Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcription profiling of lung cancer cells treated with 5-aza - a genome-wide screen for hypermethylated genes in lung cancer

ABSTRACT: Abstract; Background: Promoter hypermethylation coupled with loss of heterozygosity at the same locus results in loss of gene function in many tumor cells. The ârulesâ governing which genes are methylated during the pathogenesis of individual cancers, how specific methylation profiles are initially established, or what determines tumor-type specific methylation are unknown. However, DNA methylation markers that are highly specific and sensitive for common tumors would be useful for the early detection of cancer, and those required for the malignant phenotype identify pathways important as therapeutic targets. Methods and Findings: In an effort to identify new cancer-specific methylation markers, we employed a high throughput global expression profiling approach in lung cancer cells. We identified 132 genes that have 5â CpG islands, are induced from undetectable levels by 5-aza-2â-deoxycytidine (5-aza) in multiple non-small cell lung cancer cell lines, and are expressed in immortalized human bronchial epithelial cells. As expected, these genes were also expressed in normal lung, but often not in companion primary lung cancers. Methylation analysis of a subset (45/132) of these promoter regions in primary lung cancer (N=20) and adjacent non-malignant tissue showed that 31 genes had acquired methylation in the tumors, but did not show methylation in normal lung or lymphocytes. We studied the eight most frequently and specifically methylated genes from our lung cancer data set in breast cancer (N=37), colon cancer (N=24), and prostate cancer (N=24) along with counterpart non-malignant tissues. We found that seven loci were frequently methylated in both breast and lung cancers, with four showing extensive methylation in all four epithelial tumors. Conclusions: By using a systematic biological screen we identified multiple genes that are methylated with high penetrance in primary lung, breast, colon, and prostate cancers. The cross-tumor methylation pattern we observed for these novel markers suggests that we have identified a partial promoter hypermethylation signature for these common malignancies. These data suggest that while tumors in different tissues vary substantially with respect to gene expression, there may be commonalities in their promoter methylation profiles that represent potential targets for early detection screening or therapeutic intervention. Experiment Overall Design: Drug treatment: control, 100 nM, 1 uM Experiment Overall Design: Cancer vs. Normal Comparison: NSCLC vs. Normal

ORGANISM(S): Homo sapiens

SUBMITTER: David Shames

PROVIDER: E-GEOD-5816 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

A genome-wide screen for promoter methylation in lung cancer identifies novel methylation markers for multiple malignancies.

Shames David S DS Girard Luc L Gao Boning B Sato Mitsuo M Lewis Cheryl M CM Shivapurkar Narayan N Jiang Aixiang A Perou Charles M CM Kim Young H YH Pollack Jonathan R JR Fong Kwun M KM Lam Chi-Leung CL Wong Maria M Shyr Yu Y Nanda Rita R Olopade Olufunmilayo I OI Gerald William W Euhus David M DM Shay Jerry W JW Gazdar Adi F AF Minna John D JD

PLoS medicine 20061201 12

<h4>Background</h4>Promoter hypermethylation coupled with loss of heterozygosity at the same locus results in loss of gene function in many tumor cells. The "rules" governing which genes are methylated during the pathogenesis of individual cancers, how specific methylation profiles are initially established, or what determines tumor type-specific methylation are unknown. However, DNA methylation markers that are highly specific and sensitive for common tumors would be useful for the early detect ...[more]

PMID: 17194187

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Project description:BACKGROUND: Promoter hypermethylation coupled with loss of heterozygosity at the same locus results in loss of gene function in many tumor cells. The "rules" governing which genes are methylated during the pathogenesis of individual cancers, how specific methylation profiles are initially established, or what determines tumor type-specific methylation are unknown. However, DNA methylation markers that are highly specific and sensitive for common tumors would be useful for the early detection of cancer, and those required for the malignant phenotype would identify pathways important as therapeutic targets. METHODS AND FINDINGS: In an effort to identify new cancer-specific methylation markers, we employed a high-throughput global expression profiling approach in lung cancer cells. We identified 132 genes that have 5' CpG islands, are induced from undetectable levels by 5-aza-2'-deoxycytidine in multiple non-small cell lung cancer cell lines, and are expressed in immortalized human bronchial epithelial cells. As expected, these genes were also expressed in normal lung, but often not in companion primary lung cancers. Methylation analysis of a subset (45/132) of these promoter regions in primary lung cancer (n = 20) and adjacent nonmalignant tissue (n = 20) showed that 31 genes had acquired methylation in the tumors, but did not show methylation in normal lung or peripheral blood cells. We studied the eight most frequently and specifically methylated genes from our lung cancer dataset in breast cancer (n = 37), colon cancer (n = 24), and prostate cancer (n = 24) along with counterpart nonmalignant tissues. We found that seven loci were frequently methylated in both breast and lung cancers, with four showing extensive methylation in all four epithelial tumors. CONCLUSIONS: By using a systematic biological screen we identified multiple genes that are methylated with high penetrance in primary lung, breast, colon, and prostate cancers. The cross-tumor methylation pattern we observed for these novel markers suggests that we have identified a partial promoter hypermethylation signature for these common malignancies. These data suggest that while tumors in different tissues vary substantially with respect to gene expression, there may be commonalities in their promoter methylation profiles that represent targets for early detection screening or therapeutic intervention. Experiment Overall Design: We compared the gene expression changes HBEC and NSCLC cells before and after treatment with 5-aza-2'deoxycytidine.

Project description:Tumor angiogenesis requires intricate regulation of gene expression in endothelial cells (EC). We recently showed that DNA methyltransferase (DNMT)- and histone deacetylase (HDAC) inhibitors directly repress EC growth and tumor angiogenesis, suggesting that epigenetic modifications mediated by DNMTs and HDACs are involved in regulation of EC gene expression during tumor angiogenesis. To understand the mechanisms behind the epigenetic regulation of tumor angiogenesis, we used microarray analysis to perform a comprehensive screen to identify genes downregulated in tumor-conditioned versus quiescent EC, and re-expressed by 5-aza-2â-deoxycytidine and trichostatin A. Among the 81 genes identified, 77% harboured a promoter CpG island. Validation of mRNA levels of a subset of genes confirmed significant downregulation in tumor-conditioned EC and reactivation by treatment with a combination of 5-aza-2â-deoxycytidine and trichostatin A, as well as by both compounds separately. Silencing of these genes in tumor-conditioned EC correlated with promoter histone H3 deacetylation and loss of H3 lysine 4 methylation, however did not involve DNA methylation of promoter CpG islands. For six genes, downregulation in microdissected human tumor endothelium was confirmed. Functional validation by RNA interference revealed that clusterin, fibrillin 1 and quiescin Q6 are negative regulators of EC growth and angiogenesis. In summary, our data identify novel angiogenesis suppressing genes which become silenced in tumor-conditioned EC in association with promoter histone modifications and reactivated by DNMT- and HDAC inhibitors through reversal of these epigenetic modifications, providing a mechanism for epigenetic regulation of tumor angiogenesis. Experiment Overall Design: A commercial pool (a mixture of 32 donors) of HUVEC (Tebu-bio, Heerhugowaard, The Netherlands) was used for DNA microarray experiments. Total RNA was isolated using the RNeasy RNA isolation kit (Qiagen) according to the supplier's protocol. Possible genomic DNA contaminations were removed by on column DNase treatment with the RNase-free DNase set (Qiagen). The purified RNA was quantified using a Nanodrop spectrophotometer, and RNA quality was evaluated using the Agilent 2100 Bioanalyzer. cDNA synthesis was performed using the Agilent Fluorescent Direct Labelkit with direct incorporation of either cyanine 5 (Cy5) or Cy3 âdCTP nucleotides (Perkin Elmer) according to the manufacturerâs instructions. Labeled cDNA was purified using QIAquick PCR purification columns (Qiagen), followed by concentration by vacuum centrifugation. The Agilent human 1A cDNA microarray (Agilent Technologies, Amstelveen, The Netherlands) contained ~15000 cDNA probes. Labeled cDNA was resuspended in hybridisation buffer and hybridised to Agilent human 1A cDNA microarray for 17 h at 65Â°C, according to the Agilent protocols. All hybridisations were replicated with cy dyes switched. Two fluorescent microarray comparisons were performed: (1) A comparison of tumor-conditioned HUVEC and quiescent HUVEC and (2) a comparison of tumor-conditioned HUVEC treated with or without a combination of DAC and TSA. Experiment Overall Design: Microarray data processing and statistical analysis Experiment Overall Design: The image file was processed using Agilent's Feature Extraction software (version A.6.1.1, Agilent Technologies). This Feature Extraction program was used to identify pixels corresponding to fluorescent signal (as opposed to background) and to remove pixels with intensities that met the default criteria for outliers. The different normalisation routines applied (Local Background, Minimum signal (feature or background) & Average of all background areas) resulted in comparable results. For each identified area of signal and each of the two dyes, the basic measure of RNA abundance was taken to be the mean intensity over pixels in the identified signal area. The log ratio of the red to green intensities for each signal area was used for statistical analyses, with all subsequent analyses done using the R statistical software package (version 1.2). We selected fold change 1.5 as a threshold, since the 4 hybridisations increase the likelihood of statistical reliability.

Dataset Information

Transcription profiling of lung cancer cells treated with 5-aza - a genome-wide screen for hypermethylated genes in lung cancer

Publications

A genome-wide screen for promoter methylation in lung cancer identifies novel methylation markers for multiple malignancies.

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