The metastatic cascade is a complex and extremely inefficient process with many potential barriers. Understanding this process is of critical importance because the majority of cancer mortality is associated with metastatic disease. Recently, it has become increasingly clear that microRNAs (miRNA) play important roles in tumorigenesis and metastasis, yet few studies have examined how germline variations may dysregulate miRNAs, in turn affecting metastatic potential. To explore this possibility, the highly metastatic MMTV-PyMT mice were crossed with 25 AKXD (AKR/J × DBA/2J) recombinant inbred strains to produce F1 progeny with varying metastatic indices. When mammary tumors from the F1 progeny were analyzed by miRNA microarray, miR-290 (containing miR-290-3p and miR-290-5p) was identified as a top candidate progression-associated miRNA. The microarray results were validated in vivo when miR-290 upregulation in two independent breast cancer cell lines suppressed both primary tumor and metastatic growth. Computational analysis identified breast cancer progression gene Arid4b as a top target of miR-290-3p, which was confirmed by luciferase reporter assay. Surprisingly, pathway analysis identified estrogen receptor (ER) signaling as the top canonical pathway affected by miR-290 upregulation. Further analysis showed that ER levels were elevated in miR-290–expressing tumors and positively correlated with apoptosis. Taken together, our results suggest miR-290 targets Arid4b while simultaneously enhancing ER signaling and increasing apoptosis, thereby suppressing breast cancer progression. This, to the best of our knowledge, is the first example of inherited differences in miRNA expression playing a role in breast cancer progression. Cancer Res; 73(8); 2671–81. ©2013 AACR.

The metastatic cascade is a complex and extremely inefficient process with many potential barriers. Because as much as 90% of cancer-associated mortality is associated with metastatic disease rather than primary tumor burden, understanding the molecular mechanisms and pathways exploited by cancer cells during the metastatic process is of critical importance. Decades of metastasis research have led to a variety of different models of the metastatic process (1). Although the metastatic process likely involves a combination of models, our laboratory has focused on inherited genetic variants that influence metastatic potential (2). These efforts have resulted in the identification of a number of genes associated with metastatic potential in both mouse and humans (2–5). As these genes do not appear to be frequently somatically mutated in breast cancer, identification and characterization of inherited metastasis susceptibility genes provide novel insights into the mechanisms underlying the terminal stages of breast cancer progression.

More recently, there has been a significant interest in characterizing the role of microRNAs in tumor progression. The discovery of microRNAs (miRNA—small, noncoding RNAs) in 1993 sparked a paradigm shift in our understanding of gene regulation. Initially, miRNAs were classified as functionless stretches within “junk DNA” but are currently viewed as powerful regulators of gene expression as it relates to cellular development, death, and proliferation (6), as well as disease (7). Because miRNAs negatively regulate gene expression posttranscriptionally, they can act as either tumor suppressors or oncogenes (8), depending on their gene targets and cellular context (9). Recently, much attention has been focused on the role dysregulated miRNAs have on tumorigenesis (10) and metastasis (11) and whether miRNA profiles can be used for diagnosis and prognosis in cancer (12). Most interesting, germline mutations in microRNAs have been associated with skeletal and growth defects (13), chronic lymphocytic leukemia (CLL; ref. 14), and breast cancer (15), thereby supporting the notion that germline mutations in certain miRNAs may create a predisposition towards tumorigenesis.

In this study, we analyzed miRNA expression levels in the mammary tumors of the (PyMT × AKXD) F1 progeny described previously (16) to determine whether a link exists between inherited differences in miRNA expression and mammary tumor progression. We provide evidence that miR-290-3p, a member of the pluripotency miR-290-295 cluster (17–19), an ortholog of the human miR-371-373 cluster (19), is differentially expressed between the highly metastatic AKR/J and poorly metastatic DBA2/J strains of mice. Increased expression of miR-290 suppresses tumor growth and progression in a tumor autonomous manner, in part, by targeting the recently described tumor progression gene, Arid4b (20). To the best of our knowledge, this is the first example of an inherited miRNA expression difference being associated with tumor progression.

Cell lines

All cells were cultured in Dulbecco's Modified Eagle's Media (DMEM) with 10% FBS and antibiotics. Puromycin was used for selection.

Mouse strains

The AKXD RI mice were generated as described (21). The PyMT mouse strain FVB/N-TgN(MMTV-PyVT)634Mul (22) was then bred to 18 of the AKXD RI strains to generate 18 (PyMT × AKXD) F1 sublines (16).

miRNA microarray analysis of AKXD RI tumors

The LMT_miRNA_v2 microarray was designed using the Sanger miR9.0 database (http://microrna.sanger.ac.uk) and manufactured as custom-synthesized 8 × 15K microarrays (Agilent Technologies). Each mature miRNA was represented by + and − (reverse complement) strand sequences. Each probe has 4 replicates within each microarray, providing technical replicates for consistency and performance of the microarray. Each unique mature miRNA was represented by 8 probes (4 + strand and 4 − strand). A total of 3,556 unique LMT seq IDs (miRNA, positive and negative controls, ±strand) were on the microarray, each with 4 replicates.

Total RNA (1 μg) was labeled using the miRCURY LNA microRNA Array Power Labeling Kit (Exiqon Inc.). The 3′-end of the RNA was enzymatically labeled with Hy3 for the sample RNA and Hy5 fluorescent dye (Exiqon) for the reference RNA (Ambion reference RNA) and cohybridized onto the microarrays. The washed and dried slides were scanned using the Agilent scanner. The Feature Extraction program extracted spot intensities. The log2 ratio of all signals was used for data analysis.

mRNA microarray analysis of 6DT1 miRNA tumors

The Agilent 2100 Bioanalyzer (Agilent Technologies) verified that each sample RNA had a high quality score (RIN >9). The RNA (100 ng) was reverse-transcribed and amplified using the Ambion WT Expression Kit. Sense strand cDNA was fragmented and labeled using the GeneChip WT Terminal Labeling and Controls Kit. Four replicates of each sample were hybridized to GeneChip Mouse Gene 1.0 ST Array in the GeneChip Hybridization Oven 645 while shaking at 60 rpm at 45°C for 16 hours. Washing and staining were conducted on the GeneChip Fluidics Station 450 and scanned on the GeneChip Scanner 3000. Data were collected using the GeneChip Command Console Software (AGCC). All reagents, software, and instruments used, except for the Agilent 2100 Bioanalyzer, were from Affymetrix.

RNA isolation

Tumors were snap-frozen upon harvesting and stored at −80°C. All tumors were homogenized on dry ice in an RNase-free environment. The RNA was isolated using the mirVana miRNA Isolation Kit (Ambion). The RNA for all remaining samples, including cell lines, was isolated using the RNAeasy Kit (Qiagen).

Quantitative real-time PCR and Western blot

Total RNA was reverse-transcribed with iScript cDNA Synthesis Kit (Bio-Rad) and PCR-amplified using QuantiTect SYBR Green PCR Kit (Qiagen) on the Applied Biosystems 7900HT Fast Real-Time PCR System (Applied Biosytems). The standard curve method was used for quantitation and normalized to endogenous control Ppib levels. TaqMan MicroRNA Assays (Applied Biosystems) were used to measure miRNA expression. Expression of miRNA was defined from the threshold cycle, and relative expression levels were calculated using the |$2 - \Delta \Delta C_{\rm t}$| method (23) after normalization with reference snoRNA135.

Primers for reverse transcription (RT)-PCR: Ppib and Arid4b.

  • Mouse PPIB: 5′-GGAGATGGCACAGGAGGAAAGAG-3′ (forward)

  • Mouse PPIB: 5′-TGTGAGCCATTGGTGTCTTTGC-3′ (reverse)

  • Mouse ARID4B: 5′-AACAAAGGTGCAGGTGAAGC-3′ (forward)

  • Mouse ARID4B: 5′-ACATCAGTGCCCACTGTCAA-3′ (reverse)

  • Mouse ESR1: 5′-TCTCTGGGCGACATTCTTCT-3′ (forward)

  • Mouse ESR1: 5′-CATGGTCATGGTAAGTGGCA-3′ (reverse)

  • Mouse EGFR: 5′-GGCGTTGGAGGAAAAGAAAG-3′ (forward)

  • Mouse EGFR: 5′-ATCCTCTGCAGGCTCAGAAA-3′ (reverse)

  • Mouse C3: 5′-GGCCTTCTCTCTAACAGCCA-3′ (forward)

  • Mouse C3: 5′-TGCAGGTGACTTTGCTTTTG-3′ (reverse)

  • Mouse DLC1: 5′-CCTGGCTGGAATAGCATCAT-3′ (forward)

  • Mouse DLC1: 5′-ATGCATGGGTCAAGGAAGAG-3′ (reverse)

  • Mouse IL6ST: 5′-CTGAGGGACCGGTGGTGT-3′ (forward)

  • Mouse IL6ST: 5′-TCATGTTCCTTCTATCGGGTC-3′ (reverse)

  • Mouse IL2RA: 5′-TTGCTGATGTTGGGGTTTCT-3′ (forward)

  • Mouse IL2RA: 5′-AGGAGAGGGCTTTGAATGTG-3′ (reverse)

  • Mouse CDH1: 5′-GAGGTCTACACCTTCCCGGT-3′ (forward)

  • Mouse CDH1: 5′-AAAAGAAGGCTGTCCTTGGC-3′ (reverse)

Western blotting with Arid4b was conducted as described in (20). For the remaining Western blotting, a PARP antibody (#9544, Cell Signaling) and an estrogen receptor (ER)-α antibody (ab2746, Abcam) were used. Protein lysate was generated from tumors by homogenizing in radioimmunoprecipitation assay (RIPA) buffer without detergents. Detergents were subsequently added (1% NP-40, 0.5% Na deoxycholate, and 0.1% SDS) after homogenizing. Western blot bands were quantified using ChemiDoc-It Imaging System with VisionWorks LS software.

Generation of stable cell lines

The pEZX-MR06 plasmids (GeneCopoeia) that contained either miR-290 or a scrambled insert were transformed into GCl-L3 chemically competent Escherichia coli cells (GeneCopoeia) and purified using Plasmid Midi Kit (Qiagen). Two days before transfection, 293ta packaging cells (GeneCopoeia) were plated in a 10-cm dish with DMEM and 10% heat-inactivated FBS. The cells were transfected at 70% to 80% confluency according to the manufacturer's instructions in the Lenti-Pac FIV Expression Kit (GeneCopoeia). Mvt-1 and 6DT1 cells were transduced using 200 μL of purified lentivirus and polybrene. Media containing puromycin were applied 5 days posttransduction. Stable clones were generated by plating 5 cells/mL of media in each well of a 96-well plate. Fresh media containing puromycin were added to each well 48 hours later. When the cells were approximately 80% confluent, they were collected and transferred to a T25 flask.

In vivo analysis

FVB/N (24) female mice at 6 weeks of age were orthotopically injected with 1.0 × 105 cells. Mice were euthanized 30 days after injection of 6DT1 cells and 42 days after injection of Mvt-1. Mammary tumors were removed and weighed, and each lung was analyzed for surface metastases and internal metastasis after histologic sectioning: calculated as the mean number of metastases detected from 3 separate lung sections having 20 sections between each section.

Luciferase reporter assays

HEK293T cells were seeded in a 24-well plate and a construct containing the full-length 3′-untranslated region (UTR) of human ARID4B adjacent to the Renilla gene (SwitchGear) was cotransfected with 50 nmol/L of miRIDAN miRNA mimic (Dharmacon) and a control vector pGL4.13 [luc2/SV40] (Promega) using DharmaFECT DUO (Dharmacon) when the cells were ≥80% confluent. After 24 hours, the plate was stored at −80°C overnight. The following day, the cells in each well were mixed by pipetting and then divided evenly. Half of the sample was treated with Steady-Glo reagent (Promega) and the remaining half was treated with LightSwitch reagent (Switchgear). All samples were quantified using the Lumat LB 9507 (EG&G Berthold) luminometer, and the Renilla luciferase activity was normalized to the Firefly luciferase activity. miRIDAN miRNA mimic negative control was used as the control miRNA. Experiments were carried out in triplicate.

Statistical and bioinformatic analysis

Error bars depict SEM. GraphPad Prism was applied to calculate statistical significance. The in vivo data were analyzed using Mann–Whitney whereas remaining calculations were conducted using the unpaired Student t test. Microarray data were analyzed using Partek Genomics Suite. Differentially expressed genes were identified with ANOVA analysis; genes with a P < 0.05 were considered significant. Significant genes were analyzed for pathway enrichment using Ingenuity Pathway Analysis (IPA) software. The MFold program was used to determine the structure of mmu-miR-290. TargetScan, miRDB, and microRNA.org were applied using default parameters. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Site-directed mutagenesis

Site-directed mutagenesis was conducted using high-fidelity DNA polymerase, ARID4B 3′-UTR vector (#S210100, SwitchGear Genomics), and 2 synthetic oligonucleotide primers containing the desired mutation in a temperature cycler. The mutagenesis reaction was transformed into DH5a supercompetent cells and then the mutant plasmid DNA (isolated from a single colony) was verified by DNA sequencing.

miR-290 expression is significantly correlated with tumor latency and metastatic index

Previously, the highly metastatic FVB/N-TgN (MMTV-PyMT) mice were crossed with 18 AKXD RI (AKR/J × DBA/2J) strains to map modifiers associated with tumor latency, tumor burden, and metastatic index (Supplementary Fig. S1; ref. 16). To identify miRNAs that might contribute to metastatic progression, we evaluated miRNA expression levels within all mammary tumors by miRNA microarray. The miRNA microarray data were analyzed using Partek Genomics Suite software and correlated with tumor burden, tumor latency, and metastatic index [log2(number of metastases/lung area (μm2)); Table 1]. One-way ANOVA revealed a significant positive correlation between miR-290 and latency and a significant negative correlation with metastatic index. Furthermore, 2-way ANOVA analysis produced a significant negative correlation between miR-290 with metastatic index and tumor burden; and metastatic index and latency. Finally, 3-way ANOVA analysis produced a significant negative correlation between miR-290 with metastatic index, latency, and tumor burden. Importantly, these ANOVA results suggested that miR-290 may play a suppressor role in during breast cancer progression.

Table 1.

Correlation between miR-290 and phenotype

PhenotypePFactorCorrelation
1-way metastatic index 0.014 7.62 −0.56 
2-way metastatic index and tumor burdena 0.006 10.3 −0.63 
2-way metastatic index and latencya 0.021 6.66 −0.54 
PhenotypePFactorCorrelation
1-way metastatic index 0.014 7.62 −0.56 
2-way metastatic index and tumor burdena 0.006 10.3 −0.63 
2-way metastatic index and latencya 0.021 6.66 −0.54 

NOTE: Only parameters producing a mean f ratio much greater than 1.0 are listed. This was not the case with 1-way ANOVA of tumor burden (F = 1.02) and 2-way ANOVA of tumor burden (F = 1.04) and latency (F = 1.48).

aOnly the metastatic index statistics are listed.

The structure of miR-290 includes 2 functionally active mature miRNAs: miR-290-3p and miR-290-5p (Supplementary Fig. S2). To validate the microarray findings and determine whether only one or both of the mature miRNAs are correlated with tumor progression, the expression levels of miR-290-3p and miR-290-5p were compared in mammary tumors from AKXD strain #18 (the most metastatic) and AKXD strain #22 (the least metastatic; Fig. 1A). The miR-290-3p expression was downregulated more than 50% in AKXD strain #18 than in AKXD strain #22. In contrast, miR-290-5p displayed virtually no expression difference between the 2 strains. Likewise, the expression levels of miR-290-3p and miR-290-5p were evaluated in mammary tumors from the AKXD parental mouse strains, the highly metastatic AKR/J mice, and poorly metastatic DBA/2J mice (Fig. 1B). Consistent with an antimetastatic role, the expression of miR-290-3p was downregulated 70% in the AKR/J tumors compared with the DBA/2J tumors, whereas miR-290-5p was only slightly downregulated. Finally, to explore whether certain genetic strains may contain a predisposition toward tumorigenesis and metastasis through an inherited up- or downregulation of specific miRNAs, miR-290-3p expression was measured in normal mammary and normal lung tissue from AKR/J mice and DBA/2J mice (Fig. 1C). Interestingly, both tissues displayed more than 50% downregulation of miR-290-3p expression in the AKR/J mice, thereby supporting a potential predisposition toward breast carcinogenesis in the AKR/J strain. Taken together, these results further suggested miR-290-3p may act as a tumor and metastasis suppressor miRNA and thereby warranted further investigation.

Figure 1.

miR-290-3p and miR-290-5p expression analysis in mammary tumors and normal tissues. A, miR-290-3p expression was measured in mammary tumors from the highly metastatic AKXD strain #18 versus the less metastatic AKXD strain #22 and in the highly metastatic AKR/J strain (n = 5; B) versus the less metastatic DBA/2J strain (n = 4). C, miR-290-3p expression was measured in normal lung and normal breast tissues from the AKR/J versus the DBA/J strain. RNA from the AKXD samples was analyzed 5 times via qRT-PCR. *, P < 0.05, **, P < 0.01; ***, P < 0.001.

Figure 1.

miR-290-3p and miR-290-5p expression analysis in mammary tumors and normal tissues. A, miR-290-3p expression was measured in mammary tumors from the highly metastatic AKXD strain #18 versus the less metastatic AKXD strain #22 and in the highly metastatic AKR/J strain (n = 5; B) versus the less metastatic DBA/2J strain (n = 4). C, miR-290-3p expression was measured in normal lung and normal breast tissues from the AKR/J versus the DBA/J strain. RNA from the AKXD samples was analyzed 5 times via qRT-PCR. *, P < 0.05, **, P < 0.01; ***, P < 0.001.

Close modal

miR-290 suppresses mammary tumor progression

To determine the phenotypic effects of miR-290 expression on breast cancer progression in vivo, a lentivirus construct was used to upregulate miR-290 expression in the highly metastatic mouse mammary tumor cell lines 6DT1 and Mvt-1 (Supplementary Table S1). Heterogeneous populations expressing the miR-290 construct were injected orthotopically into the mammary fat pad of FVB/N mice. In vivo analysis showed that miR-290 expression in the 6DT1 cell population significantly decreased the primary tumor weight approximately 50% (Fig. 2A) and the number of lung metastases from approximately 7 lesions per lung section to zero lesions (Fig. 2B). Likewise, miR-290 expression in the Mvt-1 cell population reduced primary tumor weight by approximately 50% (Fig. 2C) and the number of lung metastases from approximately 5 lesions to zero lesions (Fig. 2D).

Figure 2.

miR-290 suppresses breast cancer tumorigenesis and lung metastasis. miR-290 expression in the 6DT1 and Mvt-1 heterogeneous populations suppressed primary tumor weight (A and C) and the number of lung metastasis (B and D). Clones generated from the heterogeneous populations also suppressed primary tumor weight (E and G) and the number of lung metastasis (F and H).

Figure 2.

miR-290 suppresses breast cancer tumorigenesis and lung metastasis. miR-290 expression in the 6DT1 and Mvt-1 heterogeneous populations suppressed primary tumor weight (A and C) and the number of lung metastasis (B and D). Clones generated from the heterogeneous populations also suppressed primary tumor weight (E and G) and the number of lung metastasis (F and H).

Close modal

To confirm these results and eliminate any potential bias due to population heterogeneity, single-cell clones were generated from both the Mvt1 and 6DT1 heterogeneous populations and injected as above. The results were even more dramatic with mammary tumor burden reduced 75% (Fig. 2E) and the average number of lung lesions reduced from 10 to zero (Fig. 2F) for the miR-290 6DT1 clone. Similarly, the miR-290 Mvt-1 clone displayed an 80% reduction in primary tumor size (Fig. 2G) and an average of 1 versus 4 lung lesions in the negative control (Fig. 2H). To be sure miRNA expression was maintained after orthotopic injection, the mammary tumors generated from the 6DT1 clones were analyzed. Expression analysis showed all the miR-290 tumors displayed elevated miR-290-3p and miR-290-5p expression compared with the negative control tumors (Supplementary Fig. S3).

miR-290-3p targets breast cancer progression gene Arid4b

To identify interesting potential targets of miR-290-3p, bioinformatic analysis using TargetScan, miRDB, and microRNA.org was applied. Targetscan and miRDB identified Arid4b as the top target, whereas microRNA.org identified Arid4b as the second-ranked target (Table 2). Interestingly, elevated levels of Arid4b have been previously linked to breast carcinogenesis (25, 26), and recent studies in our laboratory have shown that Arid4b promotes breast cancer progression (20). Therefore, analysis was conducted to determine whether miR-290-3p might inhibit mammary cancer progression, in part, by suppression of Arid4b.

Table 2.

Top bioinformatic targets of miR-290-3p

ProgramGeneGene nameScoreaRank
TargetScan ARID4B AT-rich interactive domain 4B −0.85 
miRDB ARID4B AT-rich interactive domain 4B 96 
microRNA.org RBL2 Retinoblastoma-like 2 −2.98 
 ARID4B AT-rich interactive domain 4B −2.22 
ProgramGeneGene nameScoreaRank
TargetScan ARID4B AT-rich interactive domain 4B −0.85 
miRDB ARID4B AT-rich interactive domain 4B 96 
microRNA.org RBL2 Retinoblastoma-like 2 −2.98 
 ARID4B AT-rich interactive domain 4B −2.22 

aTarget scan, total context score; miRDB, score; microRNA.org, mirSVR score.

First, Arid4b RNA levels were compared in the clones and heterogeneous populations. Expression analysis detected an approximate 50% reduction in Arid4b mRNA levels (Fig. 3A) and a 10% to 30% reduction in protein levels (Fig. 3B) in the miR-290–upregulated cells. Next, to determine whether miR-290-3p directly targets the 3′-UTR of Arid4b, a luciferase construct containing the 3′-UTR of Arid4b was transfected with an miRNA-negative control mimic, an miR-290-3p mimic, or an miR-290-5p mimic; a 50% reduction in luciferase activity was observed only with the miR-290-3p mimic (Fig. 3C). To further show that miR-290-3p interacts directly with 2 seed-binding regions within the Arid4b 3′-UTR, 2 point mutations were generated in each seed-binding region and were denoted as Mut123 and Mut1110 (Fig. 3D). Although a significant reduction in luciferase activity was observed for the WT construct, high luciferase activity was maintained in all of the mutants (Fig. 3E), thereby supporting the direct interaction between miR-290-3p and these 2 targeted regions within the Arid4b 3′-UTR.

Figure 3.

miR-290-3p targets the 3′-UTR of Arid4b. Arid4b expression was measured by qRT-PCR (A) and Western blotting (B) in all 4 stable cell lines with upregulated miR-290. C, luciferase activity was measured after transfection of a vector containing the 3′-UTR of ARID4B adjacent to a Renilla gene with miR-290-3p or miR-290-5p. D, two miR-290-3p–targeted regions within the 3′-UTR of ARID4B were identified and then mutated using 2 point mutations each. E, luciferase activity was measured for the WT 3′-UTR of ARID4B and the mutants after miR-290-3p transfection. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Figure 3.

miR-290-3p targets the 3′-UTR of Arid4b. Arid4b expression was measured by qRT-PCR (A) and Western blotting (B) in all 4 stable cell lines with upregulated miR-290. C, luciferase activity was measured after transfection of a vector containing the 3′-UTR of ARID4B adjacent to a Renilla gene with miR-290-3p or miR-290-5p. D, two miR-290-3p–targeted regions within the 3′-UTR of ARID4B were identified and then mutated using 2 point mutations each. E, luciferase activity was measured for the WT 3′-UTR of ARID4B and the mutants after miR-290-3p transfection. *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Close modal

miR-290 upregulates ER expression, ER signaling, and ER-associated apoptosis

Because miRNAs affect numerous signaling pathways and genes, rather than a single gene, and because the tumor microenvironment can dramatically influence gene expression, cDNA microarray analysis was conducted on the tumors generated from the 6DT1 clones. These samples were chosen because they displayed the greatest suppression of pulmonary metastasis (Fig. 2F) and because they were previously shown to maintain miR-290 expression within the individual tumors (Supplementary Fig. S3). A total of 4 negative control tumors and 3 miR-290–expressing tumors (the remaining 2 tumors were too small for analysis) were arrayed.

Bioinformatic analysis of the 6DT1 tumors generated a list of 7,015 differentially regulated genes (P < 0.05) with fold-changes between approximately −6-fold to 74-fold in the miR-290 tumors (Supplemental Table S2). The top 10 downregulated genes included granzyme D and E precursors and mast cell protease 1 precursor. The list of top 10 upregulated genes included the metabolism-associated genes Cyp2e1 and Ces1d. Interestingly, many genes associated with adipocytes were upregulated in the miR-290 tumors: Retn (27) and Cfd (28) are both secreted by adipocytes; Adipoq (29) is expressed exclusively in adipose tissue and has anti-inflammatory activities (30); lgals12 is a primary regulator of the early stages of adipose tissue development (31); and finally, Cidec may mediate adipocyte apoptosis (32).

The expression data were then processed using IPA to identify biologic functions associated with miR-290 expression; a total of 38 dysregulated canonical pathways (P < 0.01) were identified. The most significantly dysregulated pathways identified were pyrimidine and purine metabolism, aminoacyl-tRNA biosynthesis, cell-cycle control of chromosomal replication, assembly of RNA polymerase II complex, nucleotide excision repair pathway, and a number of pathways associated with breast cancer and ER signaling: hereditary breast cancer signaling, role of BRCA1 in DNA damage response, estrogen-mediated S-phase entry, and ER signaling (Fig. 4A). Dysregulation of the ER signaling pathway in the miR-290 tumors was further validated by measuring the expression level of a subset of genes within the ER signaling pathway (EGFR, C3, DLC1, IL6ST, IL2RA, CDH1; refs. 33, 34) via microarray and quantitative RT-PCR (qRT-PCR; Supplementary Fig. S4). Furthermore because ER signaling was the most significantly dysregulated, we sought to understand whether the enhanced signaling was a consequence of upregulated ER expression. For this, we analyzed the mRNA levels observed in the microarray results (Fig. 4B) and validated those via qRT-PCR (Fig. 4C); both methods confirmed ER upregulation in the miR-290 tumors. Likewise, Western blot analysis (Fig. 4D) confirmed significantly elevated ER levels in the miR-290 tumors (Fig. 4E). Furthermore, because previous publications have documented a link between elevated ER signaling and apoptosis (35, 36), this relationship was explored in vivo. Apoptosis was measured via Western blotting in the 6DT1 tumors through detection of the 89-kDa PARP fragment resulting from caspase cleavage (Fig. 5A). Interestingly, a significantly elevated level of apoptosis was observed in the miR-290 tumors (Fig. 5B) that was positively correlated with ER expression (r2 = 0.711; Fig. 5C).

Figure 4.

miR-290 enhances ERα signaling and expression levels. A, the top 10 canonical pathways dysregulated by miR-290. The blue bar represents P value and the yellow squares represent the ratio of genes dysregulated in the pathway. ER mRNA levels were measured in the miR-290 tumors by microarray analysis (B) and qRT-PCR (C). ER protein levels were detected in the miR-290 tumors by Western blotting (D) and quantified (E).

Figure 4.

miR-290 enhances ERα signaling and expression levels. A, the top 10 canonical pathways dysregulated by miR-290. The blue bar represents P value and the yellow squares represent the ratio of genes dysregulated in the pathway. ER mRNA levels were measured in the miR-290 tumors by microarray analysis (B) and qRT-PCR (C). ER protein levels were detected in the miR-290 tumors by Western blotting (D) and quantified (E).

Close modal
Figure 5.

Elevated ER expression is positively correlated with apoptosis. The level of apoptosis was measured in the 6DT1 tumors through Western blot detection of cleaved PARP (A) and quantitation (B). C, a correlation between cleaved PARP and ER expression was observed in the 6DT1 tumors.

Figure 5.

Elevated ER expression is positively correlated with apoptosis. The level of apoptosis was measured in the 6DT1 tumors through Western blot detection of cleaved PARP (A) and quantitation (B). C, a correlation between cleaved PARP and ER expression was observed in the 6DT1 tumors.

Close modal

Metastasis is a complex phenotype that requires many molecular and cellular events. Recent evidence suggests that in addition to tumor cell autonomous events, such as somatic mutation, nontumor cells and tissues also play a significant role in tumor progression. Cancer initiation and metastasis should therefore be considered a disease of the whole organism, rather than focused on the loss of proliferative control in an individual tissue. As a consequence, a complete understanding of the metastatic process requires better characterization of factors that influence the biology of the entire organism not just a specific cell or tissue type.

In addition to key environmental factors that affect organismal biology, genetic background is an underappreciated factor that can dramatically influence tumor initiation and progression. By influencing gene expression and efficiency of gene function, inherited polymorphisms determine not only the morphologic features that make individuals unique but also establish the spectrum of sensitivity or resistance to particular disease states, such as cancer, heart disease, diabetes, etc. Along these lines, studies from our laboratory have previously established the strong relationship between genetic predisposition and the metastatic process (37). Therefore, the identification and characterization of polymorphic factors that establish sensitivity or resistance to metastatic disease should in turn provide invaluable incite about the mechanistic basis of tumor dissemination and progression.

Our laboratory previously focused on how polymorphisms can influence the metastatic function of protein-coding genes. Yet recent evidence has shown that miRNA dysregulation can significantly alter signaling networks thereby affecting tumor progression and metastasis (38). We therefore extended our research focus to explore whether constitutional differences in miRNA expression might influence the metastatic potential of mammary tumors. This involved profiling miRNA expression across an AKXD recombinant inbred genetic reference panel to identify miRNAs that are associated with metastatic progression. The AKXD RI panel consisted of 20 substrains of mice derived from an original cross between the high metastatic AKR/J and low metastatic DBA/2J inbred strains of mice (21). By profiling miRNA expression across this panel, we observed a correlation between miR-290 expression and metastatic burden, thus suggesting that miR-290 is a potential metastasis-associated miRNA. Subsequent in vivo analysis confirmed miR-290 suppresses breast cancer progression through suppression of both primary and metastatic tumor growth. In addition, analysis of nonneoplastic tissues from AKR/J and DBA/2J mice showed that differential expression of miR-290 exists before oncogenesis, thereby suggesting that miR-290-associated suppressive effects are, at least in part, an inherited rather than a purely somatic event, yet the exact nature of the expression polymorphism has not been established.

When bioinformatics analysis was conducted to establish a mechanistic basis for the suppressive effect of miR-290, the recently described metastasis susceptibility gene Arid4b (20) was unexpectedly identified as a top target, and the direct targeting was subsequently validated by luciferase assay. Arid4b had previously been identified as a potential metastasis susceptibility gene in our laboratory based on the strong correlation observed between Arid4b expression with both tumor growth and metastatic burden in the AKXD RI panel. In brief, multiple amino acid substitutions in the Arid4b gene were identified in the AKR/J strain compared with the reference mouse genome. These nonsynonymous substitutions increased binding of the AKR/J strain of Arid4b to members of the SIN3 HDAC complex and reduced metastatic burden compared with the DBA/2J allele. Similarly, overexpression of either allele was found to increase tumor growth compared with the control. In contrast, knockdown of endogenous Arid4b reduced metastatic burden yet did not significantly affect primary tumor growth. Hence, the suppression of tumor growth and metastasis associated with miR-290-3p targeting of Arid4b is consistent with previous findings in our laboratory and suggests that the tumor suppression properties of miR-290 are at least partially related to Arid4b targeting.

Although a part of the suppressive effect of miR-290-3p may be accounted for by Arid4b targeting, additional pathways are likely affected because, unlike the Arid4b knockdown, miR-290 expression significantly suppresses primary tumor burden. Along these lines, subsequent microarray analysis showed that miR-290 enhances ER expression and signaling. These unexpected results are particularly noteworthy because they are contrary to the currently accepted and long held belief that stimulation of ER by estrogen fuels cell proliferation and breast cancer progression (39), thereby creating a vast industry focused on the development of antiestrogen therapies and aromatase inhibitors to block ERα activity (40, 41). Yet, as mentioned previously, some reports have documented the therapeutic benefits of high-dose estrogen therapy for treatment of breast cancer (42–44), arguing that estrogen can collapse the enhanced survival pathways (i.e., HER2/neu and NF-κβ) supported by exhaustive antiestrogen treatment while simultaneously promoting apoptosis by activating caspase-8 and synthesis of Fas receptor (35, 36). Consequently, the activation of apoptosis as measured by PARP cleavage in miR-290–expressing cells is consistent with the latter scenario, which suggests this relationship may contribute to the tumor-suppressive effects observed.

Another potential mechanism of tumor and metastasis suppression by miR-290 is suppression of differentiation. Support for this mechanism comes from reports showing miR-290 is enriched in embryonic stem (ES) cells and reduced after differentiation (45), and members of the miR-290-295 cluster make up greater than 70% of the miRNAs in ES cells (46). In addition, current hypotheses suggest that cells at the invading fronts of tumor masses, as well as disseminating cells, undergo an obligate epithelial-to-mesenchymal transition and acquire stem cell–like characteristics. Once established in a secondary site, cells are thought to reverse this process to reestablish a more epithelial-like state before proliferation resumes. Furthermore, recent work on induced pluripotent stem cells (iPSC) suggests that miR-290 may play an important role in reprogramming and maintenance of a stem cell–like state. In short, reprogramming has been achieved when the 4 reprogramming factors OSKM (Oct3/4, Sox2, Klf4, and c-Myc) are ectopically expressed in mouse embryonic fibroblasts and human fibroblasts (47). Reports show that c-Myc induces the expression of miRNAs associated with pluripotency such as the miR-290-295 (48), miR-302 (49), and the miR-17-92 (50) clusters. Hence, sustained expression of miR-290 within tumor cells might inhibit the subsequent mesenchymal-to-epithelial transition required for tumor growth and proliferation at a secondary site.

In summary, we have shown that expression of miR-290 in highly metastatic breast cancer cell lines significantly decreases tumor progression. Moreover, our results suggest that inherited differences in expression of miRNAs, in addition to somatically acquired alterations in expression, may be important determinants of tumor progression. The precise mechanism for miR-290 tumor suppression is unclear but likely involves multiple factors, such as Arid4b targeting; enhanced ER signaling and apoptosis; and cellular reprogramming. Further research into each of these explanations is certainly required to determine whether any or a combination of these is a contributing to the effect. Nonetheless, the results described herein certainly spark future investigations by providing additional molecular insights into distinct factors that regulate breast cancer progression.

No potential conflicts of interest were disclosed.

Conception and design: K.W. Hunter, N. Goldberger

Development of methodology: N. Goldberger

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): N. Goldberger, R.C. Walker, C.H. Kim, S. Winter

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): N. Goldberger, C.H. Kim, S. Winter

Writing, review, and/or revision of the manuscript: K.W. Hunter, N. Goldberger, C.H. Kim

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): N. Goldberger

Study supervision: K.W. Hunter, N. Goldberger

The authors thank Dr. Glenn Merlino for critical reading of the manuscript and helpful discussion.

This work was supported by the NCI, Center for Cancer Research, Intramural Research Program, NIH, and the Howard Hughes Medical Institute-NIH Research Scholars Program.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Hunter
KW
,
Crawford
NP
,
Alsarraj
J
. 
Mechanisms of metastasis
.
Breast Cancer Res
2008
;
10
Suppl 1:S2
.
2.
Crawford
NP
,
Ziogas
A
,
Peel
DJ
,
Hess
J
,
Anton-Culver
H
,
Hunter
KW
. 
Germline polymorphisms in SIPA1 are associated with metastasis and other indicators of poor prognosis in breast cancer
.
Breast Cancer Res
2006
;
8
:
R16
.
3.
Crawford
NP
,
Alsarraj
J
,
Lukes
L
,
Walker
RC
,
Officewala
JS
,
Yang
HH
, et al
Bromodomain 4 activation predicts breast cancer survival
.
Proc Natl Acad Sci U S A
2008
;
105
:
6380
5
.
4.
Crawford
NP
,
Qian
X
,
Ziogas
A
,
Papageorge
AG
,
Boersma
BJ
,
Walker
RC
, et al
Rrp1b, a new candidate susceptibility gene for breast cancer progression and metastasis
.
PLoS Genet
2007
;
3
:
e214
.
5.
Hsieh
SM
,
Lintell
NA
,
Hunter
KW
. 
Germline polymorphisms are potential metastasis risk and prognosis markers in breast cancer
.
Breast Dis
2006
;
26
:
157
62
.
6.
Ambros
V
. 
The functions of animal microRNAs
.
Nature
2004
;
431
:
350
5
.
7.
Alvarez-Garcia
I
,
Miska
EA
. 
MicroRNA functions in animal development and human disease
.
Development
2005
;
132
:
4653
62
.
8.
Kent
OA
,
Mendell
JT
. 
A small piece in the cancer puzzle: microRNAs as tumor suppressors and oncogenes
.
Oncogene
2006
;
25
:
6188
96
.
9.
Shenouda
SK
,
Alahari
SK
. 
MicroRNA function in cancer: oncogene or a tumor suppressor?
Cancer Metastasis Rev
2009
;
28
:
369
78
.
10.
Calin
GA
,
Croce
CM
. 
MicroRNA-cancer connection: the beginning of a new tale
.
Cancer Res
2006
;
66
:
7390
4
.
11.
Nicoloso
MS
,
Spizzo
R
,
Shimizu
M
,
Rossi
S
,
Calin
GA
. 
MicroRNAs–the micro steering wheel of tumour metastases
.
Nat Rev Cancer
2009
;
9
:
293
302
.
12.
Calin
GA
,
Croce
CM
. 
MicroRNA signatures in human cancers
.
Nat Rev Cancer
2006
;
6
:
857
66
.
13.
de Pontual
L
,
Yao
E
,
Callier
P
,
Faivre
L
,
Drouin
V
,
Cariou
S
, et al
Germline deletion of the miR-17 approximately 92 cluster causes skeletal and growth defects in humans
.
Nat Genet
2011
;
43
:
1026
30
.
14.
Calin
GA
,
Ferracin
M
,
Cimmino
A
,
Di Leva
G
,
Shimizu
M
,
Wojcik
SE
, et al
A MicroRNA signature associated with prognosis and progression in chronic lymphocytic leukemia
.
N Engl J Med
2005
;
353
:
1793
801
.
15.
Li
W
,
Duan
R
,
Kooy
F
,
Sherman
SL
,
Zhou
W
,
Jin
P
. 
Germline mutation of microRNA-125a is associated with breast cancer
.
J Med Genet
2009
;
46
:
358
60
.
16.
Hunter
KW
,
Broman
KW
,
Voyer
TL
,
Lukes
L
,
Cozma
D
,
Debies
MT
, et al
Predisposition to efficient mammary tumor metastatic progression is linked to the breast cancer metastasis suppressor gene Brms1
.
Cancer Res
2001
;
61
:
8866
72
.
17.
Sinkkonen
L
,
Hugenschmidt
T
,
Berninger
P
,
Gaidatzis
D
,
Mohn
F
,
Artus-Revel
CG
, et al
MicroRNAs control de novo DNA methylation through regulation of transcriptional repressors in mouse embryonic stem cells
.
Nat Struct Mol Biol
2008
;
15
:
259
67
.
18.
Houbaviy
HB
,
Dennis
L
,
Jaenisch
R
,
Sharp
PA
. 
Characterization of a highly variable eutherian microRNA gene
.
RNA
2005
;
11
:
1245
57
.
19.
Houbaviy
HB
,
Murray
MF
,
Sharp
PA
. 
Embryonic stem cell-specific MicroRNAs
.
Dev Cell
2003
;
5
:
351
8
.
20.
Winter
SF
,
Lukes
L
,
Walker
RC
,
Welch
DR
,
Hunter
KW
. 
Allelic variation and differential expression of the mSIN3A histone deacetylase complex gene Arid4b promote mammary tumor growth and metastasis
.
PLoS Genet
2012
;
8
:
e1002735
.
21.
Mucenski
ML
,
Taylor
BA
,
Jenkins
NA
,
Copeland
NG
. 
AKXD recombinant inbred strains: models for studying the molecular genetic basis of murine lymphomas
.
Mol Cell Biol
1986
;
6
:
4236
43
.
22.
Guy
CT
,
Cardiff
RD
,
Muller
WJ
. 
Induction of mammary tumors by expression of polyomavirus middle T oncogene: a transgenic mouse model for metastatic disease
.
Mol Cell Biol
1992
;
12
:
954
61
.
23.
Livak
KJ
,
Schmittgen
TD
. 
Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method
.
Methods
2001
;
25
:
402
8
.
24.
Taketo
M
,
Schroeder
AC
,
Mobraaten
LE
,
Gunning
KB
,
Hanten
G
,
Fox
RR
, et al
FVB/N: an inbred mouse strain preferable for transgenic analyses
.
Proc Natl Acad Sci U S A
1991
;
88
:
2065
9
.
25.
Cao
J
,
Gao
T
,
Stanbridge
EJ
,
Irie
R
. 
RBP1L1, a retinoblastoma-binding protein-related gene encoding an antigenic epitope abundantly expressed in human carcinomas and normal testis
.
J Natl Cancer Inst
2001
;
93
:
1159
65
.
26.
Cui
D
,
Jin
G
,
Gao
T
,
Sun
T
,
Tian
F
,
Estrada
GG
, et al
Characterization of BRCAA1 and its novel antigen epitope identification
.
Cancer Epidemiol Biomarkers Prev
2004
;
13
:
1136
45
.
27.
Kim
KH
,
Lee
K
,
Moon
YS
,
Sul
HS
. 
A cysteine-rich adipose tissue-specific secretory factor inhibits adipocyte differentiation
.
J Biol Chem
2001
;
276
:
11252
6
.
28.
White
RT
,
Damm
D
,
Hancock
N
,
Rosen
BS
,
Lowell
BB
,
Usher
P
, et al
Human adipsin is identical to complement factor D and is expressed at high levels in adipose tissue
.
J Biol Chem
1992
;
267
:
9210
3
.
29.
Hu
E
,
Liang
P
,
Spiegelman
BM
. 
AdipoQ is a novel adipose-specific gene dysregulated in obesity
.
J Biol Chem
1996
;
271
:
10697
703
.
30.
Peake
PW
,
Shen
Y
,
Campbell
LV
,
Charlesworth
JA
. 
Human adiponectin binds to bacterial lipopolysaccharide
.
Biochem Biophys Res Commun
2006
;
341
:
108
15
.
31.
Yang
RY
,
Hsu
DK
,
Yu
L
,
Chen
HY
,
Liu
FT
. 
Galectin-12 is required for adipogenic signaling and adipocyte differentiation
.
J Biol Chem
2004
;
279
:
29761
6
.
32.
Tang
X
,
Xing
Z
,
Tang
H
,
Liang
L
,
Zhao
M
. 
Human cell-death-inducing DFF45-like effector C induces apoptosis via caspase-8
.
Acta Biochim Biophys Sin
2011
;
43
:
779
86
.
33.
Cardamone
MD
,
Bardella
C
,
Gutierrez
A
,
Di Croce
L
,
Rosenfeld
MG
,
Di Renzo
MF
, et al
ERalpha as ligand-independent activator of CDH-1 regulates determination and maintenance of epithelial morphology in breast cancer cells
.
Proc Natl Acad Sci U S A
2009
;
106
:
7420
5
.
34.
Leunen
K
,
Gevaert
O
,
Daemen
A
,
Vanspauwen
V
,
Michils
G
,
De Moor
B
, et al
Recurrent copy number alterations in BRCA1-mutated ovarian tumors alter biological pathways
.
Hum Mutat
2009
;
30
:
1693
702
.
35.
Osipo
C
,
Gajdos
C
,
Liu
H
,
Chen
B
,
Jordan
VC
. 
Paradoxical action of fulvestrant in estradiol-induced regression of tamoxifen-stimulated breast cancer
.
J Natl Cancer Inst
2003
;
95
:
1597
608
.
36.
Liu
H
,
Lee
ES
,
Gajdos
C
,
Pearce
ST
,
Chen
B
,
Osipo
C
, et al
Apoptotic action of 17beta-estradiol in raloxifene-resistant MCF-7 cells in vitro and in vivo
.
J Natl Cancer Inst
2003
;
95
:
1586
97
.
37.
Lifsted
T
,
Le Voyer
T
,
Williams
M
,
Muller
W
,
Klein-Szanto
A
,
Buetow
KH
, et al
Identification of inbred mouse strains harboring genetic modifiers of mammary tumor age of onset and metastatic progression
.
Int J Cancer
1998
;
77
:
640
4
.
38.
Ma
L
,
Teruya-Feldstein
J
,
Weinberg
RA
. 
Tumour invasion and metastasis initiated by microRNA-10b in breast cancer
.
Nature
2007
;
449
:
682
8
.
39.
Jordan
VC
,
Brodie
AM
. 
Development and evolution of therapies targeted to the estrogen receptor for the treatment and prevention of breast cancer
.
Steroids
2007
;
72
:
7
25
.
40.
Osipo
C
,
Liu
H
,
Meeke
K
,
Jordan
VC
. 
The consequences of exhaustive antiestrogen therapy in breast cancer: estrogen-induced tumor cell death
.
Exp Biol Med (Maywood)
2004
;
229
:
722
31
.
41.
Jordan
VC
. 
Tamoxifen: a most unlikely pioneering medicine
.
Nat Rev Drug Discov
2003
;
2
:
205
13
.
42.
Haddow
A
,
Watkinson
JM
,
Paterson
E
,
Koller
PC
. 
Influence of synthetic oestrogens on advanced malignant disease
.
Br Med J
1944
;
2
:
393
8
.
43.
Lonning
PE
,
Taylor
PD
,
Anker
G
,
Iddon
J
,
Wie
L
,
Jorgensen
LM
, et al
High-dose estrogen treatment in postmenopausal breast cancer patients heavily exposed to endocrine therapy
.
Breast Cancer Res Treat
2001
;
67
:
111
6
.
44.
Ingle
JN
. 
Estrogen as therapy for breast cancer
.
Breast Cancer Res
2002
;
4
:
133
6
.
45.
Gu
P
,
Reid
JG
,
Gao
X
,
Shaw
CA
,
Creighton
C
,
Tran
PL
, et al
Novel microRNA candidates and miRNA-mRNA pairs in embryonic stem (ES) cells
.
PLoS One
2008
;
3
:
e2548
.
46.
Marson
A
,
Levine
SS
,
Cole
MF
,
Frampton
GM
,
Brambrink
T
,
Johnstone
S
, et al
Connecting microRNA genes to the core transcriptional regulatory circuitry of embryonic stem cells
.
Cell
2008
;
134
:
521
33
.
47.
Park
IH
,
Zhao
R
,
West
JA
,
Yabuuchi
A
,
Huo
H
,
Ince
TA
, et al
Reprogramming of human somatic cells to pluripotency with defined factors
.
Nature
2008
;
451
:
141
6
.
48.
Chen
X
,
Xu
H
,
Yuan
P
,
Fang
F
,
Huss
M
,
Vega
VB
, et al
Integration of external signaling pathways with the core transcriptional network in embryonic stem cells
.
Cell
2008
;
133
:
1106
17
.
49.
Li
H
,
Collado
M
,
Villasante
A
,
Strati
K
,
Ortega
S
,
Canamero
M
, et al
The Ink4/Arf locus is a barrier for iPS cell reprogramming
.
Nature
2009
;
460
:
1136
9
.
50.
O'Donnell
KA
,
Wentzel
EA
,
Zeller
KI
,
Dang
CV
,
Mendell
JT
. 
c-Myc-regulated microRNAs modulate E2F1 expression
.
Nature
2005
;
435
:
839
43
.