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[Cancer Research 66, 2765-2777, March 1, 2006]
© 2006 American Association for Cancer Research

Experimental Therapeutics, Molecular Targets, and Chemical Biology

Pharmacogenomic Identification of Novel Determinants of Response to Chemotherapy in Colon Cancer

John Boyer1, Wendy L. Allen1, Estelle G. McLean1, Peter M. Wilson1, Andrea McCulla1, Stephen Moore2, Daniel B. Longley1, Carlos Caldas3 and Patrick G. Johnston1

1 Department of Oncology, Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, Northern Ireland; 2 Almac Diagnostics Ltd., Seagoe Industrial Estate, Craigavon, Northern Ireland; and 3 Department of Oncology, Hutchinson/Medical Research Council Research Centre, University of Cambridge, Cambridge, United Kingdom

Requests for reprints: Patrick G. Johnston, Department of Oncology, Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast City Hospital, University Floor, Lisburn Road, Belfast, BT9 7AB, Northern Ireland. Phone: 44-28-90263911; Fax: 44-28-90263744; E-mail: oncology{at}qub.ac.uk.


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
DNA microarray analysis was used to analyze the transcriptional profile of HCT116 colorectal cancer cells that were treated with 5-fluorouracil (5-FU) or oxaliplatin and selected for resistance to these agents. Bioinformatic analyses identified sets of genes that were constitutively dysregulated in drug-resistant cells and transiently altered following acute exposure of parental cells to drug. We propose that these genes may represent molecular signatures of sensitivity to 5-FU and oxaliplatin. Using real-time reverse transcription-PCR (RT-PCR), the robustness of our microarray data was shown with a strong overall concordance of expression trends for ≥82% (oxaliplatin) and ≥85% (5-FU) of a representative subset of genes. Furthermore, strong correlations between the microarray and real-time RT-PCR measurements of average fold changes in gene expression were observed for both the 5-FU (R2 ≥ 0.73) and oxaliplatin gene sets (R2 ≥ 0.63). Functional analysis of three genes identified in the microarray study [prostate-derived factor (PDF), calretinin, and spermidine/spermine N1-acetyl transferase (SSAT)] revealed their importance as novel regulators of cytotoxic drug response. These data show the power of this novel microarray-based approach to identify genes which may be important markers of response to treatment and/or targets for therapeutic intervention. (Cancer Res 2006; 66(5): 2765-77)


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Colorectal cancer is the second leading cause of cancer-related deaths in the Western world. Approximately one quarter of patients have incurable cancer at diagnosis and one half of patients who undergo potentially curative surgery will ultimately develop metastatic disease. The most active drug against this malignancy is the antimetabolite 5-fluorouracil (5-FU). In advanced colorectal cancer, 5-FU therapy modulated with folinic acid produces response rates of only 20% to 25% with a mean survival of 11 months (1). Efforts to improve efficacy have led to combinations of 5-FU and folinic acid with the topoisomerase I inhibitor irinotecan and the platinum-based agent oxaliplatin. These newer combinations have significantly improved response rates (to 40-50%) and prolonged overall survival (2, 3). Novel biological agents, such as the monoclonal antibodies cetuximab (an epidermal growth factor receptor inhibitor) and bevacizumab (a vascular endothelial growth factor inhibitor), have recently been shown to provide additional clinical benefit for patients with metastatic colorectal cancer (4, 5). Despite these improvements, >50% of patients undergo chemotherapy for advanced disease without any significant benefit.

The greatest problem associated with effective cancer treatment is drug resistance. Over recent years, a large number of studies have attempted to define molecular and biochemical markers that may be useful predictors of response to treatment. The introduction of DNA microarray technology has revolutionized our approach to understanding the molecular events regulating the drug-resistant phenotype, allowing the simultaneous assessment of thousands of genes. This approach provides a valuable means to identify novel biomarkers of response to treatment as well as novel molecular targets for therapeutic intervention. Despite the plethora of experimental and clinical data that exists for 5-FU and oxaliplatin, the molecular signaling pathways that regulate response to these agents are not clearly defined.

In this study, we used DNA microarray technology to analyze the mRNA expression profile of human colon cancer cell lines that were either transiently treated with 5-FU or oxaliplatin or selected for resistance to these agents. Bioinformatic analysis of these data identified sets of genes of which expression was constitutively altered in drug-resistant cells and induced or repressed following acute exposure of parental cells to drug. We propose that these genes may represent a molecular signature of response to 5-FU and oxaliplatin and provide important insight into the complex mechanisms of action of these drugs and the diverse signaling pathways involved in the development of resistance. We also report the validation and functional characterization of several novel target genes and investigate their roles in modulating the response of colorectal cancer cells to drug treatment.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Materials. 5-FU and oxaliplatin were purchased from Sigma Chemical Co. (St. Louis, MO) and Sanofi-Synthelabo (Malvern, PA), respectively. The polyamine analogue DENSpm and the SSAT polyclonal antibody were kind gifts from Dr. Robert Casero (John Hopkins University, Baltimore, MD). Prostate-derived factor (PDF) and calretinin antibodies were purchased from Upstate Biotechnology (Charlottesville, VA) and Chemicon International (Temecula, CA), respectively. ß-Tubulin and poly(ADP-ribose) polymerase (PARP) antibodies were purchased from Sigma and PharMingen (San Diego, CA).

Tissue culture. The p53 wild-type HCT116 human colon cancer cell line was kindly provided by Prof. Bert Vogelstein (John Hopkins University, Baltimore, MD). The 5-FU- and oxaliplatin-resistant HCT116 sublines and the p53 mutant R248W HCT116 cell line were generated in our laboratory as previously described (6, 7). All HCT116-derived cell lines were maintained in McCoy's 5A medium supplemented with 10% dialysed FCS, 50 µg/mL penicillin-streptomycin, 2 mmol/L L-glutamine, and 1 mmol/L sodium pyruvate. RKO and HT-29 cells (obtained from the National Cancer Institute, Bethesda, MD) were maintained in DMEM and supplemented as above. LoVo cells (kindly provided by AstraZeneca, Macclesfield, United Kingdom) were maintained in DMEM supplemented as above minus sodium pyruvate (all medium and supplements from Invitrogen Life Technologies Corp., Paisley, United Kingdom). All cell lines were maintained at 37°C in a humidified atmosphere containing 5% CO2.

Microarray analysis. HCT116 parental cells were treated with 5 µmol/L 5-FU or 1 µmol/L oxaliplatin for 0, 6, 12, and 24 hours as outlined in Fig. 1. Untreated 5-FU- and oxaliplatin-resistant cells were also analyzed to allow identification of constitutively dysregulated genes relative to the untreated (0 hour) parental line. Total RNA was isolated from three independent experiments using the RNeasy Total RNA Isolation Midi kit (Qiagen Ltd., Crawley, United Kingdom). Ten micrograms of total RNA were used to synthesize double-stranded cDNA using SuperScript II reverse transcriptase (Invitrogen) and a T7-(dT)24 primer (GENSET Corp., San Diego, CA). Biotinylated cRNA was synthesized from double-stranded cDNA using the Enzo Bioarray High Yield Transcript Labelling Kit (Enzo Life Sciences, Farmingdale, NY), purified using the GeneChip Sample Cleanup Module (Affymetrix, Santa Clara, CA) and fragmented. Twenty micrograms of fragmented cRNA were hybridized to an Affymetrix HGU133 Plus 2.0 oligonucleotide array, which was washed and stained with streptavidin-phycoerythrin and analyzed (CRUK Microarray Facility, Paterson Institute for Cancer Research, Manchester, United Kingdom). Detailed experimental protocols and raw expression data are available at http://www.ebi.ac.uk/arrayexpress/ (accession no. E-MEXP-390). Microarray data analysis was done using GeneSpring v7.1 (Agilent Technologies UK Ltd., Stockport, United Kingdom; Supplementary Materials and Methods).


Figure 1
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  		Figure 1. DNA microarray experimental design.

 
Real-time reverse transcription-PCR analysis. Total RNA was isolated using the RNA STAT-60 reagent (Biogenesis, Poole, United Kingdom) according to the instructions of the manufacturer. Reverse transcription was carried out with 1 µg of RNA using a Moloney murine leukemia virus–based reverse transcriptase kit (Invitrogen) according to the instructions of the manufacturer. Real-time reverse transcription-PCR (RT-PCR) amplification was carried out in a final volume of 10 µL containing 5 µL of 2x SYBR green master mix (Qiagen), 4 µL of primers (20 µmol/L), and 1 µL of cDNA using an Opticon DNA Engine Thermal Cycler (Bio-Rad Laboratories, Inc., Waltham, MA). All amplifications were primed by pairs of chemically synthesized 18- to 22-mer oligonucleotides designed using published DNA sequences (Supplementary Table S1). Before each experiment, primer-specific annealing temperatures were optimized by temperature gradient analysis. Reaction conditions were activation at 95°C for 15 minutes, denaturation at 95°C for 15 seconds, annealing 54 to 62°C for 30 seconds, and extension 72°C for 1 minute. All PCR amplifications were done for 40 cycles and melt curve analysis was used to examine the specificity of an amplified product. Standard curves were generated to quantify the absolute expression levels of each target gene and the 18S rRNA reference gene in each sample. The relative expression level of each gene in samples of interest was calculated by dividing the amount of normalized target by the value in an untreated calibrator sample.

Statistical analysis. R2 values were calculated using Pearson's correlation coefficient. The statistical significance of R2 was calculated using a one-tailed test of significance (SPSS 11.0 for Windows).

Small interfering RNA transfections. Small interfering RNAs (siRNA) were designed using the Invitrogen RNAi designer tool (http://www.rnaidesigner.invitrogen.com/rnaiexpress/) and purchased from Dharmacon, Inc. (Lafayette, CO). The target sequences used were calretinin, AAGGCTCTGGCATGATGTCAA; PDF, AATCCCATGGTGCTCATTCAA; and control siRNA (SC), AATTCTCCGAACGTGTCACGT. siRNA transfections were done on subconfluent cells incubated in unsupplemented Optimem medium using the oligofectamine reagent (both from Invitrogen) according to the instructions of the manufacturer. Cells were drug treated 4 hours after transfection and analyzed by flow cytometry or Western blot analysis.

Flow cytometry. Cells were harvested in PBS/0.5 mmol/L EDTA and pelleted by centrifugation at 1,000 rpm/4°C for 5 minutes. Cell pellets were washed once with PBS/1% FCS, fixed in 70% ethanol, and stained with propidium iodide. Analyses were done on a Beckman Coulter Epics XL flow cytometer (Miami, FL). Cells were gated on a dot plot display of forward scatter versus side scatter to extract aggregates and cell cycle populations were quantified using Wincycle histogram analysis software (Phoenix Flow Systems, San Diego, CA).

Immunoblotting. Western blots were done as previously described (6). Immunodetection was done using an SSAT, calretinin, PDF, PARP, or ß-tubulin primary antibody and a 1/2,000 dilution of a horseradish peroxidase–conjugated secondary antibody (Amersham, Buckinghamshire, United Kingdom). The fluorescent signal was detected using the Super Signal chemiluminescent detection system (Pierce, Rockford, IL) according to the instructions of the manufacturer.

Supplementary information. Supplementary data are available at Cancer Research Online.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Gene expression analysis. HCT116 colorectal cancer cells were treated with 5 µmol/L 5-FU or 1 µmol/L oxaliplatin (approximately IC60 doses at 72 hours) for 0, 6, 12, and 24 hours (Fig. 1). Following 5-FU treatment, 855 genes showed altered expression (≥2-fold) compared with untreated cells in at least one time point (Supplementary Fig. S1A). Following exposure to oxaliplatin for 24 hours, 1,233 up-regulated and down-regulated genes were identified (Supplementary Fig. S1B). When basal gene expression profiles were analyzed in the HCT116 5-FU- and oxaliplatin-resistant cell lines, 155 and 123 dysregulated genes (relative to parental cells) were identified, respectively (Supplementary Fig. S1C and D). Of the 855 genes acutely altered following 5-FU treatment, 116 (~14%) were also shown to be constitutively up-regulated or down-regulated in the 5-FU-resistant daughter cell line (Table 1; Supplementary Fig. S2A). Within this overlapping 5-FU gene set, regulators of cell growth such as PDF and cyclin G2 (CCNG2) were identified (Fig. 2). In addition, modulation of genes involved in regulating cell death, such as the caspase inhibitor AVEN, regulators of macromolecule metabolism, such as spermidine/spermine N1-acetyltransferase (SSAT), and the precursor of the Notch-1 ligand Jagged-1, Jag-1, was shown. Of the 1,233 genes acutely induced or repressed in the HCT116 parental cell line following oxaliplatin treatment, 37 genes (~3%) were also shown to be constitutively dysregulated in cells rendered resistant to oxaliplatin (Table 2; Supplementary Fig. S2B). Within the overlapping oxaliplatin gene set, two members of the collagen gene superfamily were identified, COL12A1 and COL13A1 (Fig. 2), which have been implicated in a number of diverse cellular processes including phosphate transport, skeletal development, and cell adhesion. Several calcium-binding proteins, including two members of the S100 gene family (S100A4 and S100A14) and the EF-hand family member calretinin (CALB2), were also identified. Of interest, several genes common to both the 5-FU and oxaliplatin gene sets were noted, including COL12A1, the coagulation factor, F3, the mucinlike adhesion molecule, CD24, and the developmental transcription factor, SOX-4.


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  		Table 1. 5-FU overlapping gene set

 

Figure 2
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  		Figure 2. Grouping of 5-FU and oxaliplatin gene sets according to biological process using the FatiGO data-mining web-tool (http://fatigo.bioinfo.cnio.es/). Examples of genes in each subcategory are highlighted.

 

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  		Table 2. Oxaliplatin overlapping gene set

 
Validation of microarray results. Real-time RT-PCR was used to verify the expression of a representative subset of genes from the overlapping 5-FU and oxaliplatin gene sets. Genes were selected on the basis of fold induction and signal intensity, with both highly and moderately inducible genes selected, as well as genes of high and low signal intensity. In addition, both up-regulated and down-regulated genes were analyzed, as well as genes which were common to both the 5-FU and oxaliplatin gene lists. For 20 5-FU and 11 oxaliplatin target genes, the average fold change in gene expression levels, as determined by microarray and real-time RT-PCR analysis, was log transformed and the correlation between both data sets examined using R2. As shown in Fig. 3A, an R2 value of 0.76 (P < 0.0005) was calculated for the 5-FU gene subset following treatment of HCT116 parental cells with 5-FU. When the same gene set was analyzed in the context of constitutive dysregulation in the 5-FU-resistant cell line, an R2 value of 0.73 (P < 0.0005) was obtained (Fig. 3B). Similarly, for the oxaliplatin gene subset, an R2 value of 0.63 (P < 0.0005) was calculated following acute exposure of parental cells to drug (Fig. 3C) and 0.73 (P = 0.05) when basal gene expression levels were analyzed in oxaliplatin-resistant and oxaliplatin-sensitive cells (Fig. 3D).


Figure 3
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  		Figure 3. Correlation between average fold change in gene expression levels for 20 5-FU and 11 oxaliplatin target genes as determined by DNA microarray and real-time RT-PCR analysis following treatment of HCT116 parental cells with 5-FU (A) and oxaliplatin (C) and analysis of basal gene expression levels in 5-FU- (B) and oxaliplatin-resistant cells (D) compared with parental cells. All data are displayed as log10.

 
Overall, statistically significant correlations (P < 0.05) were observed for 7 of 11 (64%) up-regulated genes and 3 of 9 (33%) down-regulated genes following treatment of HCT116 parental cells with 5-FU (Table 3A). Average correlations between the microarray and real-time RT-PCR analyses for these subsets of genes were 0.88 and 0.89, respectively. Furthermore, 7 of 8 (88%) up-regulated genes and 0 of 3 (0%) down-regulated genes following oxaliplatin treatment showed significant correlations (P < 0.05; Table 3B). For the oxaliplatin-inducible gene subset, an average correlation of 0.94 was observed.


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  		Table 3. R2 values for selected 5-FU (A) and oxaliplatin (B) target genes following treatment of HCT116 cells with each respective chemotherapy

 
In terms of expression trends, 19 of 20 (95%) genes induced or repressed following 5-FU treatment and 17 of 20 (85%) genes constitutively dysregulated in 5-FU-resistant cells were positively validated by real-time RT-PCR. Furthermore, for the oxaliplatin gene set, 9 of 11 (82%) up-regulated or down-regulated genes following drug treatment and 10 of 11 (91%) stably underexpressed or overexpressed genes were confirmed. Collectively, these data suggest a strong overall concordance of expression trends between the microarray and real-time RT-PCR data and strong correlations in terms of average fold change in gene expression levels for both the 5-FU and oxaliplatin gene sets.

Biological role of selected target genes in modulating drug resistance. To investigate the functional relevance of our data, several targets from the lists of genes identified both in cells transiently treated with 5-FU or oxaliplatin and cells selected for resistance to these agents were chosen for further analysis. Among target selection criteria were a strong correlation between the microarray and real-time RT-PCR measurements of average fold change in gene expression (thus showing that our microarray data for the selected target was robust); a potential novel role in drug resistance; and any overlap with previous data sets generated in our laboratory. The chosen targets were PDF, calretinin, and SSAT.

PDF is the first member of a divergent group within the transforming growth factor-ß (TGF-ß) superfamily. The major function of PDF is still unclear although it has been described as being able to inhibit tumor necrosis factor {alpha} production from lipopolysaccharide-stimulated macrophages (8), induce cartilage formation in the early stages of endochondral bone formation (9), and act as a neutrophic factor (10). In this study, PDF was identified in our microarray analysis as a highly inducible target gene following exposure of HCT116 parental cells to 5-FU (3.3-fold increase) for 24 hours as well as constitutively overexpressed (2.9-fold) in 5-FU-resistant cells (Table 1). Furthermore, a strong correlation was shown (R2 = 0.86; P = 0.036) between our real-time RT-PCR and microarray data comparing average fold change in PDF mRNA levels following 5-FU treatment (Table 3A). Our microarray data also showed inducible expression of PDF following treatment with oxaliplatin (5.2-fold); however, no significant difference in constitutive PDF expression levels was observed between the oxaliplatin-resistant and oxaliplatin-sensitive cells (data not shown). To determine whether our observations in the HCT116 cell line model were representative of human colon cancer drug response, our analyses were expanded to include a panel of colorectal cancer cell lines. As shown in Fig. 4A, PDF mRNA expression was increased by ~2- and 4-fold in the RKO cells following treatment for 24 hours with IC50 doses of oxaliplatin and 5-FU, respectively. Furthermore, an ~2- and 3-fold induction in PDF mRNA levels was observed in the LoVo and HT-29 cells following exposure to both agents. In the HCT248 cell line, a 2-fold increase in PDF mRNA levels was shown following treatment with 5-FU although no alteration in expression was observed in response to oxaliplatin.


Figure 4
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  		Figure 4. A, real-time RT-PCR analysis showing fold induction of PDF mRNA in RKO, HT-29, LoVo, and HCT248 cells following treatment with IC50 concentrations of oxaliplatin and 5-FU for 24 hours. B, Western blot analysis of PDF and PARP expression levels following treatment of HCT116 parental cells with 1 µmol/L oxaliplatin and 5 µmol/L 5-FU for 48 hours in the presence or absence of 5 nmol/L PDF-targeted siRNA. Line graphs illustrate the fraction of apoptotic cells (as measured by flow cytometry) following treatment of HCT116 cells with oxaliplatin (C) and 5-FU (D) for 48 hours in the presence or absence of 5 nmol/L PDF siRNA. ***, P < 0.0001; **, P = 0.0035, two-tailed unpaired t test (GraphPad Prism v4.02).

 
To examine the functional significance of PDF and elucidate its role in modulating response to 5-FU and oxaliplatin, a siRNA complementary to the PDF coding region was generated. Transfection of this siRNA (5 nmol/L) into HCT116 parental cells for 48 hours resulted in ~80% knockdown of PDF protein compared with cells transfected with a scrambled control siRNA duplex (Fig. 4B). Consistent with the transcriptional expression data, induction of PDF protein was observed following treatment of HCT116 cells with ~IC60 doses of 5-FU and oxaliplatin (Fig. 4B). However, transfection of drug-treated cells with 5 nmol/L PDF-targeted siRNA prevented chemotherapy-induced up-regulation of PDF. To analyze the effect of PDF down-regulation on drug sensitivity, expression of PARP protein was measured as an indicator of the levels of apoptotic cell death. As shown in Fig. 4B, transient transfection of HCT116 cells with PDF siRNA alone caused a moderate decrease in PARP levels compared with cells transfected with scrambled control siRNA. Treatment of cells with 1 µmol/L oxaliplatin or 5 µmol/L 5-FU alone for 48 hours also resulted in a moderate decrease in full-length PARP. However, when cells pretreated with PDF siRNA were exposed to either chemotherapy, expression of full-length PARP was almost completely lost. This would suggest that down-regulation of PDF sensitizes cells to drug treatment by enhancing apoptosis. To confirm these findings, flow cytometric analyses were carried out. As shown in Fig. 4C and D, transfection of HCT116 cells with PDF siRNA alone resulted in an ~5% to 10% increase in the levels of spontaneous apoptosis as measured by the subdiploid (<2N) cell population. In accordance with the PARP analysis, combined treatment with PDF siRNA and chemotherapy resulted in dramatic sensitization of cells to treatment with both oxaliplatin and 5-FU (Fig. 4C and D). A maximum increase in the sub-G0/G1 cell populations (~30%) was noted when 1 µmol/L oxaliplatin and 5 µmol/L 5-FU (equivalent to approximate IC60 doses) were combined with PDF-targeted siRNA. These results indicate that PDF inhibits chemotherapy-induced cell death in these cells.

Calretinin (CALB2) is a cytosolic calcium-modulating protein which was first identified in chick retinal tissue (11). It is a member of the EF-hand family of proteins, with expression limited to several distinct tissues including central nervous tissues and excitable cells, interstitial cells of the rat ovary (12), avian thymus tissue (13), and colorectal carcinoma cells (14). The specific function of calretinin still remains to be elucidated but a role in intracellular Ca2+ movement has been suggested in excitable cells and a role in cell cycle progression in nonexcitable tissue (15). In the present study, calretinin was identified in the microarray analysis as one of several calcium-binding proteins that were up-regulated in response to oxaliplatin treatment (2.0-fold; Table 2). Validation of calretinin induction following treatment with oxaliplatin was shown by real-time RT-PCR with an R2 value of 0.93 (P = 0.017; Table 3 B). Further studies revealed that calretinin mRNA expression was highly inducible following extended exposure (72 hours) of HCT116 parental cells to both 1 µmol/L oxaliplatin (9-fold increase) and 5 µmol/L 5-FU (5.4-fold increase; data not shown). Our microarray analyses also showed constitutive overexpression of calretinin in oxaliplatin-resistant cells (3.1-fold; Table 2). Of note, these data are consistent with a proteomic study carried out in our laboratory, which used two-dimensional gel electrophoresis in combination with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry to analyze differences in global protein expression in HCT116 parental and drug-resistant cells. Using this approach, calretinin was identified as one of several proteins constitutively overexpressed (~3-fold) in oxaliplatin-resistant cells relative to the parental cell line.4 Analysis of drug-induced calretinin expression in a panel of colorectal cancer cell lines showed an ~2- and 4-fold increase in calretinin mRNA levels in HT-29 and HCT248 cells following exposure to IC50 doses of 5-FU and oxaliplatin, respectively (Fig. 5A). In the LoVo cells, a 2-fold induction in calretinin mRNA was observed following treatment with oxaliplatin, with only a moderate increase in expression in response to 5-FU. In contrast, in the RKO cells, a 2-fold increase in calretinin levels was shown following 5-FU treatment with no significant change in expression in response to oxaliplatin.


Figure 5
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  		Figure 5. A, real-time RT-PCR analysis showing fold induction of calretinin mRNA in RKO, HT-29, LoVo, and HCT248 cells following treatment with IC50 concentrations of oxaliplatin and 5-FU for 24 hours. B, Western blot analysis of calretinin and PARP expression levels following treatment of HCT116 parental cells with 5 µmol/L 5-FU and 1 µmol/L oxaliplatin for 72 hours in the presence or absence of 5 nmol/L calretinin-targeted siRNA. Line graphs illustrate the fraction of apoptotic cells (as measured by flow cytometry) following treatment of HCT116 cells with 5-FU (C) and oxaliplatin (D) for 72 hours in the presence or absence of 5 nmol/L calretinin siRNA. ***, P < 0.0001, two-tailed unpaired t test (GraphPad Prism v4.02).

 
To elucidate the biological significance of drug-induced calretinin expression, HCT116 cells were treated with chemotherapy for 72 hours in both the presence and absence of calretinin-targeted siRNA (5 nmol/L) and the levels of apoptosis examined by PARP cleavage analysis (Fig. 5B). A moderate increase in calretinin protein expression (~2-fold) was observed following treatment with 5-FU or oxaliplatin in cells transfected with a scrambled control siRNA (5 nmol/L). Knockdown of basal calretinin expression (~80%) did not result in alterations in PARP expression, suggesting no change in the levels of programmed cell death. However, when either 5-FU or oxaliplatin was combined with calretinin siRNA, drug-induced apoptosis was reduced, as indicated by attenuated PARP cleavage (Fig. 5B). This was supported by flow cytometric analysis, which showed a dramatic reduction in the subdiploid fraction of 5-FU- and oxaliplatin-treated cells that were pretreated with calretinin siRNA (Fig. 5C and D). Collectively, these data suggest that calretinin may be an important positive regulator of cytotoxic drug-induced cell death.

Spermidine/spermine N1-acetyl transferase (SSAT) catalyzes the rate-limiting step in the two-step catabolism of eukaryotic polyamines, which play critical roles in proliferation, differentiation, and homeostasis in both normal and cancer cells (16). SSAT activity is highly regulated and prevents the accumulation of cytotoxic levels of spermidine and spermine by facilitating their excretion and oxidative catabolism. SSAT has also been reported to be induced by a broad range of agents such as hormones, growth factors, toxic compounds, and drugs (17). In this study, DNA microarray analysis showed potent up-regulation (3.2-fold) of SSAT mRNA expression following treatment of HCT116 cells with 5-FU for 24 hours (Table 1). This result was confirmed by real-time RT-PCR with an R2 value of 0.85 (P = 0.038; Table 3A). Constitutive overexpression of SSAT mRNA (2.4-fold) was also observed in cells resistant to 5-FU (Table 1). In accordance with these data, a previous study carried out by our group identified SSAT as one of the most highly induced target genes in a cDNA microarray screen designed to examine global transcriptional changes in MCF-7 breast cancer cells treated with 5-FU (18). Furthermore, elevated levels of SSAT mRNA (~2-fold) were noted in a H630-derived 5-FU-resistant colorectal cancer cell line relative to parental cells. In the present study, we also showed inducible expression of SSAT mRNA (~5-fold) following acute exposure to oxaliplatin although no alterations in basal SSAT levels were observed in oxaliplatin-resistant cells (data not shown). Treatment of an extended panel of colorectal cancer cell lines with an IC50 dose of oxaliplatin for 24 hours showed highly inducible expression of SSAT mRNA (between 5- and 15-fold induction relative to control cells; Fig. 6A). In contrast, only moderate increases in SSAT expression were observed following exposure to 5-FU, with the exception of the LoVo cells, which showed an ~10-fold increase in SSAT mRNA.


Figure 6
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  		Figure 6. A, real-time RT-PCR analysis showing fold induction of SSAT mRNA in RKO, HT-29, LoVo, and HCT248 cells following treatment with IC50 concentrations of oxaliplatin and 5-FU for 24 hours. B, Western blot analysis of SSAT expression levels following treatment of HCT116 parental cells with 1 µmol/L oxaliplatin and 5 µmol/L 5-FU in the presence or absence of 1 µmol/L DENSpm for 24, 48, and 72 hours. Cell cycle distribution of HCT116 cells following treatment with 0.1 and 1 µmol/L oxaliplatin (C) and 1 and 5 µmol/L 5-FU (D) for 72 hours in the presence or absence of 1 µmol/L DENSpm.

 
To investigate the functional significance of polyamine metabolism in modulating the response to chemotherapy in colorectal cancer cells, we employed the synthetic polyamine analogue N1,N11-diethylnorspermine (DENSpm), which has been shown to potently induce SSAT levels (19) and has been evaluated in phase I and II clinical trials (20, 21). Basal SSAT protein expression was not detected in HCT116 cells; however, cellular levels of SSAT are usually low (Fig. 6B). Following treatment with 1 µmol/L DENSpm, little induction of SSAT protein was observed. Of note, despite increased SSAT mRNA expression, no increase in SSAT protein was observed following treatment of cells with an IC60 dose of either 5-FU or oxaliplatin for 24 to 72 hours. However, when cells were concurrently treated with DENSpm and chemotherapy, a synergistic induction of SSAT protein expression was observed. Using flow cytometry, we found that treatment of cells with 1 µmol/L DENSpm or 0.1 µmol/L oxaliplatin (~IC25 dose) alone for 72 hours had no effect on the cell cycle profile relative to untreated control cells (Fig. 6C). However, following cotreatment with the same concentrations of both agents, an accumulation of cells in S phase was noted. Following exposure to 1 µmol/L oxaliplatin, the majority of cells were arrested in G2-M phase (Fig. 6C) and an increase in the sub-G0/G1 apoptotic content was observed. However, following combined treatment with 1 µmol/L oxaliplatin and 1 µmol/L DENSpm, a significant increase (P < 0.0001) in the apoptotic fraction of cells was observed (58.6% compared with 41.9% in cells treated with oxaliplatin alone). Similarly, following cotreatment of cells with 1 µmol/L DENSpm and 1 µmol/L 5-FU (~IC25 dose) for 72 hours, the majority of cells were arrested in S phase with a moderate increase in the subdiploid fraction compared with cells treated with either agent alone in which only moderate perturbations of the cell cycle were noted (Fig. 6D). Following treatment with a higher concentration of 5-FU (~IC60 dose), cells were arrested in S phase and an increase in the subdiploid content was observed. When this concentration of 5-FU was combined with 1 µmol/L DENSpm, a further increase in the apoptotic fraction was shown (53.5% compared with 35.8% in cells treated with 5-FU alone; P < 0.0001). These data indicate that SSAT protein levels are synergistically induced following combined treatment with DENSpm and 5-FU or oxaliplatin, and that cotreatment with DENSpm and these chemotherapies results in enhanced cell cycle arrest and apoptosis.


   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
We have used DNA microarray expression profiling to analyze the complex signaling pathways regulating the response to 5-FU and oxaliplatin to identify novel predictive markers of sensitivity to these agents and candidate genes that may be targeted to counteract drug resistance and increase therapeutic efficacy. Recently, a number of studies have examined the potential of DNA microarrays to provide insight into the factors that determine 5-FU response by analyzing genes acutely altered following exposure to this agent (22–24). Despite this, the downstream molecular events underlying the cytotoxic effects of 5-FU remain poorly characterized. To our knowledge, this is the first study to examine dynamic changes in gene expression following treatment with oxaliplatin. Of note, a number of the aforementioned studies were limited by virtue of inadequate validation of microarray expression data by alternative means. In addition, in many cases it remains to be determined whether the genes identified confer sensitivity or resistance by directly modulating drug response or whether they represent secondary transcriptional changes that may be downstream and potentially less relevant to the resistance phenotype. In addition to examining dynamic changes in gene expression following drug treatment, a number of investigators have applied microarray expression profiling to gain insight into the mechanisms of acquired 5-FU and oxaliplatin resistance using drug-resistant cell line models (25–27). Similar to studies examining drug-induced gene alterations, a major criticism of a number of these studies is the casual relationships often presented linking gene expression and response to treatment.

In an effort to identify genes directly associated with 5-FU and oxaliplatin resistance or sensitivity, we adopted a novel DNA microarray-based approach whereby drug-inducible gene sets were cross-referenced with panels of genes identified as constitutively dysregulated in 5-FU- and oxaliplatin-resistant cells. Using this approach, we identified distinct subsets of genes that we propose may represent unique molecular signatures indicative of 5-FU and oxaliplatin resistance.

Using real-time RT-PCR, we validated the expression changes of a representative subset of targets in the 5-FU and oxaliplatin gene sets obtained from the microarray analysis. Confirmation of the robustness of our data was shown with a strong overall concordance of expression trends for ≥82% (oxaliplatin) and ≥85% (5-FU) of genes analyzed. These data are similar to two previous studies that reported confirmation of ~70% to 75% of gene expression trends determined by cDNA microarray analysis using real-time RT-PCR (28, 29). Furthermore, we showed strong correlations in terms of average fold change in gene expression levels for both our 5-FU (R2 ≥ 0.73) and oxaliplatin gene sets (R2 ≥ 0.63). Of note, significant correlations (P < 0.05) were observed for 10 of 20 (50%) and 7 of 11 (64%) 5-FU and oxaliplatin gene targets, respectively, following acute exposure to chemotherapy. These results indicate that drug-induced alterations in gene expression may be accurately predicted by oligonucleotide microarray analysis but emphasize the need for rigorous validation of gene expression measurements.

Given the number of other studies that have used DNA microarrays to identify determinants of 5-FU response, we cross-referenced our 5-FU gene set with expression profiles identified in these studies to examine the degree of overlap. In two studies which used constitutively resistant gastric cell line models to identify genes with altered expression compared with parental cells, several targets common to our gene set were identified, including the nephroblastoma (Nov) gene, the HIF-1 responsive gene, RTP801, the coagulation factor, F3, the developmental transcription factor, SOX4, cyclin G2, and SSAT (25, 30). Of note, however, we did not observe any overlap with a gene set identified by Mariadason et al. (31) who used a cDNA microarray-generated basal gene expression profile to predict apoptotic response to 5-FU in a panel of 30 colon carcinoma cell lines. There are several explanations that may account for this difference, not least the use of different technology platforms and different normalization procedures and data analysis methods in each study. However, this may also reflect the different mechanisms involved in regulating inherent 5-FU resistance and resistance following chronic 5-FU exposure. This would imply that distinct subsets of genes may define inherent and acquired 5-FU resistance.

To begin to functionally characterize our data, three targets were selected for phenotypic analyses. PDF is a divergent member of the TGF-ß superfamily of cytokines, which was identified in our microarray analysis as acutely induced following treatment with 5-FU and oxaliplatin and constitutively overexpressed in 5-FU-resistant cells. Additional transcriptional analyses showed inducible expression of PDF following treatment of a panel of colorectal cancer cells lines with these agents. Consistent with our HCT116 transcriptional data, we showed inducible expression of PDF protein following treatment of parental cells with 5-FU and oxaliplatin. Importantly, we have shown that down-regulation of PDF using siRNA significantly sensitized HCT116 cells to 5-FU- and oxaliplatin-induced apoptosis. These data suggest that PDF may be a novel inhibitor of cytotoxic drug-induced cell death in colorectal cancer and that inhibition of PDF in combination with chemotherapy may increase therapeutic efficacy. The major function of PDF remains unclear although elevated levels of secreted PDF protein detected in the serum of patients with metastatic colorectal, breast, and prostate carcinomas would suggest a role in the development and progression of these tumors (32). Of note, PDF has been shown to be induced in human colon cancer cells by several nonsteroidal anti-inflammatory drugs (33), as well as antitumorigenic compounds such as reveratrol (34), genistein (35), diallyl disulfide (36), and 2-(4-amino-3-methylphenyl)-5-fluorobenzothiazole (5F-203; ref. 37), suggesting that PDF expression may be an important regulator of drug response. In contrast to our own data, the majority of previous studies suggest a proapoptotic role for PDF, with overexpression resulting in increased levels of spontaneous apoptosis in HCT116 colorectal (38) and DU-145 prostate cell lines (39) and enhanced indomethacin-induced apoptosis in HCT116 cells (33). In addition, knockdown of PDF using siRNA has shown marked protection against tetradecanoyl phorbol acetate–induced apoptosis in LNCaP prostate cancer cells (40). These data suggest a potentially diverse and multifunctional role for PDF depending on cellular context and stimuli. Further studies are currently under way in our laboratory to comprehensively characterize the role of PDF in modulating response to chemotherapy in colorectal cancer cells.

Calretinin was one of several calcium-binding proteins identified in our microarray analysis, which was acutely induced following treatment with oxaliplatin and 5-FU. Furthermore, inducible expression of calretinin mRNA was shown in a panel of colorectal cancer cell lines following exposure to both agents. Using both transcriptional and proteomic approaches,4 we also showed stable overexpression of calretinin in HCT116 oxaliplatin-resistant cells. Down-regulation of calretinin expression in HCT116 parental cells using siRNA resulted in a dramatic decrease in 5-FU- and oxaliplatin-induced apoptosis as measured by PARP cleavage and flow cytometric analysis. To our knowledge, this is the first report to suggest a proapoptotic role for calretinin in regulating the response to cytotoxic drug treatment in tumor cells. Furthermore, these observations are generally inconsistent with previous studies using in vitro cell line models in the neurologic setting. D'Orlando et al. (41) reported that overexpression of calretinin in murine embryonic carcinoma P19 cells led to short-term cytoprotection against excitotoxic stimulation and calcium overload. Other calcium-binding proteins, including the closest homologue of calretinin, calbindin, have also been shown to confer cytoprotection in nerve cells (41). It is generally thought that these calcium-binding proteins act by blunting pathologic calcium transients using their buffering capacities; however, their function(s) in cancer cells has not been fully characterized and may differ markedly from that in neurones. Given the integral role of calcium in apoptotic cell death, it is feasible that calretinin may facilitate the transport of calcium between the endoplasmic reticulum and the mitochondria in cancer cells or regulate apoptotic signals via as yet unknown mechanisms. The biological function of calretinin in colorectal cancer and its role in modulating the response to chemotherapy are currently being investigated.

SSAT is the rate-limiting enzyme in polyamine catabolism. This enzyme is essential for maintaining tightly regulated intracellular concentrations of polyamines which are essential for the growth and function of cells. In this study, we showed acute induction of SSAT mRNA levels following treatment of HCT116 cells with 5-FU and oxaliplatin by DNA microarray analysis and stable overexpression in cells resistant to 5-FU. Potent induction of SSAT mRNA was also observed in a panel of colorectal cancer cell lines following exposure to both agents. In contrast to our HCT116 transcriptional analyses, we did not detect a concurrent increase in SSAT protein levels in response to treatment with drug alone. This is consistent with a known translational regulatory mechanism that limits induction of SSAT activity (42). Previously, overexpression studies aimed at understanding the role of SSAT in determining sensitivity to various agents have been hampered by an inability to obtain efficient translation of the transfected SSAT cDNA sequences in human cells (43). As a result, several investigators have used synthetic polyamine analogues, such as DENSpm, which potently induce SSAT activity by enhanced mRNA transcription (44), stabilization of mRNA and protein (43, 45), and enhanced translation (42). In this study, we showed potentiation of 5-FU- and oxaliplatin-induced SSAT protein expression in HCT116 cells following combined treatment with DENSpm. Furthermore, this "superinduction" was associated with enhanced cell cycle arrest and cell death. These data are consistent with two recent studies, which reported a synergistic decrease in tumor cell viability following cotreatment with DENSpm and 5-FU or oxaliplatin, which was also associated with SSAT superinduction (46, 47). These data suggest that both 5-FU and oxaliplatin modulate polyamine catabolism in tumor cells by inducing SSAT and show that this response as well as growth inhibition can be augmented by cotreatment with the polyamine analogue DENSpm. We are currently investigating the effect of additional colon cancer therapies on SSAT expression in vitro as well as the specific role that this enzyme plays in regulating the synergistic interaction between DENSpm and chemotherapy.

In summary, we have shown the power of DNA microarray expression profiling to identify novel genes that regulate the response of colon cancer cells to 5-FU and oxaliplatin. Moreover, we have highlighted the effectiveness of cross-referencing genes that are transiently altered following acute drug treatment with genes that are also constitutively dysregulated in cells resistant to chemotherapy as a method of identifying novel and potentially key components of drug-induced signaling networks. Although our results also show the robustness of oligonucleotide microarray analysis in the accurate prediction of drug-induced alterations in gene expression, we would emphasize the need for rigorous validation of microarray data before embarking on functional studies. From these data, we have identified three genes that may serve as useful determinants of 5-FU and oxaliplatin sensitivity. Future studies will examine the relevance of these and other genes identified in this study as predictive biomarkers as well therapeutic targets to enhance drug efficacy.


   Acknowledgments
 
Grant support: Cancer Research UK, Action Cancer, Research and Development Office, Northern Ireland, and Department for Employment and Learning, Northern Ireland.

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.


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

J. Boyer and Wendy L. Allen contributed equally to this work.

4 J. Boyer, P. Wilson, W.L. Allen, A. McCulla, E.G. McLean, D.B. Longley, and P.G. Johnston. A proteomic approach to identifying novel determinants of response to chemotherapy in colon cancer - the role of Calretinin, in preparation. Back

Received 8/ 3/05. Revised 11/24/05. Accepted 12/28/05.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

  1. Johnston PG, Kaye S. Capecitabine: a novel agent for the treatment of solid tumors. Anticancer Drugs 2001;12:639–46.[CrossRef][Medline]
  2. Giacchetti S, Perpoint B, Zidani R, et al. Phase III multicenter randomized trial of oxaliplatin added to chronomodulated fluorouracil-leucovorin as first-line treatment of metastatic colorectal cancer. J Clin Oncol 2000;18:136–47.[Abstract/Free Full Text]
  3. Douillard JY, Cunningham D, Roth AD, et al. Irinotecan combined with fluorouracil compared with fluorouracil alone as first-line treatment for metastatic colorectal cancer: a multicentre randomised trial. Lancet 2000;355:1041–7.[CrossRef][Medline]
  4. Cunningham D, Humblet Y, Siena S, et al. Cetuximab monotherapy and cetuximab plus irinotecan in irinotecan-refractory metastatic colorectal cancer. N Engl J Med 2004;351:337–45.[Abstract/Free Full Text]
  5. Hurwitz H, Fehrenbacher L, Novotny W, et al. Bevacizumab plus irinotecan, fluorouracil, and leucovorin for metastatic colorectal cancer. N Engl J Med 2004;350:2335–42.[Abstract/Free Full Text]
  6. Boyer J, McLean EG, Aroori S, et al. Characterization of p53 wild-type and null isogenic colorectal cancer cell lines resistant to 5-fluorouracil, oxaliplatin, and irinotecan. Clin Cancer Res 2004;10:2158–67.[Abstract/Free Full Text]
  7. McDermott U, Longley DB, Galligan L, et al. Effect of p53 status and STAT1 on chemotherapy-induced, Fas-mediated apoptosis in colorectal cancer. Cancer Res 2005;65:8951–60.[Abstract/Free Full Text]
  8. Bootcov MR, Bauskin AR, Valenzuela SM, et al. MIC-1, a novel macrophage inhibitory cytokine, is a divergent member of the TGF-ß superfamily. Proc Natl Acad Sci U S A 1997;94:11514–9.[Abstract/Free Full Text]
  9. Paralkar VM, Vail AL, Grasser WA, et al. Cloning and characterization of a novel member of the transforming growth factor-ß/bone morphogenetic protein family. J Biol Chem 1998;273:13760–7.[Abstract/Free Full Text]
  10. Strelau J, Sullivan A, Bottner M, et al. Growth/differentiation factor-15/macrophage inhibitory cytokine-1 is a novel trophic factor for midbrain dopaminergic neurons in vivo. J Neurosci 2000;20:8597–603.[Abstract/Free Full Text]
  11. Rogers JH. Calretinin: a gene for a novel calcium-binding protein expressed principally in neurons. J Cell Biol 1987;105:1343–53.[Abstract/Free Full Text]
  12. Pohl V, Van Rampelbergh J, Mellaert S, Parmentier M, Pochet R. Calretinin in rat ovary: an in situ hybridization and immunohistochemical study. Biochim Biophys Acta 1992;1160:87–94.[Medline]
  13. Kiraly E, Celio MR. Parvalbumin and calretinin in the avian thymus. Anat Embryol (Berl) 1993;188:339–44.[Medline]
  14. Gotzos V, Schwaller B, Gander JC, Bustos-Castillo M, Celio MR. Heterogeneity of expression of the calcium-binding protein calretinin in human colonic cancer cell lines. Anticancer Res 1996;16:3491–8.[Medline]
  15. Gander JC, Gotzos V, Fellay B, Schwaller B. Inhibition of the proliferative cycle and apoptotic events in WiDr cells after down-regulation of the calcium-binding protein calretinin using antisense oligodeoxynucleotides. Exp Cell Res 1996;225:399–410.[Medline]
  16. Gerner EW, Meyskens FL, Jr. Polyamines and cancer: old molecules, new understanding. Nat Rev Cancer 2004;4:781–92.[CrossRef][Medline]
  17. Seiler N. Functions of polyamine acetylation. Can J Physiol Pharmacol 1987;65:2024–35.[Medline]
  18. Maxwell PJ, Longley DB, Latif T, et al. Identification of 5-fluorouracil-inducible target genes using cDNA microarray profiling. Cancer Res 2003;63:4602–6.[Abstract/Free Full Text]
  19. Porter CW, Ganis B, Libby PR, Bergeron RJ. Correlations between polyamine analogue-induced increases in spermidine/spermine N1-acetyltransferase activity, polyamine pool depletion, and growth inhibition in human melanoma cell lines. Cancer Res 1991;51:3715–20.[Abstract/Free Full Text]
  20. Hahm HA, Ettinger DS, Bowling K, et al. Phase I study of N(1),N(11)-diethylnorspermine in patients with non-small cell lung cancer. Clin Cancer Res 2002;8:684–90.[Abstract/Free Full Text]
  21. Wolff AC, Armstrong DK, Fetting JH, et al. A Phase II study of the polyamine analog N1,N11-diethylnorspermine (DENSpm) daily for five days every 21 days in patients with previously treated metastatic breast cancer. Clin Cancer Res 2003;9:5922–8.[Abstract/Free Full Text]
  22. Clarke PA, George ML, Easdale S, et al. Molecular pharmacology of cancer therapy in human colorectal cancer by gene expression profiling. Cancer Res 2003;63:6855–63.[Abstract/Free Full Text]
  23. Park JS, Young Yoon S, Kim JM, et al. Identification of novel genes associated with the response to 5-FU treatment in gastric cancer cell lines using a cDNA microarray. Cancer Lett 2004;214:19–33.[Medline]
  24. Takahashi Y, Nagata T, Ishii Y, et al. Up-regulation of vitamin D3 up-regulated protein 1 gene in response to 5-fluorouracil in colon carcinoma SW620. Oncol Rep 2002;9:75–9.[Medline]
  25. Kang HC, Kim IJ, Park JH, et al. Identification of genes with differential expression in acquired drug-resistant gastric cancer cells using high-density oligonucleotide microarrays. Clin Cancer Res 2004;10:272–84.[Abstract/Free Full Text]
  26. Samimi G, Manorek G, Castel R, et al. cDNA microarray-based identification of genes and pathways associated with oxaliplatin resistance. Cancer Chemother Pharmacol 2005;55:1–11.[CrossRef][Medline]
  27. Schmidt WM, Kalipciyan M, Dornstauder E, et al. Dissecting progressive stages of 5-fluorouracil resistance in vitro using RNA expression profiling. Int J Cancer 2004;112:200–12.[Medline]
  28. Jenson SD, Robetorye RS, Bohling SD, et al. Validation of cDNA microarray gene expression data obtained from linearly amplified RNA. Mol Pathol 2003;56:307–12.[Abstract/Free Full Text]
  29. Rajeevan MS, Vernon SD, Taysavang N, Unger ER. Validation of array-based gene expression profiles by real-time (kinetic) RT-PCR. J Mol Diagn 2001;3:26–31.[Abstract/Free Full Text]
  30. de Angelis PM, Fjell B, Kravik KL, et al. Molecular characterizations of derivatives of HCT116 colorectal cancer cells that are resistant to the chemotherapeutic agent 5-fluorouracil. Int J Oncol 2004;24:1279–88.[Medline]
  31. Mariadason JM, Arango D, Shi Q, et al. Gene expression profiling-based prediction of response of colon carcinoma cells to 5-fluorouracil and camptothecin. Cancer Res 2003;63:8791–812.[Abstract/Free Full Text]
  32. Welsh JB, Sapinoso LM, Kern SG, et al. Large-scale delineation of secreted protein biomarkers overexpressed in cancer tissue and serum. Proc Natl Acad Sci U S A 2003;100:3410–5.[Abstract/Free Full Text]
  33. Baek SJ, Kim KS, Nixon JB, Wilson LC, Eling TE. Cyclooxygenase inhibitors regulate the expression of a TGF-ß superfamily member that has proapoptotic and antitumorigenic activities. Mol Pharmacol 2001;59:901–8.[Abstract/Free Full Text]
  34. Baek SJ, Wilson LC, Eling TE. Resveratrol enhances the expression of non-steroidal anti-inflammatory drug-activated gene (NAG-1) by increasing the expression of p53. Carcinogenesis 2002;23:425–34.[Abstract/Free Full Text]
  35. Wilson LC, Baek SJ, Call A, Eling TE. Nonsteroidal anti-inflammatory drug-activated gene (NAG-1) is induced by genistein through the expression of p53 in colorectal cancer cells. Int J Cancer 2003;105:747–53.[CrossRef][Medline]
  36. Bottone FG, Jr., Baek SJ, Nixon JB, Eling TE. Diallyl disulfide (DADS) induces the antitumorigenic NSAID-activated gene (NAG-1) by a p53-dependent mechanism in human colorectal HCT 116 cells. J Nutr 2002;132:773–8.[Abstract/Free Full Text]
  37. Monks A, Harris E, Hose C, Connelly J, Sausville EA. Genotoxic profiling of MCF-7 breast cancer cell line elucidates gene expression modifications underlying toxicity of the anticancer drug 2-(4-amino-3-methylphenyl)-5-fluorobenzothiazole. Mol Pharmacol 2003;63:766–72.[Abstract/Free Full Text]
  38. Kim KS, Baek SJ, Flake GP, et al. Expression and regulation of nonsteroidal anti-inflammatory drug-activated gene (NAG-1) in human and mouse tissue. Gastroenterology 2002;122:1388–98.[CrossRef][Medline]
  39. Liu T, Bauskin AR, Zaunders J, et al. Macrophage inhibitory cytokine 1 reduces cell adhesion and induces apoptosis in prostate cancer cells. Cancer Res 2003;63:5034–40.[Abstract/Free Full Text]
  40. Shim M, Eling TE. Protein kinase C-dependent regulation of NAG-1/placental bone morphogenic protein/MIC-1 expression in LNCaP prostate carcinoma cells. J Biol Chem 2005;280:18636–42.[Abstract/Free Full Text]
  41. D'Orlando C, Fellay B, Schwaller B, et al. Calretinin and calbindin D-28k delay the onset of cell death after excitotoxic stimulation in transfected P19 cells. Brain Res 2001;909:145–58.[CrossRef][Medline]
  42. Fogel-Petrovic M, Vujcic S, Miller J, Porter CW. Differential post-transcriptional control of ornithine decarboxylase and spermidine-spermine N1-acetyltransferase by polyamines. FEBS Lett 1996;391:89–94.[CrossRef][Medline]
  43. Parry L, Balana Fouce R, Pegg AE. Post-transcriptional regulation of the content of spermidine/spermine N1-acetyltransferase by N1N12-bis(ethyl)spermine. Biochem J 1995;305:451–8.[Medline]
  44. Xiao L, Casero RA, Jr. Differential transcription of the human spermidine/spermine N1-acetyltransferase (SSAT) gene in human lung carcinoma cells. Biochem J 1996;313:691–6.[Medline]
  45. Fogel-Petrovic M, Vujcic S, Brown PJ, Haddox MK, Porter CW. Effects of polyamines, polyamine analogs, and inhibitors of protein synthesis on spermidine-spermine N1-acetyltransferase gene expression. Biochemistry 1996;35:14436–44.[CrossRef][Medline]
  46. Choi W, Gerner EW, Ramdas L, et al. Combination of 5-fluorouracil and N1,N11-diethylnorspermine markedly activates spermidine/spermine N1-acetyltransferase expression, depletes polyamines, and synergistically induces apoptosis in colon carcinoma cells. J Biol Chem 2005;280:3295–304.[Abstract/Free Full Text]
  47. Hector S, Porter CW, Kramer DL, et al. Polyamine catabolism in platinum drug action: Interactions between oxaliplatin and the polyamine analogue N1,N11-diethylnorspermine at the level of spermidine/spermine N1-acetyltransferase. Mol Cancer Ther 2004;3:813–22.[Abstract/Free Full Text]



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