Background
Epigenetic modifications involve genomic methylation changes and the alteration of chromatin-associated proteins such as linker histones, polycomb groups, nuclear scaffold proteins and transcription factors, (reviewed in [
1]). Epigenetic abnormalities contribute in several ways to oncogenesis and may activate oncogenes or silence tumor suppressor genes. In addition, epigenetic processes can enhance chromosomal instability [
2], (reviewed in [
3]), and have recently been shown to be involved in the regulation of the DNA double-strand break and repair process [
4], (reviewed in [
5]). Typically, a general pattern of demethylation of the genome is observed in tumor DNA, while increased methylation of a subset of promoter-associated CpG islands associated with the transcriptional start sites (TSS) of genes may also be observed. Abnormal methylation of genes is far more frequent than classical genetic mutation.
There is increasing interest in the therapeutic modulation of such processes, since epigenetic alterations are amenable to physiological alteration by drugs that change patterns of DNA methylation or histone acetylation, (reviewed in [
6]). The most powerful DNA methyltransferase inhibitor in clinical use is 5-aza-2'-deoxycytidine (decitabine). Decitabine is a cytosine analog that inhibits DNA methylation and reactivates silenced genes. Decitabine has shown promising clinical efficacy in the treatment of myelodysplastic syndromes, with evidence of gene target expression modulation by demethylation with less toxicity than conventional cancer chemotherapies [
7,
8]. Studies regarding the implications of epigenetic modification in osteosarcoma (OS) have been limited, but have suggested a role in bone differentiation [
9‐
11], transcription factor expression, and histone modifications [
12,
13]. No study to date has utilized decitabine to modify gene expression in an OS-derived cell to identify gene-specific targets for demethylation that may have therapeutic importance.
A full assessment of tumor cell response to treatment requires integrating experimental data from both
in vitro and
in vivo observations. One major advantage of preclinical animal models of xenografts of human tumor cell lines is that they provide both tissue vascularization and a tumor microenvironment that is closer to human tumors so that an evaluation of the therapeutic impact on tissue differentiation, cell growth and proliferation levels is possible [
14], (reviewed in [
15]). Such analyses are providing opportunities for a detailed assessment of new classes of anti-neoplastic drugs that target the epigenome, such as decitabine.
Discussion
This study draws attention to the possibility that therapeutic levels of decitabine could orchestrate the interplay between DNA damage genes, induce growth arrest, apoptosis and potentially modulate genomic fidelity. At present, neoadjuvant and adjuvant chemotherapy is favored in the treatment of OS and the agents most commonly used include doxorubicin, high-dose methotrexate, cis-platinum and ifosfamide either alone or with etoposide. The use of these agents in OS treatment has been well established and yielded 5-year disease-free-survival and overall-survival of greater than 60%, (reviewed in [
21]). However, the lack of a near-complete response to chemotherapy in a sub-group of patients reflects inherent biologic resistance to these agents, hence poorer prognosis [
22], especially since attempts at changing chemotherapy regimens for poor responders have generally not improved outcome [
23‐
25]. In recent years, another approach that has been used in other tumors is targeting the epigenome of tumor cells. The most promising of which is the re-activation of epigenetically silenced genes, using DNA methylation inhibitors or histone deacetylase inhibitors (HDAC) [
26‐
28].
Previous studies have implicated a role of epigenetics in OS biology; Methylation of osteocalcin has been linked to bone differentiation [
9‐
11], transcription factor expression, and histone modifications [
12,
13]. Abnormal promoter methylation of p16INK4a/p14ARF promoters was observed in OS-derived cell lines [
29]. Aberrant methylation of specific genes was also correlated with poor survival in OS patients [
30,
31]. The number of genes found methylated in OS is increasing [
32,
33] which further supports the implication of DNA methylation in OS tumorigenesis.
Up-to-date studies regarding the epigenetics OS have either been based on a single gene or focused on a small number of genes, and limited with respect to elucidating the target pathways suitable for epigenetic therapeutics in OS. Our study is the first to use demethylation treatment to modify global gene expression in an OS cell line in order to identify pathway-specific methylation targets that may have therapeutic importance. Analysis of decitabine-induced cellular changes in U2OS xenografts suggested that apoptotic pathways may be the earliest pathways to be affected. The decitabine dose was based on previous studies [
34‐
36] and has been shown to reduce the methylation of tumor suppressor genes and decrease tumor growth in xenografts. The xenografts in decitabine-treated mice decreased in volume size significantly (p < 0.05) when compared to the xenografts in untreated control mice. Recent reports showed similar effects of xenograft size and growth parameters from other tumor types grown in mice treated with decitabine [
37,
38].
The effect of decitabine on tumor cell mitotic index, apoptosis and extracellular matrix (osteoid) formation in OS has not been previously reported. In our series, decitabine treatment significantly decreased the number of mitoses and increased the number of apoptotic cells. In addition, treatment with decitabine significantly increased the amount of osteoid associated with the tumors. Such results suggest that decitabine treatment reduces the proliferative capacity of U2OS cells, whilst concurrently driving the cells toward terminal differentiation and apoptosis.
Gene expression profiling by microarray analysis showed that 88 genes showed increased expression
in vitro after decitabine treatment, which represented 0.6% out of the 14500 genes on the array and this global modulation of genes is at a level comparable to other studies. The effect of decitabine on global gene expression has been previously reported in several studies [
39‐
41]; comparable to our study, the number of up-regulated genes varied from 1.9% in ovarian cancer cells [
40] to 1.1% in malignant glioma cells [
41] and 0.6% in bladder cancer cells [
39]. The differences in the number of decitabine up-regulated genes were possibly a result of cell line-to-cell line variations, microarray platforms, and experimental designs including differences in drug dosage and duration of treatment. Indeed, Karpf et al [
42] screened the expression of approximately 38,000 human transcripts in several decitabine treated cell types and observed changes in genes expression for 0.2 to 1.4% of those transcripts, depending on the cell type treated [
42] and (reviewed in, [
43]). Further investigation of several OS cell lines will further assist to more accurately define the level of global change of expression induced by decitabine using the same experiment conditions and microarray platform.
The global expression profiling of U2OS represents the majority of cells that were viable at the time of harvest. These cells were undergoing fine molecular modulations of their epigenome eventually leading to changes in gene expression resulting in the observed cell death and apoptosis
in vitro and
in vivo. Decitabine treatment in U2OS induced a level of cell death that was comparable to observations in acute myeloid leukemia (AML) cells using similar treatment regimens
in vitro [
44]. In this study differentiation activation was considered to be an early effect of decitabine in AML. The genes identified in U2OS with a role in apoptosis were
GADD45A,
HSPA9B,
PAWR,
PDCD5,
NFKBIA, and TNFAIP3. These proteins have potential roles in regulating a number of key apoptotic events including the p53 related apoptosis, bcl2 related apoptosis, and the nfkb related apoptosis [
40‐
50]. While the exact function of
GADD45A is not known, the protein, like p53, is considered to be involved in cell growth control, maintenance of genomic stability, DNA repair, cell cycle control, and apoptosis. Interestingly, Gadd45a has recently been shown to promote epigenetic gene activation by repair-mediated DNA demethylation [
45].
The findings of this study suggest that the cellular effects detected are a result of up-regulation of apoptotic genes. These data are in general agreement with the increasing evidence that decitabine's antineoplastic effects may be through modulation of apoptotic pathways [
46,
47]. Our data also demonstrate that the re-activation of genes involves CpG-island demethylation. Dinucleotide clusters of CpGs in CG-rich regions of genomes or CpG-islands are present in the promoters and exonic regions of at least 40% of mammalian genes some reports, however, other reports indicate that up to 70% of mammalian genes have CpG islands in their promoter [
48,
49]. Methylation of promoter associated CpG islands in the genome of cancer cells has shown non-random and tumor-type-specific patterns [
50]. While some tumors exhibit hypermethylation of low number of specific CpG islands, other tumors possess hypermethylation of a higher number CpG island associated promoters [
50]. It is important, though, to realize that not all genes with methylated CpG islands are re-activated by decitabine treatment possibly because some methylated CpG islands may have other chromatin structural alterations that are dominate over DNA methylation for their silencing, (reviewed in [
43]).
In our study, 63 (71%) of the 88 decitabine up-regulated genes possessed CpG-island at their 5' region, a proportion that is higher than that observed in other reports; including AML [
44], and human glioma cell lines, where decitabine re-activated 50% and 40%, respectively, of genes with potential CpG islands [
50]. This observation is particularly intriguing because it suggests that decitabine treatment of U2OS induced CpG-island associated genes more frequently than previously reported. Moreover, of the 13 U2OS genes with a ≥2-fold change, there were 11 genes (84%) with CpG-island in there promoter region. The further enrichment of the frequency of CpG-island-associated genes in the genes with a strong induction of expression (≥2-fold change) after decitabine treatment, further suggested that expression induction reflects either a more direct effect of decitabine through CpG-island demethylation, or indirect activation effects. In four of six apoptotic genes studied in detail, we showed a significant increase in expression following decitabine treatment was accompanied by a marked loss of promoter methylation, which points out to the potential direct effect of decitabine on methylated CpG sequences. Decitabine-induction of
GADD45A expression was examined in two other osteosarcoma cell lines (MG63 and HOS), to determine the specificity of this effect. Whilst MG63 responded in a similar way to U2OS, HOS did not become demethylated in the promoter region of
GADD45A, and expression was not activated by decitabine. These data are consistent with cell-type differences in response to this drug (Al-Romaih et al., in preparation), and the notion that mechanisms other than demethylation may effect a subset of genes in some cell types. Interestingly, out of 88 significantly induced genes there were 25 genes with no apparent CpG island at their promoter region. Similarly, two genes out of the 13 genes with ≥2-fold change have no potential CpG islands close to the TSS and promoter region. Methylation-independent induction of gene expression has also observed in other studies [
39,
51,
44] indicating that genes without CpG islands may respond to this drug. Decitabine mechanism of action is not restricted to its demethylation capability and was reported to have effects on histone methylation and RB phosphorylation [
52,
53]. This also draws attention to the possibility that the other two apoptotic genes (
NFKBIA, and
TNFAIP3) were expressed by induction of methylation-independent mechanisms.
Decitabine treatment re-activated several apoptotic genes in U2OS cells that were identified in this work. Our microarray screen also identified other decitabine re-activated genes with potential roles in regulating proliferation and differentiation of mammalian cells. IGF-binding proteins IGFBP6 and IMP-3 (Table
1) have been shown to be potential regulators of IGFs with anti-growth properties [
54‐
57]. Hypermethylation of these genes functions as a mechanism for increased proliferation capacity, reduction of apoptosis and loss of the differentiated phenotype in U2OS. Targeting hypermethylation in U2OS by decitabine indicated the potential power of this drug for OS treatment.
Methods
Cell line culture and treatment
The human OS cell line U2OS was obtained from the American Type Culture Collection (ATCC) (Rockville, MD) and maintained in alpha-Minimum Essential Medium (alpha-MEM) supplemented with 10% heat inactivated Fetal Bovine Serum and 2 mM L-Glutamine. Treatment with decitabine was performed as described by Liang et al [
39]. Briefly, 5 × 10
5 cells were plated in 56 cm
2 culture plates with 10 ml growth medium. 12 hours after plating they were treated with freshly prepared decitabine (Sigma Chemical Co., St Louis, MO) to a final concentration of 1 μM without changing the medium. The cells were harvested by trypsinization after 3 days of treatment, where the cells were portioned and used for total RNA extraction, DNA extraction, or Propidium Iodide (PI) staining for cell death by flow cytometery. A control (medium only) culture was maintained and processed over the same period of time under the same condition as the treated cells. To establish growth curves for U2OS cells with or without 1 μM decitabine, cells were plated at 5 × 10
5 cells/56 cm
2 culture plates with 4 mm
2 grids. The cells were allowed to attach to the surface of the plates for 12 hours before the start of the treatment. Adherent cells were counted in 2 independent cultures in multiple 4 mm
2 grids every 12 hours after plating and the experiment was repeated after culturing the cells for 5 passages. When cell growth was near confluent, the cells were trypsinized, re-suspended in growth medium (10% serum) and cell viability was determined using Vi-CELL™ XR (Beckman Coulter, Fullerton, CA) after Trypan Blue staining.
Immunohistochemistry and image analysis
Xenograft tissue sections were de-paraffinized using xylene and re-hydrated in a series of alcohols. The tissue sections were then incubated at room temperature (RT) in 3% H202 in PBS for 10 minutes to inactivate endogenous peroxidase. Following incubation the slides were washed 3 times in PBS for 3 minutes each. Antigen retrieval was obtained by heating in a microwave at maximum heat for 20 minutes in Tris-EDTA buffer (10 mM Tris Base, 1 mM EDTA solution, 0.05% Tween 20, pH 9.0) and cooling for 20 minutes at RT. Slides were again washed 3 times in PBS for 3 minutes each. The slides were blocked (30 minutes in a humid chamber at RT) with serum to reduce non-specific binding. Serum was removed from the slides and the slides were then incubated with the primary antibody 5-methylcytidine (5-mc-Ab) (Eurogentec, San Diego, CA) at 1:500 dilution at 4°C overnight.
Following incubation the slides were washed 3 times in PBS for 3 minutes each. The slides were then incubated with a secondary antibody [Polyclonal rabbit anti-mouse immunoglobulins/biotinylated rabbit F(ab')2; Dako] for 30 minutes at RT, followed by 3 washes in PBS for 3 minutes each. The slides were then incubated with StreptABCComplex/HRP (Dako, Glostrup, Denmark) for 30 minutes in a humid chamber at RT, followed by 3 washes in PBS for 3 minutes each. A 3,3'diaminobenzidine (DAB) substrate (Vector Laboratories, Burlingame, CA) was used for detection and hematoxylin was used for counterstain. The slides were then dehydrated and mounted.
Whole sections from xenografts were scanned by ScanScope CS (Aperio technologies, Vista, CA). The slides were digitized to 20× magnification (~0.5 microns/pixel). Images were then viewed with Aperio's image viewer software (ImageScope), which allows performing quantitative analysis of stain intensity on snapshots from the sections. Five to ten ~0.3 mm2 snapshots (each containing 3,000 to 5,000 cells) were analyzed per section using the following parameters: compression quality = 30, and color saturation threshold = 0.04. Positivity thresholds were150 to 220 = high positive, 100 to 150 = low positive, and 0 to 100 = negative. Descriptive analysis such as mean and standard deviation for 5-mc immunostaining intensity were calculated based on the percentage of positivity (total positivity/total negativity per snapshot). Comparison between control and decitabine-treated sections was done using the student t-test and p < 0.05 was considered significant.
In vivo studies: U2OS xenograft and treatment
Six- to eight-week old male immune-deficient NOD-SCID and Rag-2M mice were bred and maintained by the Animal Resource Centre at the British Colombia Cancer agency, Vancouver, Canada. U2OS cells, in general, were considered as non-tumorigenic in mice while grafting the cells subcutaneously or orthotopically [
17]. As such, in order to establish their xenografts, U2OS cells were grafted under the renal capsule, a site proven to be an excellent site for tumor engraftment [
18‐
20]. Briefly U2OS cells were cultured in alpha-MEM and washed in growth medium containing 20% FBS. The viable cells were counted after trypan blue staining. 2 × 10
6 cells were pelleted, re-suspended and grafted beneath the renal capsule of adult male SCID mice as previously described [
18]. After 5 months, a well-grown xenograft was selected for re-grafting to establish multiple stable U2OS xenografts under the kidney capsules of NOD-SCID mice (two per kidney per mouse). The re-grafted U2OS xenograft had a 100% take rate, with a doubling time of ~10 days and exponential growth phase starting after ~2 of tumor growth doubling. After 5 generations of tumor growth doubling the xenografts were surgically removed from the mice and cut into approximately 4 mm
3 portions then were re-grafted under the renal capsules of 6 male Rag-2M mice (4 grafts per mouse, 2 per kidney). Four weeks after grafting (~2–3 doubling of tumor growth), the host mice were divided into two groups. One group (3 mice) was given decitabine (2.5 mg/kg body weight) dissolved in saline (0.9% w/v NaCl), intraperitoneally [
8] on days 29, 31 and 33. The other group (3 mice) was given saline alone as a treatment control over the same schedule. On day 37, mice from both groups were sacrificed. Tumor volumes were measured using a digital caliper, recorded and expressed in mm
3, using the formula: volume (mm
3) = (0.52) × length (mm) × width (mm) × height (mm). Data were presented as means ± Standard Deviation (SD) and student t-test was used to analyze the difference between the two treatment groups. The xenograft tissues were then snap frozen, or prepared in paraffin and sectioned according to standard procedures [
18].
Histopathological analysis and TUNEL assay
The tissues from the control (no treatment) and decitabine treated groups were paraffin embedded using routine protocols [
18] and stained with hematoxylin and eosin. Sections were assessed blindly. Extracellular matrix was defined as eosinophilic osteoid-like material surrounding individual cells and small clusters of 3–5 cells, and the percentage of tumor with osteoid was then calculated. Mitotic counts were performed in areas with the highest mitotic rate, and ten high-powered fields (× 400) were counted per section.
In situ hybridization for terminal deoxynucleotidyl transferase-mediated nick end labeling (TUNEL) was performed on paraffin sections as recommended by the manufacturer (Ventana Medical Systems, Tucson, AZ). Scoring of the sections was performed using Simple PCI analytical software (Nikon, Tokyo, Japan). Sections were examined and the most intense areas of staining were photographed using a DXM1200 digital camera (Nikon) at a power of x200. The digital image was then scanned using the Simple PCI program and the numbers of positive and negative nuclei were obtained. Control and treatment images were all photographed at a uniform brightness, and all images were subjected to uniform binary image modification and size calibration prior to counting by Simple PCI. The positivity index was obtained by dividing the number of positive nuclei by the total number of nuclei (positive + negative). The number of nuclei counted was always over 1000, and ranged from 1100 to 2000. Positivity indices were compared by Student's t-test and a p-value of <0.05 was considered significant.
Affymetrix expression analysis
Total RNA was extracted using the RNeasy kit (Qiagen, Germany) from duplicate experiments of U2OS cells at day 3 after treatment with 1 μM decitabine or medium alone (control). In each experiment RNA yields were pooled from two independent cultures per treatment arm to minimize experimental noise. For each case, 10 μg of RNA was labeled and hybridized to the Affymetrix HG-U133A GeneChips using the manufacturer's protocol (Affymetrix, Santa Clara, CA) by the Centre of Applied Genomics at the Hospital for Sick Children (Toronto, Canada). Data were extracted using the Microarray Suite (MAS) version 5.0 (Affymetrix) and linearly scaled to achieve an average intensity of 150 across each chip. The candidate gene list obtained from the MAS 5.0-extracted data was selected by eliminating genes that were not present in at least one experiment. The arrays were subjected to a pair wise comparison using MAS 5.0, with signal intensities from the no-treatment cells as the baseline. The statistical significance for the change of expression for each probe set between the decitabine treated and control was calculated by the MAS 5.0 software. The criteria for gene selection for real-time expression validation analysis was based on the statistically significant up-regulation (p < 0.0025) and fold change of ≥2 for expression after decitabine treatment. The gene list was annotated based on the NetAffx data-base [
58] and further verified using the Human Genome Browser data base [
59]. All the raw data for expression arrays is available in [
60] under the series record number (GSE7454).
In silico analysis of CpG-island association, gene annotation, and pathway enrichment
The criteria for a CpG-island was based on those outlined by Takai and Jones [
61], where the GC ≥ 55%, Obs/Exp ≥ 0.65, and length > 300 bp which was reported to exclude most
Alu-repetitive elements. We identified the genes that harbored CpG-island within a 2000 bp window upstream or downstream from the transcription start site based Human Genome Browser data base [
59]. To be certain that there were no CpG island closer to the TSS and gene promoter regions, we submitted the sequences of interest (including a 2000 bp window upstream and downstream from TSS) to the CpG search engine available in reference [
61] and verified that there was no CpG islands that are closer to TSS for the genes we tested. Up-regulated genes with CpG-island associations were further analyzed through the Microarray Literature-based Annotation tool MILANO [
62] to look for evidence of epigenetic modifications in the literature. MILANO is a web-based tool that allows annotation of lists of genes derived from microarray results by user defined terms [
62]. Using MILANO we searched for literature associations between our list of genes and the terms 'epigenetics', 'methylation' 'chromatin modification' 'cancer', and 'disease'. To identify the putative functional pathways for each gene list, we used the functional annotation enrichment tool. This tool utilizes the Gene Ontology database and uses GO Terms to identify enriched biological themes in the gene lists [
63,
63]. The Fisher Exact test was applied to determine the significance in the proportions of genes falling into a certain pathway in each gene list. We used this tool to look for enriched pathways of up- or down- regulated genes with CpG-island associations from the gene lists from the cell lines.
Expression validation using reverse transcription and quantitative real-time PCR
Total RNA from xenografts was extracted using the TRIzol reagent method. 1 ml of TRIzol (Invitrogen, Osaka, Japan) was used for every 50–100 mg of tumor tissue and homogenized in an RNase free environment. Chloroform was then added (200 μl for each 1 ml TRIzol) and the samples were centrifuged at high speed for 15 minutes at 4°C. The aqueous layer was then transferred into a new tube and RNA was precipitated with iso-propanol followed by one wash using 70% ethanol. The RNA precipitate was then dissolved in 10–15 μl of RNAse free water and analyzed for quantity and quality using a spectrophotometer. A two-step reverse transcription-PCR procedure was performed. Total RNA was reverse transcribed using the GeneAmp kit (Applied Biosystems; ABI, Foster City, CA). 20 ng of the resulting cDNA was then used in the real-time PCR step. Six genes were tested by real-time PCR including:
growth arrest and DNA-Damage inducible, alpha (
GADD45A),
heat chock70KDA protein 9b (
HSPA9B),
parkc apoptosis wt1-regulatort (
PAWR),
programmed cell death 5 gene (
PDCD5),
nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha (
NFKBIA),
tumor necrosis factor, alpha-induced protein 3 (
TNFAIP3). We used the TaqMan primers (ABI) for all the genes that we tested (primer information is provided the in [Additional file
5]). All real-time PCR assays were performed in triplicate in a 96-well plate using the 7900 Sequence Detector System (ABI) according to the manufacturer's protocol. Data analysis was performed using the Sequence Detector System (SDS) software (ABI) and the results were expressed as fold-change in relative mRNA expression level, calculated using the ΔΔCt method with β-actin (
ACTB) as the reference gene and the non-treated cells as baseline. The validation was carried out on RNA from three replicate experiments of U2OS cells, three decitabine-treated U2OS xenograft tumors (Xeno- 1, 2 and 3), three no-treatment (control) U2OS xenograft tumors (Xeno- 4, 5, and 6) and three replicate experiments of NHOst.
Quantitative-bisulfite pyrosequencing
Quantitative Bisulfite Pyrosequencing for CpG islands (Pyro Q-CpG) is a sequencing-based analysis of DNA methylation that quantifies multiple CpG sites per amplicon using Pyro Q-CpG software. 2 μg of DNA from the control and decitabine treatment were bisulfite-treated using the Zymo DNA Methylation Kit (Zymo Research, Orange, CA). Bisulfite-treated DNA was amplified by PCR then sequenced according to the manufacturer's protocol (Biotage, Kungsgatan, Sweden). The target sequences inside the CpG-islands of the candidate genes and the primer sequences are shown in [Additional file
5]. The percentage of C content (methylated alleles) versus T content (unmethylated alleles) is calculated by the Pyro-Q-CpG software for each CpG position in each sample. Analysis was performed on DNA samples from 3 replicate experiments of U2OS cells
in vitro and six U2OS xenograft tumors; three decitabine treated (Xeno-1, Xeno-2 and Xeno-3), and three saline (control) treated (Xeno-4, Xeno-5 and Xeno-6). Universally methylated DNA was used as a methylation positive control. DNA isolated from early embryos (Biotage, Kungsgatan, Sweden) was used for methylation negative control. DNA from low-passage normal human osteoblasts (PromoCell, Germany) was used for experiment control.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
K A performed the in vitro treatment assays, the AffyChip expression assays, the AffyChip data analysis, the real-time expression assays and analysis in vitro and in vivo, the DNA methylation data analysis, the meth5-C data analysis and conceived and wrote the manuscript draft; G R S performed the histopathological analysis and TUNEL analysis; J B contributed to the writing of the paper; S H performed the immunostaining using the Methyl-C-Ab; M P contributed to the AffyChip data analysis; J-C C contributed to the mice experiments; H X and Y W performed the mice experiment; M Z and J A S conceived and contributed to the writing of the manuscript. All authors read and approved the final manuscript.