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Gene Expression Patterns and Profile Changes Pre- and Post-Erlotinib Treatment in Patients with Metastatic Breast Cancer

Authors' Affiliations: ¹ Cancer Therapeutics Branch in Center for Cancer Research and ² Biometrics Research Branch, National Cancer Institute, NIH, Bethesda, Maryland

Requests for reprints: Sherry X. Yang or Sandra M. Swain, Cancer Therapeutics Branch, Center for Cancer Research, National Cancer Institute, Room 5101, Building 8, 8901 Wisconsin Avenue, Bethesda, MD 20889. Phone: 301-451-6882; Fax: 301-496-0047; E-mail: xy32m{at}nih.gov or swains{at}mail.nih.gov.

	Abstract

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Purpose: To delineate gene expression patterns and profile changes in metastatic tumor biopsies at baseline and 1 month after treatment with the epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor erlotinib in patients with metastatic breast cancer.

Experimental Design: Patients were treated with 150 mg of oral erlotinib daily. Gene expression profiles were measured with Affymetrix U133A GeneChip and immunohistochemistry was used to validate microarray findings.

Results: Estrogen receptor (ER) status by immunohistochemistry is nearly coincided with the two major expression clusters determined by expression of genes using unsupervised hierarchical clustering analysis. One of 10 patients had an EGFR-positive tumor detected by both microarray and immunohistochemistry. In this tumor, tissue inhibitor of metalloproteinases-3 and collagen type 1 2, which are the EGF-down-regulated growth repressors, were significantly increased by erlotinib. Gene changes in EGFR-negative tumors are those of G-protein-linked and cell surface receptor–linked signaling. Gene ontology comparison analysis pretreatment and posttreatment in EGFR-negative tumors revealed biological process categories that have more genes differentially expressed than expected by chance. Among 495 gene ontology categories, the significant differed gene ontology groups include G-protein-coupled receptor protein signaling (34 genes, P = 0.002) and cell surface receptor–linked signal transduction (74 genes, P = 0.007).

Conclusions: ER status reflects the major difference in gene expression pattern in metastatic breast cancer. Erlotinib had effects on genes of EGFR signaling pathway in the EGFR-positive tumor and on gene ontology biological process categories or genes that have function in signal transduction in EGFR-negative tumors.

Erlotinib has been tested in various phases of clinical trials for its safety, tolerability, and treatment efficacy with the most activity seen in non–small cell lung cancer. Mutations in the EGFR-TK domain were found in non–small cell lung cancer and these mutations cause hypersensitivity to growth inhibition by gefitinib and is predictive of sensitivity to the receptor tyrosine kinase inhibitors (3, 4). In our pilot trial, 18 previously treated patients with metastatic breast cancer were given erlotinib alone at an oral dose of 150 mg daily, and no clinical responses and no significant changes in tumor proliferation after erlotinib treatment were observed (5). One tumor expressed significant levels of EGFR protein and erlotinib had inhibitory effects on the EGFR-TK of the EGFR-positive tumor as shown by the decreased activities of phosphorylated-EGFR, phosphorylated-mitogen-activated protein kinase, and phosphorylated-Akt (5). However, erlotinib had no apparent inhibitory effect on markers examined in EGFR-negative tumors. Therefore, it is of interest to further explore the effects of erlotinib in tumors that express or do not express EGFR at the gene expression profile levels.

Gene ontology is a term used to describe the genes in their associated biological processes, cellular components and molecular function (http://www.geneontology.org). Comparison analysis of gene ontology categories pretreatment and posttreatment is expected to identify the differentially expressed genes within a biological context. It is thus of interest to explore what effects erlotinib exerts on various gene ontology biological process categories in patient tumors.

Estrogen receptor (ER) has been shown to be critical in molecular classification of human primary breast cancer (6, 7). ER-positive tumors based on expression of genes were classified as epithelial/luminal-like subtype and ER-negative tumors were defined as myoepithelial/basal-like subtype (6). However, it is largely unknown whether ER status is still associated with the gene expression pattern in metastatic breast cancer.

With 10 pairs of tumor biopsies pretreatment and posttreatment available, we have undertaken the current study in an attempt to explore the gene expression patterns in metastatic breast cancer. Furthermore, the gene expression profiles pre- and post-erlotinib in one pair of EGFR-positive and nine pairs of EGFR-negative tumor samples were compared, respectively, and the significantly changed genes were identified. Gene ontology comparison has revealed the differentially expressed gene ontology categories that have higher than expected numbers of genes between pre- and post-erlotinib in EGFR-negative tumor samples.

	Materials and Methods

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Patients. Patient eligibility criteria, treatment plan, and clinical and toxicity evaluation have been described previously (5). The study was approved by the Institutional Review Board of the National Cancer Institute. All patients gave written informed consent.

Tumor core biopsies and RNA extraction. Tumor core biopsies were obtained at different metastatic sites including liver, lymph node, and chest wall using an 18-gauge needle or punch biopsy (chest wall mets) at baseline and 1 month after erlotinib treatment. For each of the patients, paired biopsies were both from the same metastatic organ site. They were immediately snap-frozen and stored at –80°C until use. The presence of tumor was evaluated using H&E-stained paraffin-embedded sections and >80% of tumor cells were present in each of the core biopsies. The cores used for formalin fixation and paraffin embedding were obtained at the same time as the frozen ones. Total RNA was extracted from the tumor cores available in pairs using Trizol reagent (Invitrogen, Carlsbad, CA) recommended by Affymetrix (Santa Clara, CA; ref. 8). The RNA yields ranged from 5 to 15 µg. Quality of each RNA sample extracted from the tumor core biopsies was examined by the BioAnalyzer (Agilent Technologies, Palo Alto, CA) before the subsequent double strand cDNA, cRNA syntheses, and array hybridization.

cRNA synthesis, oligonucleotide array hybridization, and analysis. First- and second-strand cDNA were synthesized from 5 to 15 µg of total RNA using the SuperScript Double-Stranded cDNA Synthesis Kit and oligo-dT₂₄-T7 primer according to the manufacturer's instructions. cRNA synthesized was labeled with biotinylated UTP and CTP by in vitro transcription using T7 promoter with the double-stranded cDNA as template.

cRNA (10.0 µg) was fragmented by heat and ion-mediated hydrolysis and was hybridized to oligonucleotide arrays (HG-U133A GeneChip, Affymetrix) containing 22,215 probe sets representing 18,400 transcripts of human genes. After wash, arrays were stained with phycoerythrein-conjugated streptavidin and the fluorescence intensities were determined using a laser confocal scanner (Hewlett-Packard, Palo Alto, CA). The scanned images were analyzed using Microarray suite 5.0 (Affymetrix). Sample loading and variations in staining were standardized by scaling the average of fluorescent intensities of all genes on an array to the constant target intensity (500) for all arrays used. The signal intensity for each gene was calculated as the average intensity difference, represented by [E (PM – MM) / (number of probe pairs)], where PM and MM denote perfect match and mismatch probes. All data from 22,215 probe sets were uploaded into the statistical package BRB-ArrayTools version 3.2 (available at http://linus.nci.nih.gov) for filtering and statistical analysis (9, 10).

Immunohistochemistry and quantitative analysis. Immunohistochemistry was used to validate the expression of EGFR, IGFBP4, and tumor proliferation (Ki67). Staining on tissue sections from formalin-fixed paraffin-embedded core biopsies was described previously (5). Antibody to IGFBP4 was purchased from Upstate Biotechnology (Lake Placid, NY) and was applied to tumor sections at a dilution of 1:500. Antibodies to EGFR (monoclonal antibody clone H11) and Ki67 (MIB-1, mouse monoclonal) were from DAKO Corp. (Carpinteria, CA). Binding of the antibodies to IGFBP4, EGFR, or Ki67 in tissue sections was amplified using Vectastain Elite avidin-biotin-peroxidase complex kits (Vector Laboratories, Burlingame, CA). Cell lines known to express EGFR (MCF10A human breast epithelial cells) and IGFBP4 (glioblastoma T98G cells) were used as the positive controls. Normal tonsil was used as the positive control for Ki67. Negative controls were done using isotype immunoglobulins appropriate to the primary antibodies used (Zymed Laboratories, South San Francisco, CA). Stained tumor core sections were scored quantitatively by an investigator (S.Y.) with the assistance of the Automated Cellular Imaging System (Chromavision, San Juan Capistrano, CA) using a previously described method (5, 11). Six areas of each tumor section were scored using a free-scoring or 40x magnification tool to generate an averaged percentage and intensity of stained tumor cells. Staining index is expressed as the percentage of staining multiplied by staining intensity after subtracting the tissue readouts of the corresponding negative control for each marker/100. Ki67 was reported as a labeling percentage.

Statistical analysis. All analyses were done on data from log₂-transformed median-normalized expression values. Genes that were not present in at least 15% of the array samples were excluded from analysis with 12,325 genes retained. Among these, 20% of genes with the greatest variation among samples (1,575 genes) were used for statistical data analysis because they gave the best intrapatient reproducibility.

An average linkage unsupervised hierarchical clustering on 20 samples was done on the 1,575 median-centered genes using the correlation similarity metric (12). To identify significantly changed genes in one pair of EGFR-positive tumors, each probe set on post-therapy array (11 probes per probe set) was compared with the corresponding one of pre-therapy array using Wilcoxon's signed rank test. It was considered to be statistically significant if P < 0.003 for increase or P > 0.997 for decrease (Affymetrix Microarray Suite 5.0 Comparison Analysis). To identify significantly changed genes by erlotinib treatment in EGFR-negative tumors, data from nine pairs of EGFR-negative tumors were analyzed with a paired t test with the random variance model by BRB-ArrayTools and genes significant at the P < 0.01 level were reported (13).

The evaluation of which gene ontology classes are differentially expressed between pretreatment and posttreatment samples was done using a functional class scoring analysis as described by Gavlidis et al. (14). For each gene in a gene ontology class, the random variance paired t test P value for comparing pretreatment versus posttreatment samples was computed. The set of P values for a class was summarized by two summary statistics: (i) The LS summary is the average log P values for the genes in that class and (ii) the KS summary is the Kolmogorov-Smirnov statistic computed on the P values for the genes in that class. The statistical significance of the gene ontology class containing n genes represented on the array was evaluated by computing the empirical distribution of these summary statistics in random samples of n genes. Gene ontology classes with P < 0.01 for the LS or KS statistic are reported. Functional class scoring is a more powerful method of identifying differentially expressed gene classes than the more common overrepresentation analysis or annotation of gene lists based on individually analyzed genes. The functional class scoring analysis for gene ontology classes was done using BRB-ArrayTools.

	Results

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Patient and baseline clinicopathologic characteristics. Patient and clinicopathologic characteristics have been described previously (5). Briefly, these 10 patients had more than one regimen of prior chemotherapy for metastatic disease. Five patients with ER-positive tumors and thre with HER-2/neu-positive tumors had received hormonal therapy or trastuzumab treatment. These metastatic tumors were from liver (three pairs), lymph nodes (three pairs), and chest wall (four pairs) and were all pathologically confirmed poorly differentiated carcinoma consistent with the breast origin.

Gene expression pattern of metastatic breast tumors pre- and post-erlotinib treatment. To investigate the intrinsic gene expression pattern in metastatic breast cancer, an average linkage hierarchical clustering approach was used to analyze the 20 tumor biopsies. As shown in the dendrogram in Fig. 1, all tumor pairs were grouped together at each of the terminal branches, suggesting the relatedness of each tumor pairs. To study the ER status in relation to the gene expression pattern, ER expression by both gene expression profiling and immunohistochemistry were aligned to tumor clusters determined by expression of genes. Most of ER-negative tumors appeared on the left major branch (blue colored) and ER-positive tumors on the right major branch (red colored) of the dendrogram. Only one ER-negative tumor pair was classified into the ER-positive cluster (green colored; Fig. 1B). Next, HER-2/neu status by immunohistochemistry was aligned to gene expression clusters. Seven tumors were HER-2/neu negative, in which five were in the left major expression cluster and two in the right major expression cluster of the dendrogram. Three HER-2/neu-positive tumors appeared in the right major expression cluster of the dendrogram (Fig. 1B). Furthermore, expression of EGFR by immunohistochemistry was aligned to the gene expression clusters. The pair of EGFR-positive tumor appeared in the left major expression cluster (Fig. 1B). Its expression at both transcriptional and protein level in 10 pairs of tumors is shown in Fig. 2, in which a high level of EGFR mRNA expression is concordant with that of EGFR protein expression in the EGFR-positive tumor.

View larger version (39K): [in this window] [in a new window]   Fig. 1. A, hierarchical cluster analysis of 1,575 genes in 20 metastatic breast tumors. Luminal/ER gene cluster (red bar) and basal-like cluster (green bar). B, dendrogram showing each of tumor pairs at terminal branches (top). Expression of ER RNA (middle). Expression of ER, HER-2/neu, and EGFR protein by immunohistochemistry (bottom). ER-, HER-2/neu-, and EGFR-positive tumors (red bar) and ER-, HER-2/neu, and EGFR-negative tumors (white bar).

View larger version (16K): [in this window] [in a new window]   Fig. 2. Expression of EGFR mRNA (A) by microarray and EGFR protein (B) by immunohistochemistry in 10 tumor pairs pre- (unfilled) and post-erlotinib (black) treatment.

fibronectin 1, collagen, type I, 1 and 2 (COL1A1 and 2), and tissue inhibitor of metalloproteinases-1 and -3

To investigate the effect of erlotinib treatment on EGFR-negative tumors, the gene expression profiles pre- and post-erlotinib in nine pairs of EGFR-negative tumor were compared and the top 33 genes associated with erlotinib treatment was identified (P < 0.01; Table 1). Although the false discovery rate associated with the use of significance at the P < 0.01 level is high (16 of 33, 48%), a significant portion of the changed genes are those whose biological function is associated with the ATP or GTP molecules. These are genes encoding ATPases, GTPases, ATP or GTP binding, and various energy-dependent membrane transporters or G-protein-coupled receptor signaling pathway proteins. In addition, expression of a cytochrome P450 protein CYP1B1 was decreased after erlotinib treatment.

View larger version (17K): [in this window] [in a new window]   Fig. 3. Expression of IGFBP4 mRNA (A) and protein (B) and Ki67 mRNA (C) and protein (D) in 10 pairs of tumor biopsy pre- (unfilled) and post-erlotinib (black) treatment. IGFBP4 mRNA overall was called decrease shown in (A) in contrast to Ki67 mRNA in (B) for which was called no change by paired t tests in BRB-ArrayTool. B, three tumor biopsy pairs that were available for analysis.

	Discussion

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In the EGFR-positive tumor detected by both cDNA microarray and immunohistochemistry, erlotinib treatment led to the decrease in cyclin E2 and cell division cycle 27, and the increase in TIMP-3 and COL1A2. The latter two are EGF-regulated growth repressors that are the downstream targets of RAS and are specifically repressed by EGF-induced transformation (15). Therefore, these changes may have been derived from the inhibition of EGFR pathway; however, they were not translated into clinical response. The tumor likely progressed through other pathway signaling (17).

Thirty-three genes changed with erlotinib treatment were identified for the receptor-negative tumors at the P < 0.01 level (Table 1). Notably, IGFBP4 was significantly decreased by erlotinib. Previously, erlotinib at higher concentrations has been shown to inhibit the kinase activity of IGFIR (1, 18). Addition of IGF-I diminished the inhibitory effect of anti-EGFR antibody on DiFi human colorectal tumor cells (18). IGF-binding proteins alter the interaction of IGFs with their cell surface receptors such as IGFIR (18). Therefore, it warrants further study to examine the effect of erlotinib on the expression of IGF-I receptor and the tyrosine kinase activities of IGF-I receptor, level of IGF-I, and IGFBPs. Gene profiling data from this cohort showed no significant change in expression of IGF-I mRNA and IGFIR by erlotinib (data not shown).

Overall in both EGFR-positive and EGFR-negative tumors, few apoptosis, antiapoptosis, or proliferation genes were significantly changed. This is consistent with our clinical findings (no objective responses) in this cohort of patients (5). However, the change in genes at the transcriptional level in EGFR-negative tumors are those whose function were coupled to ATP or GTP molecule including ATPases, GTPases, ATP- and GTP-binding proteins, or G-protein-coupled receptor signaling proteins (Table 1). This would not be surprising as erlotinib, the small-molecule competitive inhibitor of ATP on the EGFR-TK, may have directly or indirectly at varying levels of affinity affected other ATP- and GTP-coupling or -dependent molecules at the transcriptional level. For example, erlotinib decreased RAB31 at RNA level, a member of Ras-like small GTP-binding protein superfamily whose biological function is GTPase activity (19). Other members of the family have been found to have a role on vesicular (receptors) protein transport in both endocytic and exocytic pathways (20). It will be of interest to know if RAB31 is involved in the transport of the receptors of EGF or other growth factors. Erlotinib reduced the expression level of CYP1B1, which encodes a member of the cytochrome P450 superfamily of enzymes (21). The cytochrome P450 proteins are monooxygenases that catalyze many reactions involved in drug metabolism and synthesis of cholesterol, steroids, and other lipids. It has been reported that erlotinib is a substrate for CYP1B1 (22). The decrease in CYP1B1 transcription by erlotinib may be due to a feedback inhibitory mechanism, a feature commonly seen in the action of many drugs.

We used gene ontology analysis to confirm the findings at the individual gene level in EGFR-negative tumors. Gene ontology functional class analysis identified gene ontology groups differentially expressed from pretreatment to posttreatment that are generally consistent with the findings at the individual gene level. These include groups of G-protein-linked and cell surface receptor–linked signal transduction, extracellular matrix protein and protein tyrosine phosphatase activity. The other top differed gene ontology categories such as oxidoreductase activity acting on CH-OH group of donors and class of peptide binding may be due to the structural effects of erlotinib (Table 2).

In summary, we have shown in this study that ER status by immunohistochemistry is associated with the gene expression pattern determined by expression of genes in metastatic breast cancer. It seems that erlotinib had effects on the signaling pathway genes regulated by EGF in the EGFR-positive tumor besides the EGFR-TK. These data indicate that erlotinib may have hit the target but proliferation of the EGFR-positive tumor was not dependent on EGFR signaling. The significantly differed genes or gene ontology categories in pre- and post-erlotinib treatment in EGFR-negative tumors are those related to the erlotinib structural effect and molecules that have function in association with signal transduction. Gene expression profiling with larger numbers of both EGFR-positive and -negative tumors is warranted in future studies.

	Acknowledgments

 
We thank Drs. Paul Meltzer and Stan Lipkowitz for their critical review and helpful discussion of the article and Amy Lam for her technical advice on BRB-ArrayTools.

	Footnotes

 
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Note: Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/).

Received 2/ 7/05; revised 6/ 7/05; accepted 7/ 1/05.

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