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Molecular classification of selective oestrogen receptor modulators on the basis of gene expression profiles of breast cancer cells expressing oestrogen receptor alpha.

Levenson AS, Kliakhandler IL, Svoboda KM, Pease KM, Kaiser SA, Ward JE, Jordan VC - Br. J. Cancer (2002)

Bottom Line: This observation may be explained by the use of the same cell context for treatments with compounds that essentially belong to the same class of drugs with oestrogen receptors related mechanisms.The most surprising observation was that ICI 182,780 clustered together with oestrodiol and raloxifene for cells expressing wtERalpha and clustered together with EM 652 for cells expressing mutant(351)ERalpha.These data provide a rationale for a more precise and elaborate study in which custom made oligonucleotide arrays can be used with comprehensive sets of genes known to have consensus and putative oestrogen response elements in their promoter regions.

View Article: PubMed Central - PubMed

Affiliation: Robert H Lurie Comprehensive Cancer Center, Northwestern University Medical School, 303 E. Chicago Avenue, Chicago, Illinois, IL 60611, USA.

ABSTRACT
The purpose of this study was to classify selective oestrogen receptor modulators based on gene expression profiles produced in breast cancer cells expressing either wtERalpha or mutant(351)ERalpha. In total, 54 microarray experiments were carried out by using a commercially available Atlas cDNA Expression Arrays (Clontech), containing 588 cancer-related genes. Nine sets of data were generated for each cell line following 24 h of treatment: expression data were obtained for cells treated with vehicle EtOH (Control); with 10(-9) or 10(-8) M oestradiol; with 10(-6) M 4-hydroxytamoxifen; with 10(-6) M raloxifene; with 10(-6) M idoxifene, with 10(-6) M EM 652, with 10(-6) M GW 7604; with 5 x 10(-5) M resveratrol and with 10(-6) M ICI 182,780. We developed a new algorithm 'Expression Signatures' to classify compounds on the basis of differential gene expression profiles. We created dendrograms for each cell line, in which branches represent relationships between compounds. Additionally, clustering analysis was performed using different subsets of genes to assess the robustness of the analysis. In general, only small differences between gene expression profiles treated with compounds were observed with correlation coefficients ranged from 0.83 to 0.98. This observation may be explained by the use of the same cell context for treatments with compounds that essentially belong to the same class of drugs with oestrogen receptors related mechanisms. The most surprising observation was that ICI 182,780 clustered together with oestrodiol and raloxifene for cells expressing wtERalpha and clustered together with EM 652 for cells expressing mutant(351)ERalpha. These data provide a rationale for a more precise and elaborate study in which custom made oligonucleotide arrays can be used with comprehensive sets of genes known to have consensus and putative oestrogen response elements in their promoter regions.

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Gene expression patterns of cells expressing wtER related to treatment with compounds (E2, SERMs and ICI). Two-dimensional hierarchical clustering was applied to up-regulated subset of expression data from a total of 588 cDNAs measured across seven different treatments. A cluster dendrogram representing the hierarchical relationships between gene expression profiles of cells (vertically) and compounds (horizontally) was then generated. Colour-coded gene expression values for genes are shown. The colour reflects the mean-adjusted expression level of the gene: black is the mean, red is greater than the mean and green is less than the mean.
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fig3: Gene expression patterns of cells expressing wtER related to treatment with compounds (E2, SERMs and ICI). Two-dimensional hierarchical clustering was applied to up-regulated subset of expression data from a total of 588 cDNAs measured across seven different treatments. A cluster dendrogram representing the hierarchical relationships between gene expression profiles of cells (vertically) and compounds (horizontally) was then generated. Colour-coded gene expression values for genes are shown. The colour reflects the mean-adjusted expression level of the gene: black is the mean, red is greater than the mean and green is less than the mean.

Mentions: The hierarchical clustering (Eisen et al, 1998) is often used as a multivariate technique to find groups of genes with similar expression profiles across a number of experiments and to group the experimental samples according to the similarities in their overall patterns of gene expression. This method was successfully applied for tumour, tissue or cancer cell line classification purposes (Ben-Dor et al, 2000; Perou et al, 2000; Ross et al, 2000; Gruvberger et al, 2001; Sorlie et al, 2001; Welsh et al, 2001). To examine the robustness of the observed clustering patterns by ‘Expression signatures’, hierarchical cluster analysis (see Materials and Methods) was performed by using subsets of up-regulated genes for each of seven experimental conditions for cells expressing wtERα (Figure 3Figure 3


Molecular classification of selective oestrogen receptor modulators on the basis of gene expression profiles of breast cancer cells expressing oestrogen receptor alpha.

Levenson AS, Kliakhandler IL, Svoboda KM, Pease KM, Kaiser SA, Ward JE, Jordan VC - Br. J. Cancer (2002)

Gene expression patterns of cells expressing wtER related to treatment with compounds (E2, SERMs and ICI). Two-dimensional hierarchical clustering was applied to up-regulated subset of expression data from a total of 588 cDNAs measured across seven different treatments. A cluster dendrogram representing the hierarchical relationships between gene expression profiles of cells (vertically) and compounds (horizontally) was then generated. Colour-coded gene expression values for genes are shown. The colour reflects the mean-adjusted expression level of the gene: black is the mean, red is greater than the mean and green is less than the mean.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2376139&req=5

fig3: Gene expression patterns of cells expressing wtER related to treatment with compounds (E2, SERMs and ICI). Two-dimensional hierarchical clustering was applied to up-regulated subset of expression data from a total of 588 cDNAs measured across seven different treatments. A cluster dendrogram representing the hierarchical relationships between gene expression profiles of cells (vertically) and compounds (horizontally) was then generated. Colour-coded gene expression values for genes are shown. The colour reflects the mean-adjusted expression level of the gene: black is the mean, red is greater than the mean and green is less than the mean.
Mentions: The hierarchical clustering (Eisen et al, 1998) is often used as a multivariate technique to find groups of genes with similar expression profiles across a number of experiments and to group the experimental samples according to the similarities in their overall patterns of gene expression. This method was successfully applied for tumour, tissue or cancer cell line classification purposes (Ben-Dor et al, 2000; Perou et al, 2000; Ross et al, 2000; Gruvberger et al, 2001; Sorlie et al, 2001; Welsh et al, 2001). To examine the robustness of the observed clustering patterns by ‘Expression signatures’, hierarchical cluster analysis (see Materials and Methods) was performed by using subsets of up-regulated genes for each of seven experimental conditions for cells expressing wtERα (Figure 3Figure 3

Bottom Line: This observation may be explained by the use of the same cell context for treatments with compounds that essentially belong to the same class of drugs with oestrogen receptors related mechanisms.The most surprising observation was that ICI 182,780 clustered together with oestrodiol and raloxifene for cells expressing wtERalpha and clustered together with EM 652 for cells expressing mutant(351)ERalpha.These data provide a rationale for a more precise and elaborate study in which custom made oligonucleotide arrays can be used with comprehensive sets of genes known to have consensus and putative oestrogen response elements in their promoter regions.

View Article: PubMed Central - PubMed

Affiliation: Robert H Lurie Comprehensive Cancer Center, Northwestern University Medical School, 303 E. Chicago Avenue, Chicago, Illinois, IL 60611, USA.

ABSTRACT
The purpose of this study was to classify selective oestrogen receptor modulators based on gene expression profiles produced in breast cancer cells expressing either wtERalpha or mutant(351)ERalpha. In total, 54 microarray experiments were carried out by using a commercially available Atlas cDNA Expression Arrays (Clontech), containing 588 cancer-related genes. Nine sets of data were generated for each cell line following 24 h of treatment: expression data were obtained for cells treated with vehicle EtOH (Control); with 10(-9) or 10(-8) M oestradiol; with 10(-6) M 4-hydroxytamoxifen; with 10(-6) M raloxifene; with 10(-6) M idoxifene, with 10(-6) M EM 652, with 10(-6) M GW 7604; with 5 x 10(-5) M resveratrol and with 10(-6) M ICI 182,780. We developed a new algorithm 'Expression Signatures' to classify compounds on the basis of differential gene expression profiles. We created dendrograms for each cell line, in which branches represent relationships between compounds. Additionally, clustering analysis was performed using different subsets of genes to assess the robustness of the analysis. In general, only small differences between gene expression profiles treated with compounds were observed with correlation coefficients ranged from 0.83 to 0.98. This observation may be explained by the use of the same cell context for treatments with compounds that essentially belong to the same class of drugs with oestrogen receptors related mechanisms. The most surprising observation was that ICI 182,780 clustered together with oestrodiol and raloxifene for cells expressing wtERalpha and clustered together with EM 652 for cells expressing mutant(351)ERalpha. These data provide a rationale for a more precise and elaborate study in which custom made oligonucleotide arrays can be used with comprehensive sets of genes known to have consensus and putative oestrogen response elements in their promoter regions.

Show MeSH
Related in: MedlinePlus