- Open Access
Gene expression and pathway analysis of ovarian cancer cells selected for resistance to cisplatin, paclitaxel, or doxorubicin
© Sherman-Baust et al; licensee BioMed Central Ltd. 2011
- Received: 12 October 2011
- Accepted: 05 December 2011
- Published: 05 December 2011
Resistance to current chemotherapeutic agents is a major cause of therapy failure in ovarian cancer patients, but the exact mechanisms leading to the development of drug resistance remain unclear.
To better understand mechanisms of drug resistance, and possibly identify novel targets for therapy, we generated a series of drug resistant ovarian cancer cell lines through repeated exposure to three chemotherapeutic drugs (cisplatin, doxorubicin, or paclitaxel), and identified changes in gene expression patterns using Illumina whole-genome expression microarrays. Validation of selected genes was performed by RT-PCR and immunoblotting. Pathway enrichment analysis using the KEGG, GO, and Reactome databases was performed to identify pathways that may be important in each drug resistance phenotype.
A total of 845 genes (p < 0.01) were found altered in at least one drug resistance phenotype when compared to the parental, drug sensitive cell line. Focusing on each resistance phenotype individually, we identified 460, 366, and 337 genes significantly altered in cells resistant to cisplatin, doxorubicin, and paclitaxel, respectively. Of the 845 genes found altered, only 62 genes were simultaneously altered in all three resistance phenotypes. Using pathway analysis, we found many pathways enriched for each resistance phenotype, but some dominant pathways emerged. The dominant pathways included signaling from the cell surface and cell movement for cisplatin resistance, proteasome regulation and steroid biosynthesis for doxorubicin resistance, and control of translation and oxidative stress for paclitaxel resistance.
Ovarian cancer cells develop drug resistance through different pathways depending on the drug used in the generation of chemoresistance. A better understanding of these mechanisms may lead to the development of novel strategies to circumvent the problem of drug resistance.
- Ovarian Cancer
- Drug Resistance
- Ovarian Cancer Cell
In the United States, ovarian cancer represents 3% of all the new cancer cases in women, but accounts for 5% of all the cancer deaths . This discrepancy is due, in part, to the common resistance of ovarian cancer to current chemotherapy regimens. The vast majority of ovarian cancer patients with advanced disease are treated with surgery followed by adjuvant chemotherapy consisting of a platinum agent (typically carboplatin) in combination with a taxane (paclitaxel). Unfortunately, while most patients initially respond to this combination chemotherapy, a majority of the patients (up to 75%) will eventually relapse within 18 months, many with drug resistant disease . The optimal management of patients with recurrent tumors is unclear, especially for drug resistant disease (by definition, a recurrence that has occurred within 6 months of initial treatment), and various studies have suggested different second line chemotherapy approaches, all with limited success . Ultimately, the frequent development of drug resistance and the lack of alternatives for the treatment of drug resistant disease are responsible for a 5-year survival of approximately 30% in ovarian cancer patients with advanced disease. Indeed, 90% of the deaths from ovarian cancer can be attributed to drug resistance .
Studies have shown that ovarian cancer resistance is multifactorial and may involve increased drug inactivation/efflux, increased DNA repair, alterations in cell cycle control, and changes in apoptotic threshold. For example, the copper transporter CTR1 has been shown to mediate cisplatin uptake and cells with decreased CTR1 exhibit increased resistance to cisplatin [5, 6]. Another pathway, the PTEN-PI3K-AKT axis, has been suggested to play an important role in the development of drug resistance in several malignancies , including ovarian cancer [8–10]. Overall, these studies indicate that a better understanding of the mechanisms of drug action and drug resistance may ultimately lead to new approaches for circumventing resistance and improve patient survival. However, in spite of recent advances, the exact pathways important for the development of drug resistance in ovarian cancer remain unclear. A better understanding of the molecular mechanisms leading to drug resistance may provide new opportunities for the development of strategies for reversing or circumventing drug resistance [4, 11].
In this manuscript, we generate novel drug resistant ovarian cancer cell lines independently selected for resistance to cisplatin, doxorubicin or paclitaxel, and we use gene expression profiling to identify genes and pathways that may be important to the development of drug resistance in ovarian cancer.
Cell line and generation of drug resistance sub-lines
The ovarian cancer cell line OV90 was obtained from The American Type Culture Collection (ATCC) and grown in MCDB 105 (Sigma-Aldrich):Media 199 (Invitrogen) containing 15% bovine serum and antibiotics (100 units/ml penicillin and 100 μg/ml streptomycin) at 37°C in a humidified atmosphere of 5% CO2. The chemotherapeutic drugs cisplatin, doxorubicin, and paclitaxel were purchased from Sigma. The resistant sub-lines were generated by exposure to the drugs for four to five cycles. For each cycle, the cells were exposed to each individual drug for twenty-four hours, and then transferred to normal media where they were allowed to grow for 2 weeks. Following this two-week period, the cells were re-exposed to the drug to initiate the next cycle.
Illumina Microarray and data analysis
RNA samples were purified using the RNeasy kit (Qiagen). Biotinylated cRNA was prepared using the Illumina RNA Amplification Kit (Ambion, Inc.) according to the manufacturer's directions starting with approximately 500 ng total RNA. Hybridization to the Sentrix HumanRef-8 Expression BeadChip (Illumina, Inc.), washing and scanning were performed according to the Illumina BeadStation 5006 manual (revision C). Array data processing and analysis was performed using Illumina Bead Studio software. Hierarchical clustering analysis of significant genes was done using an algorithm of the JMP 6.0.0 software. Microarray analysis was performed essentially as described . Raw microarray data were subjected to filtering and z-normalization. Sample quality was assessed using scatterplots and gene sample z-score-based hierarchical clustering. Expression changes for individual genes were considered significant if they met 4 criteria: z-ratio above 1.4 (or below -1.4 for down-regulated genes); false detection rate <0.30; p-value of the pairwise t-test <0.05; and mean background-corrected signal intensity z-score in each comparison group is not negative. This approach provides a good balance between sensitivity and specificity in the identification of differentially expressed genes, avoiding excessive representation of false positive and false negative regulation . All the microarray data are MIAME compliant and the raw data were deposited in Gene Expression Omnibus database [GEO:GSE26465].
Real-time reverse transcription quantitative-PCR (RT-PCR)
Total RNA was extracted with Trizol (Invitrogen) according to the manufacturer's instructions. RNA was quantified and assessed using the RNA 6000 Nano Kit in the 2100 Bioanalyzer (Agilent Technologies UK Ltd). One μg of total RNA from each cell line was used to generate cDNA using Taqman Reverse Transcription Reagents (PE Applied Biosystems). The SYBR Green I assay and the GeneAmp 7300 Sequence Detection System (PE Applied Biosystems) were used for detecting real-time PCR products. The PCR cycling conditions were as follows: 50°C, 2 min for AmpErase UNG incubation; 95°C, 10 min for AmpliTaq Gold activation; and 40 cycles of melting (95°C, 15 sec) and annealing/extension (60°C for 1 min). PCR reactions for each template were performed in duplicate in 96-well plates. The comparative CT method (PE Applied Biosystems) was used to determine the relative expression in each sample using GAPDH as normalization control. The PCR primer sequences are available from the authors.
Antibodies and Immunoblotting
All the antibodies used for this work were obtained from commercial sources. Anti-ABCB1 was purchased from GeneTex. Anti-SPOCK2 and anti-CCL26 were obtained from R&D Systems. Anti-PRSS8 and anti-MSMB were obtained from Novus Biologicals. Anti-GAPDH was purchased from Abcam. Immunoblotting was performed as previously described .
We used WebGestalt version 2 (http://bioinfo.vanderbilt.edu/webgestalt/) to test for the enrichment of any pathway/terms that may be related to the drug resistance phenotypes. Two different databases (KEGG, and GO) were analyzed using Webgestalt. Overrepresentation analysis was also performed using the Reactome database . Ingenuity Pathway Analysis software (Ingenuity Systems) was used to identify and draw networks relevant to the pathways identified.
Statistical analysis was conducted using Student's t-test. A p-value of <0.05 was considered statistically significant.
Generation of drug resistant cell lines
Microarray analysis of gene expression in drug resistant ovarian cancer cell lines
Top 20 genes down- and up-regulated in each drug resistance phenotype
As further validation, we investigated the protein expression levels of selected candidates by immunoblotting. We found five genes whose protein level changed significantly in the drug resistant cell lines (Figure 3C). Consistent with our RT-PCR findings, the P-glycoprotein (encoded by ABCB1), a well-studied protein which has been implicated in multi-drug resistance, was found elevated in all three drug-resistant cell lines, including OV90C, in spite of a relatively small increase in mRNA levels observed in cisplatin cell lines (Figure 3A). On the other hand, the CCL26, PRSS8, and MSMB proteins were found to be significantly decreased in all three drug resistant cell lines. The SPOCK2 protein was only found decreased in the paclitaxel resistant lines (OV90P).
Pathway analysis of drug resistance
Pathway analysis: Pathways/Terms found enriched in the indicated databases for each of the resistance phenotype are shown.
KEGG (P < 0.001)
GO (P < 0.1)
Reactome (P < 5e-04)
Leukocyte transendothelial migration (P = 2.7e-06)
cell-substrate adhesion (adjP = 0.0011)
Nephrin interactions (P = 5.1e-05)
Focal adhesion (P = 4.76e-06)
response to chemical stimulus (adjP = 0.0012)
Recruitment of Proteins To Vesicles (P = 2.7e-04)
ECM-receptor interaction (P = 0.0001)
cellular component movement (adjP = 0.0015)
Activation of PPARA by Fatty Acid (P = 2.8e-04)
Ribosome (P = 0.0001)
homeostasis of number of cells (adjP = 0.0028)
Cell-Cell communication (P = 3.3e-04)
TGF-beta signaling pathway (P = 0.0001)
Proteasome (P = 2.28e-09)
regulation of ubiquitin-protein ligase
Proteasomal cleavage/Cell cycle (P = 3.2e-06)
Chemokine signaling pathway (P = 7.16e-06)
(mitosis) (adjP = 1.74e-05)
Platelet activation/degranulation (P = 4.7e-06)
Steroid biosynthesis (P = 8.46e-06)
Cholesterol biosynthesis (P = 1.5e-05)
Tight junction (P = 8.91e-06)
Oocyte meiosis (P = 1.79e-05)
Leukocyte transendothelial migration (P = 2.1e-05)
Melanogenesis (P = 4.87e-05)
cellular response to oxidative stress (adjP = 0.08)
Platelet activation/degranulation(P = 7.7e-06)
Glycolysis/Gluconeogenesis (P = 0.0002)
cellular amino acid metabolism (adjP = 0.0782)
Translation (P = 4.2e-04)
Tight junction (P = 0.0002)
hexose metabolic process (adjP = 0.0782)
Leukocyte transendothelial migration (P = 0.0005)
translation (adjP = 0.0782)
Glutathione metabolism (P = 0.0005)
Ribosome (P = 0.0006)
Drug resistance remains a major obstacle in cancer therapy and significant efforts have been directed at understanding the mechanisms leading to the development of resistance. Gene expression profiling has played a key role in providing us with important clues regarding genes and pathways that may be affected in drug resistance. Overall, the picture that has emerged is that the drug resistance is a multifactorial process involving mechanisms that are both drug- and tissue-dependent. To address these issues in ovarian cancer, we have generated cell lines that are individually resistant to cisplatin, paclitaxel, or doxorubicin. The combination of a platinum compound (cisplatin) and paclitaxel represent the standard initial chemotherapy for ovarian cancer, while doxorubicin has shown some promise in the treatment of recurrent drug-resistant disease . Various studies have investigated drug resistance, but few have compared the drug resistance mechanisms associated with the development of resistance to different drugs.
We found that the gene expression changes associated with the development of drug resistance was dependent on the drug used (Figure 1B), but the individual lines generated from a given drug were extremely similar to each other. This suggests that while cell lines adopted different mechanisms to develop resistance to different drugs, a given drug and conditions seem to favor similar pathways. Interestingly, the patterns of expression associated with cisplatin and doxorubicin resistance were more similar to each other than they were to cell lines developed through paclitaxel exposure (Figure 2A). This is further supported by the observation that the number of differentially expressed genes shared by cisplatin and doxorubicin (149) was greater than the number of genes shared by cisplatin and paclitaxel (115) or paclitaxel and doxorubicin (97) (Figure 1C). Doxorubicin and paclitaxel resistance can both arise through a multi-drug resistance (MDR)-type mechanism, which generally results from overexpression of ATP Binding cassette (ABC) transporters , while cisplatin resistance is not believe to have a significant MDR component. On the other hand, cisplatin and doxorubicin are both DNA-damaging agents (albeit acting through different mechanisms), while paclitaxel is a microtubule stabilizing agent. Our data suggest that the overall changes in gene expression tend to reflect the drug target rather than an association with the MDR phenotype.
Overall, relatively few genes were simultaneously altered in the 3 drug resistance phenotypes studied: only 18 genes were elevated and 44 genes decreased. Many of these genes were validated and shown to be differentially expressed at the protein level (Figure 3C). Pathway enrichment analysis of these genes revealed that the most significantly enriched pathway was "fatty acid metabolism and oxidation" (4 genes were part of this pathway). Certain genes consistently downregulated in all the drug resistant lines were particularly interesting. In particular, MSMB was found highly downregulated in drug resistant cells at both the mRNA and the protein levels (Figure 3B,C). Interestingly, MSMB has been found decreased in prostate cancer and has been suggested to function through its ability to regulate apoptosis . With this function in mind, it is intriguing that we identified MSMB as one of the most downregulated genes following the development of drug resistance for all three drugs. These findings suggest that MSMB or derivatives may be useful in sensitizing ovarian cancer cells to chemotherapy. In particular, a small peptide derived from the MSMB protein has been shown to exhibit anti-tumor properties  and has been suggested as a potential therapeutic agent in prostate cancer . It will be interesting to determine whether this peptide may be useful in reversing drug resistance in ovarian cancer and we are currently investigating this enticing possibility. RFTN1 is another gene consistently downregulated in all three drug resistance phenotype and it encodes a lipid raft protein. RFTN1 is located on chromosome 3p24, a region shown to be frequently deleted in ovarian cancer, including in OV90 cells . This gene has also been shown to be mutated in some ovarian tumors , suggesting that it may represent a genuine tumor suppressor gene in this disease. Our results suggest that it may also be involved in drug resistance.
Multiple mechanisms can mediate the development of drug resistance and include 1) changes in the regulation or repair of the primary target of the drug (DNA, microtubule, etc), 2) drug retention (increased influx or decreased uptake), 3) increased drug inactivation or sequestration, 4) signaling pathways that affect survival. For cisplatin, copper transporter CTR1 has been shown to play a crucial role in cisplatin uptake and knockout of the CTR1 alleles can lead to resistance to cisplatin toxicity . On the other hand, paclitaxel and doxorubicin are known substrates for the ATP-dependent efflux pump P-glycoprotein (MDR transporter system, ABCB1) and up-regulation of MDR1 has been associated with clinical drug resistance in multiple systems . While we failed to observe changes in the expression of CTR1 in cisplatin (or other) resistant lines, we did identify MDR1 (ABCB1) as one of our most up-regulated genes in all the resistant phenotypes, including cisplatin resistant cells. Genes of the GAGE and MAGEA family have also been found elevated in drug resistance. In particular, MAGEA3,6,11,12 as well as GAGE2,4,5,6 and 7 were found elevated in ovarian cancer cells resistant to paclitaxel and doxorubicin . In this study, we also find GAGE5,6,7 and XAGE1 to be consistently elevated in the various drug resistant lines, although the levels varied according to the resistance phenotype.
While drug resistance development clearly involves changes in a large number of genes and pathways, we wondered whether pathway analysis may help us identify "dominant" pathways for each drug resistance phenotype. Using pathway analysis, we were indeed able to identify several dominant pathways altered in the different drug resistant cells (Table 2 and Figure 4). Different pathway databases identified different pathways, likely because of variations in annotation and curation, but comparison of the results from different databases allowed us to find pathways that were consistently identified (Figure 4). In cisplatin-derived resistance, we frequently found changes in ECM pathways altered. ECM-Integrin interactions have previously been shown to control cell survival  and ECM has been implicated in ovarian cancer drug resistance  as well as lung cancer drug resistance . The development of doxorubicin resistance exhibited strong changes in pathways associated with proteasome degradation, This is particularly interesting considering that bortezomib, a proteasome inhibitor, has been found effective in combination therapy with doxorubicin in several studies [28, 29]. Because of the specific proteasome genes found altered, as well as the presence of cell cycle genes differentially expressed (such as CDK7), it is likely that the proteasome pathway changes affect the cell cycle. It has been shown that doxorubicin can affect G2/M transition and cyclin B1 activity , and changes in the cell cycle may therefore influence the response to doxorubicin through changes in apoptosis sensitivity . Paclitaxel resistance was associated with changes in pathways important for mRNA and protein synthesis, oxidative stress and glycolysis. The exact mechanisms by which these pathways can affect the resistance to paclitaxel remain under investigation, but changes in apoptosis sensitivity is a certain possibility since general mRNA degradation and oxidative stress have been implicated in apoptosis [32, 33].
In conclusion, we have generated drug resistant ovarian cancer cell lines through exposure to three different chemotherapeutic drugs and identified gene expression patterns altered during the development of chemoresistance. Among the genes that are consistently elevated we identify previously known genes such as ABCB1 and genes of the MAGEA family. Among the genes downregulated, we find genes such as MSMB and PRSS family members that are implicated for the first time in drug resistance. Overall, we find that different drug resistance phenotypes have different expression patterns and we identify many novel genes that may be important in the development of cisplatin, doxorubicin and paclitaxel resistance. Pathway analysis suggests enticing new mechanisms for the development of resistance to cisplatin, doxorubicin, and paclitaxel in ovarian cancer and we find that each resistance phenotype is associated with specific pathway alterations (Figure 5). Whether the identified pathways are causally related to drug resistance remains to be determined and it will be important to follow up these findings with mechanistic studies to better understand the roles of the genes and pathways we have identified.
We thank the members of our laboratory for useful comments on the manuscript. We thank Dr. Bingxue Yan for technical help on various aspects of this work. This research was supported entirely by the Intramural Research Program of the NIH, National Institute on Aging.
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