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. 2011 Jul 1;71(13):4443-53.
doi: 10.1158/0008-5472.CAN-11-0608. Epub 2011 May 17.

MicroRNA sequence and expression analysis in breast tumors by deep sequencing

Thalia A Farazi  1 , Hugo M Horlings, Jelle J Ten Hoeve, Aleksandra Mihailovic, Hans Halfwerk, Pavel Morozov, Miguel Brown, Markus Hafner, Fabien Reyal, Marieke van Kouwenhove, Bas Kreike, Daoud Sie, Volker Hovestadt, Lodewyk F A Wessels, Marc J van de Vijver, Thomas Tuschl
Affiliations

MicroRNA sequence and expression analysis in breast tumors by deep sequencing

Thalia A Farazi et al. Cancer Res. .

Abstract

MicroRNAs (miRNA) regulate many genes critical for tumorigenesis. We profiled miRNAs from 11 normal breast tissues, 17 noninvasive, 151 invasive breast carcinomas, and 6 cell lines by in-house-developed barcoded Solexa sequencing. miRNAs were organized in genomic clusters representing promoter-controlled miRNA expression and sequence families representing seed sequence-dependent miRNA target regulation. Unsupervised clustering of samples by miRNA sequence families best reflected the clustering based on mRNA expression available for this sample set. Clustering and comparative analysis of miRNA read frequencies showed that normal breast samples were separated from most noninvasive ductal carcinoma in situ and invasive carcinomas by increased miR-21 (the most abundant miRNA in carcinomas) and multiple decreased miRNA families (including miR-98/let-7), with most miRNA changes apparent already in the noninvasive carcinomas. In addition, patients that went on to develop metastasis showed increased expression of mir-423, and triple-negative breast carcinomas were most distinct from other tumor subtypes due to upregulation of the mir~17-92 cluster. However, absolute miRNA levels between normal breast and carcinomas did not reveal any significant differences. We also discovered two polymorphic nucleotide variations among the more abundant miRNAs miR-181a (T19G) and miR-185 (T16G), but we did not identify nucleotide variations expected for classical tumor suppressor function associated with miRNAs. The differentiation of tumor subtypes and prediction of metastasis based on miRNA levels is statistically possible but is not driven by deregulation of abundant miRNAs, implicating far fewer miRNAs in tumorigenic processes than previously suggested.

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Conflict of interest statement

Conflict of interest:

T. T. is cofounder and scientific advisor to Alnylam Pharmaceuticals and scientific advisor to Regulus Therapeutics.

Figures

Figure 1
Figure 1
Unsupervised hierarchical clustering with complete linkage and Spearman correlation for patient samples conducted using the miRNA sequence families making up 85% of the sequence reads within each sample. Color histogram represents miRNA rf standardized across each miRNA.
Figure 2
Figure 2
Unsupervised hierarchical clustering with complete linkage and Spearman correlation depicting (A) the 85% top expressed mature miRNAs, (B) miRNA genomic clusters, and (C) sequence families. Every clustering includes 4 samples from each ER/HER2 IHC category, normal breast and 6 cell lines. Color histogram represents log2 miRNA abundance (rf). Examples of lineage specific miRNA genomic clusters are noted.
Figure 3
Figure 3
Comparison of clustering using miRNA sequence families (characterizing seed-sequence-dependent miRNA target regulation) and mRNA profiles. A. Unsupervised hierarchical clustering performed for miRNA sequence families, using the top 98% expressed families within each sample (221 families) (Spearman correlation). B. Unsupervised hierarchical clustering of 161 samples with available mRNA profiles, using the 221 genes with the highest variance (Pearson correlation).
Figure 4
Figure 4
Results of EdgeR comparison analysis between groups of samples. Results plotted as log2 of the fold change between normal and/or tumor categories as a function of the log2 of the average miRNA abundance in the two categories compared. Colored dots represent miRNAs that are significantly differentially expressed (p value≤0.001). miRNAs in green are over-expressed, while miRNAs in red are under-expressed in the second category of samples within each comparison. Abundant miRNAs (log2 concentration ≥-8) are labeled in black font, while low abundant miRNAs are labeled in grey font. A. miRNA sequence families (sf) differentially represented in tumor stages. B. miRNA sf that differentiate tumor IHC types. C. miRNA sf that predict metastasis in IDC, also separately evaluated in HER2 positive and TNBC patients.
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