Original article

A. RYBICKA1, J. MUCHA1, K. MAJCHRZAK2, B. TACIAK1, E. HELLMEN3, T. MOTYL1, M. KROL1

ANALYSIS OF MICRORNA EXPRESSION IN CANINE MAMMARY CANCER STEM-LIKE CELLS INDICATES EPIGENETIC REGULATION OF TRANSFORMING GROWTH FACTOR-BETA SIGNALING

1Department of Physiological Sciences, Faculty of Veterinary Medicine, Warsaw University of Life Sciences, Warsaw, Poland; 2Department of Animal Environment Biology, Faculty of Animal Sciences, Warsaw University of Life Sciences, Warsaw, Poland; 3Swedish University of Agricultural Sciences, Department of Anatomy, Physiology and Biochemistry, Uppsala, Sweden
Cancer stem cells (CSCs) display both unique self-renewal ability as well as the ability to differentiate into many kinds of cancer cells. They are supposed to be responsible for cancer initiation, recurrence and drug resistance. Despite the fact that a variety of methods are currently employed in order to target CSCs, little is known about the regulation of their phenotype and biology by miRNAs. The aim of our study was to assess miRNA expression in canine mammary cancer stem-like cells (expressing stem cell antigen 1, Sca-1; CD44 and EpCAM) sorted from canine mammary tumour cell lines (CMT-U27, CMT-309 and P114). In order to prove their stem-like phenotype, we conducted a colony formation assay that confirmed their ability to form colonies from a single cell. Profiles of miRNA expression were investigated using Agilent custom-designed microarrays. The results were further validated by real-time rt-PCR analysis of expression of randomly selected miRNAs. Target genes were indicated and analysed using Kioto Encyclopedia of Genes and Genomes (KEGG) and BioCarta databases. The results revealed 24 down-regulated and nine up-regulated miRNAs in cancer stem-like cells compared to differentiated tumour cells. According to KEGG and BioCarta databases, target genes (n=240) of significantly down-regulated miRNAs were involved in transforming growth factor-beta signaling, mitogen-activated protein kinases (MAPK) signaling pathway, anaplastic lymphoma receptor tyrosine kinase (ALK) and peroxisome proliferator-activated receptor gamma, coactivator 1 alpha (PGC1A) pathways. The analysis of single-gene overlapping with different pathways showed that the most important genes were: TGFBR1, TGFBR2, SOS1, CHUK, PDGFRA, SMAD2, MEF2A, MEF2C and MEF2D. All of them are involved in tumor necrosis factor-beta signaling and may indicate its important role in cancer stem cell biology. Increased expression of TGFBR2, SMAD2, MEF2A and MEF2D in canine mammary cancer stem-like cells was further confirmed by real-time-qPCR. The results of our study point at epigenetic differences between cancer stem-like cells and differentiated tumour cells, which may be important not only for veterinary medicine but also for comparative oncology.
Key words:
cancer stem cells, microRNA, canine mammary cancer, transforming growth factor, tumor necrosis factor-beta

INTRODUCTION

Although the concept that cancer might arise from a population of stem-like cells was proposed about 150 years ago, only recent technological development has been able to provide evidence supporting this theory (1). Cancer stem cells (CSCs) have the ability to self-renew. They also frequently display resistance to chemotherapy. Due to their asymmetric divisions, drug efflux ability, immortality, and mesenchymal phenotype, they are thought to be responsible for cancer initiation, recurrence and metastasis (1-5). Therefore, the possibility of identifying and isolating these cells has provided a promising opportunity for novel therapeutic strategies that render the conventional cancer therapy just a tip of the iceberg of anti-cancer approach. Targeting crucial stem cell pathways including Wnt/β-catenin (6-8), Hedgehog (9) or Notch (10) as well as specific CSCs surface markers (11) enables CSCs to maintain their properties. However, the activity and high level of ATP-efflux pumps on CSCs play an important role in resistance to anticancer drugs and challenge effective chemotherapy (12). For this reason, CSCs are not only interesting for basic science but are also important for clinical practice. Recently, in addition to alterations in protein-coding genes, abnormalities in non-coding RNAs (e.g. miRNAs) have been shown to play an important role in the regulation of CSCs properties (13).

Thus, the aim of this study was to investigate miRNA expression profiles of canine mammary cancer stem-like cells and to compare these results to miRNA expression patterns observed in differentiated tumour cells. Using this approach, we examined the expression of all known canine miRNA, which enabled us to select a few miRNAs that may be responsible for maintaining CSCs phenotype, and then to find their target genes and indicate cellular pathways that may be crucial for this process.

Dogs serve as excellent models for human cancer studies as they naturally develop the same tumors as humans with an intact immune system and with a syngeneic host and tumor microenvironment (13). Moreover, both species share similar environmental, age, sex, and reproductive factors that lead to the development and progression of cancers (13). Due to a similar activity of the P450 cytochrome in dogs and humans, the canine model could be used in clinical trials of human anticancer drugs (13). Therefore, the presented results are important not only for veterinary medicine but also for comparative oncology.

MATERIALS AND METHODS

Cell lines, culture and co-culture conditions, cell sorting

Canine mammary tumour cell lines

Three mammary cancer cell lines were used in this study. Their isolation and characteristics were reported in previous manuscripts (14-16). All these cell lines were also described in our previous studies (17, 18). In brief, an anaplastic carcinoma cell line (P114) was kindly donated by Dr. Gerard Rutteman (Utrecht University, The Netherlands), a simple carcinoma cell line (CMT-U27) and a spindle cell mammary tumour cell line (CMT-U309) were established by Prof. Dr. Eva Hellmen (Swedish University of Agricultural Sciences, Sweden). Cells were cultured in RPMI 1640 medium supplemented with 10% (v/v) heat-inactivated foetal bovine serum, penicillin-streptomycin (50 IU/mL), and fungizone (2.5 mg/mL; Sigma Aldrich, USA) in an atmosphere of 5% CO2 and 95% humidified air at 37°C (17, 18). Using various canine mammary cancer cell lines originating from different tumour types, we were able to find miRNAs regulated in canine mammary cancer stem cells in general, but not in particular tumour types.

Staining and fluorescence-activated cell sorting (FACS) analysis and sorting

The rat anti-mouse Ly-6A/E allelic members of the Ly6 multigene family (Sca-1) FITC-conjugated antibody (BD Bioscience, USA) was used at the concentration of 1:100 in order to stain stem-like cells in the neoplastic cells lines. Additionally, propidium iodide (PI) solution (Sigma-Aldrich) was added to stain and exclude necrotic or apoptotic cells. Cells were analysed and sorted using FACSAria II (BD Bioscience) into two tubes: Sca-1pos/PIneg and Sca-1neg/PIneg. FACS sorting isolated a 97–99% pure population on the post-sort, as assessed using BD FACS Diva 5.0 software.

Colony formation assay

The colony formation assay was conducted to examine the ability of a single cell to form a colony (19). Sca-1pos and Sca-1neg neoplastic cells from each cell line were seeded onto 24-well plate (BD Biosciences) at a concentration of 300 cells per well in 500 µl of cell culture medium. The growth pattern of these cells was observed every day under Olympus BX60 microscope (Olympus, Germany) until Sca-1pos colonies consisted of minimum 50 cells (usually after 5 days). Then, the plates were fixed with 96% ethanol and stained with 0.5% crystal violet solution in 25% ethanol. The number of colonies was counted using Image J software. Additionally, the same number of Sca-1 positive cells from each cell line was seeded on a 6-well plate and harvested by trypsinization and FACS analysed after 7 and 14 days to estimate the percentage of remaining Sca-1pos cells in the culture.

MiRNA microarray analysis

Total-RNA (t-RNA) was isolated from the samples using a MicroRNA kit (A&A Biotechnology, Poland) according to the manufacturer's protocol. The quantity of t-RNA was measured using a NanoDrop instrument (NanoDrop Technologies, USA), and the final RNA quality and integrity were assessed using a BioAnalyzer (Agilent, USA). Only high-quality samples (RIN=10) were used in further analyses.

A microRNA Labeling & Hybridization Kit (Agilent) was used to amplify and label mature miRNAs for microarray experiments. The canine miRNA microarray containing 323 miRNA sequences was custom designed using an eArray Agilent online service. All sequences from miRBase 17.0 were included. This microarray platform has been deposited in NCBI's Gene Expression Omnibus and is accessible via the GEO Series accession number GPL16580.

The miRNA expression in Sca-1pos cells was compared with that in Sca-1neg. The acquisition and analysis of hybridization intensities were performed by means of a microarray scanner (Agilent), and data were extracted using Agilent's Feature Extraction software with normalization and robust statistical analyses.

Biostatistical analysis

Statistical analyses were performed using Future Extraction and Gene Spring software (Agilent). Unpaired t-tests with a Benjamin-Hochberg false discovery rate (FDR) of <5% correction were applied, with a P value cut-off of <0.05. Only miRNAs that differed significantly in all Sca-1pos samples (in all cell lines) were analysed. Significant miRNAs were selected from eight technical and three biological repetitions (three cell lines). Areas of these analyses have been deposited in NCBI's Gene Expression Omnibus and are accessible using the GEO Series accession number GSE45622.

Genes targeted by significantly regulated miRNAs were analysed using miRBase, and corresponding signaling pathways were identified using Molecular Signature Database v. 4.0 (20). We computed KEGG and BioCarta gene sets and pathways overlaps (vs. Human Tissue Compendium provided by Novartis and vs. Global Cancer Map provided by Broad Institute). The significantly overrepresented pathways were selected on the basis of P value <0.05 and FDR <5%.

Real-time RT-PCR

Real-time rt-PCR analyses were used to validate microarray data. Total RNA was reverse transcribed using Universal cDNA synthesis kit II (Exiqon, USA) and cDNA was synthesized. Quantitative RT-PCR was performed using a fluorogenic SYBR Green Master Mix kit (Exiqon) and the Mx3005P QPCR System (Strategene). Commercially available microRNA LNA PCR primer sets (Exiqon) were used to amplify randomly selected miR-27a, miR-197 and miR-370. Data were expressed relative to spike-in controls and analysed using the comparative Ct method. (21). The experiment was repeated three times.

In order to confirm the data obtained from KEGG and Biocarta and to investigate whether Sca-1pos cells express also other stem-cell markers, real-time rt-PCR analysis was conducted. Sequences of key genes were obtained from the NCBI database. Primers were designed using Primer3 software (free online access) and were checked using Oligo Calculator (free online access) and Primer-Blast (NCBI database). Primer sequences are listed in Table 1. The housekeeping gene Rps19 was used as internal control (22, 23). Quantitative RT-PCR was performed using a fluorogenic Lightcycler Fast Strand DNA SYBR Green kit (Roche) and a Light Cycler (Roche). Data were analysed using the comparative Ct method (23). The experiment was repeated five times.

Table 1. Primer sequences used in this study and their annealing optimal temperature and time. The mRNA sequences of the key genes were obtained from NCBI database. Primers were designed with PRIMER3 software (free on-line access) and checked using Oligo Calculator (free on-line access) and Primer-Blast (NCBI database).
Table 1

Statistical analysis

Statistical analyses were performed using Prism version 5.00 software (GraphPad Software, USA). Two-way analysis of variance (ANOVA), ANOVA with Tukey's honestly significant difference post-hoc test, t-test, and Spearman's correlation were applied, and the differences were considered significant when

P <0.05 or highly significant when P <0.01 or P <0.001.

RESULTS

Number of cancer stem-like cells within population of canine mammary neoplastic cell lines

Cancer stem-like cells (Sca-1pos) constituted between 0.2% and 1.2% of the whole population of canine mammary neoplastic cell lines. After seven days the FACS sort (98–99% of sort purity confirmed on post-sort by FACS Diva) was followed by sorted cells culture, giving the percentage of Sca-1pos cells between 28.1% and 56.3% in all examined cell lines whereas after 14 days this number decreased to 0.4–2.2% (Fig. 1A).

Figure 1
Fig. 1. Representative histograms showing the percentage of cancer stem-like cells (Sca-1pos) of P114 cell line sorted on the day of staining and 7 and 14 days after (A). Graphs show the expression of EpCAM and CD44 in Sca-1pos cells sorted from CMT-U27, CMT-309 and P114 cell lines compared to differentiated cells examined using real-time rt-PCR (B). Representative images of CMT-U27 Sca-1pos and Sca-1neg in colony formation assay (×10) (C). Representative pictures showing colonies of CMT-U27 Sca-1pos cells and monolayer of Sca-1neg cells in colony formation assay. Cells were stained with crystal violet (×10). On the graph, the number of colonies formed by Sca-1pos CMT-U27, CMT-309, P114 cells compared to differentiated cells in colony formation assay (CFA) is presented (D).

Real-time RT-PCR analysis showed significantly higher expression of EPCAM gene in Sca-1pos cells sorted from CMT-U27 (P <0.001), CMT-U309 (P <0.01) and P114 (P <0.05) cell lines compared to differentiated cells (Fig. 1B). Similarly, we observed a significantly higher expression of the other stem-cell marker CD44 in Sca-1pos cells sorted from the CMT-U27 (P <0.05), CMT-U309 (P <0.001) and P114 (P <0.01) cell lines, compared with Sca-1neg cells (Fig. 1B).

Canine mammary cancer stem-like cells show the ability to form colonies

Colony formation assay confirmed that only Sca-1pos cells were able to form colonies from a single cell in CMT-U27, CMT-U309 and P114 lines (Fig. 1C and 1D). These colonies consisted of minimum 50 cells and demonstrated undifferentiated morphology. The number of colonies in P114 and CMT-U309 was 10–13 per well in 24-well plate. However, in CMT-U27 we observed approximately 212 colonies per well (Fig. 1D). Sca-1neg cells formed a uniform layer in each cell line (Fig. 1D).

miRNA expression in canine mammary cancer stem-like cells and differentiated neoplastic cells

Microarray experiment revealed 33 significantly deregulated miRNAs in cancer stem-like cells compared to differentiated tumour cells. Twenty-four of them were down-regulated whereas nine were up-regulated (Table 2). Out of these, miR-451 was the most significantly up-regulated and miR-135b most significantly down-regulated.

Table 2. The list of 33 miRNAs significantly deregulated in canine mammary cancer stem-like cells compared to differentiated tumour cells in CMT-U27, CMT-309 and P114 cell lines. The table is based on microarray data and miRBase. The area of the analyses covered in this publication has been deposited in NCBI's Gene Expression Omnibus and is accessible via GEO Series accession number GSE45622.
Table 2

For the purposes of validation of microarray experiment, we randomly selected three miRNAs (miR-27a, miR-197 and miR-370) down-regulated in Sca-1pos cells (compared with Sca-1neg) and confirmed their expression using real-time-qPCR. We observed 100-fold lower (P <0.01) miR-27a expression in Sca-1pos cells than in Sca-1neg cells in CMT-309 cell line, 333-fold lower (P <0.001) in P114, and 1.4-fold lower (P <0.01) in CMT-U27 cell line. Similar results were observed in miR-197 with 203-fold (P <0.01), 200-fold (P <0.001) and 66-fold (P <0.01) lower expression in Sca-1pos CMT-309, P114 and CMT-U27 cells, respectively, compared with Sca-1neg cells. Moreover, miR-370 expression was 6-fold (P <0.01), 3.28-fold (P <0.01) and 2.5-fold (P <0.001) lower in Sca-1pos cells compared with Sca-1neg in CMT-U309, P114 and CMT-U27 cell lines, respectively.

Using miRBase, we found target genes for significantly dysregulated miRNAs (Table 2). In addition, we graded these genes by the number of regulating miRNAs. We selected 240 target genes of down-regulated miRNAs (these genes were targeted by at least 8 miRNAs) and 81 target genes targeted by up-regulated miRNAs (these genes were targeted by at least 4 miRNAs). Then, using Gene Set Enrichment Analysis (GSEA), we analysed the over-representation of the pathways in which target genes are involved (Fig. 2 and 3). The analysis showed that the genes targeted by down-regulated miRNAs were mainly involved in MAPK signaling pathway (KEGG and BioCarta databases). According to KEGG database, these genes were mainly involved in chronic myeloid leukemia pathway and TGF-beta signaling pathway (Fig. 2A). According to BioCarta database, they were also involved in ALK, p38 MAPK and PGC1A pathways (Fig. 2B). Genes targeted by up-regulated miRNAs were not involved in common pathways. However, the analysis of single-gene overlapping with different pathways showed that according to KEGG database, the most important regulated genes were: TGFBR1, TGFBR2, SOS1, CHUK and PDGFRA. According to BioCarta, however, the most important genes were: MEF2C, TGFBR1, MEF2D and MEF2A (Fig. 3).

Figure 2 Fig. 2. List of cellular pathways regulated by genes targeted by down-regulated miRNAs in canine mammary cancer stem cell-like cells according to KEGG database (A) and BioCarta database (B).
Figure 3
Fig. 3. List of genes involved in TGF-beta signaling that are targeted by miRNAs down-regulated in canine mammary cancer stem-like cells according to KEGG and BioCarta database.

Canine mammary cancer stem-like cells reveal higher expression of selected TGF-beta pathway genes

RT-qPCR analysis confirmed the results obtained from KEGG and BioCarta databases. A significantly higher expression of TGFBR2, SMAD2, MEF2A and MEF2D has been shown in Sca-1pos compared to Sca-1neg cells in all three cell lines (Table 3). We observed 2.2-fold (P <0.001), 3.9-fold (P <0.01) and 6.2-fold (P <0.01) higher expression of TGFBR2 in Sca-1pos cells sorted from CMT-U27, CMT-309 and P114 cell lines, respectively, compared to Sca-1neg cells. A similar trend was observed in SMAD2 expression with 8.29-fold (P <0.01), 1.5-fold (P <0.05) and 40.6-fold (P <0.05) higher expression in Sca-1ppos cells in CMT-U27, CMT-U309 and P114 cell lines, respectively, compared to Sca-1neg cell lines. In Sca-1pos cells sorted from CMT-U27, CMT-U309 and P114 cell lines the expression of MEF2A was higher 14.23-fold (P <0.01), 1.2-fold (P <0.05) and 11.17-fold (P <0.05), respectively, compared to Sca-1neg cells. In Sca-1pos cells, compared to Sca-1neg cells, the expression of MEF2D was 20.21-fold (P <0.01), 1.56-fold (P <0.01) and 2-fold (P <0.05) higher in CMT-U27, CMT-309 and P114 cell lines, respectively (Fig. 4).

Figure 4 Fig. 4. Graphs showing the expression of TGFBR2, SMAD2, MEF2A and MEF2D genes in Sca-1pos and Sca-1neg cells sorted from CMT-U27, CMT-309 and P114 cell lines. The expression was examined using real-time rt-PCR, followed by ΔΔCt analysis and GraphPad statistical analysis. Values that differed significantly (P <0.05) are marked as *, values whose difference was highly significant with P <0.01 and P <0.001 are marked with ** and ***, respectively.

DISCUSSION

A small population of cells, called cancer stem cells, is responsible for tumour chemo resistance, recurrence and metastasis. Therefore, novel anti-cancer therapy often aims to target these cells. A substantial number of studies describe novel compounds used in experimental anti-cancer strategies. According to Skidan and Steiniger these anti-CSCs drugs can be divided into three groups: chemical compounds, small molecules targeting CSCs and therapeutic biologics including microRNA-based therapeutics (24). However, prior to this, finding specific CSCs markers is crucial for a better characterisation and isolation of these cells. Despite the fact that a variety of markers are used, the most "universal" ones expressed by tumors of different histological types are: Sca-1, CD24, CD44, CD133, CD166, EpCAM, and various integrins (25). These proteins enrich murine stem/progenitor cell activity both in vitro in sphere-forming analysis and in vivo assays (26).

So far only a few studies of canine mammary cancer stem cells have been published and in each of them different surface markers have been used (27-30). One study showed that a culture of Sca-1pos cells enriched side-population (SP) within the mammary cancer cell line. Moreover, these cells expressed other stem cell-specific surface-markers: CD44, CD49f, Sox2, and Oct4 (29). Since suitable markers for canine stem-like cancer cells have not yet been established, we used anti-Sca-1 antibody for FACS sorting and we further showed the expression of other stem-cell markers, that is EpCAM and CD44, in these cells.

Cancer stem cells generally constitute just a small fraction of a tumour mass. For example in murine Brca1 mammary tumour cell lines, the fraction of CD44+/CD24 cells constituted about 1.23% to 5% of all cells (31). In breast cancer, the population of ESA+/CD44+/CD24/Lineage- cells accounted for 2–4% of all cells (32) or, if distinguished according to various origin of the cells, 0.02% to 0.5% ESA+/CD44+/CD24 cells in luminal and 2.5% in basal breast cancer cell lines (33). These findings are in accordance with our study. We found only 0.2–1.2% cancer stem-like cells in the whole population of canine mammary neoplastic cell lines. The ability of these cells to self-renewal was proved in colony formation assay (CFA). Our results in CMT-U309 and P114 cell lines were similar to those described by Fillmore and Kupperweisser (33) in human breast cancer stem cell lines (CD44+/CD24/ESA+). The stem-like cells sorted out from CMT-U27 cell line had the highest clonogenic capacity. However, we also found out that after 14 days of culture of Sca1pos cells they differentiated into tumour cells and the percentage of cancer stem-like cells in the whole population was similar to the one before the sort.

The utility of miRNAs as possible CSCs targets in therapy is still a matter of discussion and needs further investigation. So far, only several original studies have been performed to reveal the role of miRNAs in CSCs development and maintenance (13, 34-36).

Despite the fact that only a small number of studies about the epigenetic regulation of cancer stem cells have been published, a few of the miRNAs that significantly differed in expression pattern between Sca-1pos and Sca-1neg canine mammary cancer cells have been reported by other investigators. So far, only let-7, miRNA-128 and miRNA-27a have been described as involved in breast cancer stem-like cells maintenance (36-41). Yu et al. have shown that down-regulation of let-7 molecules, a family of miRNAs involved in cell differentiation mainly by overexpression of RAS and HMGA2 genes, is important for self-renewal and mammosphere formation of breast cancer stem cells. A transfection of let-7 into those cells significantly reduced tumour formation and metastasis in NOD-SCID mice (36). Likewise, Zhu at al. have pointed out at a significant down-regulation of miRNA-128 in breast cancer stem cells. A reduced expression of miRNA-128 in patients was correlated with poor survival due to a higher resistance to chemotherapy via Bmi-1 and ABCC5 overexpression. Transduction of lenti-miRNA-128 increased DNA-damaging and pro-apoptotic effect of doxorubicin (37). Recent paper by Tang et al. has shown an increased expression of miRNA-27a and a reduced level of its target gene ZBTB10 in breast cancer stem cells, which is contrary to our results. They demonstrated that up-regulated miRNA27a increased tumourigenesis, angiogenesis and metastasis of breast cancer stem cells in NOD-SCID mice (39).

It seems that in order to find a more "universal" miRNA set that could characterize mammary cancer stem cells, more studies in this field using different cell lines and models are required. The regulation of TGF-beta signaling by miRNA is nowadays extensively investigated in many pathologic stages, including cancer (42, 43). Some miRNAs have been pointed so far as regulators of TGF-beta pathway in cancer stem-like cells (44). For instance, Wang et al. discovered miRNA-181 as the one involved in TGF-beta signaling in breast cancer stem-like cells (45). Our study has shown that in TGF-beta signaling the following miRNAs are mainly involved: let-7 family, miRNA-27a, miRNA23a, miRNA-128, miRNA-106a and miRNA144.

Our data followed by the KEGG database analysis of genes targeted by down-regulated miRNAs show major implications for TGF-beta signaling pathway (TGFBR1, TGFBR2, SOS1, CHUK, PDGFRA, MEF2A, MEF2C and MEF2D). Our real-time-qPCR analysis confirmed a significantly higher expression of selected genes involved in this pathway in cancer-stem like cells compared to differentiated cancer cells from the same cell lines. This finding is in accordance with previous publications which have revealed that TGF-beta is involved in the maintenance of stem cells-properties of embryonic stem cells and the inhibition of its members results in a decreased expression of stemness markers (46). Similarly, our results suggest that TGF-beta might also be involved in the maintenance of stem cell properties of canine mammary cancer stem cells and it may support tumorigenesis. Recently, it has also been shown that TGF-beta signaling induced by paclitaxel, commonly used in triple negative breast cancer, increases cancer stem cells population and enhances tumour recurrence. The inhibition of TGF-beta in mice treated with paclitaxel results in a slower tumour growth compared to the control group treated only with paclitaxel (47). Beyond doubt, the above findings together with the results of our study point to the important role of TGF-beta in cancer stem-cell biology and set it up as a crucial target in further therapy.

Acknowledgements: The authors would like to thank Dr. Alicja Majewska for her excellent technical support. This work was supported by the grant no. 2011/03/B/NZ5/05299 from National Science Centre (NCN) and by COST Action CM1106.

Conflict of interests: None declared.

REFERENCES

  1. Dalerba P, Cho RW, Clarke MF. Cancer stem cells: models and concepts. Annu Rev Med 2007; 58: 267-284.
  2. Tang DG. Understanding cancer stem cell heterogeneity and plasticity. Cell Res 2012; 22: 457-472.
  3. Grange C, Lanzardo S, Cavallo F, Camussi G, Bussolati B. Sca-1 identifies the tumor-initiating cells in mammary tumors of BALB-neuT transgenic mice. Neoplasia 2008; 10: 1433-1443.
  4. Shafee N, Smith CR, Wei S, et al. Cancer stem cells contribute to cisplatin resistance in Brca1/p53-mediated mouse mammary tumors. Cancer Res 2008; 68: 3243-3250.
  5. Soltysik K, Czekaj P. Membrane estrogen receptors - is it an alternative way of estrogen action? J Physiol Pharmacol 2013; 64: 129-142.
  6. Lamb R, Ablett MP, Spence K, Landberg G, Sims AH, Clarke RB. Wnt pathway activity in breast cancer sub-types and stem-like cells. PLoS One 2013; 8: e67811.
  7. Heidel FH, Bullinger L, Feng Z, et al. Genetic and pharmacologic inhibition of beta-catenin targets imatinib-resistant leukemia stem cells in CML. Cell Stem Cell 2012; 10: 412-424.
  8. Korbut E, Ptak-Belowska A, Brzozowski T. Mechanisms promoting physiological cells progression into tumorigenesis. J Physiol Pharmacol 2012; 63: 565-570.
  9. Liu S, Dontu G, Mantle ID, et al. Hedgehog signaling and Bmi-1 regulate self-renewal of normal and malignant human mammary stem cells. Cancer Res 2006; 66: 6063-6071.
  10. Harrison H, Farnie G, Howell SJ, et al. Regulation of breast cancer stem cell activity by signaling through the Notch4 receptor. Cancer Res 2010; 70: 709-718.
  11. Sachlos E, Risueno RM, Laronde S, et al. Identification of drugs including a dopamine receptor antagonist that selectively target cancer stem cells. Cell 2012; 149: 1284-1297.
  12. Chuthapisith S, Eremin J, El-Sheemey M, Eremin O. Breast cancer chemoresistance: emerging importance of cancer stem cells. Surg Oncol 2010; 19: 27-32.
  13. Barh D, Malhotra R, Ravi B, Sindhurani P. MicroRNA let-7: an emerging next-generation cancer therapeutic. Curr Oncol 2010; 17: 70-80.
  14. Hellmen E. Characterization of four in vitro established canine mammary carcinoma and one atypical benign mixed tumor cell lines. in vitro Cell Dev Biol 1992; 28A: 309-319.
  15. Hellmen E, Moller M, Blankenstein MA, Andersson L, Westermark B. Expression of different phenotypes in cell lines from canine mammary spindle-cell tumours and osteosarcomas indicating a pluripotent mammary stem cell origin. Breast Cancer Res Treat 2000; 61: 197-210.
  16. Van Leeuwen IS, Hellmen E, Cornelisse CJ, Van den Burgh B, Rutteman GR. P53 mutations in mammary tumor cell lines and corresponding tumor tissues in the dog. Anticancer Res 1996; 16: 3737-3744.
  17. Krol M, Pawlowski KM, Skierski J, et al. Transcriptomic "portraits" of canine mammary cancer cell lines with various phenotypes. J Appl Genet 2010; 51: 169-183.
  18. Krol M, Mucha J, Majchrzak K, et al. Macrophages mediate a switch between canonical and non-canonical Wnt pathways in canine mammary tumors. PLoS One 2014; 9: e83995.
  19. Franken NA, Rodermond HM, Stap J, Haveman J, van Bree C. Clonogenic assay of cells in vitro. Nat Protoc 2006; 1: 2315-2319.
  20. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 2005; 102: 15545-15550.
  21. Schmittgen TD, Livak KJ. Analyzing real-time PCR data by the comparative C(T) method. Nat Protoc 2008; 3: 1101-1108.
  22. Brinkhof B, Spee B, Rothuizen J, Penning LC. Development and evaluation of canine reference genes for accurate quantification of gene expression. Anal Biochem 2006; 356: 36-43.
  23. Etschmann B, Wilcken B, Stoevesand K, von der Schulenburg A, Sterner-Kock A. Selection of reference genes for quantitative real-time PCR analysis in canine mammary tumors using the GeNorm algorithm. Vet Pathol 2006; 43: 934-942.
  24. Skidan I, Steiniger SC. in vivo models for cancer stem cell research: a practical guide for frequently used animal models and available biomarkers. J Physiol Pharmacol 2014; 65: 157-169.
  25. Marhaba R, Klingbeil P, Nuebel T, Nazarenko I, Buechler MW, Zoeller M. CD44 and EpCAM: cancer-initiating cell markers. Curr Mol Med 2008; 8: 784-804.
  26. Mulholland DJ, Xin L, Morim A, Lawson D, Witte O, Wu H. Lin-Sca-1+CD49fhigh stem/progenitors are tumor-initiating cells in the Pten-null prostate cancer model. Cancer Res 2009; 69: 8555-8562.
  27. Ferletta MJ, Grawe J, Hellmen E. Canine mammary tumors contain cancer stem-like cells and form spheroids with an embryonic stem cell signature. Int J Dev Biol 2011; 55: 791-799.
  28. Pang LY, Cervantes-Arias A, Else RW, Argyle DJ. Canine mammary cancer stem cells are radio- and chemo- resistant and exhibit an epithelial-mesenchymal transition phenotype. Cancers (Basel) 2011; 3: 1744-1762.
  29. Magalhaes GM, Terra EM, de Oliveira Vasconcelos R, et al. Immunodetection of cells with a CD44+/CD24 phenotype in canine mammary neoplasms. BMC Vet Res 2013; 9: 205.
  30. Michishita M, Akiyoshi R, Suemizu H, et al. Aldehyde dehydrogenase activity in cancer stem cells from canine mammary carcinoma cell lines. Vet J 2012; 193: 508-513.
  31. Wright MH, Calcagno AM, Salcido CD, Carlson MD, Ambudkar SV, Varticovski L Brca1 breast tumors contain distinct CD44+/CD24 and CD133+ cells with cancer stem cell characteristics. Breast Cancer Res 2008; 10: R10.
  32. Al-Hajj M, Wicha MS, Benito-Hernandez A, Morrison SJ, Clarke MF. Prospective identification of tumorigenic breast cancer cells. Proc Natl Acad Sci USA 2003; 100: 3983-3988.
  33. Fillmore CM, Kuperwasser C. Human breast cancer cell lines contain stem-like cells that self-renew, give rise to phenotypically diverse progeny and survive chemotherapy. Breast Cancer Res 2008; 10: R25.
  34. Schwarzenbacher D, Balic M, Pichler M. The role of microRNAs in breast cancer stem cells. Int J Mol Sci 2013; 14: 14712-1423.
  35. Yu F, Deng H, Yao H, Liu Q, Su F, Song E. Mir-30 reduction maintains self-renewal and inhibits apoptosis in breast tumor-initiating cells. Oncogene 2010; 29: 4194-4204.
  36. Yu F, Yao H, Zhu P, et al. let-7 regulates self renewal and tumorigenicity of breast cancer cells. Cell 2007; 131: 1109-1123.
  37. Zhu Y, Yu F, Jiao Y, et al. Reduced miR-128 in breast tumor-initiating cells induces chemotherapeutic resistance via Bmi-1 and ABCC5. Clin Cancer Res 2011; 17: 7105-7115.
  38. Ma L. Role of miR-10b in breast cancer metastasis. Breast Cancer Res 2010; 12: 210.
  39. Tang W, Yu FF, Yao H, et al. miR-27a regulates endothelial differentiation of breast cancer stem like cells. Oncogene 2014; 33: 2629-2638.
  40. Hu Y, Zhu Q, Tang L. MiR-99a antitumor activity in human breast cancer cells through targeting of mTOR expression. PLoS One 2014; 9: e92099.
  41. Chiang CH, Hou MF, Hung WC. Up-regulation of miR-182 by beta-catenin in breast cancer increases tumorigenicity and invasiveness by targeting the matrix metalloproteinase inhibitor RECK. Biochim Biophys Acta 2013; 1830: 3067-3076.
  42. Butz H, Racz K, Hunyady L, Patocs A. Crosstalk between TGF-beta signaling and the microRNA machinery. Trends Pharmacol Sci 2012; 33: 382-393.
  43. Masri S, Liu Z, Phung S, Wang E, Yuan YC, Chen S. The role of microRNA-128a in regulating TGFbeta signaling in letrozole-resistant breast cancer cells. Breast Cancer Res Treat 2010; 124: 89-99.
  44. Gal H, Pandi G, Kanner AA, et al. MIR-451 and Imatinib mesylate inhibit tumor growth of glioblastoma stem cells. Biochem Biophys Res Commun 2008; 376: 86-90.
  45. Wang Y, Yu Y, Tsuyada A, et al. Transforming growth factor-beta regulates the sphere-initiating stem cell-like feature in breast cancer through miRNA-181 and ATM. Oncogene 2011; 30: 1470-1480.
  46. Watabe T. Miyazono K. Roles of TGF-beta family signaling in stem cell renewal and differentiation. Cell Res 2009; 19: 103-115.
  47. Bhola NE, Balko JM, Dugger TC, et al. TGF-beta inhibition enhances chemotherapy action against triple-negative breast cancer. J Clin Invest 2013; 123: 1348-1358.
R e c e i v e d : August 20, 2014
A c c e p t e d : November 25, 2014
Author’s address: Prof. Magdalena Krol, Department of Physiological Sciences, Faculty of Veterinary Medicine, Warsaw University of Life Sciences, 159 Nowoursynowska Street, Warsaw, Poland e-mail: magdalena_krol@sggw.pl