Integrated analysis of DNA methylation and gene expression reveals specific signaling pathways associated with platinum resistance in ovarian cancer
© Li et al; licensee BioMed Central Ltd. 2009
Received: 25 November 2008
Accepted: 08 June 2009
Published: 08 June 2009
Cisplatin and carboplatin are the primary first-line therapies for the treatment of ovarian cancer. However, resistance to these platinum-based drugs occurs in the large majority of initially responsive tumors, resulting in fully chemoresistant, fatal disease. Although the precise mechanism(s) underlying the development of platinum resistance in late-stage ovarian cancer patients currently remains unknown, CpG-island (CGI) methylation, a phenomenon strongly associated with aberrant gene silencing and ovarian tumorigenesis, may contribute to this devastating condition.
To model the onset of drug resistance, and investigate DNA methylation and gene expression alterations associated with platinum resistance, we treated clonally derived, drug-sensitive A2780 epithelial ovarian cancer cells with increasing concentrations of cisplatin. After several cycles of drug selection, the isogenic drug-sensitive and -resistant pairs were subjected to global CGI methylation and mRNA expression microarray analyses. To identify chemoresistance-associated, biological pathways likely impacted by DNA methylation, promoter CGI methylation and mRNA expression profiles were integrated and subjected to pathway enrichment analysis.
Promoter CGI methylation revealed a positive association (Spearman correlation of 0.99) between the total number of hypermethylated CGIs and GI50 values (i.e., increased drug resistance) following successive cisplatin treatment cycles. In accord with that result, chemoresistance was reversible by DNA methylation inhibitors. Pathway enrichment analysis revealed hypermethylation-mediated repression of cell adhesion and tight junction pathways and hypomethylation-mediated activation of the cell growth-promoting pathways PI3K/Akt, TGF-beta, and cell cycle progression, which may contribute to the onset of chemoresistance in ovarian cancer cells.
Selective epigenetic disruption of distinct biological pathways was observed during development of platinum resistance in ovarian cancer. Integrated analysis of DNA methylation and gene expression may allow for the identification of new therapeutic targets and/or biomarkers prognostic of disease response. Finally, our results suggest that epigenetic therapies may facilitate the prevention or reversal of transcriptional repression responsible for chemoresistance and the restoration of sensitivity to platinum-based chemotherapeutics.
Ovarian cancer is the most deadly gynecological malignancy, with an overall U.S. five-year survival rate of only 46% . While highly curable if diagnosed in the early (ovary-confined) stages, over 75% of initial diagnoses are Stage III or IV malignancies, for which the survival index is only 30.6% . While most patients initially respond to surgical debulking and treatment with taxanes combined with platinum-based chemotherapies [2, 3], over 80% of those responders eventually relapse with fully chemoresistant disease . While a number of signal transduction cascades have been hypothesized to contribute to this devastating clinical phenomenon, the mechanism(s) underlying the onset of chemoresistance remains poorly understood, reviewed in .
Similar to most chemotherapies, the antitumor activity of cisplatin is dependent upon DNA damage of rapidly dividing cells, and is mediated primarily by the formation of intra- and interstrand cisplatin-DNA adducts . The resulting accumulation of these DNA lesions is believed to lead to steric obstruction of DNA-binding proteins necessary for vital intracellular functions, including transcription and DNA replication, with recognition of the resulting lesions by high mobility group and mismatch repair proteins eventually leading to p53-initiated apoptosis . Thus, drug inactivation, decreased accumulation of DNA-cisplatin adducts, defective DNA damage recognition, enhanced nucleotide-excision repair, and impaired apoptotic responses are hypothesized as broad-based mechanisms responsible for the drug-resistant phenotype [5, 8, 9]. While dysregulation of genes and pathways is often due to various rearrangements (e.g., deletions, mutations, or translocations) to the DNA molecule itself, epigenetic changes (e.g., DNA methylation and histone modifications) are likely even more prominent in the onset of chemoresistance [10–14]. Specifically, transcriptional silencing of distinct DNA repair and apoptosis-associated genes by hypermethylation of promoter "CpG islands" (CGIs), CG-rich DNA regions typically unmethylated in normal cells , has now been associated with platinum drug resistance in numerous cancers, including ovarian [9, 16–21]. Moreover, the degree of aberrant methylation (i.e., the total number of methylated genes) has also been directly correlated with ovarian tumor progression and recurrence, and specific methylated loci have been statistically associated with poor progression-free survival in ovarian cancer [22–24]. However, no previous global studies of the accumulation of DNA methylation aberrations, during the gradual acquisition of chemoresistance, or their likely impact on specific biological signaling pathways, have been reported in cancer.
To identify epigenetically regulated genes directly associated with ovarian cancer cisplatin resistance, and their associated biological pathways, we established a cell culture model to emulate the time-dependent development of drug resistance in patients suffering from this condition. In this model, a single clone of the platinum-sensitive ovarian cancer cell line A2780 was exposed to incrementally increasing doses of cisplatin, generating A2780 sublines having varying degrees of chemoresistance. By categorizing distinct aberrations in DNA methylation and gene expression associated with specific time-points during the development of resistance, we demonstrated statistically significant correlations between promoter CpG island methylation and gene expression changes, and also between methylation and drug resistance, with consequent alterations in specific drug-response signaling pathways. In accord with a causal role for aberrant DNA methylation in cisplatin resistance, treatment of the drug-resistant sublines with DNA methylation inhibitors resulted in significant promoter demethylation and the re-establishment of chemosensitivity. While other studies have profiled gene expression [13, 25, 26], proteomic [27, 28], and chromosomal aberrations [29, 30] related to ovarian cancer cisplatin resistance, we believe this is the first report integrating chemoresistance-associated alterations in DNA methylation and gene expression to determine likely epigenetically regulated biological pathways related to drug sensitivity. Based on these results, we suggest that aberrant DNA methylation may contribute to the disruption of key biological pathways during ovarian tumor progression to a drug-resistant phenotype. We believe our findings justify further cellular and molecular biologic studies for the development of more effective approaches for the clinical use of platinum-based chemotherapeutics.
Cell lines, drug treatments, and cell proliferation assays
All cells were maintained in RPMI 1640 media with 2 mM L-glutamine, 50 U/ml penicillin, 50 mg/ml streptomycin, and 10% fetal bovine serum, at 30°C and 5% CO2. 5-aza-2'-deoxycytidine (5-aza-dC) was purchased from Sigma (St. Louis, MO) and zebularine [1-(β-D-ribofuranosyl)-1,2-dihydropyrimidin-2-one] was a kind gift from Dr. Victor Marquez (Developmental Therapeutics Program, National Cancer Institute, Frederick, MD). A2780 ovarian cancer cells were obtained from ATCC (Manassas, VA), restriction enzymes from New England Biolabs (Beverly, MA), and cell culture reagents from Invitrogen (Carlsbad, CA). Using serial dilution cell seeding, a single clone of the cisplatin-sensitive, epithelial ovarian cancer cell line A2780 was cultured for multiple cycles ("treatment rounds") with incrementally increasing doses of cis-diamminedichloroplatinum(II) dichloride (CDDP, cisplatin) (Sigma). 5-aza-dC or zebularine treatment was performed after cell seeding for 48 hours prior to cisplatin treatment . MTT assays were used to determine both GI50 values and growth curves of the cells, as we have described previously . Briefly, 96-well dishes were plated with 2,000 cells per well one day before cisplatin treatment. The next day, cells were treated with various dosages of cisplatin (0.5 μM to 100 μM) for three hours and allowed to recover for three days. Following drug treatments, total viable cell numbers were determined by 4-hr treatments with 3-(4.5-dimethylthiazol-2-yl)-2.5-diphenyl tetrazolium bromide (MTT) assay, with cell viability (as determined by MTT metabolism to formazan) determined by measuring absorbance at 600 nm using a Bio-Tek (Winooski, VT) microplate spectrophotometer. Dose-response curves were then generated and 50% growth inhibitory (GI50) dose values determined by Microsoft Excel or Prism 4.0 (GraphPad Software, San Diego, CA), using sigmoidal dose (variable slope) curve fitting, as we have described previously [31, 32].
Differential methylation hybridization and data processing
Genomic DNA was isolated from parental (drug-sensitive) A2780, Round1, Round3, and Round5 cells using DNeasy purification kits (Qiagen, Valencia, CA). Differential methylation hybridization (DMH) was then performed as previously described [22, 33, 34]. Briefly, isolated DNA was digested with the methylation-insensitive restriction enzyme BfaI (C^TAG), followed by ligation of linkers. Linker-ligated DNA was then digested by the methylation sensitive enzymes HinP1I (G^CGC) and HpaII (C^CGG), and digestion products were then amplified by linker PCR. The PCR products were further amplified using aminoallyl-dUTP incorporation to facilitate labeling with the fluorophores Cy3 (parental A2780) or Cy5 (A2780 following various "rounds" of cisplatin treatment). The labeled DNA samples were then combined and hybridized to a customized 60-mer oligo microarray containing 40,000 CpG-rich fragments from 12,000 known gene promoters (Agilent, Santa Clara, CA) (see Additional file 1 for full annotations). Following hybridization and washing, microarray images were scanned and generated using an Axon GenePix 4200A scanner (Molecular Devices, Sunnyvale, CA). The raw microarray data was first corrected for background noise by background subtraction and then for system and technical noise by LOESS normalization. The relative methylation levels (i.e., "folds-change"), for each hybridized probe, in cisplatin-resistant sublines, were approximated by the ratios of the Cy5 (drug-treated cell lines) to Cy3 (parental A2780 cell line) fluorescence, and were defined as positive or negative values according to their respective increases or decreases compared to the parental cells. To avoid potential technical variations between probes, the methylation levels of multiple probes for a single gene (average of four probes per gene) were not collapsed or averaged, and genes having multiple discordant probe intensities were eliminated from the analysis. The DMH data have been deposited in NCBI Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/projects/geo/) and are accessible through GEO SuperSeries GSE15709.
Gene expression microarray analysis and data processing
For gene expression assessments, total RNA was isolated from parental A2780 cells and Round5 cells using Qiagen RNeasy purification kits (Valencia, CA) and further purified with RNase-free DNase following our previously described method . All microarray hybridizations were performed at the Indiana University Center for Medical Genomics (IUCMG). Five replicates were performed for each cell line using Human U133 plus 2.0 GeneChips (Affymetrix, Santa Clara, CA). Using Bioconductor , present (P), absent (A) or marginal (M) calls were determined using an MAS5 algorithm. Fraction presence, defined as the average present/absent (P/A) detection call (scores were given as P = 1, M = 0.5 and A = 0) for the experimental or control groups, was calculated for each microarray probe, and probes with at least one group having a fraction presence of 0.5 were selected for future use. Welch's t-test was performed for each probe using their log-transformed signals, with p-values less than 0.01 considered significant. To further support the statistical significance of probes having p < 0.01, the false discovery rate (FDR) was also calculated [36, 37], with probe significance defined as an FDR of less than 5%. A moderately stringent fold-change cutoff of ≥ 1.5 (or ≤ -1.5 for downregulation), which allows for an acceptable balance between false discovery and false negative rates , was applied (in addition to the p-value cutoffs) to determine genes with significant expression alterations. The gene expression microarray data have been deposited in NCBI Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/projects/geo/) and are accessible through GEO SuperSeries GSE15709.
Clustering, functional pathway prediction, gene ontology (GO) analysis, and statistics
Hierarchical clustering of the expression profiles was done by Cluster 3.0 . Expression profiles were first pre-filtered by fraction present and then input into the program. The input data was further log transformed and normalized by array median centering and gene median centering. Average linkage clustering was performed by correlation (centered) similarity metric. Clustering results were viewed using TreeView version 1.60 . The online program Pathway-Express (Onto-Tools, Wayne State University, Detroit, MI) [40, 41] was used to explore the most biologically relevant pathways impacted by a list of input genes. Specific biological pathways were defined by the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (Kanehisa Laboratories, Japan) . Given a list of genes (for example, upregulated genes in Round5), Pathway-Express selects pathways based on impact analysis that considers not only conventional statistical analysis but also other biological factors, such as expression levels (i.e., fold-change) of input genes, type and position in a given pathway, and protein-protein interactions, among other variables. Thus, this approach is considered to be more powerful than analyses based on statistics only. Corrected p-values of less than 0.05 were used as an empirical cutoff for retrieving altered pathways . GO enrichment of expression profiles was also analyzed by the online program Functional Annotation Analysis Tools, provided by the Database for Annotation, Visualization and Integrated Discovery (DAVID) bioinformatics database (NIAID, NIH, Bethesda, MD) [43–45]. All statistics were done in R and Microsoft Excel, unless otherwise stated.
Establishment of a cell culture model of acquired ovarian cancer platinum resistance
Positive correlation between DNA methylation and cisplatin-resistance development
To examine changes in DNA methylation during the development of drug resistance, we utilized a genome-wide methylation microarray approach, differential methylation hybridization (DMH) , to compare CpG island (CGI) methylation profiles of: 1) Round1 (one cisplatin treatment) vs. parental A2780; 2) Round3 (three cisplatin treatments) vs. parental A2780; and 3) Round5 (five cisplatin treatments) vs. parental A2780. The methylation status of any specific gene was estimated by its ratio of normalized Cy5 to Cy3 fluorescent signal (i.e., fold-change). To select differentially methylated genes, a moderately stringent fold-change cutoff of 1.5, allowing for an acceptable balance between false discovery and false negative rates , was used (i.e., hypermethylation was defined as fold-change ≥ 1.5 and hypomethylation as fold-change ≤ -1.5). Using that cutoff limit, the total numbers of hypermethylated genes for the Round1, Round3 and Round5 sublines were 595, 870 and 1176, respectively, relative to the parental ("Round0") A2780 cells. Spearman correlation testing further demonstrated a significant and positive linear correlation between the total number of hypermethylated genes and the GI50 values of the cisplatin-resistant sublines (Figure 1B), in accord with previous studies demonstrating increasing CGI methylation during ovarian tumor progression [23, 24].
mRNA expression level changes of DNA methyltransferases (DNMTs) in Round5 vs. parental A2780 cells
DNA (cytosine-5-)-methyltransferase 1
DNA (cytosine-5-)-methyltransferase 2
DNA (cytosine-5-)-methyltransferase 3 alpha
DNA (cytosine-5-)-methyltransferase 3 beta
Gene expression profiles reveal cisplatin-induced alterations in drug-resistant ovarian cancer
To identify changes in gene expression associated with cisplatin resistance (in possible correlation with promoter DNA methylation), microarray studies were performed on the Round5 subline (chemoresistant) vs. the parental line (A2780; chemosensitive). Total RNA was isolated from Round5 and parental (Round0) cells, labeled and hybridized to Human U133 plus 2.0 Affymetrix microarrays. Probes with fraction presence (see methods) < 0.5 were removed. The remaining 23,723 probes were then log-transformed and median-centered between arrays and within genes. Unsupervised hierarchical clustering (average linkage clustering method) was then utilized to visualize common expression patterns between the samples , demonstrating clustering of all five replicates of the Round5 subline gene expression microarray results, while analogously, the five replicate microarray results of the parental line formed a separate cluster. Figure 1C and Additional file 2 showed the clustering results of all (3127) significantly up- or down- regulated probes (p-value < 0.01, FDR < 5%, fold-change ≥ 1.5 or ≤ -1.5), these probes corresponded to a total of 1036 genes upregulated and 1286 genes downregulated in Round5 (see Additional file 3). The functional clustering analysis, an algorithm capable of clustering similar annotations across several often-used annotation types (e.g., GO terms, pathways, Swiss-Prot knowledgebase keywords) or within different levels of annotation terms (to highlight functional changes), provided by DAVID (Database for Annotation, Visualization and Integrated Discovery, developed by NIAID/NIH) [43–45], was then used to investigate gene ontology (GO)  term enrichment. Functional clustering analysis of all 1036 genes upregulated in Round5, using a high classification stringency (Kappa similarity term overlap ≥ 3 and threshold of 0.85; classification multiple linkage threshold of 0.5), showed 31 functional groups to be highly enriched (geometric p-values < 0.01), with cell cycle progression, cellular metabolic processes and DNA damage response as the top three ranked GO biological process terms (see Additional file 4). Conversely, using the same high classification stringency, all 1286 downregulated genes segregated into 16 enriched functional groups, with cellular metabolic processing, biological regulation, macromolecule and protein metabolic processes and positive regulation of transcription as the highest ranking GO biological pathway terms (see Additional file 5).
Cisplatin resistance is associated with promoter CpG island DNA methylation of key genes in vital intracellular pathways
Biological pathways repressed by hypermethylation
Hypermethylated and downregulated genes
Fisher's Exact Test p-values§
Input genes in pathway
Input genes in pathway
Genes in pathway
Cell adhesion molecules
ITFAV, CLDN11, NEO1, CDH2
CLDN11, PPP2R4, INADL
PPAR signaling pathway
Leukocyte transendothelial migratio'n
Biological pathways activated by hypomethylation
Hypomethylated and upregulated genes
Fisher's Exact Test p-values§
Input genes in pathway
Input genes in pathway
Genes in pathway
PIK3R3, PDGFRA, E2F1
PIK3R3, PDGFRA, E2F1
PIK3R3, E2F1, TGFBR2
PIK3R3, PDGFRA, E2F1
PIK3R3, E2F1, TGFBR2
Chronic myeloid leukemia
PIK3R3, E2F1, TGFBR2
Non-small cell lung cancer
Phosphatidylinositol signaling system
MAPK signaling pathway
TGF-beta signaling pathway
Regulation of actin cytoskeleton
PIK3R3, PDGFRA, MSN
Calcium signaling pathway
Leukocyte transendothelial migration
Platinum compounds have served as a standard therapy for post-surgical ovarian cancer patients for over two decades, as well as for other malignancies, including testicular, bladder, lung, endometrial, and head and neck cancers . Acquired or de novo resistance to platinum-based chemotherapy is commonly observed in ovarian cancer, with numerous underlying mechanisms now proposed to explain this phenomenon, including drug inactivation, elevated resistance to apoptosis, decreased recognition of DNA damage, and increased DNA repair [8, 9, 53, 54]. Accumulating evidence now shows that aberrant epigenetic alterations contribute to these chemoresistance-associated phenomena, perhaps even more so than genetic aberrations [10, 16]. By comparing and combining genome-wide gene expression and methylation changes observed in cisplatin-sensitive and -resistant ovarian cancer sublines, we discovered both novel and reported pathways and gene ontology (GO) groups likely to mediate acquired cisplatin resistance. These included cell cycle progression (G2/M checkpoint), response to DNA damage, nucleotide binding, and various cellular metabolic processes, in agreement with previous reports  and our previous study , further supporting a role for promoter CpG island (CGI) methylation in disrupting gene expression during tumor progression.
One possible explanation for our observed chemoresistance-associated changes in DNA methylation patterns is aberrant activity or substrate specificity of DNA methyltransferase (DNMT) enzymes [56, 57]. In the current study, we observed modest but highly significant upregulation of both DNMT1 and DNMT3B in the cisplatin-resistant Round5 (i.e., treated for five drug cycles) A2780 subline (Table 1), suggesting that the altered methylation profile in these cells may be associated with increased or altered DNMT activity. In support of this possibility, a number of other studies have demonstrated pharmacologic or genetic downregulation of DNMT1 and DNMT3B enhanced chemosensitivity to various platinum drugs, including cisplatin [31, 58–61]. Our current results demonstrate that 5-aza-dC and another methylation inhibitor, zebularine, dose-dependently restored chemosensitivity to Round5 cells (Figure 2), and previously we reported upregulation of DNMT1 and DNMT3B in ovarian cancer cell lines, along with a potential positive correlation between DNMT1 overexpression and tumor aggressiveness . Alterations in DNMT isoforms have also been reported in ovarian cancer, with a complex relationship between global DNA hypomethylation and regional hypermethylation [63, 64], while one study of serous endometrioid cancer actually demonstrated DNMT downregulation .
In addition to aberrant DNMT enzyme activity, drug-induced de novo promoter methylation has been hypothesized . This phenomenon is believed to be due to DNA structural distortions resulting from the formation of cisplatin-DNA adducts, allowing subsequent access of DNMTs and/or other auxiliary methylation machinery components to target DNA regions [67, 68]. It has also been demonstrated that endogenous DNA damage can alter the site selectivity of DNMT1 or recruit DNMTs to sites of repair [69, 70]. The fact that chemoresistance is reproducibly reversible by inhibition of DNA methyltransferases further suggests that patients whose cancers have methylation of multiple relevant genes might be selected as candidates for demethylating therapy, in addition to platinum-based ovarian cancer chemotherapy, an approach that our group (National Institutes of Health, NIH, Study NCT00477386) and others (NIH studies NCT00748527 and NCT00529022) are taking to the clinic .
Our promoter CGI methylation profiles from A2780 sublines representing early, intermediate and late-stage cisplatin resistance demonstrated that the total number of hypermethylated genes linearly increased (Spearman correlation 0.99) with increasing cisplatin resistance (Figure 1B). This positive correlation suggests that in concert with altered promoter DNA methylation, distinct methylation profiles progressively may emerge during the development of drug resistance. Our previous studies of methylation profiling of late stage ovarian cancer patient tumors correlated CGI methylation and disease recurrence [22, 23], further supporting an association between progressive methylation patterns and advanced disease [24, 72]. Such methylation patterns may disrupt specific intracellular signaling pathways, and while using genome-wide approaches to demonstrate direct regulation of gene expression by aberrant promoter CGI methylation has been a challenge in the epigenomics field, direct or indirect biological outcomes of epigenetic modifications commonly associate with specific cellular behavior changes.
To assess the role of promoter CGI methylation in cisplatin resistance, we examined biological pathways potentially dysregulated by hypermethylation (Table 2), showing likely DNA hypermethylation-downregulated pathways such as CAMs, tight junction formation, PPAR signaling, and leukocyte transendothelial migration pathways. CAMs and tight junctions, by affecting signal transduction pathways, are both directly involved in the regulation of cell proliferation, differentiation, and apoptosis [73, 74], and loss of functional tight junctions has been associated with tumorigenesis . Specifically, hypermethylation-associated downregulation of numerous claudins, integral membrane protein constituents of tight junctions, has been demonstrated to associate with tumorigenesis and tumor invasion in ovarian and other cancers, including those of the breast, bladder, and colon [76–80]. In addition to alterations in claudin and CAM functions, we observed hypermethylation and downregulation of gene products previously hypothesized as suppressors of ovarian tumor progression, including alpha-integrins (possible regulators of cell proliferation and adhesion), carnitine palmitoyltransferase I (CPT1A, a protein believed to play a role in histone deacetylase inhibition), and N-cadherin (CDH2), a member of the cell adhesion proteins often lost during tumor progression [81–84]. Consequently, using global approaches, our analyses identified several potential links between cell adhesion and the acquisition of chemoresistance, in the context of regulation by epigenetic modification.
By using a similar approach for hypermethylated and downregulated genes in the cisplatin-resistant cells, we identified several pathways likely regulated by promoter CGI hypomethylation, including those characteristic of other cancers, such has pancreatic, prostate, colorectal, non-small cell lung cancers, chronic myeloid leukemia, glioma, and melanoma (Table 3). Interestingly, all significantly enriched pathways were found to be cancer-related, and those broad-based cancer categories commonly included signal transduction pathways such as phosphatidylinositol kinase/Akt, transforming growth factor-beta, the E2F transcription factor family, and platelet-derived growth factor signaling (PDGFR). Specifically, the PI3K/Akt pathway has been shown to contribute to cisplatin resistance by promoting cell proliferation and increasing drug metabolism and resistance to apoptosis [85, 86], while E2F transcription factors have been previously implicated in platinum-resistant ovarian cancer [87–89]. Moreover, while regulation of TGFBR2 by promoter CGI methylation has previously been reported in lymphoma , a role for promoter hypomethylation in dysregulation of that pathway was previously unknown.
Taken together, our pathway analyses suggest significant upregulation of tumor-promoting cascades by hypomethylation and disruption of tumor-suppressive functions by hypermethylation. While many of these genes/pathways have been previously implicated in cisplatin drug response/resistance , we also identified various cellular mediators (CIPT1A, alpha-integrins) previously unreported in the action of that widely used chemotherapeutic agent. Finally, although others have reported proteomic [27, 28], chromosomal [29, 30], gene expression [13, 25, 54], or histone/DNA modification  profiles of cancer cisplatin resistance, we believe this is the first report integrating aberrations in DNA methylation with changes in gene expression to identify likely drug sensitivity-associated biological pathways.
This study establishes a valuable cell culture model system for the study of promoter CpG island DNA methylation aberrations related to the development of platinum resistance in ovarian cancer and their associated intracellular signaling pathways. We further demonstrate the value of rigorous bioinformatics analyses of integrating DNA methylation and gene expression profiles to elucidate epigenetically regulated pathways associated with the time-dependent acquisition of chemoresistance. Such experimental and computational approaches will be highly valuable for identifying key mediators of chemotherapy resistance as potential biomarkers or therapeutic targets.
differential methylation hybridization
Database for Annotation, Visualization and Integrated Discovery
Kyoto Encyclopedia of Genes and Genomes
cell adhesion molecule
- GI50 :
50% growth inhibition dose.
The authors wish to thank Dr. V. Marquez (National Institutes of Health, National Cancer Institute) for kindly providing the zebularine used in this study and Mr. H. Paik and D. Sledge for helpful discussion. Supported in part by NIH grants CA085289 and CA113001, Walther Cancer Institute Foundation (Indianapolis), Phi Beta Psi Sorority (Brownsburg, IN), Ovar'coming Together (Indianapolis). The microarray studies were carried out using the facilities of the Center for Medical Genomics at Indiana University School of Medicine. The Center for Medical Genomics is supported in part by the Indiana Genomics Initiative of Indiana University, INGEN, which is supported in part by the Lilly Endowment, Inc.
- Ries LAG, Melbert D, Krapcho M, Stinchcomb DG, Howlader N, Horner MJ, Mariotto A, Miller BA, Feuer EJ, Altekruse SF, et al: SEER Cancer Statistics Review. 1975, National Cancer Institute. Bethesda, MD, [http://seer.cancer.gov/csr/1975_2005/]Google Scholar
- McGuire WP, Hoskins WJ, Brady MF, Kucera PR, Partridge EE, Look KY, Clarke-Pearson DL, Davidson M: Cyclophosphamide and cisplatin compared with paclitaxel and cisplatin in patients with stage III and stage IV ovarian cancer. N Engl J Med. 1996, 334 (1): 1-6. 10.1056/NEJM199601043340101.PubMedGoogle Scholar
- Ozols RF: Treatment goals in ovarian cancer. Int J Gynecol Cancer. 2005, 15 (Suppl 1): 3-11. 10.1111/j.1525-1438.2005.15351.x.PubMedGoogle Scholar
- Agarwal R, Kaye SB: Ovarian cancer: strategies for overcoming resistance to chemotherapy. Nat Rev Cancer. 2003, 3 (7): 502-516. 10.1038/nrc1123.PubMedGoogle Scholar
- Stewart DJ: Mechanisms of resistance to cisplatin and carboplatin. Crit Rev Oncol Hematol. 2007, 63 (1): 12-31. 10.1016/j.critrevonc.2007.02.001.PubMedGoogle Scholar
- Crul M, van Waardenburg RC, Beijnen JH, Schellens JH: DNA-based drug interactions of cisplatin. Cancer Treat Rev. 2002, 28 (6): 291-303. 10.1016/S0305-7372(02)00093-2.PubMedGoogle Scholar
- Cepeda V, Fuertes MA, Castilla J, Alonso C, Quevedo C, Perez JM: Biochemical mechanisms of cisplatin cytotoxicity. Anticancer Agents Med Chem. 2007, 7 (1): 3-18. 10.2174/187152007779314044.PubMedGoogle Scholar
- Kelland L: The resurgence of platinum-based cancer chemotherapy. Nat Rev Cancer. 2007, 7 (8): 573-584. 10.1038/nrc2167.PubMedGoogle Scholar
- Siddik ZH: Cisplatin: mode of cytotoxic action and molecular basis of resistance. Oncogene. 2003, 22 (47): 7265-7279. 10.1038/sj.onc.1206933.PubMedGoogle Scholar
- Balch C, Huang TH, Brown R, Nephew KP: The epigenetics of ovarian cancer drug resistance and resensitization. Am J Obstet Gynecol. 2004, 191 (5): 1552-1572. 10.1016/j.ajog.2004.05.025.PubMedGoogle Scholar
- Feinberg AP, Tycko B: The history of cancer epigenetics. Nat Rev Cancer. 2004, 4 (2): 143-153. 10.1038/nrc1279.PubMedGoogle Scholar
- Fojo T, Bates S: Strategies for reversing drug resistance. Oncogene. 2003, 22 (47): 7512-7523. 10.1038/sj.onc.1206951.PubMedGoogle Scholar
- Jones PA, Baylin SB: The epigenomics of cancer. Cell. 2007, 128 (4): 683-692. 10.1016/j.cell.2007.01.029.PubMedPubMed CentralGoogle Scholar
- Konstantinopoulos PA, Fountzilas E, Pillay K, Zerbini L, Libermann TA, Cannistra SA, Spentzos D: Carboplatin-induced gene expression changes in vitro are prognostic of survival in epithelial ovarian cancer. BMC Med Genomics. 2008, 1 (1): 59-10.1186/1755-8794-1-59.PubMedPubMed CentralGoogle Scholar
- Takai D, Jones PA: Comprehensive analysis of CpG islands in human chromosomes 21 and 22. Proc Natl Acad Sci USA. 2002, 99 (6): 3740-3745. 10.1073/pnas.052410099.PubMedPubMed CentralGoogle Scholar
- Balch C, Montgomery JS, Paik HI, Kim S, Huang TH, Nephew KP: New anti-cancer strategies: epigenetic therapies and biomarkers. Front Biosci. 2005, 10: 1897-1931. 10.2741/1668.PubMedGoogle Scholar
- Brown R, Hirst GL, Gallagher WM, McIlwrath AJ, Margison GP, Zee van der AG, Anthoney DA: hMLH1 expression and cellular responses of ovarian tumour cells to treatment with cytotoxic anticancer agents. Oncogene. 1997, 15 (1): 45-52. 10.1038/sj.onc.1201167.PubMedGoogle Scholar
- Cunningham JM, Christensen ER, Tester DJ, Kim CY, Roche PC, Burgart LJ, Thibodeau SN: Hypermethylation of the hMLH1 promoter in colon cancer with microsatellite instability. Cancer Res. 1998, 58 (15): 3455-3460.PubMedGoogle Scholar
- Kitajima Y, Miyazaki K, Matsukura S, Tanaka M, Sekiguchi M: Loss of expression of DNA repair enzymes MGMT, hMLH1, and hMSH2 during tumor progression in gastric cancer. Gastric Cancer. 2003, 6 (2): 86-95.PubMedGoogle Scholar
- Martini M, Ciccarone M, Garganese G, Maggiore C, Evangelista A, Rahimi S, Zannoni G, Vittori G, Larocca LM: Possible involvement of hMLH1, p16(INK4a) and PTEN in the malignant transformation of endometriosis. Int J Cancer. 2002, 102 (4): 398-406. 10.1002/ijc.10715.PubMedGoogle Scholar
- Dai W, Teodoridis JM, Graham J, Zeller C, Huang TH, Yan P, Vass JK, Brown R, Paul J: Methylation Linear Discriminant Analysis (MLDA) for identifying differentially methylated CpG islands. BMC Bioinformatics. 2008, 9: 337-10.1186/1471-2105-9-337.PubMedPubMed CentralGoogle Scholar
- Wei SH, Balch C, Paik HH, Kim YS, Baldwin RL, Liyanarachchi S, Li L, Wang Z, Wan JC, Davuluri RV, et al: Prognostic DNA methylation biomarkers in ovarian cancer. Clin Cancer Res. 2006, 12 (9): 2788-2794. 10.1158/1078-0432.CCR-05-1551.PubMedGoogle Scholar
- Wei SH, Chen CM, Strathdee G, Harnsomburana J, Shyu CR, Rahmatpanah F, Shi H, Ng SW, Yan PS, Nephew KP, et al: Methylation microarray analysis of late-stage ovarian carcinomas distinguishes progression-free survival in patients and identifies candidate epigenetic markers. Clin Cancer Res. 2002, 8 (7): 2246-2252.PubMedGoogle Scholar
- Watts GS, Futscher BW, Holtan N, Degeest K, Domann FE, Rose SL: DNA methylation changes in ovarian cancer are cumulative with disease progression and identify tumor stage. BMC Med Genomics. 2008, 1: 47-10.1186/1755-8794-1-47.PubMedPubMed CentralGoogle Scholar
- Hsu DS, Balakumaran BS, Acharya CR, Vlahovic V, Walters KS, Garman K, Anders C, Riedel RF, Lancaster J, Harpole D, et al: Pharmacogenomic strategies provide a rational approach to the treatment of cisplatin-resistant patients with advanced cancer. J Clin Oncol. 2007, 25 (28): 4350-4357. 10.1200/JCO.2007.11.0593.PubMedGoogle Scholar
- Riedel RF, Porrello A, Pontzer E, Chenette EJ, Hsu DS, Balakumaran B, Potti A, Nevins J, Febbo PG: A genomic approach to identify molecular pathways associated with chemotherapy resistance. Mol Cancer Ther. 2008, 7 (10): 3141-3149. 10.1158/1535-7163.MCT-08-0642.PubMedGoogle Scholar
- Stewart JJ, White JT, Yan X, Collins S, Drescher CW, Urban ND, Hood L, Lin B: Proteins associated with Cisplatin resistance in ovarian cancer cells identified by quantitative proteomic technology and integrated with mRNA expression levels. Mol Cell Proteomics. 2006, 5 (3): 433-443.PubMedGoogle Scholar
- Yan XD, Pan LY, Yuan Y, Lang JH, Mao N: Identification of platinum-resistance associated proteins through proteomic analysis of human ovarian cancer cells and their platinum-resistant sublines. Journal of proteome research. 2007, 6 (2): 772-780. 10.1021/pr060402r.PubMedGoogle Scholar
- Prasad M, Bernardini M, Tsalenko A, Marrano P, Paderova J, Lee CH, Ben-Dor A, Barrett MT, Squire JA: High definition cytogenetics and oligonucleotide aCGH analyses of cisplatin-resistant ovarian cancer cells. Genes, chromosomes & cancer. 2008, 47 (5): 427-436. 10.1002/gcc.20547.Google Scholar
- Wasenius VM, Jekunen A, Monni O, Joensuu H, Aebi S, Howell SB, Knuutila S: Comparative genomic hybridization analysis of chromosomal changes occurring during development of acquired resistance to cisplatin in human ovarian carcinoma cells. Genes, chromosomes & cancer. 1997, 18 (4): 286-291. 10.1002/(SICI)1098-2264(199704)18:4<286::AID-GCC6>3.0.CO;2-X.Google Scholar
- Balch C, Yan P, Craft T, Young S, Skalnik DG, Huang TH, Nephew KP: Antimitogenic and chemosensitizing effects of the methylation inhibitor zebularine in ovarian cancer. Molecular cancer therapeutics. 2005, 4 (10): 1505-1514. 10.1158/1535-7163.MCT-05-0216.PubMedGoogle Scholar
- Fan M, Yan PS, Hartman-Frey C, Chen L, Paik H, Oyer SL, Salisbury JD, Cheng AS, Li L, Abbosh PH, et al: Diverse gene expression and DNA methylation profiles correlate with differential adaptation of breast cancer cells to the antiestrogens tamoxifen and fulvestrant. Cancer Res. 2006, 66 (24): 11954-11966. 10.1158/0008-5472.CAN-06-1666.PubMedGoogle Scholar
- Yan PS, Wei SH, Huang TH: Differential methylation hybridization using CpG island arrays. Methods Mol Biol. 2002, 200: 87-100.PubMedGoogle Scholar
- Yan PS, Chen CM, Shi H, Rahmatpanah F, Wei SH, Huang TH: Applications of CpG island microarrays for high-throughput analysis of DNA methylation. J Nutr. 2002, 132 (8 Suppl): 2430S-2434S.PubMedGoogle Scholar
- Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L, Ge Y, Gentry J, et al: Bioconductor: open software development for computational biology and bioinformatics. Genome Biol. 2004, 5 (10): R80-10.1186/gb-2004-5-10-r80.PubMedPubMed CentralGoogle Scholar
- Benjamini Y, Hochberg Y: Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B (Methodological). 1995, 57 (1): 289-300.Google Scholar
- Benjamini Y, Yekutieli D: The control of the false discovery rate in multiple testing under dependency. Ann Stat. 2001, 29: 1165-1168. 10.1214/aos/1013699998.Google Scholar
- Norris AW, Kahn CR: Analysis of gene expression in pathophysiological states: balancing false discovery and false negative rates. Proc Natl Acad Sci USA. 2006, 103 (3): 649-653. 10.1073/pnas.0510115103.PubMedPubMed CentralGoogle Scholar
- Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 1998, 95 (25): 14863-14868. 10.1073/pnas.95.25.14863.PubMedPubMed CentralGoogle Scholar
- Onto-Tools (The Intelligent Systems and Bioinformatics Laboratory). [http://vortex.cs.wayne.edu/Projects.html]
- Draghici S, Khatri P, Tarca AL, Amin K, Done A, Voichita C, Georgescu C, Romero R: A systems biology approach for pathway level analysis. Genome Res. 2007, 17 (10): 1537-1545. 10.1101/gr.6202607.PubMedPubMed CentralGoogle Scholar
- KEGG: Kyoto Encyclopedia of Genes and Genomes. [http://www.genome.jp/kegg/pathway.html]
- Dennis G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA: DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003, 4 (5): P3-10.1186/gb-2003-4-5-p3.PubMedGoogle Scholar
- Huang da W, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009, 4 (1): 44-57. 10.1038/nprot.2008.211.PubMedGoogle Scholar
- Database for Annotation, Visualization and Integrated Discovery (DAVID). [http://david.abcc.ncifcrf.gov/]
- Eva A, Robbins KC, Andersen PR, Srinivasan A, Tronick SR, Reddy EP, Ellmore NW, Galen AT, Lautenberger JA, Papas TS, et al: Cellular genes analogous to retroviral onc genes are transcribed in human tumour cells. Nature. 1982, 295 (5845): 116-119. 10.1038/295116a0.PubMedGoogle Scholar
- Behrens BC, Hamilton TC, Masuda H, Grotzinger KR, Whang-Peng J, Louie KG, Knutsen T, McKoy WM, Young RC, Ozols RF: Characterization of a cis-diamminedichloroplatinum(II)-resistant human ovarian cancer cell line and its use in evaluation of platinum analogues. Cancer Res. 1987, 47 (2): 414-418.PubMedGoogle Scholar
- Gal-Yam EN, Saito Y, Egger G, Jones PA: Cancer epigenetics: modifications, screening, and therapy. Annu Rev Med. 2008, 59: 267-280. 10.1146/annurev.med.59.061606.095816.PubMedGoogle Scholar
- Cheng JC, Weisenberger DJ, Gonzales FA, Liang G, Xu GL, Hu YG, Marquez VE, Jones PA: Continuous zebularine treatment effectively sustains demethylation in human bladder cancer cells. Mol Cell Biol. 2004, 24 (3): 1270-1278. 10.1128/MCB.24.3.1270-1278.2004.PubMedPubMed CentralGoogle Scholar
- Foubister V: Drug reactivates genes to inhibit cancer. Drug Discov Today. 2003, 8 (10): 430-431. 10.1016/S1359-6446(03)02697-7.PubMedGoogle Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000, 25 (1): 25-29. 10.1038/75556.PubMedPubMed CentralGoogle Scholar
- Lebwohl D, Canetta R: Clinical development of platinum complexes in cancer therapy: an historical perspective and an update. Eur J Cancer. 1998, 34 (10): 1522-1534. 10.1016/S0959-8049(98)00224-X.PubMedGoogle Scholar
- Lamendola DE, Duan Z, Yusuf RZ, Seiden MV: Molecular description of evolving paclitaxel resistance in the SKOV-3 human ovarian carcinoma cell line. Cancer Res. 2003, 63 (9): 2200-2205.PubMedGoogle Scholar
- Sakamoto M, Kondo A, Kawasaki K, Goto T, Sakamoto H, Miyake K, Koyamatsu Y, Akiya T, Iwabuchi H, Muroya T, et al: Analysis of gene expression profiles associated with cisplatin resistance in human ovarian cancer cell lines and tissues using cDNA microarray. Hum Cell. 2001, 14 (4): 305-315.PubMedGoogle Scholar
- Li M, Paik HI, Balch C, Kim Y, Li L, Huang TH, Nephew KP, Kim S: Enriched transcription factor binding sites in hypermethylated gene promoters in drug resistant cancer cells. Bioinformatics. 2008, 24 (16): 1745-1748. 10.1093/bioinformatics/btn256.PubMedPubMed CentralGoogle Scholar
- Rhee I, Bachman KE, Park BH, Jair KW, Yen RW, Schuebel KE, Cui H, Feinberg AP, Lengauer C, Kinzler KW, et al: DNMT1 and DNMT3b cooperate to silence genes in human cancer cells. Nature. 2002, 416 (6880): 552-556. 10.1038/416552a.PubMedGoogle Scholar
- Robertson KD, Uzvolgyi E, Liang G, Talmadge C, Sumegi J, Gonzales FA, Jones PA: The human DNA methyltransferases (DNMTs) 1, 3a and 3b: coordinate mRNA expression in normal tissues and overexpression in tumors. Nucleic Acids Res. 1999, 27 (11): 2291-2298. 10.1093/nar/27.11.2291.PubMedPubMed CentralGoogle Scholar
- Mishra MV, Bisht KS, Sun L, Muldoon-Jacobs K, Awwad R, Kaushal A, Nguyen P, Huang L, Pennington JD, Markovina S, et al: DNMT1 as a molecular target in a multimodality-resistant phenotype in tumor cells. Mol Cancer Res. 2008, 6 (2): 243-249. 10.1158/1541-7786.MCR-07-0373.PubMedGoogle Scholar
- Plumb JA, Strathdee G, Sludden J, Kaye SB, Brown R: Reversal of drug resistance in human tumor xenografts by 2'-deoxy-5-azacytidine-induced demethylation of the hMLH1 gene promoter. Cancer Res. 2000, 60 (21): 6039-6044.PubMedGoogle Scholar
- Qiu YY, Mirkin BL, Dwivedi RS: Inhibition of DNA methyltransferase reverses cisplatin induced drug resistance in murine neuroblastoma cells. Cancer Detect Prev. 2005, 29 (5): 456-463. 10.1016/j.cdp.2005.05.004.PubMedGoogle Scholar
- Strathdee G, MacKean MJ, Illand M, Brown R: A role for methylation of the hMLH1 promoter in loss of hMLH1 expression and drug resistance in ovarian cancer. Oncogene. 1999, 18 (14): 2335-2341. 10.1038/sj.onc.1202540.PubMedGoogle Scholar
- Ahluwalia A, Hurteau JA, Bigsby RM, Nephew KP: DNA methylation in ovarian cancer. II. Expression of DNA methyltransferases in ovarian cancer cell lines and normal ovarian epithelial cells. Gynecol Oncol. 2001, 82 (2): 299-304. 10.1006/gyno.2001.6284.PubMedGoogle Scholar
- Ehrlich M, Woods CB, Yu MC, Dubeau L, Yang F, Campan M, Weisenberger DJ, Long T, Youn B, Fiala ES, et al: Quantitative analysis of associations between DNA hypermethylation, hypomethylation, and DNMT RNA levels in ovarian tumors. Oncogene. 2006, 25 (18): 2636-2645. 10.1038/sj.onc.1209145.PubMedPubMed CentralGoogle Scholar
- Teodoridis JM, Hall J, Marsh S, Kannall HD, Smyth C, Curto J, Siddiqui N, Gabra H, McLeod HL, Strathdee G, et al: CpG island methylation of DNA damage response genes in advanced ovarian cancer. Cancer Res. 2005, 65 (19): 8961-8967. 10.1158/0008-5472.CAN-05-1187.PubMedGoogle Scholar
- Xiong Y, Dowdy SC, Xue A, Shujuan J, Eberhardt NL, Podratz KC, Jiang SW: Opposite alterations of DNA methyltransferase gene expression in endometrioid and serous endometrial cancers. Gynecol Oncol. 2005, 96 (3): 601-609. 10.1016/j.ygyno.2004.11.047.PubMedGoogle Scholar
- Wei SH, Brown R, Huang TH: Aberrant DNA methylation in ovarian cancer: is there an epigenetic predisposition to drug response?. Annals of the New York Academy of Sciences. 2003, 983: 243-250.PubMedGoogle Scholar
- Nyce J, Leonard S, Canupp D, Schulz S, Wong S: Epigenetic mechanisms of drug resistance: drug-induced DNA hypermethylation and drug resistance. Proc Natl Acad Sci USA. 1993, 90 (7): 2960-2964. 10.1073/pnas.90.7.2960.PubMedPubMed CentralGoogle Scholar
- Nyce J, Liu L, Jones PA: Variable effects of DNA-synthesis inhibitors upon DNA methylation in mammalian cells. Nucleic Acids Res. 1986, 14 (10): 4353-4367. 10.1093/nar/14.10.4353.PubMedPubMed CentralGoogle Scholar
- Mortusewicz O, Schermelleh L, Walter J, Cardoso MC, Leonhardt H: Recruitment of DNA methyltransferase I to DNA repair sites. Proc Natl Acad Sci USA. 2005, 102 (25): 8905-8909. 10.1073/pnas.0501034102.PubMedPubMed CentralGoogle Scholar
- Valinluck V, Sowers LC: Endogenous cytosine damage products alter the site selectivity of human DNA maintenance methyltransferase DNMT1. Cancer Res. 2007, 67 (3): 946-950. 10.1158/0008-5472.CAN-06-3123.PubMedGoogle Scholar
- ClinicalTrials.gov. [http://clinicaltrials.gov/ct2/search]
- Costello JF, Fruhwald MC, Smiraglia DJ, Rush LJ, Robertson GP, Gao X, Wright FA, Feramisco JD, Peltomaki P, Lang JC, et al: Aberrant CpG-island methylation has non-random and tumour-type-specific patterns. Nat Genet. 2000, 24 (2): 132-138. 10.1038/72785.PubMedGoogle Scholar
- Matter K, Aijaz S, Tsapara A, Balda MS: Mammalian tight junctions in the regulation of epithelial differentiation and proliferation. Curr Opin Cell Biol. 2005, 17 (5): 453-458. 10.1016/j.ceb.2005.08.003.PubMedGoogle Scholar
- Matter K, Balda MS: Signalling to and from tight junctions. Nat Rev Mol Cell Biol. 2003, 4 (3): 225-236. 10.1038/nrm1055.PubMedGoogle Scholar
- Morin PJ: Claudin proteins in human cancer: promising new targets for diagnosis and therapy. Cancer Res. 2005, 65 (21): 9603-9606. 10.1158/0008-5472.CAN-05-2782.PubMedGoogle Scholar
- Honda H, Pazin MJ, D'Souza T, Ji H, Morin PJ: Regulation of the CLDN3 gene in ovarian cancer cells. Cancer Biol Ther. 2007, 6 (11): 1733-1742.PubMedGoogle Scholar
- Nakayama F, Semba S, Usami Y, Chiba H, Sawada N, Yokozaki H: Hypermethylation-modulated downregulation of claudin-7 expression promotes the progression of colorectal carcinoma. Pathobiology. 2008, 75 (3): 177-185. 10.1159/000124978.PubMedGoogle Scholar
- Osanai M, Murata M, Chiba H, Kojima T, Sawada N: Epigenetic silencing of claudin-6 promotes anchorage-independent growth of breast carcinoma cells. Cancer Sci. 2007, 98 (10): 1557-1562. 10.1111/j.1349-7006.2007.00569.x.PubMedGoogle Scholar
- Boireau S, Buchert M, Samuel MS, Pannequin J, Ryan JL, Choquet A, Chapuis H, Rebillard X, Avances C, Ernst M, et al: DNA-methylation-dependent alterations of claudin-4 expression in human bladder carcinoma. Carcinogenesis. 2007, 28 (2): 246-258. 10.1093/carcin/bgl120.PubMedGoogle Scholar
- Litkouhi B, Kwong J, Lo CM, Smedley JG, McClane BA, Aponte M, Gao Z, Sarno JL, Hinners J, Welch WR, et al: Claudin-4 overexpression in epithelial ovarian cancer is associated with hypomethylation and is a potential target for modulation of tight junction barrier function using a C-terminal fragment of Clostridium perfringens enterotoxin. Neoplasia. 2007, 9 (4): 304-314. 10.1593/neo.07118.PubMedPubMed CentralGoogle Scholar
- Cruet-Hennequart S, Maubant S, Luis J, Gauduchon P, Staedel C, Dedhar S: alpha(v) integrins regulate cell proliferation through integrin-linked kinase (ILK) in ovarian cancer cells. Oncogene. 2003, 22 (11): 1688-1702. 10.1038/sj.onc.1206347.PubMedGoogle Scholar
- Maubant S, Cruet-Hennequart S, Poulain L, Carreiras F, Sichel F, Luis J, Staedel C, Gauduchon P: Altered adhesion properties and alphav integrin expression in a cisplatin-resistant human ovarian carcinoma cell line. Int J Cancer. 2002, 97 (2): 186-194. 10.1002/ijc.1600.PubMedGoogle Scholar
- Mazzarelli P, Pucci S, Bonanno E, Sesti F, Calvani M, Spagnoli LG: Carnitine palmitoyltransferase I in human carcinomas: a novel role in histone deacetylation?. Cancer Biol Ther. 2007, 6 (10): 1606-1613.PubMedGoogle Scholar
- Pon YL, Auersperg N, Wong AS: Gonadotropins regulate N-cadherin-mediated human ovarian surface epithelial cell survival at both post-translational and transcriptional levels through a cyclic AMP/protein kinase A pathway. J Biol Chem. 2005, 280 (15): 15438-15448. 10.1074/jbc.M410766200.PubMedGoogle Scholar
- Hennessy BT, Smith DL, Ram PT, Lu Y, Mills GB: Exploiting the PI3K/AKT pathway for cancer drug discovery. Nat Rev Drug Discov. 2005, 4 (12): 988-1004. 10.1038/nrd1902.PubMedGoogle Scholar
- Zhang L, Huang J, Yang N, Greshock J, Liang S, Hasegawa K, Giannakakis A, Poulos N, O'Brien-Jenkins A, Katsaros D, et al: Integrative genomic analysis of phosphatidylinositol 3'-kinase family identifies PIK3R3 as a potential therapeutic target in epithelial ovarian cancer. Clin Cancer Res. 2007, 13 (18 Pt 1): 5314-5321. 10.1158/1078-0432.CCR-06-2660.PubMedGoogle Scholar
- Dressman HK, Berchuck A, Chan G, Zhai J, Bild A, Sayer R, Cragun J, Clarke J, Whitaker RS, Li L, et al: An integrated genomic-based approach to individualized treatment of patients with advanced-stage ovarian cancer. J Clin Oncol. 2007, 25 (5): 517-525. 10.1200/JCO.2006.06.3743.PubMedGoogle Scholar
- Reimer D, Sadr S, Wiedemair A, Stadlmann S, Concin N, Hofstetter G, Muller-Holzner E, Marth C, Zeimet AG: Clinical relevance of E2F family members in ovarian cancer – an evaluation in a training set of 77 patients. Clin Cancer Res. 2007, 13 (1): 144-151. 10.1158/1078-0432.CCR-06-0780.PubMedGoogle Scholar
- Wang A, Li CJ, Reddy PV, Pardee AB: Cancer chemotherapy by deoxynucleotide depletion and E2F-1 elevation. Cancer Res. 2005, 65 (17): 7809-7814.PubMedGoogle Scholar
- Chen G, Ghosh P, Osawa H, Sasaki CY, Rezanka L, Yang J, O'Farrell TJ, Longo DL: Resistance to TGF-beta 1 correlates with aberrant expression of TGF-beta receptor II in human B-cell lymphoma cell lines. Blood. 2007, 109 (12): 5301-5307. 10.1182/blood-2006-06-032128.PubMedPubMed CentralGoogle Scholar
- Chekhun VF, Lukyanova NY, Kovalchuk O, Tryndyak VP, Pogribny IP: Epigenetic profiling of multidrug-resistant human MCF-7 breast adenocarcinoma cells reveals novel hyper- and hypomethylated targets. Mol Cancer Ther. 2007, 6 (3): 1089-1098. 10.1158/1535-7163.MCT-06-0663.PubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1755-8794/2/34/prepub