at least one rRNA peak above sufficient RNA were selected for further analysis on the Affymetrix platform. The purified RNA was sent to the Roche Center for Medical Genomics (RCMG; Basel, Switzerland) for analysis by microarray. The 122 RNA samples received from this pathology laboratory were further processed.

Target labeling of tissue RNA samples
Using Affymetrix (Affymetrix, Santa Clara, California), a two-cycle target labeling amplification protocol (Two-Cycle Target Labeling Amplification Protocol), target labeling was performed.
The method is based on the standard Eberwine linear amplification method, but uses two cycles of this method to generate sufficient labeled cRNA for hybridization in the microarray.
The total RNA input used in the labeling reaction is 10 ng for samples where more than 10 ng of RNA is available, and the available amount is less than this amount or (because the RNA concentration is very low) If no quantitative data was available, half of the total sample was used for the reaction. The product obtained from the labeling reaction was in the range of 20-180 μg cRNA. For all samples, the process was standardized at the level of hybridization when 15 μg of cRNA was used.

  Human Reference RNA (Stratagene, Carlsbad, CA, USA) was used as a control sample in each sample batch workflow. This 10 ng of RNA was used as an input at the same time as the test sample to confirm that the labeling and hybridization reagents were functioning as expected.

Microarray hybridization
The Affymetrix HG-U133A microarray includes over 22,000 probe sets that target variants representing approximately 18,400 transcripts and approximately 14,500 well-characterized genes.
Hybridization of all samples was performed according to Affymetrix instructions (Affymetrix Inc., Expression Analysis Technical Manual, 2004). Briefly, for each sample, 15 μg of biotin-labeled cRNA was fragmented in the presence of a divalent cation, heated and hybridized with the Affymetrix HG-U133A full-length genomic oligonucleotide array. At a later date, the arrays were stained with streptavidin-phycoerythrin (Molecular Probes; Eugene, OR) according to product instructions. Thereafter, the array was scanned using a GeneChip scanner 3000 (Affymetrix), and the signal intensity was automatically calculated using GeneChip operation software (GCOS) version 1.4 (Affymetrix).

Statistical analysis
Analysis of Affymetrix ™ data consisted of five main steps.
Step 1 was quality control. The objective was to identify and eliminate array data analysis of substandard quality profiles.
Step 2 was pretreatment and standardization. The purpose was to create a standardized and scaled “analysis data set” suitable for comparison between chips. This includes background noise estimation and removal, probe summarization and scaling.
Step 3 was investigation and description. The objective was to identify potential biases and causes of variability. This consisted of applying multivariate and univariate descriptive analysis techniques to identify influential covariates.
Step 4 was modeling and inspection. The goal was to identify a list of candidate markers based on a statistical assessment of the difference in mean expression levels between “clinically beneficial” and “non-clinically beneficial” patients. This consisted of fitting a statistically sufficient model to each probe set and deriving statistical significance measurements.
Step 5 was a robustness analysis. The goal was to create a quantified list of candidate markers that was largely independent of pretreatment methods and statistical assumptions. This was to iterate the analysis with different methodological approaches and cross the candidate list.
All analyzes were performed using the R software package.

Step 1: Quality control Data quality assessment was based on multiple parameter checks. The parameters included standard Affymetrix GeneChip ™ quality parameters, in particular, scaling factor, percentage of calls present and average background. This step includes visual inspection of the virtual chip image to detect localized hybridization problems and the average virtual chip and each chip to detect any abnormal dissociation from the average behavior. A comparison of was also included. Correlation analysis between chips was also performed to detect outlier samples. In addition, ancillary measurements of RNA quality obtained from the analysis of RNA samples by Agilent Bioanalyzer ™ 2100 were taken into account.
Based on these parameters, data from 20 arrays were excluded from the analysis. That is, data obtained from a total of 102 arrays corresponding to 102 patients were analyzed. The clinical description of the 102 sample set is reported in Table 1.

  Table 1: Description of patient clinical features included in the analysis

Step 2: Data preprocessing and standardization For preprocessing and standardization, the rma algorithm (Irizarry, RA, et al., Summaries of Affymetrix GeneChip probe level data. Nucl. Acids Res., 2003. 31 (4): p e15) was used. The mas5 algorithm (AFFYMETRIX, GeneChip® Expression: Data Analysis Fundamentals. 2004, AFFYMETRIX) was used to create detection calls for this individual probe set. In all samples, 5930 probe sets were excluded by this criterion because probe sets called “absent” or “marginal” were excluded from further analysis. Therefore, the analysis data set consisted of a matrix with 16353 (out of 22283) in 102 patients.

Step 3: Data description and survey A descriptive survey analysis was conducted to identify potential bias and major causes of variability. A set of covariates with potential impact on gene expression profiles was screened. This included both technical and clinical variability. Technical covariates include RNA treatment (described later as a 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) There was an operator and center for sample collection (as a measure of RNA quality / integrity). Clinical covariates include histology, smoking status, tumor grade, behavioral score (Oken, MM, et al., Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol, 1982. 5 (6 ): p. 649-55), population data, response status and clinical benefit status.
This analysis tool includes univariate ANOVA and principal component analysis. For each of the covariates, univariate ANOVA was applied independently to each probe set.

  A significant effect of batch variables was identified. In practice, the batch variable captured the difference between the sample processing data and the lot of Affymetrix chips. After confirming that the batch variable is almost independent of the variable of interest, Johnson, WE, C. Li, and A. Rabinovic, Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostat, 2007. 8 (1) : The batch effect was corrected using the method described on p. 118-127.

The standardized data set after the batch effect correction was used as an analysis data set in the subsequent analysis.
Tissue structure and RIN were two additional important variables highlighted by descriptive analysis.

Step 4: Data Modeling and Inspection For each probe set, a linear model was fitted individually. The variables included in the model are reported in Table 2. Model parameters were estimated by maximum likelihood technique. A parameter corresponding to the “clinical benefit” variable (X1) was used to examine the difference in expression levels between patient groups with clinical benefit and patients without clinical benefit.

  Table 2: Description of variables included in the linear model

For each probe set i, the purpose of the statistical test is to assume that the mean expression levels in patients with clinical benefit and those without clinical benefit are equal, considering the other adjustment covariates listed in Table 2 Was to deny. In accordance with the format, the null hypothesis of equality was tested for the alternative hypothesis (alternative) performed by the two-sided test. The corresponding p-value is reported in Table 3.
The reason for choosing the linear model was motivated by the following two. First, linear modeling is a versatile, well-characterized and robust approach that allows for the adjustment of confounding variables when estimating the effects of variables of interest. Second, for a sample size of 102, and for data set normalization and scaling, the assumption of normal distribution is reasonable and valid. For each probe set, the equivariance hypothesis was evaluated using the Fligner Killeen test based on the residual model. This analysis consisted of three steps.
1. Test each categorical variable for equal variance of the residual variable.
2. Note the variable V with the smallest p-value.
3. If this minimum p-value is less than 0.001, redistribution to the model gives different levels of variable V different variances.

Step 5: Robustness The purpose of the robustness analysis was to reduce the risk that the results of the analysis would be man-made and that the preprocessing steps or hypotheses would be the basis for statistical analysis. The following three aspects were considered. a) inclusion and exclusion of a few additional chips in the quality control step, b) pre-processing and standardization algorithms, c) statistical hypotheses and inspection approaches.

The list of candidate markers was defined as a subset of genes that were consistently identified as significant in different analysis settings. The different application analysis options are:
a) An additional subset of 8 chips was identified based on stricter quality control standards. A “reduced data set” was defined excluding the eight chips.
b) MAS5 has been identified as an alternative to rma for pre-processing and standardization. MAS5 uses different methods for background estimation, probe summarization and standardization.
c) Two additional statistical tests were used. The two additional tests depend on different sets on which the statistical assumptions are based.
a. Wilcoxon test for differences between clinical and non-clinical benefits, and b. Likelihood for testing logistic regression models where clinical benefit is gene expression as response variable and covariate Ratio test (LRT). For each probe set, the LRT was a chi-square with one degree of freedom:

In summary, considering two sample sets (“complete” data set and “reduced” data set) and two preprocessing algorithms (mas5 and rma), this results in four different analysis data sets. It was. For each of the four data sets, three different statistical tests were applied. Therefore, three p values were calculated for each probe set. In each analysis data set, composite criteria were applied to identify a separately controlled list of genes. This criterion defined a maximum p value of less than 0.05 and a geometric mean of p values of less than 0.01.
Robustness analysis using criteria 2 for marker identification resulted in a list of 36 probe sets corresponding to 36 different genes. These markers are reported in Table 3.

Table 3: Genetic markers of clinical benefit based on robustness analysis after applying the composite criteria.
The first column is the Affymetrix identifier of the probe set. The second column is the GenBank accession number for the corresponding gene sequence. The third column is the corresponding official gene name. The fourth column is the corresponding adjusted mean fold change in expression levels between patients with and without clinical benefit estimated with a linear model. The fifth column is the p-value of the test for differences in expression levels between patients with and without clinical benefit, derived from a linear model.

Discussion Analyzing tissue samples with high-density oligonucleotide microarray technology and applying statistical modeling to the data can identify genes whose expression levels can predict patients who will benefit clinically from treatment with erlotinib It was.
Applying the compound significance criteria (defined above), a list of 36 probe sets (see Table 3) representing 35 known genes was obtained. The functions of these genes are complex and are not always well characterized or well understood.
The gene function annotations in Table 3 were analyzed using Ingenuity software (Ingenuity® Systems, www.ingenuity.com). This software provides an interface to a proprietary knowledge base of genetic annotation that is collected and regularly updated based on scientific literature.
This extensive analysis shows that Table 3 contains genes useful for distinguishing different tumor classifications, particularly with respect to the response to the EGFR inhibitor erlotinib.

Table 4: List of marker genes of the present invention The first column is the GenBank accession number of the human gene sequence, the second column is the corresponding official gene name, and the third column is the human used in this application SEQ ID NO: nucleotide sequence. Table 4 contains one or more SEQ ID NOs because variants for a particular gene are registered in GenBank.

Claims (14)

A method for predicting in vitro a cancer patient's response to treatment with an EGFR inhibitor;
Determining the expression level of at least one gene selected from Table 3 in a patient tumor sample, and the expression level of said at least one gene in a tumor of a group of patients not receiving clinical benefit from EGFR inhibitor treatment Comparing the expression level of the at least one gene with a representative value, wherein the differential expression level of the at least one gene in the patient's tumor sample is subject to clinical benefit from the treatment. A method that will be an indicator for patients to become.   The method of claim 1, wherein the expression level is determined by microarray technology.   The method according to claim 1 or 2, wherein the expression levels of the two genes are determined.   The method according to any one of claims 1 to 3, wherein the expression levels of three genes are determined.   Said gene is selected from the group consisting of ATP6V0E1, MAPRE1, PSMA5, ACSL3, RAP1A, SLC2A3, CHMP2B, RFK, CTGF, HSPA8, AKAP12, LOX, SLMO2, NOMO3, and APOO, and said gene is in a patient tumor sample The method according to claim 1, which exhibits a lower expression level compared to a representative value of the expression level of at least one gene in a tumor of a group of patients not receiving clinical benefit from EGFR inhibitor treatment. The method described.   The genes are SDC1, CEBPA, ST6GALNAC2, PLA2G6, PMS2L11, C19orf7, DDX17, SFPQ, PMS2L3, SLC35E2, PMSL2, URG4, PPP1R13B, NRCAM, FLJ10916, FLJ13C, GPR172BL, GPR172B A higher expression level in a tumor sample of a patient selected and compared to a representative value of the expression level of at least one gene in a tumor of a group of patients not receiving clinical benefit from EGFR inhibitor treatment in a patient tumor sample The method of any one of Claims 1-4.   The method according to any one of claims 1 to 6, wherein the EGFR inhibitor is erlotinib.   The method according to any one of claims 1 to 7, wherein the cancer is NSCLC.   Use of the genes listed in Table 3 to predict cancer patient response to EGFR inhibitor treatment.   Use according to claim 9, wherein the cancer is NSCLC.   11. Use according to claim 9 or 10, wherein the EGFR inhibitor is erlotinib.   9. A method of treating a cancer patient identified by the method of any one of claims 1 to 8, comprising administering an EGFR inhibitor to the patient.   13. The method of claim 12, wherein the EGFR inhibitor is erlotinib.   14. The method of claim 12 or 13, wherein the cancer is NSCLC.

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