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Gene Expression Analysis Proposes Alternative Pathways for the Mechanism by Which Celecoxib Selectively Inhibits the Growth of Transformed but not Normal Enterocytes

Authors' Affiliation: The Integrated Cancer Prevention Center, Tel Aviv Medical Center and Tel Aviv University, Tel Aviv, Israel

Requests for reprints: Nadir Arber, Integrated Cancer Prevention Center, Tel Aviv Medical Center, 6 Weizmann Street, Tel Aviv 64239, Israel. Phone: 972-3-697-4968; Fax: 972-3-695-0339; E-mail: narber{at}post.tau.ac.il.

	Abstract

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Purpose: Cyclooxygenase-2 inhibitor (celecoxib, Pfizer) is a promising chemopreventive agent, yet its long-term use may be limited due to increased cardiovascular toxicity. This study was aimed to identify genes and pathways involved in colorectal tumorigenesis and affected by celecoxib.

Experimental Design: Normal rat enterocytes (IEC18 cells) and their Ras-transformed derivatives (R1) were exposed for 72 h or over 6 months to celecoxib and analyzed for gene expression pattern using Genechip (RG-U34). Cluster and pathway analyses were done using GeneSpring software and Gene Ontology database. Cyclin D1 was overexpressed in IEC18 cells using stable transfection; cell cycle and prostaglandin synthesis were assessed.

Results: Five hundred thirty-eight genes were differentially expressed after transformation, and 70 and 126 genes, respectively, were affected by short and long treatments with celecoxib. Clusters of expression showed different expression in the transformed cells that revert to normal after treatment; they included Ras/Erk/Ral-B, Jagged2/Notch, calcineurin, lysyl-oxidase, etc. Cyclin D1 is up-regulated under the Ras pathway and is down-regulated by celecoxib. Thus, we showed that cyclin D1–transformed cells are resistant to inhibition by celecoxib. Celecoxib was also shown to work via cyclooxygenase-2 inhibition in transformed cells.

Conclusions: Celecoxib selectively affects transformed and not normal enterocytes by targeting genes and pathways that are involved in the transformation. Thus, an alternative mechanism is proposed for the cancer-preventive role of celecoxib other than the classic mechanism of inhibiting prostaglandin synthesis, stressing mainly the role of cyclin D1. These data may help in the development of safer and more effective preventive drugs.

Up-regulation of cyclooxygenase-2 (COX-2) occurs in 40% to 50% of colorectal adenomatous polyps and in up to 85% of carcinomas (5). The lack of COX-2 expression in normal colonic mucosa, along with its increased expression in malignant tissue, rationalizes the significant and selective action of COX-2 inhibitors in both the primary and secondary preventions of colorectal cancer. The association between nonsteroidal anti-inflammatory drugs (NSAID) and colorectal neoplasia has thus been studied extensively. Works by Arber et al. (6) and Bertagnolli et al. (7) have recently shown the effectiveness of the selective COX-2 inhibitor, celecoxib, in preventing adenoma formation in the colorectal mucosa. Its long-term use may be limited due to increased cardiovascular system toxicity as, overall, three large-scale clinical studies found a hazard ratio of 1.9 for cardiovascular events (8).

This study was aimed to find genes and pathways that are unique to malignant transformed cells and are involved in the chemopreventive action of NSAIDs, as former studies teaches that a wide range of mechanisms are targeted by these family of drugs. Cellular proliferation in the colonic mucosa may be directly influenced by NSAIDs (9) by down-regulating prostaglandin E₂ (PGE₂) synthesis, a mechanism that diverts the arachidonic acid cascade into lipoxygenase metabolites by inhibiting COX enzymes (10). Programmed cell death, apoptosis, is another putative target for NSAIDs, as shown in cell culture and animal studies (11–16). We seek to elucidate the importance of these and novel alternative pathways for NSAIDs in chemoprevention.

Microarray technology for gene expression profiling was successfully used in the past to pin down genes that can be useful as targets for new treatment modalities and to predict the response of the individual patient to chemotherapy (17, 18). Two in vitro studies tested for the effect of non-COX2–specific NSAIDs on colorectal cancer cell lines detecting 130 and 140 genes that alter in sensitive cell lines but not in a resistant cell line to sulindac and aspirin, respectively (19, 20). In this study, we applied this method to analyze the mechanism of action of celecoxib in a unique in vitro model developed in our laboratory. The model consists of normal, although immortalized, rat enterocytes (IEC18 cells) and Ras-transformed IEC18 cells (R1; refs. 21–24); these cells do not go through cell senescence, yet provide the closest model for normal enterocytes grown in culture. The IEC18 cells have a near diploid karyotype, without mutations in ras, APC, or p53. Moreover, they are contact inhibited; they do not grow in soft agar; their plating efficiency is zero; and they do not produce tumors when injected s.c. into nude mice. The transformed cells proliferate faster, form colonies in soft agar, and have higher saturation density and plating efficiency. Most importantly, they form tumors when injected s.c. into nude mice.

Cyclin D1 is already a well-established oncogene. It functions as an important modulator of cell cycle, overexpressed during the G₁ (25, 26). Expression of cyclin D1 stimulates DNA synthesis, accelerates cell proliferation, and can by itself lead to a malignant transformation of enterocytes (27). Overexpression of cyclin D1 is an early event at the carcinogenesis of the gut and elevated nuclear expression levels of cyclin D1 were also found in 67% of esophageal cancers (both squamous cell and adenocarcinomas; refs. 28, 29), 48% of gastric tumors (28), 35% of pancreatic adenocarcinomas (30), and 43% of small (31) and 30% of large bowel (32) tumors, respectively.

In recent preliminary studies, we found that celecoxib inhibited cell growth and induced apoptosis in a time- and dose-dependent manner, whereas rofecoxib did not inhibit cell growth at all (22). With those cell lines, we carried out gene expression profiling [using Affymetrix rat (RG-U34) Genechip] after short (72 h) and long (6 months) durations of treatment with celecoxib, looking for a differential gene expression profile in the transformed enterocytes compared with the normal cell line, which revert to normal after exposure to the drug.

	Materials and Methods

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Cell cultures. IEC18 and IEC18-ras (R1) cells were maintained in complete medium, DMEM, and were supplemented with 5% FCS, 1% glutamine, and 1% antibiotics (Bet-Ha'Emek). R1 cells were produced by cotransfection with the drug resistance selectable marker tk-neo and the plasmid pMIKcys, which encodes a mini–human c-K-ras gene (15 kb) that contains a cysteine mutation at codon 12 (24). To evaluate the patterns of gene expression in celecoxib-sensitive and celecoxib-resistant enterocytes, the IEC18 and R1 derivatives were established from parent cells: Sensitive cells are cells that were treated with celecoxib (20 µmol/L) for 72 h. Resistant cells were prepared by exposing the cells to gradually increasing concentrations of celecoxib, 10% increase every four passages, starting at a concentration equal to IC₂₀. After 6 months, IEC18- and R1-resistant cells could tolerate 30 and 17 µmol/L of celecoxib, respectively.

Preparation of labeled RNA. Total RNA was extracted from these cells while being active in mitosis, in 70% confluence using Tri Reagent (Sigma-Aldrich). The quality and amount of total RNA were analyzed both by using an agarose gel and a spectrophotometer. The RNA was then used as a template for double-stranded cDNA synthesis with an oligo-(dT)24 primer containing a T7 RNA polymerase promoter site added to the 3' end (Genset). The cDNA was extracted with phenol/chloroform, ethanol precipitated, and used as a template for in vitro transcription (Ambion T7 Megascript system) with biotin-labeled nucleotides (Enzo Diagnostics). Labeled cRNA was fragmented and a hybridization mix was generated as recommended (Affymetrix, Inc.).

Hybridization of microarrays. Aliquots of each sample (10 µg cRNA in 200 mL hybridization mix) were hybridized to a rat (RG-U34) Genechip (Affymetrix). After hybridization, each array was washed, stained with streptavidin phycoerythrin (Molecular Probes), washed again, hybridized with biotin-labeled anti–streptavidin phycoerythrin antibodies, restained with streptavidin phycoerythrin (Molecular Probes), and scanned.

Analysis of the Genechip data. The algorithm, implanted in Affymetrix Suite Version 5.0 (MAS5), generates signal value (which designates a relative measure of the abundance of the transcript), a detection P value (which indicates the reliability of the transcript detection call), and detection call (present, absent, or marginal). The detection calls were calculated based on detection P value as follows: probe sets with P > 0.06 were designated as absent, 0.06 > P > 0.04 as marginal, and P < 0.04 as present. For interarray comparisons, the data from each array was scaled using MAS5. The mean intensity for each array was corrected by a scaling factor to a set target intensity of 150.

The bioinformatics analysis was carried out using GeneSpring version7 software (Silicon Genetics).

For the normalization procedure, values below 0.01 were set to 0.01. Each measurement was divided by the 50th percentile of all measurements in that sample (per chip normalization). Each gene was divided by the median of its measurements in all samples (per gene normalization). For filtering, genes that had a "present" detection call in at least three of six samples were chosen, with 3,242 left for further analysis.

Annotation analysis. NetAffex database was used to extract relevant probe sets according to annotation demands. Functional classification in Gene Ontology of list of genes that discriminates subclasses of samples was examined to find annotation categories that are overrepresented compared with the representation in the array. Annotation analysis was preformed using "David".¹ This is an online database hosting tools for annotation analysis, among them the EASE software application. EASE provides statistical methods for discovering enriched biological themes within gene lists using this; we used Fisher's exact probability to choose categories that were significantly overrepresented (P < 0.05).

Construction and transfection of the cyclin D1 expression plasmid. The 1.1-kb human cyclin D1 cDNA containing the entire coding sequence was subcloned into the expression vector pMV7 as was previously described (27). The resulting plasmid contained a Moloney murine leukemia virus 5' long terminal repeat and carried neomycin as a selectable marker and a 3' Moloney murine leukemia virus long terminal repeat. The vector was transfected into IEC18 cells using LipofectAMINE (Invitrogen), and resistant cells were selected in complete medium with 0.2 mg/mL G418 for 3 weeks. Drug-resistant (neo+) clones were isolated (designated D1-D10). Three clones (D1-D3) were chosen for further expansion because they expressed high levels of cyclin D1 (27) as shown in Western blot analysis (done as described ref. 27); clone D1 was chosen for analysis in this study.

Assays for growth inhibition. Cells were plated at a density of 7 x 10⁶/10-cm dish in complete medium. The next day, the medium was replaced with a complete medium containing celecoxib (Pfizer), dissolved in DMSO, at the indicated concentrations for 72 h. The adherent and nonadherent cells were collected, during exponential growth of the cells, and counted. Then, 1 x 10⁶ to 2 x 10⁶ cells were washed in PBS, and the pellet was fixed in 3 mL of ethanol for 1 h at 4°C. Cells were pelleted and resuspended in 1 mL PBS and incubated for 30 min with 0.64 mg/mL RNase at 37°C. Cells were stained with 45 µg/mL propidium iodide, at least 1 h before analysis, by flow cytometry using a standard protocol for cell cycle distribution and cell size (27, 33). All experiments were repeated thrice with similar results. Data acquisition was done on a FACScan and analyzed using CellQuest software (Becton Dickinson Immunocytometry Systems). Debris was eliminated from the analysis by using a forward-angle light scatter threshold.

Measurement of PGE₂ concentration. IEC18, R1, and D1 cell lines were treated for 72 h with 10 and 20µmol/L of celecoxib. PGE₂ concentration in the medium, as released by the cells, was determined by a commercially available PGE₂-specific enzyme-linked immunoassay (R&D Biosystems) according to the protocol of the manufacturer.

	Results

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Nonsupervised analysis differentiated transformed from nontransformed cells. Nonsupervised hierarchical clustering, using 3,242 genes that were filtered on a nonparametric basis as described in Materials and Methods, distinguished perfectly between the enterocytes that had transformed and those that had not (Fig. 1 ). Nonsupervised analysis did not differentiate between cells that were and were not treated with celecoxib, presumably because the normal enterocytes are barely affected by the drug. This result serves as one validation to the quality of the experiment.

View larger version (39K): [in this window] [in a new window] [Download PPT slide]   Fig. 1. Nonsupervised analysis using 3,242 genes that were present in at least three of the six samples carried out by GeneSpring software. A, Ras-transformed cells were clustered separately from nontransformed cells: The three left signs at the bottom stand for all three samples consisting of IEC18 cells and the three right for those consisting of R1, Ras-transformed cells. B, samples were not clustered according to their treatment status: First and fourth signs stand for the two samples of RNA produced from cells that were not exposed to celecoxib, the second and sixth signs for resistant cells that were treated with the drug for at least 6 mo as indicated, and the third and fifth for short exposure to celecoxib (72 h) as indicated.

Cluster analysis detected genes that change expression after transformation and reverts to normal after treatment with celecoxib. K-means clustering analysis with four predetermined clusters was applied. Of these, three (1, 2, and 4) are characterized by a change in expression after transformation (overexpression or underexpression) that revert to normal after treatment with celecoxib (Fig. 2A, B, and D ). These genes are essentially what we are looking for while trying to elucidate the preventive mechanism and downstream targets of celecoxib. Cluster 3 describes genes without alteration in expression at all among the six samples (Fig. 2C).

View larger version (28K): [in this window] [in a new window] [Download PPT slide]   Fig. 2. K-means clustering of the 3,242 genes filtered on a nonparametric basis as described. A, 24 genes mainly down-regulated in transformed cells under no treatment. B, 37 genes mainly up-regulated in transformed cells under no treatment. C, 35 genes that showed no change between the cells. D, 37 genes mainly up-regulated in transfected nontreated mice.

PGE₂ is oversynthesized in the transformed cells, inhibited by celecoxib. Because COX-2 is overexpressed at cancer cells, we tested the extent to which the classic mechanism of celecoxib, COX-2 inhibition, acts in our cellular model. For that reason, we determined the levels of PGE₂ secreted to the growth medium, the end-product of the cyclooxygenases. Cells after transformation, the R1 cells, showed 16-fold more PGE₂ than cells before transformation, IEC18 (Fig. 2D). Under treatment with celecoxib, for 72 h, PGE₂ synthesis is reduced to normal level (Fig. 2D). A very mild effect was seen in IEC18 cells treated with celecoxib. The inhibition was even milder for the cells that had undergone malignant transformation due to overexpression of cyclin D1, which showed little synthesis of PGE₂ to begin with.

	Discussion

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The main significance of the current study is that it suggests some of the underlying mechanisms that make celecoxib such an effective drug in the prevention of colorectal cancer. It confirms our previous observations that celecoxib selectively inhibits the growth of transformed, but not normal, intestinal epithelial cells.

The main proof lies within the cluster analysis presented (Fig. 3 ), where at all the clusters (besides the one that consists of genes that were equally expressed throughout the experiment) no changes were seen after exposures of the normal immortalized cells to the drug, but a reversion toward normal expression was clearly seen while treating the Ras-transformed cells. These results are in alignment with the recent clinical observations, in large clinical trials, that celecoxib can prevent colorectal neoplasia with no side effect. The bioinformatic analysis allows only a limited interpretation of the results. This is mainly because of the small number of samples relative to the number of comparisons that makes a correct statistical hypothesis testing with proper correction for multiple comparisons not entirely applicable; the presented data are thus mainly descriptive. This work is therefore a hypothesis-generating technique as a first step toward a deeper investigation of the discussed chemopreventive agent. However, validation to the results lies within the annotation analysis that among the differentially expressed genes revealed pathways that are interesting for the biologist in the context of the study framework, promising interesting directions for further studies. These results were biologically confirmed in other works by reverse transcription-PCR (21–24, 33).

View larger version (16K): [in this window] [in a new window] [Download PPT slide]   Fig. 3. A, Western blot analysis confirmed that the clone designated D1 overexpresses cyclin D1 as against a very weak expression in the normal cells, IEC18, and a vector control, as previously described (28). B, a scheme of the differential expression of cyclin D1 in the microarray analysis: An elevation in expression is shown after Ras transformation, which is significantly inhibited by short and long treatments with celecoxib. C, IEC18 cells, and their transformed derivative overexpressing cyclin D1 (D1) or k-Ras (R1), were analyzed through fluorescence-activated cell sorting after staining with propidium iodide for their cell cycle properties under 72 h treatments with the indicated concentrations of celecoxib. D, IEC18, D1, and R1 were treated with celecoxib for72 h at selected doses. PGE2 levels in the culture medium were measured by enzyme immunoassay as described. Columns, mean of two experiments; bars, SD.

Using pathway analysis available online, we were able to trace cellular functions that are affected by the transformation and driven back to normal activity after treatment with celecoxib and thus offer alternative pathways by which the drug serves in the prevention of tumor formation. Genes that were included among the three clusters mentioned above are potentially involved in pathways that are related to tumor formation and, hence, are targets for celecoxib. An important pathway that is represented in all three clusters is negative regulation of nucleic acid metabolism. This finding conforms to a conclusion of a former publication of our group that celecoxib reduces cell proliferation rates by inducing a G₂-M arrest in human colorectal cancer cell lines (33). A second pathway that seems to be significant is the carbohydrate metabolism, including the genes insulin 1 and insulin 2. We showed that the transformed cells are capable of a higher metabolism that is suppressed by celecoxib. The fact that glucose consumption is elevated in correlation with tumor cell density and activity is also well established, and is the basis for the positron emission tomography scanning methodology (34, 35).

Furthermore, single genes which activity is normalized after treatment are evident and are suggested to be the alternative target of celecoxib besides affecting prostaglandin synthesis. Especially important as a drug target is cyclin D1, which passed false discovery rate correction under strict criteria for its expression pattern, being overexpressed after transformation and down-regulated by exposure to the drug. Thus, we confirmed the power of our results by overexpressing it in the same IEC18 cell–based system of transformation, and showed that this alone is sufficient to abolish the growth inhibition induced by celecoxib.

Cyclin D1 was shown before to be overexpressed in cancer cells harboring a hyperactive Ras pathway, and also to be down-regulated by the nonspecific COX inhibitor sulindac sulfide (12, 23). Also, cyclin D1 is vastly overexpressed in human colorectal cancer already at an early stage of the carcinogenesis process (32), which further rationalizes the use of celecoxib in vivo in the setting of colorectal cancer. This result is confirmed by a former study that had shown underexpression of three other cyclins in SW480 human colorectal cancer cells after exposure to sulindac (19).

Nonetheless, although this study is aimed to search for alternative pathways by which celecoxib affects cancer cells, we were also interested in confirming the level to which the classic mechanism of inhibition of prostaglandin synthesis acts in our model. We showed that transformed cells produce much more PGE₂ than normal cells. Celecoxib inhibits synthesis down to normal-level PGE₂. This pathway was shown to be nonrelated to those we define in the study, as cyclin D1–transformed cells do not express a high level of PGE₂.

Thus, we believe that this study is powered enough to single out other genes as targets of COX-2 inhibitors. Among these are genes that are highly involved in oncogenic signal transduction and were found to be overexpressed in R1, for example, Erk1, a proproliferative kinase of the mitogen-activated protein kinase family, which is also a downstream effector of the Ras pathway; Ral-B expression was interestingly suppressed by celecoxib, and also in a former study (19), by aspirin, Jagged 2, involved in the Notch signaling pathway for differentiation. Calcineurin binding protein 1 interacts with and inhibits the protein phosphatase calcineurin-mediated signal transduction that leads to transcription of genes, among which interleukin-2 and interleukin-8 (36), which were shown to contribute to cancer progression under the Ras pathway (37). Other genes among these clusters are potentially involved in cell morphology and motility and the relation with the extracellular matrix such as hyaluronidase 2, TYRO3 protein tyrosine kinase 3, and -tubulin. These data prove that a mutation in oncogene change the protein and behavioral milieu of the cell by cross-talking with many other pathways in which changes are crucial. However, other genes that are not yet known in relation to cancer are proposed by this study to have a potential role in tumorigenesis and chemoprevention, such as lysyl oxidase tyrosine 3-monooxygenase, very low density-lipoprotein receptor, and more. The nuclear factor GADD45, related to maintenance of genomic stability and DNA repair, was up-regulated at transformation and was also shown in a former study to be down-regulated in SW480 colorectal cancer cells after exposure to sulindac (19).

	Footnotes

 
Grant support: Pfizer, Inc.

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.

¹ "David" database and the EASE software are available online (http://apps1.niaid.nih.gov/david/) from the NIH (Bethesda, MD).

Received 5/ 7/07; revised 7/ 1/07; accepted 7/20/07.

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