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miRNA expression profiling of 51 human breast cancer cell lines reveals subtype and driver mutation-specific miRNAs.

Riaz M, van Jaarsveld MT, Hollestelle A, Prager-van der Smissen WJ, Heine AA, Boersma AW, Liu J, Helmijr J, Ozturk B, Smid M, Wiemer EA, Foekens JA, Martens JW - Breast Cancer Res. (2013)

Bottom Line: Thirty miRNAs were associated with p16INK4 status while only a few miRNAs were associated with BRCA1, PIK3CA/PTEN and TP53 mutation status.Twelve miRNAs were associated with DNA copy number variation of the respective locus.Specific sets of miRNAs were associated with ERBB2 overexpression, p16INK4a or E-cadherin mutation or E-cadherin methylation status, which implies that these miRNAs may contribute to the driver role of these genetic aberrations.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Introduction: Breast cancer is a genetically and phenotypically complex disease. To understand the role of miRNAs in this molecular complexity, we performed miRNA expression analysis in a cohort of molecularly well-characterized human breast cancer cell lines to identify miRNAs associated with the most common molecular subtypes and the most frequent genetic aberrations.

Methods: Using a microarray carrying LNA™ modified oligonucleotide capture probes), expression levels of 725 human miRNAs were measured in 51 breast cancer cell lines. Differential miRNA expression was explored by unsupervised cluster analysis and was then associated with the molecular subtypes and genetic aberrations commonly present in breast cancer.

Results: Unsupervised cluster analysis using the most variably expressed miRNAs divided the 51 breast cancer cell lines into a major and a minor cluster predominantly mirroring the luminal and basal intrinsic subdivision of breast cancer cell lines. One hundred and thirteen miRNAs were differentially expressed between these two main clusters. Forty miRNAs were differentially expressed between basal-like and normal-like/claudin-low cell lines. Within the luminal-group, 39 miRNAs were associated with ERBB2 overexpression and 24 with E-cadherin gene mutations, which are frequent in this subtype of breast cancer cell lines. In contrast, 31 miRNAs were associated with E-cadherin promoter hypermethylation, which, contrary to E-cadherin mutation, is exclusively observed in breast cancer cell lines that are not of luminal origin. Thirty miRNAs were associated with p16INK4 status while only a few miRNAs were associated with BRCA1, PIK3CA/PTEN and TP53 mutation status. Twelve miRNAs were associated with DNA copy number variation of the respective locus.

Conclusion: Luminal-basal and epithelial-mesenchymal associated miRNAs determine the subdivision of miRNA transcriptome of breast cancer cell lines. Specific sets of miRNAs were associated with ERBB2 overexpression, p16INK4a or E-cadherin mutation or E-cadherin methylation status, which implies that these miRNAs may contribute to the driver role of these genetic aberrations. Additionally, miRNAs, which are located in a genomic region showing recurrent genetic aberrations, may themselves play a driver role in breast carcinogenesis or contribute to a driver gene in their vicinity. In short, our study provides detailed molecular miRNA portraits of breast cancer cell lines, which can be exploited for functional studies of clinically important miRNAs.

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Association of miRNA expression with genomic copy number variation in breast cancer cell lines. The top four most significant miRNAs are represented (see Table S16 in Additional file 1 for a complete list). The Kruskal-Wallis test was used to reveal significant associations of miRNAs with genome copy number (CN) variation. y axis, expression levels of miRNA; x axis, number of samples with CN loss/gain or neutral.
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Figure 6: Association of miRNA expression with genomic copy number variation in breast cancer cell lines. The top four most significant miRNAs are represented (see Table S16 in Additional file 1 for a complete list). The Kruskal-Wallis test was used to reveal significant associations of miRNAs with genome copy number (CN) variation. y axis, expression levels of miRNA; x axis, number of samples with CN loss/gain or neutral.

Mentions: Previous studies show that DNA CNVs in breast tumor tissues can lead to differential expression of genes and miRNAs [33-35]. To investigate this in our cohort of breast cancer cell lines, we first determined the DNA CNVs of the cell lines by performing whole-genome SNP profiling. These CNVs were then correlated with the expression levels of the 87 most variably expressed miRNAs in the cell lines. The correlation revealed 12 miRNAs, which were significantly associated with DNA CNVs (Kruskal-Wallis test, P < 0.05) (see Table S18 in Additional file 1). The top four most significantly associated miRNAs - hsa-miR-130a (11q12.1), hsa-miR-22 (17p13.1), hsa-miR-93 (7q22.1) and hsa-miR-383 (8p22) - with DNA CNVs in breast cancer cell lines are shown in Figure 6.


miRNA expression profiling of 51 human breast cancer cell lines reveals subtype and driver mutation-specific miRNAs.

Riaz M, van Jaarsveld MT, Hollestelle A, Prager-van der Smissen WJ, Heine AA, Boersma AW, Liu J, Helmijr J, Ozturk B, Smid M, Wiemer EA, Foekens JA, Martens JW - Breast Cancer Res. (2013)

Association of miRNA expression with genomic copy number variation in breast cancer cell lines. The top four most significant miRNAs are represented (see Table S16 in Additional file 1 for a complete list). The Kruskal-Wallis test was used to reveal significant associations of miRNAs with genome copy number (CN) variation. y axis, expression levels of miRNA; x axis, number of samples with CN loss/gain or neutral.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3672661&req=5

Figure 6: Association of miRNA expression with genomic copy number variation in breast cancer cell lines. The top four most significant miRNAs are represented (see Table S16 in Additional file 1 for a complete list). The Kruskal-Wallis test was used to reveal significant associations of miRNAs with genome copy number (CN) variation. y axis, expression levels of miRNA; x axis, number of samples with CN loss/gain or neutral.
Mentions: Previous studies show that DNA CNVs in breast tumor tissues can lead to differential expression of genes and miRNAs [33-35]. To investigate this in our cohort of breast cancer cell lines, we first determined the DNA CNVs of the cell lines by performing whole-genome SNP profiling. These CNVs were then correlated with the expression levels of the 87 most variably expressed miRNAs in the cell lines. The correlation revealed 12 miRNAs, which were significantly associated with DNA CNVs (Kruskal-Wallis test, P < 0.05) (see Table S18 in Additional file 1). The top four most significantly associated miRNAs - hsa-miR-130a (11q12.1), hsa-miR-22 (17p13.1), hsa-miR-93 (7q22.1) and hsa-miR-383 (8p22) - with DNA CNVs in breast cancer cell lines are shown in Figure 6.

Bottom Line: Thirty miRNAs were associated with p16INK4 status while only a few miRNAs were associated with BRCA1, PIK3CA/PTEN and TP53 mutation status.Twelve miRNAs were associated with DNA copy number variation of the respective locus.Specific sets of miRNAs were associated with ERBB2 overexpression, p16INK4a or E-cadherin mutation or E-cadherin methylation status, which implies that these miRNAs may contribute to the driver role of these genetic aberrations.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Introduction: Breast cancer is a genetically and phenotypically complex disease. To understand the role of miRNAs in this molecular complexity, we performed miRNA expression analysis in a cohort of molecularly well-characterized human breast cancer cell lines to identify miRNAs associated with the most common molecular subtypes and the most frequent genetic aberrations.

Methods: Using a microarray carrying LNA™ modified oligonucleotide capture probes), expression levels of 725 human miRNAs were measured in 51 breast cancer cell lines. Differential miRNA expression was explored by unsupervised cluster analysis and was then associated with the molecular subtypes and genetic aberrations commonly present in breast cancer.

Results: Unsupervised cluster analysis using the most variably expressed miRNAs divided the 51 breast cancer cell lines into a major and a minor cluster predominantly mirroring the luminal and basal intrinsic subdivision of breast cancer cell lines. One hundred and thirteen miRNAs were differentially expressed between these two main clusters. Forty miRNAs were differentially expressed between basal-like and normal-like/claudin-low cell lines. Within the luminal-group, 39 miRNAs were associated with ERBB2 overexpression and 24 with E-cadherin gene mutations, which are frequent in this subtype of breast cancer cell lines. In contrast, 31 miRNAs were associated with E-cadherin promoter hypermethylation, which, contrary to E-cadherin mutation, is exclusively observed in breast cancer cell lines that are not of luminal origin. Thirty miRNAs were associated with p16INK4 status while only a few miRNAs were associated with BRCA1, PIK3CA/PTEN and TP53 mutation status. Twelve miRNAs were associated with DNA copy number variation of the respective locus.

Conclusion: Luminal-basal and epithelial-mesenchymal associated miRNAs determine the subdivision of miRNA transcriptome of breast cancer cell lines. Specific sets of miRNAs were associated with ERBB2 overexpression, p16INK4a or E-cadherin mutation or E-cadherin methylation status, which implies that these miRNAs may contribute to the driver role of these genetic aberrations. Additionally, miRNAs, which are located in a genomic region showing recurrent genetic aberrations, may themselves play a driver role in breast carcinogenesis or contribute to a driver gene in their vicinity. In short, our study provides detailed molecular miRNA portraits of breast cancer cell lines, which can be exploited for functional studies of clinically important miRNAs.

Show MeSH
Related in: MedlinePlus