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Expression of Epiregulin and Amphiregulin and K-ras Mutation Status Predict Disease Control in Metastatic Colorectal Cancer Patients Treated With Cetuximab

Shirin Khambata-Ford, Christopher R. Garrett, Neal J. Meropol, Mark Basik, Christopher T. Harbison, Shujian Wu, Tai W. Wong, Xin Huang, Chris H. Takimoto, Andrew K. Godwin, Benjamin R. Tan, Smitha S. Krishnamurthi, Howard A. Burris, III, Elizabeth A. Poplin, Manuel Hidalgo, Jose Baselga, Edwin A. Clark, David J. Mauro

From the Bristol-Myers Squibb Pharmaceutical Research Institute, Princeton; The Cancer Institute of New Jersey, New Brunswick, NJ; Division of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL; Divisions of Medical Science and Population Science, Fox Chase Cancer Center, Philadelphia, PA; Institute for Drug Development, Cancer Therapy and Research Center, San Antonio, TX; Division of Medical Oncology, Washington University School of Medicine, St Louis, MO; Case Comprehensive Cancer Center, University Hospitals of Cleveland and Case Western Reserve University, Cleveland, OH; Sarah Cannon Cancer Center, Nashville, TN; The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD; Sir Mortimer B. David Jewish General Hospital, McGill University, Montreal, Quebec, Canada; and Oncology Program and Medical Oncology Service, Vall d'Hebron University Hospital, Universitat Autonoma de Barcelona, Barcelona, Spain

Address reprint requests to Shirin Khambata-Ford, PhD, Bristol-Myers Squibb Co, 311 Pennington-Rocky Hill Rd, 3B-2.06, Princeton, NJ 08543; e-mail: shirin.ford{at}bms.com

	ABSTRACT

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS Appendix REFERENCES

 
Purpose: The antiepidermal growth factor receptor (EGFR) antibody cetuximab shows activity in multiple epithelial tumor types; however, responses are seen in only a subset of patients. This study was conducted to identify markers that are associated with disease control in patients treated with cetuximab.

Patients and Methods: One hundred ten patients with metastatic colorectal cancer were enrolled onto a cetuximab monotherapy trial. Transcriptional profiling was conducted on RNA from mandatory pretreatment metastatic biopsies to identify genes whose expression correlates with best clinical responses. EGFR and K-ras mutation analyses and EGFR gene copy number analyses were performed on DNA from pretreatment biopsies.

Results: Gene expression profiles showed that patients with tumors that express high levels of the EGFR ligands epiregulin and amphiregulin are more likely to have disease control with cetuximab (EREG, P = .000015; AREG, P = .000025). Additionally, patients whose tumors do not have K-ras mutations have a significantly higher disease control rate than patients with K-ras mutations (P = .0003). Furthermore, patients with tumors that have high expression of EREG or AREG also have significantly longer progression-free survival (PFS) than patients with low expression (EREG: P = .0002, hazard ratio [HR] = 0.47, and median PFS, 103.5 v 57 days, respectively; AREG: P < .0001, HR = 0.44, and median PFS, 115.5 v 57 days, respectively).

Conclusion: Patients with tumors that have high gene expression levels of epiregulin and amphiregulin and patients with wild-type K-ras are more likely to have disease control on cetuximab treatment. The identified markers could be developed further to select patients for cetuximab therapy.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS Appendix REFERENCES

 
The epidermal growth factor receptor (EGFR) plays an important role in normal and malignant epithelial cell biology¹ and is, therefore, an established therapeutic target. Whereas the anti-EGFR antibody cetuximab (Erbitux; ImClone, New York, NY) and the EGFR small-molecule tyrosine kinase inhibitors (TKIs) gefitinib (Iressa; AstraZeneca, London, United Kingdom) and erlotinib (Tarceva; OSI Pharmaceuticals, Melville, NY) have demonstrated activity in a subset of patients in multiple epithelial tumor types,² their initial clinical development has not benefited from an accompanying strategy for identifying patients who would most likely derive benefit. The hypothesis that only a relatively small number of tumors are EGFR-pathway dependent and, therefore, likely to respond to EGFR inhibitors might explain the limited clinical activity that is observed with this class of therapeutics among unselected patient populations.

Clinical studies of cetuximab in metastatic colorectal cancer (CRC) failed to reveal an association between radiographic response and EGFR protein expression as measured by immunohistochemistry.^3,4 Furthermore, clinical responses have been demonstrated in patients with undetectable EGFR protein expression.^5-8 Somatic mutations in the EGFR tyrosine kinase domain are associated with sensitivity to the TKIs but not to cetuximab.^9,10 In addition, the lack of EGFR kinase domain mutations in CRC patients suggests that such mutations do not underlie the response to cetuximab. Somatic mutations in K-ras are associated with a lack of sensitivity to TKIs in non–small-cell lung cancer (NSCLC), and recent data suggest a role in cetuximab sensitivity in CRC.^11,12 EGFR gene copy number has also been evaluated as a potential predictor of response, and clinical studies of gefitinib in NSCLC have demonstrated an association between increased EGFR copy number, mutational status, and clinical response.¹³

Previous attempts to identify response predictors have focused on specific markers rather than using genomic discovery approaches. In addition, RNA-, DNA-, and protein-based markers have rarely been examined in the same patient population in a single study. Towards this end, we conducted an exploratory clinical trial to systematically identify biomarkers associated with disease control to cetuximab monotherapy. This is the largest prospective human cohort uniformly treated with an anti-EGFR antibody with the goal of identifying candidate predictive markers. RNA, DNA, and protein samples from tumors and blood were analyzed to identify markers that were correlated with clinical activity.

	PATIENTS AND METHODS

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Patient Treatment and Clinical End Points
One hundred ten patients with metastatic CRC were enrolled onto a cetuximab monotherapy study. Patients were eligible if they had histologically documented metastatic CRC. Patients must have received at least one prior chemotherapeutic regimen for advanced disease or have refused prior treatment. Because of the requirement of pretreatment core tumor biopsies of the patient's metastatic lesion, a tumor site must have been accessible to repetitive biopsies. In addition, coagulation testing, including partial thromboplastin time, prothrombin time, or international normalized ratio, must have been within laboratory normal limits. Patients were required to be at least 18 years of age with a life expectancy of 4 months, have an Eastern Cooperative Oncology Group performance status of 0 to 2, and have standard laboratory values within normal limits. Prior cytotoxic or radiation therapy had to be completed at least 4 weeks before enrollment. Women of childbearing potential were required to use an acceptable nonhormonal method of contraception. Written informed consent was obtained from all patients before they were entered onto the study. The protocol was approved by the institutional review boards at the participating institutions.

A standard cetuximab dosing regimen (400 mg/m² loading dose, followed by 250 mg/m² weekly) was followed for the first 3 weeks of therapy; thereafter, patients were eligible for dose escalation every 3 weeks to a maximum dose of 400 mg/m² provided that they had not experienced a more than grade 2 skin rash. Median duration of study therapy was 9 weeks. All patients underwent a pretreatment biopsy involving three passes with an 18-gauge needle of a single metastatic lesion. RNA and DNA were isolated from the biopsies (see Appendix, online only). All patients underwent a pretreatment blood draw for plasma collection. Tumor response was evaluated every 9 weeks (one cycle of therapy) according to the modified WHO criteria.¹⁴ Progression-free survival (PFS) was defined as the time from enrollment until disease progression or death. Overall survival was not measured in this study.

Gene Expression Profiling and Data Analysis
For each sample, 1 µg total RNA was used for profiling on U133Av2.0 GeneChips according to the manufacturer's instructions (Affymetrix, Santa Clara, CA). Data were preprocessed using MAS5.0 software (Affymetrix), and statistical analyses were performed using quantile normalized signal intensity values. Univariate analysis to compare gene expression profiles with clinical response was performed by using a two-sided unequal variance t test. Receiver operating characteristic analysis was used to assess model performance. For each sample from which RNA was available, 100 ng RNA was converted into cDNA, which was measured on the ABI-7900HT system (Applied Biosystems, Foster City, CA) using primer/probe sets directed against amphiregulin and epiregulin genes.

In addition to the RNA profiling from the clinical study, we examined a gene expression database of 164 primary colorectal tumors¹⁵ to identify potential predictive markers (Appendix). Data from the 640 probe sets that passed the filtering steps described in the results were subjected to an unsupervised hierarchical clustering using Cluster, and results were displayed with TreeView (software available at http://genome-www5.stanford.edu).

Enzyme-Linked Immunosorbent Assay
Amphiregulin and epiregulin in plasma were measured using an antibody sandwich enzyme-linked immunosorbent assay (Appendix).

Nucleotide Sequence and DNA Copy Number Analysis
Mutational analyses of EGFR, K-ras, and BRAF were performed using genomic DNAs from tumors (Appendix). Measurement of EGFR copy number was performed as previously described¹⁶ using a TaqMan assay (Applied Biosystems). Statistical analyses to examine associations between mutational status or gene copy number and best clinical response were performed using Fisher's exact test.

PFS Analysis
A Cox proportional hazards regression model was used in survival analysis. PFS curves for AREG, EREG, and K-ras status were generated using the Kaplan-Meier method, and differences based on biomarker status were evaluated with the log-rank test.

	RESULTS

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Patient Characteristics and Clinical Outcome
One hundred ten patients with measurable metastatic CRC were enrolled. Patient baseline characteristics are listed in Table 1. Assessable RNA, DNA, and/or plasma samples were available for 103 of 110 patients. Objective best responses for 103 patients were as follows: complete response (CR) in one patient, partial response (PR) in six patients, stable disease (SD) in 28 patients, and progressive disease (PD) in 56 patients; 12 patients died before their first radiographic assessment, and thus, response was unable to be determined. Thirty-four percent of the patients either responded or had SD and were considered as the disease control group (DCG), whereas 66% of patients were classified as nonresponders.

We identified an initial candidate set of genes that were variably expressed in an independent set of 164 primary colorectal tumors by filtering transcriptional data from all 22,215 probe sets. This filtering yielded 640 probe sets that were expressed at a moderate to high level in colon tumors (at least one expression value of 2x mean value for array, ie, 3,000 expression units) and with a population variance of more than 0.1. We proposed that these 640 probe sets that had a highly dynamic range of expression across a population of CRC tumors were the ones most likely to yield useful patient selection markers. Unsupervised hierarchical clustering of the 640 probe sets across the 164 primary colon tumors showed that biologically interesting genes that might be predictive of response to cetuximab were preferentially expressed in a subset of tumors (Fig 1).

View larger version (112K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 1. Filtering of candidate markers. Unsupervised clustering of expression data on 640 probe sets from 164 primary colorectal cancer tumors. One hundred sixty-four tumors were divided into class 1, 2, and 3. Six hundred forty probe sets were divided into five clusters (clusters A through E). Cluster A contains cancer antigens (eg, CEACAM 6, CD24), and epidermal growth factor receptor ligands EREG and AREG. It is most highly expressed in class 1a (approximately 25%).

View larger version (22K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 2. mRNA levels of epidermal growth factor receptor ligands epiregulin and amphiregulin. Affymetrix mRNA levels of EREG (205767_at) and AREG (205239_at) are plotted on the y-axis. Patients are ordered by best clinical response. There is a significant difference in gene expression levels between the disease control group and nonresponders (EREG: P = .000015, AREG: P = .000025). CR, complete response; PR, partial response; SD, stable disease.

We then assessed the ability of individual biomarkers to separate the DCG from nonresponders. A receiver operating characteristic curve analysis was performed to assess the performance of the most frequently identified gene, EREG, and also of AREG. EREG has an area under the curve (AUC) value of 0.845, and AREG has an AUC value of 0.815, indicating that they are strongly associated with disease control (Appendix Fig A1, online only).

Analysis of Candidate Markers Epiregulin and Amphiregulin
To determine whether gene expression of EREG or AREG was significantly related to PFS, patients were divided into low and high ligand expression groups using the median signal intensity as a cutoff. Patients with tumors that had high gene expression of EREG or AREG had significantly longer PFS than patients with low gene expression (EREG: log-rank, P = .0002; hazard ratio [HR] = 0.47; median PFS, 103.5 v 57 days, respectively; AREG: log-rank, P < .0001; HR = 0.44; median PFS, 115.5 v 57 days, respectively; Figs 3A and 3B). When patient age and ECOG status were included as covariates, the adjusted P values of EREG and AREG were still significant (EREG: P = .007; HR = 0.51; AREG: P = .0009; HR = 0.43).

View larger version (9K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 3. Progression-free survival curves. (A) EREG high and low expressers (P = .0002; hazard ratio [HR] = 0.47; 95% CI, 0.24 to 0.64; median PFS, 103.5 v 57 days, respectively). (B) AREG high and low expressers (P < .0001; HR = 0.44; 95% CI, 0.21 to 0.57; median PFS, 115.5 v 57 days, respectively). (C) K-ras wild type and mutant (P = .14; HR = 1.4; 95% CI, 0.87 to 2.6; median PFS, 61 v 59 days, respectively).

To assess whether tumor mRNA expression would be reflected in levels of protein in blood, we developed enzyme-linked immunosorbent assays to assay levels of amphiregulin and epiregulin in blood plasma samples. Overall, there was only a modest correlation between systemic protein and tumor mRNA levels of amphiregulin (Pearson correlation = 0.41; P = .0011). No significant correlation between epiregulin protein and mRNA levels was observed (Pearson correlation = –0.15; P = .2440).

Genetic Analysis of DNA Isolated From Tumor Biopsies
We evaluated DNA from 80 tumor biopsies for mutations in EGFR, K-ras, and BRAF. Not a single heterozygous mutation was detected in the EGFR kinase domain or in BRAF exon 15. K-ras exon 2 mutations were detected in 30 (38%) of 80 analyzed samples (Fig 4). K-ras mutations were detected in three (11%) of 27 DCG patients but were detected in 27 (51%) of 53 nonresponders. The data show that the presence of a K-ras mutation correlates with a lack of response to cetuximab therapy (P = .0003). A predictive model using the combination of K-ras mutation status and epiregulin mRNA levels described earlier has an AUC value of 0.89. Patients with tumors that had wild-type K-ras did not have a significantly longer PFS than patients with mutant K-ras (log-rank, P = .14; HR = 1.4; median PFS, 61 v 59 days, respectively; Fig 3C). In contrast to analysis of AREG and EREG, K-ras mutational status dichotomized the population into unequal subgroups (62.5% wild-type v 37.5% mutant). The relatively small number of patients exhibiting K-ras mutation, coupled with early events in the wild-type group, likely resulted in insufficient power to detect statistically significant differences in median PFS.

View larger version (17K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 4. Mutation analysis of K-ras exon 2. K-ras mutations were detected in three patients with stable disease (SD) out of 27 disease control group patients (five partial responses + 22 SD). K-ras mutations were detected in 27 of 53 nonresponders. There is a statistically significant difference in K-ras mutation frequency between the two groups (Fisher's exact test, P = .0003).

	DISCUSSION

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS Appendix REFERENCES

 
This study sought to identify markers that are associated with disease control in patients treated with cetuximab. The key findings from the analysis of pretreatment biopsies are that patients whose tumors express high levels of mRNA for the EGFR ligands epiregulin and amphiregulin are more likely to have antitumor activity resulting from cetuximab therapy. In addition, we found that patients whose tumors do not have K-ras mutations have a significantly higher disease control rate than patients with K-ras mutations.

The genes for epiregulin and amphiregulin are colocalized on chromosome 4q13.3.²⁰ In this study, we observed that the expression of epiregulin and amphiregulin was coordinately regulated. Epiregulin is known to bind more weakly to EGFR and erbB4 than EGF but is much more potent than EGF and leads to a prolonged state of receptor activation.²¹ Elevated expression of epiregulin and/or amphiregulin may play an important role in tumor growth and survival by stimulating an autocrine loop through EGFR (Fig 5A). This may characterize a tumor that is EGFR dependent and, therefore, particularly sensitive to the ability of cetuximab to block ligand-receptor interaction (Fig 5B). Our observations that constitutive epiregulin and amphiregulin expression in L2987 cells is decreased on EGFR-inhibitor treatment and is stimulated by EGF treatment and that cetuximab treatment blocks L2987 cell growth support the hypothesis that these ligands are beacons of an activated EGFR pathway and perhaps a positive feedback loop (Appendix Fig A2, online only).

View larger version (52K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 5. Cetuximab and K-ras modulate signaling through the epidermal growth factor receptor (EGFR) pathway. (A) Binding of ligand to EGFR triggers signaling through ras/MAPK pathway to multiple targets that may regulate ligand levels. (B) In the presence of cetuximab, ligand binding is prevented, and there is deactivation of EGFR signaling in cells dependent on this pathway. (C) K-ras mutations can lead to dysregulation of MAPK pathway and downstream signaling in the absence of ligand-dependent receptor activation.

This study also shows that patients without K-ras mutations have a higher disease control rate (48%) than patients with K-ras mutations (10%). This confirms findings from a recent study showing that patients without K-ras mutations have a higher disease control rate (76%) than patients with K-ras mutations (31%).²² K-ras plays a crucial role in the RAS/MAPK pathway, downstream of EGFR and other growth factor receptors, and is involved in cell proliferation. The presence of activating K-ras mutations might circumvent cetuximab's inhibitory activity (Fig 5C). These data support a role for K-ras mutations in patients who do not respond to cetuximab, and these mutations should continue to be evaluated in CRC, NSCLC, and pancreatic cancers, where RAS mutations are prevalent.²³

In contrast to what has been observed in NSCLC,⁹ we did not detect mutations in EGFR (exons 18 to 21) in patients in this study, confirming the paucity of mutations in CRC.¹⁰ Increased EGFR copy number was observed in less than 10% of patients evaluated in this study, and although there was a trend towards higher copy number in patients with disease control, the result was more in line with the results of Lenz et al¹⁶ and Lievre et al²² (amplification in approximately 10% of patients) than with that of Moroni et al¹² (amplification in 31% of patients).

We are currently testing these markers retrospectively in samples from additional studies in CRC and NSCLC in which cetuximab is administered either as monotherapy or in combination with chemotherapy. These analyses will determine whether the putative markers are predictive and/or prognostic and also whether they are associated with an improved clinical outcome as measured by overall survival. In addition, a prospective randomized study will be critical for the validation of these findings.

Whereas the development of cetuximab to date has proceeded by evaluating its use in nearly all patients, the data presented here suggest that a targeted approach may be more valuable. A targeted approach to patient selection based on markers such as AREG and EREG gene expression could potentially improve survival, spare patients needless toxicity, and reduce expenses associated with ineffective therapy. It may be possible to accelerate the potential use of cetuximab in front-line indications by enrichment of clinical trial populations with patients who are more likely to respond. In addition, such an approach may identify indications for cetuximab in other tumor types quicker than more traditional developmental approaches. These data have provided a strong foundation for a rational approach to the targeted development of cetuximab.

	AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST

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Although all authors completed the disclosure declaration, the following authors or their immediate family members indicated a financial interest. No conflict exists for drugs or devices used in a study if they are not being evaluated as part of the investigation. For a detailed description of the disclosure categories, or for more information about ASCO's conflict of interest policy, please refer to the Author Disclosure Declaration and the Disclosures of Potential Conflicts of Interest section in Information for Contributors.

Employment: Shirin Khambata-Ford, Bristol-Myers Squibb Co; Christopher T. Harbison, Bristol-Myers Squibb Co; Shujian Wu, Bristol-Myers Squibb Co; Tai W. Wong, Bristol-Myers Squibb Co; Xin Huang, Bristol-Myers Squibb Co; Edwin A. Clark, Bristol-Myers Squibb Co; David J. Mauro, Bristol-Myers Squibb Co Leadership: N/A Consultant: Neal J. Meropol, Amgen Inc, Genentech, Pfizer Inc, Genomic Health; Benjamin R. Tan, Imclone Systems Inc; Smitha S. Krishnamurthi, Bristol-Myers Squibb Co; Jose Baselga, Roche Stock: Shirin Khambata-Ford, Bristol-Myers Squibb Co; Shujian Wu, Bristol-Myers Squibb Co; Tai W. Wong, Bristol-Myers Squibb Co; Edwin A. Clark, Bristol-Myers Squibb Co; David J. Mauro, Bristol-Myers Squibb Co Honoraria: Christopher R. Garrett, Bristol-Myers Squibb Co; Benjamin R. Tan, Bristol-Myers Squibb Co, ImClone Systems Inc; Howard A. Burris III, Bristol-Myers Squibb Co, Amgen Inc; Elizabeth A. Poplin, Bristol-Myers Squibb Co; Jose Baselga, Roche, Merck Research Funds: Christopher R. Garrett, Funds, Bristol-Myers Squibb Co; Neal J. Meropol, Funds, Bristol-Myers Squibb Co; Mark Basik, Funds, Bristol-Myers Squibb Co; Chris H. Takimoto, Funds, Bristol-Myers Squibb Co; Jose Baselga, Funds, Bristol-Myers Squibb Co Testimony: N/A Other: N/A

	AUTHOR CONTRIBUTIONS

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Conception and design: Shirin Khambata-Ford, Christopher R. Garrett, Neal J. Meropol, Mark Basik, Howard A. Burris III, Jose Baselga, Edwin A. Clark, David J. Mauro

Financial support: Andrew K. Godwin

Administrative support: Shirin Khambata-Ford, David J. Mauro

Provision of study materials or patients: Shirin Khambata-Ford, Christopher R. Garrett, Neal J. Meropol, Mark Basik, Chris H. Takimoto, Benjamin R. Tan, Smitha S. Krishnamurthi, Howard A. Burris III, Elizabeth A. Poplin, Manuel Hidalgo, Jose Baselga, David J. Mauro

Collection and assembly of data: Shirin Khambata-Ford, Christopher R. Garrett, Neal J. Meropol, Christopher T. Harbison, Tai W. Wong, Andrew K. Godwin, Howard A. Burris III, Jose Baselga, Edwin A. Clark, David J. Mauro

Data analysis and interpretation: Shirin Khambata-Ford, Christopher R. Garrett, Neal J. Meropol, Christopher T. Harbison, Shujian Wu, Tai W. Wong, Xin Huang, Andrew K. Godwin, Howard A. Burris III, Jose Baselga, Edwin A. Clark, David J. Mauro

Manuscript writing: Shirin Khambata-Ford, Christopher R. Garrett, Neal J. Meropol, Christopher T. Harbison, Tai W. Wong, Xin Huang, Andrew K. Godwin, Edwin A. Clark, David J. Mauro

Final approval of manuscript: Shirin Khambata-Ford, Christopher R. Garrett, Neal J. Meropol, Mark Basik, Chris H. Takimoto, Andrew K. Godwin, Jose Baselga, Edwin A. Clark, David J. Mauro

	Appendix

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION AUTHORS' DISCLOSURES OF... AUTHOR CONTRIBUTIONS Appendix REFERENCES

 
Methods
RNA and DNA extraction. For each patient's tumor sample, RNA and DNA were isolated from two pretreatment core needle biopsies provided in a single tube of RNALater (Ambion, Austin, TX) at room temperature (RT) within 7 days from the date of the biopsy procedure. RNA was isolated using the RNeasy mini kit (Qiagen, Valencia, CA). The quality of RNA was checked by measuring the 28S:18S ribosomal RNA ratio using an Agilent 2100 Bioanalyzer (Agilent Technologies, Rockville, MD). DNA was isolated from the flow-through collected during the RNA isolation procedure using the DNeasy mini kit (Qiagen). Concentration of RNA and DNA was determined spectrophotometrically.

Quantitative reverse transcriptase (qRT) polymerase chain reaction (PCR) for gene expression analysis. For each sample from which RNA was available, approximately 100 ng RNA was converted into cDNA by the random priming method using MultiScribe Reverse Transcriptase according to the manufacturer's instructions (TaqMan Reverse Transcription Reagents; Applied Biosystems, Foster City, CA). The resulting cDNA was measured on the ABI 7900HT Sequence Detection System using ABI Assay-on-Demand primer/probe sets (Applied Biosystems) directed against the amphiregulin (Hs00155832_m1) and epiregulin (Hs00154995_m1) genes. Relative expression levels were calculated using the delta threshold cycle (Ct) method in which average values of duplicate reactions were compared, with GAPDH (Hs001266705_g1) serving as the internal reference. In this experimental design, low Ct values correspond to high levels of expression.

Enzyme-linked immunosorbent assay. Amphiregulin and epiregulin in human plasma were measured using an antibody sandwich enzyme-linked immunosorbent assay. All antibodies, reference standards, streptavidin-horseradish peroxidase (HRP), and the HRP substrate were obtained from R&D Systems Inc (Minneapolis, MN). Ninety-six–well flat bottom plates were coated with either mouse antiamphiregulin capture antibody (2 µg/mL) or mouse antiepiregulin capture antibody (2.5 µg/mL) in phosphate-buffered saline overnight at 4°C. Plates were washed and blocked for 1 hour in 200 µL per well of Superblock (Pierce, Rockford, IL) and then washed and incubated with 100 µL of either plasma samples or reference standards spiked into human plasma for 2 hours at RT. Plates were washed, and 100 µL of biotin antiamphiregulin or biotin antiepiregulin was added per well for 1 hour at RT. After washing, 100 µL per well of streptavidin-HRP was added for 30 minutes at RT, followed by washing and incubation with 100 µL per well of tetramethyl-benzidine HRP substrate at RT. The reaction was stopped by adding 100 µL per well of 1N H₂SO₄, and the absorbance at 450 nm was measured using a SpectraMax plate reader (Molecular Devices Inc, Sunnyvale, CA). The concentration of amphiregulin or epiregulin was determined using a calibration curve based on the reference standards.

Nucleotide sequence analysis. Mutational analyses of EGFR, K-ras, and BRAF were performed using available genomic DNAs isolated from tumor specimens. Primers used for EGFR exons 18 to 21, coding for the tyrosine kinase domain, were published previously (Lynch TJ, Bell DW, Sordella R: N Engl J Med 350:2129-2139, 2004). The primers used to evaluate exon 2 of K-ras and exon 15 of BRAF were as follows: K-ras forward: 5'-TAAGGCCTGCTGAAAATGACTG-3', and K-ras reverse: 5'-TGGTCCTGCACCAGTAATATGC-3'; BRAF forward: 5'-TCATAATGCTTGCTCTGATAGGA-3', and BRAF reverse: 5'-GGCCAAAAATTTAATCAGTGGA-3'. PCR was performed using conditions as previously described (Chen X, Truong TT, Weaver J: Hum Mutat 27:427-435, 2006). PCR fragments were cleaned with QIAquick PCR Purification Kit (Qiagen), sequenced on an ABI 3100A Capillary Genetic Analyzer (Applied Biosystems), and analyzed in both sense and antisense directions for the presence of heterozygous mutations. Analysis of the DNA sequence was performed using Sequencher v4.2 (Gene Codes, Ann Arbor, MI) followed by visual analysis of each electropherogram by two independent reviewers. Appropriate positive and negative controls were included for each of the exons evaluated. Mutational analyses were performed without knowledge of clinical outcome, including tumor response.

mRNA analysis of ligands in drug-treated cell lines. L2987 and HCT116 tumor cells were plated at 400,000 cells/well in a six-well plate and cultured in RPMI-1640 supplemented with 0.5% fetal bovine serum for 16 hours before treatment with a mitogen-activated protein/extracellular signal-regulated kinase (ERK) kinase (MEK) inhibitor PD98059 (20 µmol/L), gefitinib (1 µmol/L), or cetuximab (50 µg/mL). mRNA was isolated using mRNA Catcher Kit (Invitrogen, Carlsbad, CA), and ligand expression was determined by qRT-PCR using Sybrgreen (Eurogentec, Seraing, Belgium) normalized to glyceraldehyde 3-phosphate dehydrogenase levels. The primers and probe sequences are available on request.

Results
mRNA analysis of ligands in drug-treated cell lines. To examine whether the expression of epidermal growth factor receptor (EGFR) ligands is regulated by EGFR signaling, L2987 and HCT116 cells were treated with gefitinib, cetuximab, or a MEK inhibitor, and ligand expression was analyzed by qRT-PCR. L2987 (wild-type K-ras) and HCT116 (mutant K-ras) tumor cells were used because the tumors were shown to be sensitive and resistant to treatment with cetuximab, respectively (Wild R, Fager K, Flefleh C: Mol Cancer Ther, 5:104-113, 2006). In the absence of added growth factors, both cell lines express AREG, EREG, and TGF, but not EGF. In the sensitive L2987 cells, the expression of EREG and AREG was downregulated by treatment with gefitinib or cetuximab, suggesting that it is regulated by autocrine receptor signaling (Appendix Fig A2). The expression of these two ligands was also partially inhibited on treatment with a MEK inhibitor (PD98059). This suggests that the MEK/ERK signaling pathway downstream of the EGFR might be involved in the regulation of ligand expression. TGF expression in L2987 cells was slightly inhibited by treatment with cetuximab or gefitinib. EGF stimulation of L2987 cells resulted in an upregulation of expression of EREG and AREG, confirming a role of EGF receptor signaling in regulating the expression of these ligands (data not shown). For the resistant HCT116 cells, treatment with cetuximab or gefitinib had little to no effect on the expression of the ligands (Appendix Fig A2). These data suggest that epiregulin and amphiregulin expression is regulated by a positive feedback loop whereby EGFR signaling further upregulates their expression.

Genomic analysis of tumor-derived RNAs. Recent work by numerous groups has focused on the advantages of genomic signatures in classifying patients and predicting outcome and/or chemotherapeutic response. We examined the transcriptional profiles from 164 primary colorectal cancer tumors with the purpose of identifying candidate classes of patients who may optimally benefit from cetuximab therapy. To this end, 22,215 probes present on the U133A chip were considered as candidate predictive markers. We restricted the analysis by removing all probes expressed in less than eight of the 164 primary colorectal tumors (5% of the total) at 3,000 expression units (twice the trimmed mean value for the array); 6,014 probes passed this expression filter. Next, we sought to identify genes with variable expression in colon tumors (and therefore more likely to be able to correlate with variability in response to treatment). To restrict the analysis to gene sequences expressed at variable levels in colon tumors, we removed all probes with a population variance (VARP) value (using log10-transformed data) of less than 0.1; 640 probes passed this variance filter. Unsupervised hierarchical clustering of the 640 probe sets across the 164 primary colon tumors was performed using web-available software (Cluster and TreeView software; available at http://genome-www5.stanford.edu). The data were log-transformed and then median-centered for each sample. The patients were clustered into three classes defined by five clusters of genes. Among these genes were biologically interesting candidate markers (eg, epiregulin and amphiregulin) that were preferentially expressed in a subset of colorectal tumors (class 1). Expression of these two genes was subsequently shown to be associated with disease control in patients treated with cetuximab.

View larger version (6K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig A1. (A) Receiver operating characteristic (ROC) curve for prediction of patient treatment response. The EREG gene has an area under the ROC curve (AUC) of 0.845 on the test set, indicating a high performance for prediction. (B) The AREG gene, which is found to be coordinately regulated with the EREG gene, has an AUC of 0.815 on the test set, indicating that it too has a good prediction power as a single-gene predictor.

View larger version (23K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig A2. Effect of epidermal growth factor receptor (EGFR) or mitogen-activated protein/extracellular signal-regulated kinase kinase (MEK) inhibition on ligand expression. L2987 and HCT116 cells were treated with a MEK inhibitor PD98059, cetuximab, or gefitinib for 5 or 24 hours. Expression of AREG, EREG, and TGF was determined by reverse transcriptase polymerase chain reaction. Data shown are averages of triplicates and representative of four independent experiments.

	ACKNOWLEDGMENTS

 
We thank S. Mayfield, C. Langer, P. Fracasso, S. Shibata, D. Waterhouse and clinical teams, P. Teegarden, N. Perkins, K. Kellar, and C. Wang for technical assistance, P. Rhyne for assay development, I. Neuhaus and N. Siemers for assistance with data analysis, F. McCormick and X. Chen for advice/assistance with DNA analysis, and E. Rowinsky for comments on manuscript.

	NOTES

 
Supported by Bristol-Myers Squibb Co, Princeton, NJ. Mutation analysis work was supported by a grant from the Pennsylvania Department of Health (A.K.G. and N.J.M.).

Both S.K.-F. and C.R.G. contributed equally to this article.

The Pennsylvania Department of Health specifically disclaims responsibility for any analyses, interpretations, or conclusions.

Authors’ disclosures of potential conflicts of interest and author contributions are found at the end of this article.

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Submitted January 4, 2007; accepted April 30, 2007.

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