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Gene Expression Signature in Advanced Colorectal Cancer Patients Select Drugs and Response for the Use of Leucovorin, Fluorouracil, and Irinotecan

Maguy Del Rio, Franck Molina, Caroline Bascoul-Mollevi, Virginie Copois, Frédéric Bibeau, Patrick Chalbos, Corinne Bareil, Andrew Kramar, Nicolas Salvetat, Caroline Fraslon, Emmanuel Conseiller, Virginie Granci, Benjamin Leblanc, Bernard Pau, Pierre Martineau, Marc Ychou

From the Centre National de la Recherche Scientifique Unité Mixte de Recherche 5160, Unité de Biostatistique, Service d'Anatomie pathologique, Service d'Oncologie Digestive, Centre Régional de Lutte contre le Cancer Val d'Aurelle, Montpellier; and Département d'Oncologie, Sanofi-aventis, Vitry-sur-Seine, France

Address reprint requests to Maguy Del Rio, PhD, CNRSUMR 5160, CRLC Val d'Aurelle-Paul Lamarque, 208 rue des Apothicaires, 34298 Montpellier Cedex 5, France; e-mail: mdelrio{at}valdorel.fnclcc.fr

	ABSTRACT

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Purpose: In patients with advanced colorectal cancer, leucovorin, fluorouracil, and irinotecan (FOLFIRI) is considered as one of the reference first-line treatments. However, only about half of treated patients respond to this regimen, and there is no clinically useful marker that predicts response. A major clinical challenge is to identify the subset of patients who could benefit from this chemotherapy. We aimed to identify a gene expression profile in primary colon cancer tissue that could predict chemotherapy response.

Patients and Methods: Tumor colon samples from 21 patients with advanced colorectal cancer were analyzed for gene expression profiling using Human Genome GeneChip arrays U133. At the end of the first-line treatment, the best observed response, according to WHO criteria, was used to define the responders and nonresponders. Discriminatory genes were first selected by the significance analysis of microarrays algorithm and the area under the receiver operating characteristic curve. A predictor classifier was then constructed using support vector machines. Finally, leave-one-out cross validation was used to estimate the performance and the accuracy of the output class prediction rule.

Results: We determined a set of 14 predictor genes of response to FOLFIRI. Nine of nine responders (100% specificity) and 11 of 12 nonresponders (92% sensitivity) were classified correctly, for an overall accuracy of 95%.

Conclusion: After validation in an independent cohort of patients, our gene signature could be used as a decision tool to assist oncologists in selecting colorectal cancer patients who could benefit from FOLFIRI chemotherapy, both in the adjuvant and the first-line metastatic setting.

	INTRODUCTION

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Colorectal cancer (CRC) is one of the most common malignant diseases, with 945,000 new cases every year, and is the fourth cause of cancer-related deaths worldwide.¹ When localized, CRC is often curable by surgery, but the prognosis for patients with metastatic disease remains poor. Curative-intent resections can be performed on only 10% to 15% of liver metastases. In the majority of metastatic patients, the standard treatment remains palliative chemotherapy. Fluorouracil (FU) -based therapy has been the main treatment for metastatic CRC for the last 40 years. Major progress has been made by the introduction of regimens containing new cytotoxic drugs such as irinotecan² or oxaliplatin.³ The combinations commonly used, such as FOLFIRI (leucovorin, FU, and irinotecan) and leucovorin, FU, and oxaliplatin can reach an objective response rate of approximately 50%.^4,5 However, these new combinations remain inactive in about half of the patients, and in addition, resistance to treatment appears in almost all patients who initially were responders. More recently, two monoclonal antibodies targeting the vascular endothelial growth factor (bevacizumab) and the epidermal growth factor receptor (cetuximab) have been approved for treatment of metastatic CRC but only in combination with standard chemotherapy regimens.^6,7

A major clinical challenge is to identify a subset of patients who could benefit from chemotherapy, both in metastatic and adjuvant settings. There have been many attempts to determine predictive factors for response. Alterations in gene expression, protein expression, and polymorphic variants in genes encoding thymidylate synthase, dihydropyrimidine dehydrogenase, and thymidine phosphorylase have been reported to predict response to FU.^8-10 In addition, microsatellite-instability status could be an independent predictor of FU-based adjuvant chemotherapy,¹¹ and topoisomerase I expression has been investigated as a predictive factor for response to irinotecan.¹² High mRNA expression of excision repair cross-complementing 1 and thymidylate synthase have shown to be predictive of poor response to treatment of advanced disease with oxaliplatin and FU.¹³ However, although predictive factor testing is an exciting field of research, it has not yet been applied routinely in clinical practice.^14,15 Furthermore, an in vitro study on prediction of response of colon cancer cells demonstrated that the measurement of multiple rather than single marker genes resulted in a more accurate assessment of drug response.¹⁶

Gene expression profiling has become a strategy to predict clinical outcome or to classify molecular tumor subtypes. Several studies have been conducted that show the feasibility of identifying genes involved in the progression and the prognosis of CRC^17-21 or for predicting drug response in other cancer types, notably in breast cancer.^22-24 However, no indication on the possible added value of this approach for predicting drug response in colon cancer has been reported.²⁵ Only a recent study showed that gene expression profiling might contribute to the response prediction of rectal adenocarcinomas to preoperative chemoradiotherapy.²⁶

In this report, our objective was to build a predictor classifier for response to FOLFIRI treatment in patients with advanced CRC using microarray gene expression profiles of primary colon cancer tissue.

	PATIENTS AND METHODS

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Patients and Sample Collection
From January 2000 to June 2004, we enrolled onto a prospective study 40 CRC patients with synchronous and unresectable liver metastases at the Val d'Aurelle Regional Cancer Center (Montpellier, France). The eligibility criteria for inclusion were histologically proven adenocarcinoma of the colon, advanced and bidimensionally measurable disease, age 18 to 75 years, and WHO performance status of 2 or less. All patients were chemotherapy naive. Liver metastases were determined as unresectable when there was an impossibility of performing resection of all the lesions with clear margins or when there was an extrahepatic disease involvement.

Before receiving any chemotherapy, all patients underwent surgery for primary tumor resection independently of their symptomatic status. R0 resection was achieved in all patients. Colon tumor samples were collected at the time of surgery according to a standardized procedure to obtain high-quality RNA.²⁷

The study was approved by our local ethical committee; all participating patients were informed about the study and had to provide signed, written, informed consent before enrollment.

Chemotherapy
Patients were treated with a combination of irinotecan with an LV5FU2 (folinic acid [or leucovorin] 200 mg/m² as a 2-hour infusion followed by FU 400 mg/m² bolus and 2,400 mg/m² as a 46-hour continuous infusion) regimen (FOLFIRI) as first-line treatment. Ten patients participated in a multicenter phase II clinical trial aimed at assessing whether increasing the dose of irinotecan (from 180 to 260 mg/m²) in the FOLFIRI regimen would benefit patients with metastatic CRC. The remaining patients received a FOLFIRI regimen with a standard dose of irinotecan (180 mg/m²). For one patient, intravenous FU was replaced by an oral form of FU (uracil/ftorafur).

Tumor response was evaluated according to WHO recommendations for the evaluation of cancer treatment in solid tumors.²⁸ The size of the metastatic lesions was estimated from bidimensional measurements (the product of the longest diameter and the longest perpendicular diameter) using computed tomography scanning. Patients were evaluated for response before and after every four cycles of chemotherapy for a regimen of 3-week cycles and after every six cycles of chemotherapy for a regimen of 2-week cycles, to calculate the percent change from baseline. Best observed response was then used to classify patients into two groups. Patients with a decrease 50% of the metastatic lesion were classified as responders, and patients with a decrease less than 50% or with an increase in size of lesions were classified as nonresponders.

RNA Preparation and Assessment of RNA Quality
All tissue samples were maintained at –180°C (liquid nitrogen) until RNA extraction and were weighed before homogenization. Tissue samples were then disrupted directly into a lysis buffer using Mixer Mill MM 300 (Qiagen, Valencia, CA). Total RNA was isolated from tissue lysates using the RNeasy Mini Kit (Qiagen), and additional DNAse digestion was performed on all samples during the extraction process (RNase-Free DNase Set Protocol for DNase treatment on RNeasy Mini Spin Columns; Qiagen). After each extraction, a small fraction of the total RNA preparation was taken to determine the quality of the sample and the yield of total RNA. Controls analyses were performed by UV spectroscopy and analysis of total RNA profile using the Agilent RNA 6000 Nano LabChip Kit with the Agilent 2100 Bioanalyser (Agilent Technologies, Palo Alto, CA) to determine RNA purity, quantity, and integrity.

Gene Expression Analysis
First-strand cDNA synthesis was generated using a T7-linked oligo-dT primer, followed by second-strand synthesis. Labeled cRNA probes were then generated by reverse transcription followed by in vitro transcription, incorporating biotin labeling, as part of the standard protocol (Affymetrix Inc, Santa Clara, CA). For each sample, the probes were then hybridized to human genome U133 chips (Affymetrix) containing more than 45,000 qualifiers, corresponding to genes and expressed sequence tags. After hybridization, the probes were scanned using a laser scanner, and signal intensity for each transcript and detection call (present, absent, or marginal) were determined using MAS 5.0 Software (Affymetrix). Interarray normalization was performed using a set of internal standard genes (normalization set + internal controls) leading to the determination of a scaling factor.

Statistical Analysis
To assess differences in clinicopathologic features between responder and nonresponder patients, Fisher's exact test was used for qualitative variables with discrete categories and the Wilcoxon test was used for continuous variables. The Kaplan and Meier method was used to calculate overall survival from the treatment start date to the date of death, or the date that the surviving patients were last seen.

To determine gene signature, we kept only gene called present in at least 50% of the patients from any one group. Data analysis was performed on the 19,365 expressed genes. Differentially expressed genes between responders and nonresponders were detected by means of the significance analysis of microarrays algorithm (SAM).²⁹ This approach calculates a d score, which corresponds to a t statistic with a small positive constant added to the denominator. This value was chosen to minimize the coefficient of variation.

These genes were then classified according to this score and their statistical significance. A set of genes with a false discovery rate of 20% was then selected.

The genes selected by the SAM algorithm were then ranked by computing the empirical area under the receiver operating characteristic (ROC) curve (AUC) and the empirical partial AUC (pAUC), which is restricted to a clinically relevant pertinent range of false-positive rates.³⁰ The pAUC is an index of discrimination, and the chosen false-positive rate interval allows consideration of a high specificity to detect the responder population effectively. Then, the classification rule was defined with the support vector machines (SVM) algorithm.³¹ Two parameters were required: the radial basis function kernel method and the magnitude of the penalty for violating the soft margin. Finally, leave-one-out cross validation (LOOCV) was used to estimate the performance and accuracy of the output class prediction rule. With LOOCV, one sample is left out, and the remaining samples are used to construct a predictor classifier, which is used to classify the left-out sample.

	RESULTS

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Patients and Clinical Response
Of the 40 enrolled patients, only 27 were assessable for tumor response to FOLFIRI, given that we excluded patients who received another treatment or died before the first evaluation. Furthermore, five colon tumor samples were excluded on the basis of poor quality RNA (n = 2), low quantity RNA (n = 1), and poor chip expression quality (n = 2). Two other samples were excluded from one patient who presented two different primary tumor sites. Of the 21 eligible patients, nine (43%) were considered sensitive to FOLFIRI treatment and showed a size reduction of metastatic lesion ranging from 52% to 94%, whereas 12 (57%) were considered as nonresponders, with a tumor size decrease of not more than 44% or a tumor size increase of up to 25% from nadir (Table 1).

To determine the differentially expressed genes between responders and nonresponders, we used the SAM method. Based on a false-discovery rate of 20%, about 5,000 discriminatory genes were selected and ranked according to their statistical significance. For each gene, using a nonparametric procedure, we estimated the total AUC and the pAUC for the ROC curve. Estimation of the pAUC was restricted to the region where the specificity was at least 90%. Genes were then ranked according to AUC and pAUC values, and for each indicator we retained the top 40 genes. This process was repeated 21 times with a training set of 20 samples (each time, one sample was left out). To establish a stable signature, we selected the genes common to the 21 AUC lists (eight genes) and those common to the 21 pAUC lists (11 genes). Finally, because some genes were common to both the final AUC and pAUC lists, we retained a set of 14 discriminatory genes (Table 3). Unsupervised hierarchical clustering and principal component analysis were then applied to the 14 selected genes. This resulted, in both analyses, in a clear separation between responder and nonresponder patients (Fig 1).

View larger version (27K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 1. (A) Unsupervised clustering: column represents sample and row represents gene. Red indicates relatively high expression and green indicates relatively low expression. (B) Principal component analysis, which involves a mathematical procedure representing the maximum of the data information by reducing the space dimension: 80% of the information was explained with only three principal components.

To assess the misclassification rates, we used the approach described by Michiels et al,³² that consists in dividing the data set into training sets of different sizes (from five to 19 samples). The remaining samples were considered as a validation set (size, two to 16 samples). A total of 500 random training sets were associated with each sample size. For a given training set, a classifier was built by SVM using the14 selected genes and tested in a designated validation test. Even with the smallest training size, the misclassification rate was only 25.6% (95 CI, 19% to 34%) and from a training set size more than 13, the misclassification rate did not exceed 7.5% (Figure 2).

View larger version (11K): [in this window] [in a new window] [PowerPoint Slide for Teaching]   Fig 2. Proportion of misclassification in validation sets as a function of the corresponding training set size: (——) the misclassification rate, which is equal to mean proportion obtained from 500 random training-validation sets; (- - -) 95% CIs.

	DISCUSSION

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Current treatment strategies for CRC are far from optimal, due in part to the appearance of drug resistance in about half of the patients. In the metastatic setting, administration of chemotherapy likely to induce a maximal response in the first course of treatment is critical to enhance overall treatment success. This is even more crucial for adjuvant treatment, for which the rationale is to reduce the rate of tumor recurrence and mortality in patients who have undergone curative surgery (stages II and III). This emphasizes the importance of identifying predictive factors of response to treatment. Despite the identification of single markers capable of predicting drug response, their predictive efficacy remains insufficient to allow their use in routine clinical practice.²⁵

This study was designed to identify a pattern of gene expression able to predict response to FOLFIRI in CRC patients with synchronous and unresectable liver metastases. This combination of irinotecan, FU, and leucovorin is considered as one of the reference first-line treatments for metastatic CRC, with objective response rates of 49% and 56%.^4,33 In our series of 21 patients, the response rate to FOLFIRI was 43% and median overall survival was 21 months, in agreement with the available literature data.

To determine the gene signature for response prediction from transcriptome studies, we first selected the significant differentially expressed genes from the large data set of gene expression. Then we used statistical measures of discrimination. The ROC curve provides a visual description of the trade-off between false-positive and true-positive rates for all possible threshold values. The AUC is the most commonly used index to estimate the global discriminative power of a diagnostic test. It has been suggested that the pAUC³⁴ restricts attention to a region of the marker space associated with a high specificity (probability of detecting the responder patients). The use of both of these indices allowed us to select marker genes presenting a strong global discriminant power associated with high sensitivity and specificity. We finally obtained a 14-gene signature that accurately (95%) predicted the response to FOLFIRI. Moreover, by applying a multiple random training-validation strategy,³² we showed that from a training set of 13 patients the misclassification rate did not exceed 7.5%. This result suggests that our 14-gene signature is stable and was not affected by the small sample size.

In the gene expression profiles associated with clinical outcomes, it is not clear whether these genes are causal or merely markers. The signature does not include genes from pathways commonly known to be involved in resistance mechanisms (eg, drug inactivation, drug efflux, repair DNA damage, or defects in apoptosis).³⁵ However, among the 14 genes, three genes (galectine-8, PSG9, and SERPINE2) could be involved in the adhesion process. Galectin-8 is a matricellular protein that positively or negatively regulates cell adhesion, depending on the extracellular context.³⁶ Moreover, the quantitative determination of the immunohistochemical expression of galectin-8 in the series of colon cancer specimens clearly showed that the extensively invasive colon cancers exhibited significantly less galectin-8 than did locally invasive colon cancers.³⁷ PSG9, which is ectopically upregulated in vivo by colon cancer cells,³⁸ has an arginine, glycine, and aspartic acid (RGD) motif in a conserved region in the N-terminal domain, which suggests that these genes may function as adhesion-recognition signals for integrins. The serine proteinase inhibitor SERPINE2 could participate in maintaining the integrity of connective tissue matrices. SERPINE2 has been shown to inhibit tumor cell–mediated extracellular matrix destruction.³⁹ Two other genes, FVIII and ANGPTL2, could reflect tumor vascularization. Indeed, intratumoral angiogenesis is commonly quantified by a microvessel density measurement using immunohistochemical staining with monoclonal antibodies against factor VIII.⁴⁰ ANGPTL2 protein induces sprouting in vascular endothelial cells and promotes angiogenesis.⁴¹ In combination, these results support the idea that the responders' tumors seem more adhesive and vascularized than those of the nonresponders. Given that adhesive interactions are known to play a role in the metastatic process, we cannot exclude that this gene pattern also reflects mechanistic differences in metastasis formation between both groups.

This study provides new insights into the treatment of CRC. One major application would be to use the signature as a decision tool to assist oncologists in selecting CRC patients who could benefit from chemotherapy, both in the adjuvant and the first-line metastatic setting. Applying the 14-gene signature, we are able to detect all of the responder patients from our cohort. To our knowledge, this is the first predictor classifier based on microarray gene expression in colon cancer. In this cancer, only gene signatures predicting prognosis^19,21,42,43 have been determined. The advantage of our prospective single-center study is that the genomic and clinical data quality was homogeneous. However, because the results are based on a relatively small sample size, it is essential to validate and, if necessary, to improve this 14-gene signature in a larger independent cohort of patients. We currently are conducting a national multicenter study with five times as many patients. Then, a randomized phase III clinical trial could be set up to compare the signature classifier with the usual clinical characteristics in selecting CRC patients for chemotherapy.

	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: Caroline Fraslon, Sanofi-aventis; Emmanuel Conseiller, Sanofi-aventis Leadership: N/A Consultant: N/A Stock: N/A Honoraria: N/A Research Funds: Maguy Del Rio, Sanofi-aventis; Franck Molina, Sanofi-aventis; Virginie Copois, Sanofi-aventis; Corinne Bareil, Sanofi-aventis; Nicolas Salvetat, Sanofi-aventis; Benjamin Leblanc, Sanofi-aventis; Bernard Pau, Sanofi-aventis; Pierre Martineau, Sanofi-aventis Testimony: N/A Other: N/A

	AUTHOR CONTRIBUTIONS

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

 
Conception and design: Maguy Del Rio, Franck Molina, Bernard Pau, Marc Ychou

Provision of study materials or patients: Maguy Del Rio, Virginie Copois, Frédéric Bibeau, Patrick Chalbos, Virginie Granci, Marc Ychou

Collection and assembly of data: Maguy Del Rio, Frédéric Bibeau, Patrick Chalbos, Virginie Granci, Marc Ychou

Data analysis and interpretation: Maguy Del Rio, Franck Molina, Caroline Bascoul-Mollevi, Corinne Bareil, Andrew Kramar, Nicolas Salvetat, Caroline Fraslon, Emmanuel Conseiller, Benjamin Leblanc, Pierre Martineau, Marc Ychou

Manuscript writing: Maguy Del Rio, Franck Molina, Pierre Martineau, Marc Ychou

Final approval of manuscript: Maguy Del Rio, Franck Molina, Caroline Bascoul-Mollevi, Virginie Copois, Frédéric Bibeau, Patrick Chalbos, Corinne Bareil, Andrew Kramar, Nicolas Salvetat, Caroline Fraslon, Emmanuel Conseiller, Virginie Granci, Benjamin Leblanc, Bernard Pau, Pierre Martineau, Marc Ychou

	ACKNOWLEDGMENTS

 
We thank S.L. Salhi, PhD, for careful reading of the manuscript.

	NOTES

 
Supported by sanofi-aventis and from the Centre National de la Recherche Scientifique.

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

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Submitted May 15, 2006; accepted December 6, 2006.

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