Gene expression profiles of primary cultured ovarian cells in the presence and absence of a DNA methyltransferase inhibitor
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ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Susan Murphy
PROVIDER: E-GEOD-25427 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:Epithelial ovarian cancer is the leading cause of death among gynecologic malignancies. Diagnosis usually occurs after metastatic spread, largely reflecting vague symptoms of early disease combined with lack of an effective screening strategy. Epigenetic mechanisms of gene regulation, including DNA methylation, are fundamental to normal cellular function and also play a major role in carcinogenesis. To elucidate the biological and clinical relevance of DNA methylation in ovarian cancer, we conducted expression microarray analysis of 39 cell lines and 17 primary culture specimens grown in the presence or absence of DNA methyltransferase (DNMT) inhibitors. Two parameters, induction of expression and standard deviation among untreated samples, identified 378 candidate methylated genes, many relevant to TGF-beta signaling. We analyzed 43 of these genes and they all exhibited methylation. Treatment with DNMT inhibitors increased TGF-beta pathway activity. Hierarchical clustering of ovarian cancers using the 378 genes reproducibly generated a distinct gene cluster strongly correlated with TGF-beta pathway activity that discriminates patients based on age. These data suggest that accumulation of age-related epigenetic modifications leads to suppression of TGF-beta signaling and contributes to ovarian carcinogenesis.
2010-11-17 | GSE25427 | GEO
Project description:Epithelial ovarian cancer is the leading cause of death among gynecologic malignancies. Diagnosis usually occurs after metastatic spread, largely reflecting vague symptoms of early disease combined with lack of an effective screening strategy. Epigenetic mechanisms of gene regulation, including DNA methylation, are fundamental to normal cellular function and also play a major role in carcinogenesis. To elucidate the biological and clinical relevance of DNA methylation in ovarian cancer, we conducted expression microarray analysis of 43 cell lines and 17 primary culture specimens grown in the presence or absence of DNA methyltransferase (DNMT) inhibitors. Two parameters, induction of expression and standard deviation among untreated samples, identified 378 candidate methylated genes, many relevant to TGF-beta signaling. We analyzed 43 of these genes and they all exhibited methylation. Treatment with DNMT inhibitors increased TGF-beta pathway activity. Hierarchical clustering of ovarian cancers using the 378 genes reproducibly generated a distinct gene cluster strongly correlated with TGF-beta pathway activity that discriminates patients based on age. These data suggest that accumulation of age-related epigenetic modifications leads to suppression of TGF-beta signaling and contributes to ovarian carcinogenesis. The cancer stem cell hypothesis posits that malignant growth arises from a rare population of progenitor cells within a tumor that provide it with unlimited regenerative capacity. Such cells also possess increased resistance to chemotherapeutic agents. Resurgence of chemoresistant disease following primary therapy typifies epithelial ovarian cancer and may be attributable to residual cancer stem cells, or cancer initiating cells, that survive initial treatment. As the cell surface marker CD133 identifies cancer initiating cells in a number of other malignancies, we sought to determine the potential role of CD133+ cells in epithelial ovarian cancer. We detected CD133 on ovarian cancer cell lines, in primary cancers, and on purified epithelial cells from ascitic fluid of ovarian cancer patients. We found CD133+ ovarian cancer cells generate both CD133+ and CD133- daughter cells, whereas CD133- cells divide symmetrically. CD133+ cells exhibit enhanced resistance to platinum-based therapy, drugs commonly used as first line agents for treatment of ovarian cancer. Sorted CD133+ ovarian cancer cells also form more aggressive tumor xenografts at a lower inoculum than their CD133- progeny. Epigenetic changes may be integral to the behavior of cancer progenitor cells and their progeny. In this regard, we found that CD133 transcription is controlled by both histone modifications and promoter methylation. Sorted CD133- ovarian cancer cells treated with DNA methyltransferase and histone deacetylase inhibitors show a synergistic increase in cell surface CD133 expression. Moreover, DNA methylation at the ovarian tissue active P2 promoter is inversely correlated with CD133 transcription. We also found that promoter methylation increases in CD133- progeny of CD133+ cells, with CD133+ cells retaining a less methylated or unmethylated state. Taken together, our results show that CD133 expression in ovarian cancer is directly regulated by epigenetic modifications and support the idea that CD133 demarcates an ovarian cancer initiating cell population. The activity of these cells may be epigenetically detected and such cells might serve as pertinent chemotherapeutic targets for reducing disease recurrence. The objective of the study was to identify genes that are subject to DNA methylation through pharmacological inhibition of DNA methyltransferase activity in a panel of cancer cell lines. Cells were mock treated with culture media (mock treated) or treated with 5 µM decitabine for 72 hours. Resulting expression profiles were compared to identify genes with altered expression following decitabine treatment. These data represent two experiments: In the first, 43 established cell lines were mock treated or treated with decitabine to enable identification of genes differentially expressed as a result of inhibition of DNA methyltransferase activity. HEYA8-decitabine treated cells were run in replicate. In the second experiment, A2780 and PEO1 cells underwent flow activated cell sorting to separate CD133(+) from CD133(-) cells in each cell line; the sorted cell populations were cultured in the same manner as the first experiment and similarly mock treated or treated with decitabine. All specimens were arrayed in parallel and used for RMA normalization.
2010-11-17 | E-GEOD-25428 | biostudies-arrayexpress
Project description:Epithelial ovarian cancer is the leading cause of death among gynecologic malignancies. Diagnosis usually occurs after metastatic spread, largely reflecting vague symptoms of early disease combined with lack of an effective screening strategy. Epigenetic mechanisms of gene regulation, including DNA methylation, are fundamental to normal cellular function and also play a major role in carcinogenesis. To elucidate the biological and clinical relevance of DNA methylation in ovarian cancer, we conducted expression microarray analysis of 39 cell lines and 17 primary culture specimens grown in the presence or absence of DNA methyltransferase (DNMT) inhibitors. Two parameters, induction of expression and standard deviation among untreated samples, identified 378 candidate methylated genes, many relevant to TGF-beta signaling. We analyzed 43 of these genes and they all exhibited methylation. Treatment with DNMT inhibitors increased TGF-beta pathway activity. Hierarchical clustering of ovarian cancers using the 378 genes reproducibly generated a distinct gene cluster strongly correlated with TGF-beta pathway activity that discriminates patients based on age. These data suggest that accumulation of age-related epigenetic modifications leads to suppression of TGF-beta signaling and contributes to ovarian carcinogenesis. The cancer stem cell hypothesis posits that malignant growth arises from a rare population of progenitor cells within a tumor that provide it with unlimited regenerative capacity. Such cells also possess increased resistance to chemotherapeutic agents. Resurgence of chemoresistant disease following primary therapy typifies epithelial ovarian cancer and may be attributable to residual cancer stem cells, or cancer initiating cells, that survive initial treatment. As the cell surface marker CD133 identifies cancer initiating cells in a number of other malignancies, we sought to determine the potential role of CD133+ cells in epithelial ovarian cancer. We detected CD133 on ovarian cancer cell lines, in primary cancers, and on purified epithelial cells from ascitic fluid of ovarian cancer patients. We found CD133+ ovarian cancer cells generate both CD133+ and CD133- daughter cells, whereas CD133- cells divide symmetrically. CD133+ cells exhibit enhanced resistance to platinum-based therapy, drugs commonly used as first line agents for treatment of ovarian cancer. Sorted CD133+ ovarian cancer cells also form more aggressive tumor xenografts at a lower inoculum than their CD133- progeny. Epigenetic changes may be integral to the behavior of cancer progenitor cells and their progeny. In this regard, we found that CD133 transcription is controlled by both histone modifications and promoter methylation. Sorted CD133- ovarian cancer cells treated with DNA methyltransferase and histone deacetylase inhibitors show a synergistic increase in cell surface CD133 expression. Moreover, DNA methylation at the ovarian tissue active P2 promoter is inversely correlated with CD133 transcription. We also found that promoter methylation increases in CD133- progeny of CD133+ cells, with CD133+ cells retaining a less methylated or unmethylated state. Taken together, our results show that CD133 expression in ovarian cancer is directly regulated by epigenetic modifications and support the idea that CD133 demarcates an ovarian cancer initiating cell population. The activity of these cells may be epigenetically detected and such cells might serve as pertinent chemotherapeutic targets for reducing disease recurrence.
2010-11-17 | GSE25428 | GEO
Project description:Platinum chemoresistance results in disease recurrence in patients with high-grade serous ovarian cancer (HGSOC). Recent advances in the treatment of solid tumours using checkpoint inhibitor immunotherapy has not benefited platinum-resistant HGSOC. Epigenetic silencing of genes involved in triggering cell death and apoptosis in HGSOC is known to occur via hypermethylation during development of platinum resistance. DNA methyltransferase (DNMT) inhibitors block methylation from occurring and allow silenced genes to be expressed. In ovarian cancer, DNMT inhibitors alter the epigenome and transcriptome of the tumours, which primarily affected expression of immune reactivation pathways. We aimed to determine the epigenome and transcriptome response to sequential treatment of HGSOC cells with DNMTi followed by standard-of-care carboplatin.
2022-02-02 | GSE169617 | GEO
Project description:Platinum chemoresistance results in disease recurrence in patients with high-grade serous ovarian cancer (HGSOC). Recent advances in the treatment of solid tumours using checkpoint inhibitor immunotherapy has not benefited platinum-resistant HGSOC. Epigenetic silencing of genes involved in triggering cell death and apoptosis in HGSOC is known to occur via hypermethylation during development of platinum resistance. DNA methyltransferase (DNMT) inhibitors block methylation from occurring and allow silenced genes to be expressed. In ovarian cancer, DNMT inhibitors alter the epigenome and transcriptome of the tumours, which primarily affected expression of immune reactivation pathways. We aimed to determine the epigenome and transcriptome response to sequential treatment of HGSOC cells with DNMTi followed by standard-of-care carboplatin.
2022-02-02 | GSE168225 | GEO
Project description:Advanced ovarian cancer is the most lethal gynecologic malignancy in the United States. Ovarian cancer cells are known to have diminished response to TGF-beta, but it remains unclear whether TGF-beta can modulate ovarian cancer cell growth in an indirect manner through cancer-associated fibroblasts (CAFs). Using transcriptome profiling analyses on TGF-beta-treated ovarian fibroblasts, we identified a TGF-beta-responsive gene signature in ovarian fibroblasts. Identifying TGF-beta-regulated genes in the ovarian microenvironment helps in understanding the role of TGF-beta in ovarian cancer progression. The human telomerase-immortalized ovarian fibroblast line NOF151 was treated with 5ng/mL of either TGF-beta-1 or TGF-beta-2. Total RNA was isolated from control samples and TGF-beta-treated fibroblasts samples at 48 hours post-treatment, followed by cDNA synthesis, IVT and biotin labeling. Samples were then hybridized onto Affymetrix Human Genome U133 Plus 2.0 microarrays. For each treatment group, three independent samples were prepared for the microarray experiment.
2013-12-31 | E-GEOD-40266 | biostudies-arrayexpress
Project description:Advanced ovarian cancer is the most lethal gynecologic malignancy in the United States. Ovarian cancer cells are known to have diminished response to TGF-beta, but it remains unclear whether TGF-beta can modulate ovarian cancer cell growth in an indirect manner through cancer-associated fibroblasts (CAFs). Using transcriptome profiling analyses on TGF-beta-treated ovarian fibroblasts, we identified a TGF-beta-responsive gene signature in ovarian fibroblasts. Identifying TGF-beta-regulated genes in the ovarian microenvironment helps in understanding the role of TGF-beta in ovarian cancer progression.
2013-12-31 | GSE40266 | GEO
Project description:Chemotherapy (CT) resistance in ovarian cancer is broad and encompasses diverse, unrelated drugs, suggesting more than one mechanism of resistance. We aimed to analyze the gene expression patterns in primary serous epithelial ovarian cancer (EOC) samples displaying different responses to first-line CT in an attempt to identify specific molecular signatures associated with response to CT. Initially, the expression profiles of 15 chemoresistant serous EOC tumors [time to recurrence (TTR) ≤6 months] and 10 chemosensitive serous EOC tumors (TTR ≥30 months) were independently analyzed which allowed the identification of specific sets of differentially expressed genes that might be functionally implicated in the evolution of the chemoresistant or the chemosensitive phenotype. Our data suggest that the intrinsic chemoresistance in serous EOC cells may be attributed to the combined action of different molecular mechanisms and factors linked with drug influx and efflux and cell proliferation, as possible implications of other molecular events including altered metabolism, apoptosis and inflammation cannot be excluded. Next, gene expression comparison using hierarchical clustering clearly distinguished chemosensitive and chemo- resistant tumors from the 25 serous EOC samples (training set), and consecutive class prediction analysis was used to develop a 43-gene classifier that was further validated in an independent cohort of 15 serous EOC patients and 2 patients with other ovarian cancer histotypes (test set). The 43-gene predictor set properly classified serous EOC patients at high risk for early (≤22 months) versus late (>22 months) relapse after initial CT. Thus, gene expression array technology can effectively classify serous EOC tumors according to CT response. The proposed 43-gene model needs further validation. 2 condition experiment: chemoresistant clinical samples versus chemosensitive samples
2011-07-23 | E-GEOD-28739 | biostudies-arrayexpress
Project description:Low-grade ovarian serous carcinomas are believed to arise via an adenoma-serous borderline tumor-serous carcinoma sequence. In this study, we found that advanced-stage, low-grade ovarian serous carcinomas both with and without adjacent serous borderline tumor shared similar regions of loss of heterozygosity. We then analyzed 91 ovarian tumor samples for mutations in TP53, BRAF, and KRAS. TP53 mutations were not detected in any serous borderline tumors (n = 30) or low-grade serous carcinomas (n = 43) but were found in 73% of high-grade serous carcinomas (n = 18). BRAF (n = 9) or KRAS (n = 5) mutation was detected in 47% of serous borderline tumors, but among the low-grade serous carcinomas (39 stage III, 2 stage II, and 2 stage I), only one (2%) had a BRAF mutation and eight (19%) had a KRAS mutation. The low frequency of BRAF mutations in advanced-stage, low-grade serous carcinomas, which contrasts with previous findings, suggests that aggressive, low-grade serous carcinomas are more likely derived from serous borderline tumors without BRAF mutation. In addition, advanced-stage, low-grade carcinoma patients with BRAF or KRAS mutation have a better apparent clinical outcome. However, further investigation is needed. Low-grade ovarian serous carcinomas are believed to arise via an adenoma-serous borderline tumor-serous carcinoma sequence. In this study, we found that advanced-stage, low-grade ovarian serous carcinomas both with and without adjacent serous borderline tumor shared similar regions of loss of heterozygosity. We then analyzed 91 ovarian tumor samples for mutations in TP53, BRAF, and KRAS. TP53 mutations were not detected in any serous borderline tumors (n = 30) or low-grade serous carcinomas (n = 43) but were found in 73% of high-grade serous carcinomas (n = 18). BRAF (n = 9) or KRAS (n = 5) mutation was detected in 47% of serous borderline tumors, but among the low-grade serous carcinomas (39 stage III, 2 stage II, and 2 stage I), only one (2%) had a BRAF mutation and eight (19%) had a KRAS mutation. The low frequency of BRAF mutations in advanced-stage, low-grade serous carcinomas, which contrasts with previous findings, suggests that aggressive, low-grade serous carcinomas are more likely derived from serous borderline tumors without BRAF mutation. In addition, advanced-stage, low-grade carcinoma patients with BRAF or KRAS mutation have a better apparent clinical outcome. However, further investigation is needed. Gene expression analysis was performed on five serous borderline tumors with BRAF mutation and five serous borderline tumors without BRAF mutation randomly. RNA was extracted from microdissected tumor cells. Expression profiling was carried out with Affymetrix GeneChip Human Genome U133 Plus 2.0 arrays
2011-03-04 | E-GEOD-27659 | biostudies-arrayexpress
Project description:Epithelial to mesenchymal transition (EMT) is an extreme example of cell plasticity, important for normal development, injury repair, and malignant progression. Widespread epigenetic reprogramming occurs during stem cell differentiation and malignant transformation, but EMT-related epigenetic reprogramming is poorly understood. Here we investigated epigenetic modifications during TGF-β-mediated EMT. While DNA methylation was unchanged during EMT, we found global reduction of the heterochromatin mark H3-lys9 dimethylation (H3K9Me2), increase of the euchromatin mark H3-lys4 trimethylation (H3K4Me3), and increase of the transcriptional mark H3-lys36 trimethylation (H3K36Me3). These changes were largely dependent on lysine-specific deaminase-1 (LSD1), and LSD1 loss-of-function experiments showed marked effects on EMT-driven cell migration and chemoresistance. Genome-scale mapping revealed that chromatin changes were largely specific to large organized heterochromatin K9-modifications (LOCKs), suggesting that EMT is characterized by reprogramming of specific chromatin domains across the genome. Chromatin immunoprecipitation (ChIP) was performed with antibodies against H3K9Me2 (Abcam, ab1220), H3K36Me3 (ab9050), and H3K4Me3 (ab8580) on native (unfixed) chromatin isolated from fully differentiated mouse AML12 cells (confluent, serum starved for 48hrs) either treated with TGF-β for 36hrs to induce EMT or not treated with TGF-β (0hrs, differentiated AML12 cells). DNA purified from these samples was then either whole-genome amplified (H3K36Me3 and H3K4Me3) and hybridized or directly (H3K9Me2) hybridized to NimbleGen 2.1M economy whole-genome tiling arrays #2 (listed below as array 1) and #3 (listed below as array 2), which cover mouse chromosomes 4-14. For each sample, the immunoprecipitated DNA (IP) and the input (control) DNA were hybridized to the arrays, and the IP is normalized to the input. There are 16 total samples listed below. There are 8 sample for H3K9Me2 (TGF-β and no TGF-β for arrays 1 and 2, done in replicate for a total of 8). There are 4 samples for H3K36Me3 (TGF-β and no TGF-β done on array 1 and array 2). There are 4 total samples for H3K4Me3 (TGF-β and no TGF-β done on array 1 and array 2).
2011-07-03 | E-GEOD-28291 | biostudies-arrayexpress