Introduction
Considerable molecular pathology research has focused on invasive breast cancer (IBC); however, less attention has been given to the preinvasive nonobligate precursor, ductal carcinoma
in situ (DCIS). DCIS is the fourth most common cancer diagnosis among women and is present in the vast majority of IBC cases [
1]. Women diagnosed with DCIS are at an increased risk of subsequently developing IBC, and, when examined, DCIS and IBC also share many of the same genetic features. However, there is an increased need to better understand the early genetic events and identify biomarkers that are present prior to IBC. microRNA (miRNA) have emerged as a new class of gene regulators that may serve as both molecular biomarkers and novel therapeutic targets. In this study, we sought to investigate miRNA expression changes and their consequences in preinvasive breast cancer.
miRNA are short, non-protein-coding RNA that exert posttranscriptional control over their mRNA targets through the mechanism of RNA interference. By complementary binding to the 3' untranslated region of target mRNA, miRNA promote mRNA destabilization, thereby inducing translational repression [
2]. It has been demonstrated that miRNA control major cellular processes, including metabolism, developmental timing, stem cell division, cell growth and differentiation and apoptosis [
3‐
5]. Given this expansive role, it is unsurprising that their effect on mRNA expression contributes to the pathogenesis of many diseases, including cancer [
6,
7]. To date, more than 900 miRNA have been identified in humans, constituting more than 1% of the total coding genome. It is predicted that more than 60% of mRNA may be targeted and that a single miRNA may target as many as 200 mRNA, thus making miRNA the largest class of gene regulators [
8‐
10].
Several studies have established the role of miRNA in the pathogenesis of IBC. For example, abnormal miRNA expression has been described in breast cancer cell lines and in bulk primary normal and cancerous breast tissues [
11‐
13]. In this setting, miRNA expression has correlated with specific breast cancer biopathologic features, such as estrogen receptor (ER) and progesterone receptor (PR) expression, tumor stage, vascular invasion or proliferation index. In addition, many miRNA that are consistently downregulated may act as tumor suppressors, for example, miR-206, miR-17-5p, miR-125a, miR-125b and the let-7 family, and many that are consistently upregulated may acts as oncogenes, for example, miR-21, miR-10b and miR-27a. Other studies have shown that miRNA exhibit a specific spatial distribution of expression within breast epithelium [
14].
Almost all human breast cancers arise in the epithelial compartment, likely as a result of the transformation of epithelial cells, although the surrounding stroma and microenvironment play a crucial role in tumor progression. Therefore, the present work is focused on the genetic changes that occur within the epithelial cell population.
We hypothesized that miRNA expression might be dysregulated prior to IBC, that these changes might be associated with mRNA expression changes and that together these might help to elucidate important steps in early breast tumorigenesis. Therefore, to first obtain a profile of normal miRNA expression, we profiled miRNA in normal epithelium from healthy controls undergoing reduction mammoplasty (RM). Next, to obtain a profile of miRNA dysregulated prior to invasion, we examined miRNA expression in histologically normal (HN) epithelium and compared this to paired samples of adjacent DCIS. We then integrated the HN:DCIS miRNA expression profile with the gene expression profile from the same samples and used miRNA target prediction programs to identify putative miRNA:mRNA functional interactions. We then selected three candidate miRNA (miR-125b, miR-182 and miR-183) and six of their putative target genes (MEMO1, NRIP1, CBX7, DOK4, NMT2, and EGR1) for validation. This study represents the first report of a miRNA expression profile in normal breast epithelium and the first integrated analysis of dysregulated miRNA and mRNA expression in paired HN and DCIS samples. Many of the dysregulated miRNA identified in DCIS have previously been identified in IBC. Our data suggest an important role for miRNA in determining the parallel gene expression changes that characterize the earliest stage of breast disease.
Materials and methods
Tissue sample acquisition and preparation
Primary breast tissues not needed for diagnosis were obtained at Boston Medical Center from patients undergoing RM and breast cancer surgery (prior to any chemo- or radiation therapy). All samples were deidentified and assigned a number at the time of collection; therefore, informed consent was not required according to our specimen collection protocol preapproved by the Boston University Medical Center Institutional Review Board. Samples were processed as described previously [
15]. Epithelia from three groups were examined: normal breast tissue (
n = 9) from RM (mean age, 52.2 years; age range, 44 to 75 years) and paired samples of HN and DCIS (
n = 16) from eight individuals undergoing cancer surgery. Hematoxylin and eosin-stained sections were reviewed by a pathologist (AdlM) to verify normal epithelia and preinvasive lesions.
Laser capture microdissection and RNA isolation
Laser capture microdissection (LCM) was performed as described previously [
15‐
17] to collect breast epithelial cells of normal appearing ductal tissue (RM and HN) and epithelial cells of identified regions of DCIS. Total RNA was isolated using the RNAqueous miRNA Isolation Kit (Ambion, Austin, TX, USA) and treated with DNase I according to the manufacturer's instructions. RNA to be utilized for gene expression analysis was processed as described previously [
15,
16]. The pooled RM sample was prepared by combining 400 ng of total RNA from each of the nine RM samples.
miRNA expression profiling and statistical analysis
cDNA was synthesized from 800 ng (100 ng/multiplex pool) with the TaqMan miRNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA), according to manufacturer's instructions. miRNA expression was measured by real-time quantitative polymerase chain reaction (RT-qPCR) assay utilizing the TaqMan Human miRNA Array Panel (version 1.0, based on miRBase version 9.2; Applied Biosystems) and assayed on the 7900 Real-Time PCR System (Applied Biosystems), according to the manufacturer's instructions. miRNA expression data are available from the National Center for Biotechnology Gene Expression Omnibus (GEO) [
18] at accession number [GEO:GSE24509].
All probes with threshold cycles (Ct) = 40 in more than two of three pooled RM (PRM) replicates or more than six of eight HN samples and more than six of eight DCIS samples were considered "nonexpressed" and removed. Remaining Ct values were global median normalized by transforming all expression values by rescaling to a target value of 12 (ΔCt). Relative changes in miRNA expression among each comparison (HN-PRM, DCIS-PRM and DCIS-HN) were assessed (ΔΔCt ). A variance correction was applied to account for the pooled samples as suggested by Churchill [
19], and a
t-test was performed.
P < 0.005 in at least one of the comparisons was considered statistically significant. To address the issue of multiple comparisons, we highlight the results that remain significant using two valid procedures: the more restrictive Bonferroni correction (
P < 0.00025) and the less restrictive false discovery rate < 0.05 (
P < 0.017), which typically results in a greater number of significant results. The relative fold change for each comparison was calculated by 2^
-ΔΔCt. Heatmaps were generated using the Heatplus package in Bioconductor [
20].
Gene expression profiling and statistical analysis
Gene expression analysis was measured on the U133A GeneChip (Affymetrix, Santa Clara, CA, USA). All microarray analyses were performed at the Boston University Microarray Facility as previously described [
15]. The paired data were assembled as follows: 12 paired samples (six HN and six DCIS) were pulled from the data published by Emery
et al. [
15], two HN samples were pulled from the data published by Graham
et al. [
21], and the two matching pairs of DCIS samples (combined to equal 16 paired samples) were collected and processed from tissue acquired from the same patient. Array data were analyzed as previously described [
15]. Microarray output data were filtered by removing all probe sets present in < 15% of all samples. Next, data were analyzed by performing Bayesian Analysis of Differential Gene Expression (BADGE) as previously described by Emery
et al. [
15] and found online at the BADGE website [
22]. The gene expression data are available from GEO under accession number [GEO:GSE24509].
miRNA target prediction
SigTerms [
23] was utilized to extract predictions from PicTar [
24], TargetScan (4.1 and 5.1) [
25] and miRanda (Jan 08 and Sep 08) [
26]. miRNA target predictions were extracted two separate times, the first using TargetScan release 4.1 and miRanda release Jan 08 and the second using TargetScan release 5.1 and miRanda release Sep 08. The final prediction results are a combination of the two queries. Pearson correlations and associated
P values were calculated across all 16 HN and DCIS samples for each of the target pairs identified from the intersection of the programs.
Gene ontology and pathway analysis
Gene annotation, ontology and pathway analysis were conducted using the Database for Annotation, Visualization and Integrated Discovery [
27]. A modified Fisher's exact test/EASE (Enrichment) Score was utilized to calculate the
P-values.
miRNA pre-miR and anti-miR transient transfection
MCF7 cells were kindly provided by G. Sonenshein (Tufts-New England Medical Center, Boston, MA, USA) and were maintained in Dulbecco's modified Eagle's medium (Invitrogen, Carlsbad, CA, USA) with 4.5 g/l glucose and sodium pyruvate supplemented with 5.8 g/l L-glutamine (Cellgro, Manassas, VA, USA), 10% fetal bovine serum (Sigma-Aldrich, St. Louis, MO, USA) and 1% penicillin-streptomycin (Cellgro). For all experiments, 5 × 104 cells/well of a 12-well plate were seeded for 24 hours and then transfected with (1) 100 nM pre-miR-125b or scrambled negative control sequence (Scramble) or (2) 50 nM Anti-miR-182, Anti-miR-183 or Scramble using the siPORT NeoFX Transfection Agent (Ambion).
Cells were rinsed with 1× phosphate-buffered saline (PBS) and lysed with 600 μl of lysis buffer, and total RNA was isolated with the mirVana Isolation Kit (Ambion) and treated with DNase I, according to the manufacturer's instructions. RNA quantity was determined using Quant-it RiboGreen RNA Quantitation Reagent (Invitrogen) according to the manufacturer's instructions.
qRT-PCR for target gene expression
cDNA from 500 ng of total RNA was synthesized using TaqMan RT reagents according to the manufacturer's instructions. qRT-PCR was performed by diluting RT product in 2× Universal PCR MasterMix and 20× TaqMan Gene Expression Assay for each gene to be measured: chromobox homolog 7 (CBX7) (Hs00980916_g1), docking protein 4 (DOK4) (Hs00902919_g1), early growth response 1 (EGR1) (Hs00152928_m1), glyceraldehyde 3-phosphate dehydrogenase (GAPDH) (4333764F), mediator of ErbB2-driven cell motility (MEMO1) (Hs00831646_uH), N-myristoyltransferase 2 (NMT2) (Hs01013924_g1) and nuclear receptor-interacting protein 1 (NRIP1/RIP140) (Hs00942766_s1). PCR reactions were run on the 7500 real-time PCR instrument under the following conditions: hold at 95°C for 10 minutes, then 40 cycles of 95°C for 15 seconds and 60°C for 1 minute. All reagents were purchased from Applied Biosystems. Relative gene expression was assessed using the differences in normalized Ct (ΔΔCt) method after normalization to GAPDH. Fold changes were calculated by 2^-ΔΔCt.
Immunoblot analysis
Cells were washed with 1× PBS 48 hours posttransfection and collected in radioimmunoprecipitation assay buffer (25 mM Tris-HCl, pH 7.6, 150 mM NaCl, 1% NP-40, 1% sodium deoxycholate, 0.1% sodium dodecyl sulfate). Protein (50 μg) was electrophoresed through a 4% to 15% Tris·HCl Ready Gel (Bio-Rad, Hercules, CA, USA) under reducing conditions and transferred onto a polyvinylidene fluoride membrane. The membrane was incubated with primary antibodies against CBX7 (ab21873; Abcam, Cambridge, MA, USA), E-cadherin (610181; BD Biosciences, San Jose, CA, USA) and β-actin (A5441; Sigma-Aldrich). Immune complexes were detected using horseradish peroxidase-conjugated secondary antibodies and the SuperSignal West Pico Chemiluminescent Substrate Kit (Pierce Biotechnology, Rockford, IL, USA).
Discussion
In this study, we identified a set of miRNA that are expressed in normal breast epithelium and found that major miRNA expression changes occur at the transition from normal to DCIS epithelium, thereby defining a set of putative oncogenic and tumor suppressor miRNA that are dysregulated at the preinvasive stage of breast cancer. A greater number of miRNA were expressed in PRM compared to HN and DCIS, which is contrary to what we have observed in our gene expression studies, and we recognize that the high C
t cutoff values employed could have influenced these results [
15,
16]. However, this observation fits with the current understanding of miRNA regulation of mRNA expression, given that a greater number of expressed miRNA would correspond to fewer expressed mRNA.
Twenty of these miRNA have previously been implicated in IBC, and 62% of the miRNA dysregulated in the HN-DCIS comparison are directionally concordant with miRNA dysregulated in IBC. This work identifies a role for these previously implicated miRNA at an early stage of breast cancer development. For example, we found that miR-145 expression was underexpressed in the DCIS-HN comparison. Using an
in situ hybridization approach, Sempere
et al. [
14] found that miR-145 was restricted to the myoepithelial/basal cell compartment of normal mammary ducts and lobules and was reduced or absent in matching tumor specimens. This finding lends support to our discovery of decreased miR-145 in DCIS, because we know that our epithelial samples include myoepithelial cells.
Although we observed a high concordance rate with previous reports in IBC, in contrast to the seminal study by Iorio
et al. [
13], which examined miRNA expression in bulk tumor tissue versus normal tissue, miR-155 was identified as highly overexpressed in breast tumor tissues. However, miR-155 was not differentially expressed in any of our comparisons. This suggests that either miR-155 is an invasive, specific miRNA or its expression is not epithelium-specific, and also that it was detected because of the heterogeneous cell population presenting bulk tumor tissue. miR-155 has since been described in immune cell function, which supports the latter scenario [
51]. Comparison of our data set to others may shed light on other miRNA whose expression is specific to either cancer stage or a particular cell type.
The identification of miRNA targets is crucial to the understanding of their biological role. We hypothesized that there is a coordinate mechanism of dysregulation between the abnormal expression of miRNA and target mRNA in very early breast tumorigenesis. By combining miRNA and gene expression data and integrating miRNA target prediction, we obtained a set of candidate miRNA:mRNA target pairs. Approximately one-half of these target pairs were coordinately expressed and are either false-positive predictions or may in fact positively regulate the target mRNA, albeit a less well-understood phenomenon. However, several instances of miRNA-positive regulation of a target gene have been described [
52‐
54]. In addition, it has been noted that two classes of miRNA network motifs, corresponding to positive and negative regulation of a miRNA and its target, may coexist, and in neuronal cells miRNA tend to be coexpressed in the same direction as their target genes [
55]. This may in part explain our observations, although further studies are needed.
Our approach has identified many potentially important early-acting, cancer-promoting mRNA targets, and miRNA dysregulation is a potential mechanism causing these early mRNA changes. Many of the identified target genes have known cancer or anticancer activity. For example, TXNIP (thioredoxin-interacting protein), EGR1, CBX7, HOXA9 and FOXN3 (checkpoint repressor 1) have tumor suppressor functions and are targeted by the potentially oncogenic miRNA miR-93, miR-183, miR-181b, miR-182 and miR-7. Similarly, WWP1, SDC1 (syndecan 1), EZH2, CCND1, ADAM9 and MEMO1 have oncogenic activities and are targeted by the potentially tumor suppressor miRNA miR-195, miR-10b, let-7c, miR-17 and miR-125b.
Many of these target pairs are likely to be relevant to cancer in general and breast cancer in particular; however, we could validate only a subset of these. We found that with modulation of miR-125b, miR-182 and miR-183 expression, we obtained results that suggest these miRNA do regulate the expression of their predicted target genes. The expression of miR-125b is reduced in many cancers, including breast cancer [
13,
56] and serous ovarian carcinoma [
57]. In addition, it has been established that miR-125b targets ErbB2/Her2, and by also targeting
MEMO1, which interacts with ErbB2/Her2, miR-125b is regulating two functionally related genes. miR-182 and miR-183 are clustered at 7q31.2, a region that is frequently amplified in melanoma [
58], and both miRNA are commonly codysregulated in many cancers, including prostate, colon and breast cancer [
33,
59,
60]. In this study, we found that by suppressing the expression of miR-182 and miR-183
in vitro, the expression of their four predicted targets,
CBX7, DOK4, NMT2 and
EGR1, were upregulated. Two of these,
CBX7 and
EGR1, have well-described tumor suppressor functions, and recently
DOK4 family members (
DOK1, DOK2, and
DOK3) were identified as lung tumor suppressors [
61]. In addition, the secondary effect of miR-182 repression resulting in upregulation of E-cadherin through CBX7, which we have shown, may have important implications in reversing epithelial neoplasias to a more normal state. Furthermore, in future studies, combined modulation of miR-125b, miR-182 and/or miR-183, as well as other miRNA altered in DCIS, may be effective in reversing the forward progression to IBC.
Admittedly, our study has several limitations, most notably the small sample size and the inclusion of only ER- and PR-positive DCIS. With the use of microdissected, paired breast tissue samples and robust statistical analysis, we sought to minimize potential biases elicited by small the sample size. In fact, the many similarities between our miRNA expression profile of DCIS and others' miRNA expression profiling of IBC suggest that our results are reliable. However, an expansion of this study to include other histological categories could identify subtype-specific dysregulated miRNA.
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Competing interests
The authors declare that they have no competing interests.
Authors' contributions
BNH conceived of and designed the study; executed miRNA expression profiling, subset of gene expression profiling, target prediction and target validation; and drafted the manuscript. PS participated in the design of the study and performed gene and miRNA expression statistical analysis. AdlM reviewed and identified lesions on all histological slides. JL provided expert technical advice and helped to design the validation experiments. CLR conceived of the study, participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.