In Vitro Analysis of Integrated Global High-Resolution
DNA Methylation Profiling with Genomic Imbalance and
Gene Expression in Osteosarcoma
Bekim Sadikovic
1,2,3
, Maisa Yoshimoto
3
, Khaldoun Al-Romaih
3
, Georges Maire
3
, Maria Zielenska
1,2
,
Jeremy A. Squire
3,4
*
1 Department of Pediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada, 2 Genetics and Genome Biology Program, The Hospital for Sick
Children, Toronto, Ontario, Canada, 3 Division of Cellular and Molecular Biology, Department of Research, Ontario Cancer Institute (OCI), University Health Network (UHN),
Toronto, Ontario, Canada, 4 Department of Pathology and Molecular Medicine, Richardson Labs, Queen’s University, Kingston, Ontario, Canada
Abstract
Genetic and epigenetic changes contribute to deregulation of gene expression and development of human cancer.
Changes in DNA methylation are key epigenetic factors regulating gene expression and genomic stability. Recent progress
in microarray technologies resulted in developments of high resolution platforms for profiling of genetic, epigenetic and
gene expression changes. OS is a pediatric bone tumor with characteristically high level of numerical and structural
chromosomal changes. Furthermore, little is known about DNA methylation changes in OS. Our objective was to develop an
integrative approach for analysis of high-resolution epigenomic, genomic, and gene expression profiles in order to identify
functional epi/genomic differences between OS cell lines and normal human osteoblasts. A combination of Affymetrix
Promoter Tilling Arrays for DNA methylation, Agilent array-CGH platform for genomic imbalance and Affymetrix Gene 1.0
platform for gene expression analysis was used. As a result, an integrative high-resolution approach for interrogation of
genome-wide tumour-specific changes in DNA methylation was developed. This approach was used to provide the first
genomic DNA methylation maps, and to identify and validate genes with aberrant DNA methylation in OS cell lines. This first
integrative analysis of global cancer-related changes in DNA methylation, genomic imbalance, and gene expression has
provided comprehensive evidence of the cumulative roles of epigenetic and genetic mechanisms in deregulation of gene
expression networks.
Citation: Sadikovic B, Yoshimoto M, Al-Romaih K, Maire G, Zielenska M, et al. (2008) In Vitro Analysis of Integrated Global High-Resolution DNA Methylation
Profiling with Genomic Imbalance and Gene Expression in Osteosarcoma. PLoS ONE 3(7): e2834. doi:10.1371/journal.pone.0002834
Editor: Ju¨rg Ba¨hler, Wellcome Trust Sanger Institute, United Kingdom
Received May 27, 2008; Accepted July 9, 2008; Published July 30, 2008
Copyright:
2008 Sadikovic et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was funded by the National Cancer Institute of Canada grant #16215 with funds from the Canadian Cancer Society. BS is a recipient of the
Post Doctoral Fellowship form the Terry Fox Foundation, and the National Cancer Institute of Canada and the Restracomp Fellowship from The Hospital for Sick
Children, Toronto, Canada. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: squirej@queensu.ca
Introduction
The hallmark of cancer is the deregulation of gene expression
profiles and disruption of molecular networks [1]. Mutation and
genomic instability provide tumours with sufficient diversity, so that
cells with adaptive and proliferative selective advantage can evolve
in a Darwinian manner. However, it has become evident that
epigenetic factors, particularly heritable changes in DNA methyl-
ation, may confer additional and more diverse advantage to
tumours including deregulation of gene expression and destabiliza-
tion of chromatin. While there is some understanding of how such
genetic and epigenetic changes may influence the gene expression,
and thereby tumour evolution, it is less clear how these mechanisms
influence each other, and how cumulative changes could co-evolve
and influence gene expression during tumourigenesis.
Many human diseases have been linked to aberrant DNA
methylation or mutations in the DNA methylation pathways [2],
but the most compelling evidence of DNA methylation disorders
and human pathogenesis is evident in cancer. Malignant cells can
show major disruptions in DNA methylation profiles, which
manifest as aberrant hypermethylation and hypomethylation of
gene promoters, as well as global genomic hypomethylation [3]. To
date, many genes with aberrant promoter hypermethylation have
been identified in tumours, including cell cycle regulators, DNA
repair genes, genes associated with apoptosis, hormonal regulation,
detoxification, metastasis, angiogenesis, and many others [4,5].
Another type of cancer-related defect of DNA methylation is
genomic hypomethylation [6]. It is common in both solid tumours
such as prostate cancer [7], hepatocellular cancer [8], cervical
cancer [9], as well as in hematologic cancers such as B-cell chronic
lymphocytic leukemia [10]. Decreased levels of global DNA
methylation can be indicative of tumour progression in many types
of malignancies including breast, cervical and brain cancers [6].
Aberrant hypomethylation has been hypothesized to contribute to
cancer progression by activating oncogenes such as h-RAS, r-RAS,
c-MYC, and c-FOS [11–13], by retrotransposon activation [14–16]
or by increasing chromosome instability as in ICF syndrome [17].
Osteosarcoma (OS) is the most common primary bone
malignancy, and is characterized by complex chromosomal
abnormalities that vary widely from cell to cell. These tumours
PLoS ONE | www.plosone.org 1 June 2008 | Volume 3 | Issue 7 | e2834

exhibit high degree of aneuploidy, gene amplification, and
multiple unbalanced chromosomal rearrangements. A combined
approach of molecular cytogenetic techniques [comparative
genomic hybridization (CGH), spectral karyotyping (SKY), multi-
colour banding (mBAND), or array-CGH (a-CGH)] together with
the classical G-banded cytogenetic analysis of OS tumours have
described complex karyotypes with multiple numerical and
structural chromosomal aberrations. Collectively these studies
highlight the highly unstable nature of the OS genome [18–29].
Although genetic changes in OS have been extensively
researched, our understanding of epigenetics in this tumour is
very limited. Only a handful of studies reported changes in DNA
methylation in OS, and no genome-wide DNA methylation
analysis has been published. Changes in promoter methylation of
osteocalcin gene have been suggested to play a role in osteoblast
differentiation and carcinogenesis in human and rat [30–32].
Aberrant DNA methylation of the imprinted IGF2 and H19 loci
has also been observed and suggested to play a role in OS
tumourigenesis [33]. The RASSF1 gene was shown to be
frequently hypermethylated and as re-expressed by decitabine, in
a panel of pediatric tumours including OS [34]. We have recently
reported the first genome-wide study of gene expression in OS cell
line, by assessing the changes in expression of U2OS cells in
response to decitabine exposure [35]. In this study it was shown
that decitabine-induced DNA demethylation in a number of pro-
apoptotic genes including GADD45A resulted in apoptosis of OS
cell lines U2OS and MG63, and mouse xenografts of OS cultures,
drawing attention to the therapeutic potential of modulating DNA
methylation [36].
Recent developments in microarray technologies have revolu-
tionarized the way in which DNA methylation is studied. In
addition to using methylation-sensitive restriction enzymes,
methylated genomic DNA has been successfully enriched using
immunoprecipitation with a 5-methylcytosine antibody (Me-DIP)
and used for hybridization to genomic microarray platforms
[37,38]. These advancements have allowed for very precise
mapping of genome-wide epigenetic profiles. For example, the
first genome wide characterization of epi-toxicogenomic changes
of histone acetylation profiles at a thirty five nucleotide resolution
using Affymetrix Promoter Tiling arrays was reported [39].
The functional relevance of epigenetic changes in cancer
aetiology is only beginning to be deciphered. Since acquired
changes in gene expression may be influenced by both genetic and
epigenetic factors in humans, the objective of this study was to
develop an approach for integration of global cancer-specific
epigenomic and genomic profiles with gene expression, and study
these changes in two well-characterized OS cell lines. Our
previous published studies of MG63 and U2OS have demonstrat-
ed that both these cell lines have the typical complex genomic
structure that characterizes patient derived osteosarcomas. Be-
cause we have a detailed knowledge and have published
extensively on the genomic biology of both these particular cell
lines we are uniquely positioned to assess the validity and
technological utility of the integrated whole genome analytical
approach using this in vitro system. Furthermore, our goal was to
develop a high-resolution approach of detection of genome-wide
and gene-specific changes in DNA methylation.
Using integrative epigenetic, genome imbalance, and gene
expression analyses we provided first genomic DNA methylation
maps, and identified and validated genes with aberrant DNA
methylation in osteosarcoma cell lines. These analyses provide
evidence of the cumulative roles of epigenetic and genetic
mechanisms in deregulation of gene expression networks in OS
cell lines.
Results
To assess genome wide changes in DNA methylation related to
OS that may play a role in deregulation of gene expression, as well
as to delineate the potential genomic imbalance contributions, an
integrative and functional approach for the analysis of DNA
methylation, genomic imbalance and gene expression was created,
using two OS cell lines, U2OS and MG63, and normal human
osteoblasts. Figure 1A shows a schematic outline of the analysis
with detailed protocol description in the methods. The represen-
tative chromosome view of one of the chromosomes (chromosome
7) with differentially methylated and differentially expressed genes,
as well as regions of significant genomic imbalance for one of the
cell lines (U2OS) is shown in Figure 1B.
High resolution DNA methylation analysis
Affymetrix Promoter 1.0 Tilling Array platform covers 10–
12.5 kb regions (2.5 Kb 39 and 7.5–10 Kb) of 25,500 human gene
promoters, with an average tilling resolution of 35 nucleotides.
This platform was previously used for profiling of genomic histone
acetylation in a breast cancer model of environmental exposures
[39]. We utilized this platform in combination with the methylated
DNA immunoprecipitation (Me-DIP) to develop a comprehensive
approach for detection of hypo- and hypermethylation changes at
high resolution, and used it to detect such changes in human OS
cells in relation to the normal osteoblasts.
In order to assess the quality and reproducibility of our Me-
DIP-chip procedure the hierarchical clustering of the raw data was
performed using Partek Genomic Suite (PGS) software before and
after the background normalization (Figure S1A). This analysis
clearly showed the differential clustering of the Metlyl-C
immunoprecipitated (IP) versus the input (IN) samples across all
12 arrays, as well as differential clustering of OS (U2OS and
MG63) cells in comparison to osteoblasts both before and after
normalization. The good agreement between the replicate array
‘‘heatmaps’’ within each cell type illustrates the high level of
reproducibility of the Me-DIP-chip protocol. Similar clustering
results were reproduced with the Principal Component Analysis of
the raw data (Figure S1B).
We next proceeded to detect the significantly differentially
enriched regions in each of the cancer cell lines, with osteoblast
levels as baseline. In order to exploit the high resolution of this
platform the Hidden Markov Model algorithm was used with
specific parameters (see methods) to allow for detection of relatively
short regions at minimum of 10 probes and approximately 350
nucleotides with strongly enriched/depleted regions (s-HMM), as
well as longer and overall less enriched/depleted genomic regions
with minimum 15 probes and approximately 500 nucleotides (m-
HMM), and 40 probes or approximately 1400 nucleotides (l-
HMM). This analysis resulted in identification of 828 s-HMM, 763
m-HMM, and 582 l-HMM in U2OS cells (Table S1), and 641 s-
HMM, 529 m-HMM, and 407 l-HMM regions in MG63 cells
(Table S2). The relative enrichment mean across the probes,
representing the fold enrichment in relation to osteoblasts in a
specific HMM region, ranged from +35.8 to 224.7 in U2OS, and
from +20.1 to 221.6 in MG63 cells. As expected, a large proportion
of the three types of HMM-detected regions overlapped same
genomic locations. For example, promoter region of one of the
gene, LHX9, was detected as significantly enriched in both U2OS
and MG63 cells by both s-HMM and l-HMM algorithm, while
shorter, but more enriched s-HMM region represented a sub-
section of the larger, but less enriched l-HMM region (Figure S2).
These regions were then annotated to specific gene loci using
the Affymetrix HuGene-1_0-st-v1.na24.hg18.transcript.csv li-
Integrative Epi/Genomics in OS
PLoS ONE | www.plosone.org 2 June 2008 | Volume 3 | Issue 7 | e2834

brary. We have identified 831 enriched/hypermethylated and 397
depleted/hypomethylated gene promoters in U2OS (Table S3)
and 440 enriched/hypermethylated and 359 depleted/hypo-
methylated gene promoters in MG63 cells (Table S4).
Gene-specific validation of Me-DIP-chip
The Me-DIP-chip protocol utilized the enrichment of methyl-
ated DNA with the methyl-C-specific antibody, as well as the
processing of the IP and IN DNA including the whole-genome
amplification, DNA fragmentation, labeling, hybridization, scan-
ning and software analysis. In order to validate the technical
aspects of analysis gene-specific real-time PCR quantitation of the
IP enriched DNA was performed. In addition, the quantitative
analysis of the cytosine methylation in a number of genes in both
IP and IN fractions of U2OS, MG63, and normal osteoblasts was
carried out. Figure 2 is a representative schematic of the design of
Figure 1. Integrative epigenetic, genetic, and expression profiling. (A) Schematic workflow of microarray data analysis and integration.
Individual microarrays in replicates (grey boxes), are imported, background corrected, and significantly enriched or depleted regions are detected and
assigned to specific genes for DNA methylation, genomic imbalance, and gene expression. All data were analyzed, and integrated using Partek
Genomic Suite (PGS) software, and network analysis was performed using Ingenuity Pathway Analysis (IPA). IN – input DNA, IP – immunoprecipitated
DNA Cy3 and Cy5 – replicate experiment dye flip for a-CGH labeling, 1 and 2 – individual expression replicates, cor. – background normalized/
corrected arrays, HMM – Hidden Markov Model algorithm, gen. seg. – genomic segmentation algorithm, ANOVA – analysis of variance algorithm. (B)
Chromosome view of epigenetic, genetic, and expression changes at chromosome 7 in U2OS cells. A PGS generated visualization of the significant
regions of genes with significant changes in expression (lane 1 profile), DNA methylation (lane 2 profile), genomic segmentation algorithm results
(lane 3 heat map), and the corresponding a-CGH profile (lane 4 profile). The genomic segmentation scale and a-CGH profile values are in log
2
, while
DNA methylation and gene expression y-axis scale represents average fold change to osteoblast levels, and size of each bar is proportional to the fold
change (green – decrease, red – increase).
doi:10.1371/journal.pone.0002834.g001
Integrative Epi/Genomics in OS
PLoS ONE | www.plosone.org 3 June 2008 | Volume 3 | Issue 7 | e2834

such validation experiments in WT1, which is one of the genes
detected as enriched in both U2OS and MG63 relative to normal
osteoblasts.
Several genes were detected as either enriched or depleted in either
U2OS and/or MG63 cells relative to the osteoblast levels (Table 1)
and used for the real-time PCR validation, using the original, non-
amplified IP and IN DNA that was used in the Me-DIP-chip
experiment. Figure 3 shows relative enrichment (IP/IN) of U2OS or
MG63 versus normal osteoblasts. All fourteen gene-specific real-time
PCR experiments confirmed the microarray findings.
In order to validate the methyl-C IP reaction, and show that
Me-DIP-chip-detected enriched/depleted regions are indeed
Figure 2. Visualization and validation of Me-DIP-chip detected regions. Top panel is the PGS-generated region view of the enriched/
hypermethylated genomic region (grey box) in the WT1 gene promoter, featuring the colour-coded profile of the signal from each cell type (in log
2
),
and the corresponding heat-map of the replicate array experiments bellow (in log
2
). Note the reproducibility of the signal, as well as the dendrogram
of the hierarchical clustering for the region to the left of the heat maps. Middle panel shows the PGS-generated .wig file of this region imported into
UCSC Genome Browser mapped to the WT1 gene promoter, and the corresponding CpG island, allowing identification of genomic features of interest
at 35 nucleotide resolution, and design of the primers for the validation experiments including the real-time PCR, and bisulfite EpiTYPER quantitation
of DNA methylation. Bottom panel represents the MethPrimer-generated view of the region and the corresponding CpG dinucleotides (red bars)
whose methylation levels are quantitated using EpiTYPER.
doi:10.1371/journal.pone.0002834.g002
Integrative Epi/Genomics in OS
PLoS ONE | www.plosone.org 4 June 2008 | Volume 3 | Issue 7 | e2834

hyper-/hypomethylated at their corresponding CpGs, detailed
quantitation of CpG methylation using the EpiTYPER (Seque-
nom) mass-spec analysis of bisulfite converted DNA was
performed. The six regions analysed belonged to genes that were
detected as differentially enriched by both Me-DIP-chip (Table
S3, and S4) and gene-specific real-time PCR (Table 1). AMIGO2
was significantly depleted and PTDGR significantly enriched in
U2OS cells only. WT1, PCDHB8, and LHX9 were significantly
enriched and STK32C was significantly depleted in both U2OS
and MG63. Quantitative analysis of DNA methylation across all
these genes confirmed both Me-DIP-chip and real-time data
(Figure 4). Detailed analysis of these data shows that our Me-DIP-
chip approach is capable of detecting both robust and more subtle
hypomethylation and hypermethylation events (compare AMIGO2
with STK32C, and WT1 with PCDHB8). Furthermore, the primers
for LHX9 were designed to overlap the 59end of the s-HMM
region (CpGs 16–21) and extend into l-HMM region (CpGs 1–15)
(Figure S2). Although majority of the CpGs are relatively
hypermethylated across the regions, the relative levels of CpG
16 through 21 hypermethylation were clearly more exaggerated.
Integration of genomic DNA methylation profiles with
genome-wide expression and copy number changes
The Me-DIP-chip approach resulted in identification of
hundreds of hypo- and hypermethylated gene targets in both
U2OS and MG63 cells in relation to normal osteoblasts. In order
to further narrow down the list of functionally relevant changes in
DNA methylation (i.e. ones that may play a role in cancer-specific
deregulation of gene expression) we integrated Me-DIP-chip
Table 1. Me-DIP-chip detected genes for real-time PCR and EpiTYPER validation.
Chromosome Gene Name Gene Symbol Status
U2OS
1 LIM homeobox 9 LHX9 Enriched
5 protocadherin beta 8 PCDHB8 Enriched
11 Wilms tumor 1 WT1 Enriched
3 SLIT and NTRK-like family, member 3 SLITRK3 Enriched
4 mab-21-like 2 (C. elegans) MAB21L2 Enriched
14 prostaglandin D2 receptor (DP) PTGDR Enriched
8 exostoses (multiple) 1 EXT1 Depleted
10 serine/threonine kinase 32C STK32C Depleted
12 adhesion molecule with Ig-like domain 2 AMIGO2 Depleted
MG63
1 LIM homeobox 9 LHX9 Enriched
5 protocadherin beta 8 PCDHB8 Enriched
11 Wilms tumor 1 WT1 Enriched
1 potassium voltage-gated channel, member 3 KCND3 Enriched
10 serine/threonine kinase 32C STK32C Depleted
The chromosomal location, gene name and symbol, and Me-DIP-chip enrichment status relative to osteoblasts for U2OS and MG63 cells are indicated.
doi:10.1371/journal.pone.0002834.t001
Figure 3. Gene-specific real-time PCR validation of the Me-DIP-chip data. Me-DIP-chip detected genes in Table 1 were subject to real-time
PCR quantitative analysis of enrichment. The y-axis represents fold enrichment values generated by calculating the ration of U2OS or MG63 IP (Ct)/
IN(Ct) over osteoblast IP(Ct)/IN(Ct). Each real-time PCR reaction was performed in triplicate and average values were used for enrichment calculation.
doi:10.1371/journal.pone.0002834.g003
Integrative Epi/Genomics in OS
PLoS ONE | www.plosone.org 5 June 2008 | Volume 3 | Issue 7 | e2834

findings with the genome-wide expression profiling. Furthermore,
the studies of genomic imbalance in MG63 and U2OS cell lines in
our laboratory, using FISH, SKY, mBAND, and recently a-CGH
have identified many specific structural and copy number changes
in these cell lines [20–22,24,25]. We analyzed the significant
changes in gene copy number detected by a-CGH profiling to
Figure 4. Validation of Me-DIP-chip data using quantitative DNA methylation analysis. Six genes from Table 1 were subject to EpiTYPER
quantitative analysis of DNA methylation in CpG dinucleotides across 400 nucleotide regions detected as significantly enriched/depleted in Me-DIP-
chip experiment. On left, the bar charts show levels of methylation (0–1–0–100%), on y-axis and individual CpG dinucleotides on the x-axis, and the
corresponding error bars based on triplicate experiment. On right, the PGS-generated region views of the corresponding significantly enriched/
depleted genes are labelled as in Figure 3.
doi:10.1371/journal.pone.0002834.g004
Integrative Epi/Genomics in OS
PLoS ONE | www.plosone.org 6 June 2008 | Volume 3 | Issue 7 | e2834

evaluate the relative contributions of genomic and epigenomic
imbalance to gene expression changes. The integration of
epigenomic, genomic imbalance and gene expression profiles
was performed using PGS software.
Microarray analysis of gene expression (p,0.01, +2/22 fold
change, 2 arrays/sample) revealed significant changes in gene
expression in U2OS and MG63 cells relative to normal osteoblast
levels. U2OS had 1881 overexpressed, and 1400 uderexpressed
genes (Table S5), while MG63 cells exhibited overexpression in
1060, and underexpression in 1072 genes (Table S6). VENN
analysis of Me-DIP-chip results and expression array data revealed
106 genes that were hypermethylated and underexpressed in
U2OS (Table S7), and 34 in MG63 (Table S8). Eight of the
hypermethylated and underexpressed genes were common to both
cell lines (Table S9). The methylation and enrichment status of
one of the common genes, LHX9, has been interrogated and
confirmed in detail (Figure 3, and 4). Alternatively, significant
hypomethylation and overexpression was evident in 76 genes in
U2OS (Table S10), and 92 genes in MG63 cells (Table S11).
In order to identify genes belonging to regions of significant
copy number change, the results of the segmentation algorithm of
a-CGH profiles were annotated with the Affymetrix HuGene-1_0-
st-v1.na24.hg18.transcript.csv library. In U2OS, 371 genes were
detected in significant regions of gain detected on chromosomes 7,
8, 14, 17, and 22, while 178 genes in regions of significant genomic
loss on chromosomes 1, 2, 7, 8, 10, 12, 13, 19, and 21 (Table S12).
There were 433 genes in regions of genomic gain in MG63 cells
overlapping portions of chromosomes 1, 2, 4, 5, 6, 8, 9, 10, 16, and
19, and 171 genes in regions of significant genomic loss on
chromosomes 3, 4, 6, 7, 9, 11, 13, and 14 (Table S13).
We next analyzed the correlation in gene specific changes from
the global epigenomic, genomic, and gene expression profiles
between U2OS and MG63 cell lines (Figure 5). VENN analysis of
genomic expression profiles showed the most consistent overlap,
where nearly half of the aberrantly-expressed genes from MG63
also exhibited significant changes in gene expression in U2OS
cells. Importantly, majority of these genes (857 out of 988) showed
same type of change (i.e. increase or decrease) in both cell lines. In
contrast, genes with genomic imbalance showed very little overlap
between the two cell lines. Significantly, overlap was frequently
seen in regions of genomic gain, with 43 genes belonging to the
commonly gained region of chromosome 8q in both U2OS and
MG63 cells. Conversely, epigenomic profiling revealed that a large
proportion of genes with significant changes in DNA methylation
were common to both U2OS and MG63. Out of 238 commonly
affected genes, 151 genes were hypermethylated, and 43 genes
were hypomethylated in both cell lines.
In order to examine the global gene-specific changes in
relation to epigenetic, genetic and gene expression changes
within each cell line we performed 3-way VENN analysis of the
significantly affected genes (Figure 6A). This analysis revealed a
considerable overlap between genes affected by changes in the
2-way cross sections of either epigenetic, genetic or expression
profiles. The intersects between DNA methylation and gene
expression showed 330 and 177 genes; genomic imbalance and
gene expression included 159 and 197; and DNA methylation
and genomic imbalance 93 and 137 genes in U2OS and MG63
cells respectively. To further interrogate the status of these genes
in relation to either gain or loss of DNA methylation, genomic
content, or gene expression, more detailed comparisons were
performed (Figure 6B). This analysis revealed that majority of
genes with genomic imbalance and gene expression alterations,
display genomic gain and over-expression in U2OS and MG63
cells. Furthermore genomic imbalance most strongly correlated
to DNA methylation disruptions in the form of genomic gain
and hypomethylation in both cell lines. The correlation between
DNA methylation and gene expression showed distinct profiles
in the two cell lines. In MG63 cells, large proportion of the
genes with aberrant epigenetic and in gene expression profiles
were hypomethylated and overexpressed, while in the U2OS
cells in addition to hypomethylation and overexpression, large
number of genes were affected by hypermethylation and
underexpression, and hypermethylation and overexpression.
Examination of the genes belonging to the 3-way intersect in
Figure 6A revealed that majority of the genes affected by
epigenetic, genetic, and gene expression changes were hypo-
Figure 5. VENN analysis of gene-specific epigenetic, genetic, and gene expression changes between U2OS and MG63 cells. The lists
of PGS generated genes with significant changes in DNA methylation, genomic imbalance, and gene expression, in relation to normal osteoblasts are
compared using VENN analysis between the U2OS and MG63 cells.
doi:10.1371/journal.pone.0002834.g005
Integrative Epi/Genomics in OS
PLoS ONE | www.plosone.org 7 June 2008 | Volume 3 | Issue 7 | e2834

methylated, gained and overexpressed in both U2OS and MG63
cells (Figure 6C). Tables with the lists of these genes, their
relative methylation levels, expression values, and genomic copy
number status for U2OS (Table S14) and MG63 (Table S15)
include genes that we have previously identified in the common
regions of gain in these cell lines including regions in
chromosome 8q [20–22,24,25]. One of such genes in MG63
cells was the c-MYC oncogene.
Figure 6. VENN analysis of gene-specific epigenetic, genetic, and gene expression changes in U2OS and MG63. (A) The lists of PGS
generated genes with significant changes in DNA methylation, genomic imbalance, and gene expression, in relation to normal osteoblasts are
compared using VENN analysis in U2OS and MG63 cells. (B) Analysis of the two-way intersects for the gain/loss form Figure 6A between DNA
methylation and gene expression (left), genomic imbalance and gene expression (middle), and DNA methylation and genomic imbalance (right). y-
axis represents the number of genes. (C) Pie chart of the 3-way intersect for the gain/loss changes from Figure 6A for DNA methylation, genomic
imbalance, and gene expression.
doi:10.1371/journal.pone.0002834.g006
Integrative Epi/Genomics in OS
PLoS ONE | www.plosone.org 8 June 2008 | Volume 3 | Issue 7 | e2834

Gene network analysis
In order to identify gene networks with the disrupted expression
profiles in U2OS and MG63 cells, relative to normal osteoblasts,
and the possible epigenetic and genetic contributions to these
networks, we performed Ingenuity Pathway Analysis (IPA) of the
genes with aberrant expression profiles. This analysis revealed that
four out of five most significantly affected biological functions were
common to U2OS and MG63, and included cellular movement,
cellular growth and proliferation, cell to cell signalling and
interaction, and cell death (Figure S3). Furthermore, comparative
analysis of this dataset revealed a significant contribution of genes
with significant changes in DNA methylation and genomic
imbalance to all of these biological functions (Figure S3). The
top three networks with most significant changes in gene
expression in U2OS and MG63 cells were associated with
cancer-related disruptions in cell cycle, proliferation, gene
expression, and cell death (Table S16). One of these networks
that was identified in MG63 cells centers around the c-MYC
oncogene and includes genes involved in cellular function and
maintenance, small molecule biochemistry, and cancer (Figure 7).
Four out of five hypomethylated genes, including c-MYC, were also
overexpressed in this pathway. Furthermore, four genes showing
genomic gain, including c-MYC, were also overexpressed, while the
only gene showing significant loss, the tumour suppressor
CDKN2B, was also underexpressed.
To further analyse the copy number status of the c-MYC
oncogene FISH was performed. In order to provide additional
validation our a-CGH data we analysed another gene located near
the telomere of 8q, the damage repair-associated RECQL4, which
was detected as significantly enriched in U2OS, but not MG63
cells. Figure 8A shows the results of PGS genomic segmentation
analysis in U2OS and MG63 cell lines, revealing a high-level gain
of c-MYC in MG63 and no significant change in RECQL4, and
low-level gains in c-MYC and RECQL4 in U2OS cells. FISH
Figure 7. MYC network-related changes in gene expression, DNA methylation, and genomic imbalance in MG63 cells. IPA analysis of
gene expression, DNA methylation , and genomic imbalance changes in MYC oncogene related pathways. Red denotes gain, and green loss of the
corresponding variable.
doi:10.1371/journal.pone.0002834.g007
Integrative Epi/Genomics in OS
PLoS ONE | www.plosone.org 9 June 2008 | Volume 3 | Issue 7 | e2834

analysis showed on average ten copies of c-MYC and only two
copies of RECQL4 in MG63 (Figure 8B), with average four copies
of both c-MYC and RECQL4 in U2OS cells (Figure 8C), thereby
confirming our a-CGH data for this region.
Discussion
Both epigenetic and genetic changes contribute to development
of human cancer. In this paper we describe an integrative
approach for analysis of the cumulative effects of genome-wide
changes in DNA methylation, genomic imbalance, and gene
expression in OS cell lines relative to the normal human
osteoblasts. The identification of differential methylation in the
originally described Me-DIP-chip protocol involves detection of
enriched regions in the IP fraction relative to the IN fraction
within a particular sample, and subsequent comparison between
the samples [37,38]. Although this approach resulted in successful
mapping of genome-wide DNA methylation profiles it suffered
from some limitations, including low resolution arrays, as well as
an indirect approach for detection of differentially methylated
genes between cell lines.
We utilized Affymetrix Promoter Tilling Arrays that were
previously used for high-resolution mapping of epi-toxicogenomic
profiles of global histone acetylation patterns in a breast cancer model
of environmental exposures to a carcinogen benzopyrene [39]. By
applying this Me-DIP-chip protocol, including specific HMM-based
detection algorithms on arrays, significant changes in DNA
methylation in regions as short as 300 nucleotides were detected
(Table S1 and S2) (Figure 4). This is a particularly important finding,
given the evidence that changes in DNA methylation profiles of very
short spans of DNA in gene promoters can significantly affect gene
expression [40–42]. Such high resolution analysis also allowed for a
precise detection of differentially methylated regions within gene
promoters (both hypo- and hypermethylation), as well as highly
efficient design of gene-specific validation experiments (Figure 2)
including gene-specific real-time PCR for Me-DIP enrichment
(Figure 3), and EpiTYPER quantitative methylation analysis
(Figure 4) across many gene promoters.
Figure 8. Validation of array-CGH abnormality calls by metaphase FISH. A: the copy number abnormality calls identified by array-CGH
analysis are shown on the left side of the chromosome 8 ideogram (850-band resolution) for each cell lines. The log
2
ratios of a-CGH enrichment
detected by genomic segmentation algorithm are represented by a spectrum from green (23) to red (+3). Arrows indicate the chromosomal
localization of the FISH probes. Metaphases from MG-63 (B), and U-2 OS (C), were co-hybridized with the following probes: chromosome 8
centromere (pale blue), RP11-440N18 (8q24.21) (red) and RP11-349C2 (8q24.3) (green).
doi:10.1371/journal.pone.0002834.g008
Integrative Epi/Genomics in OS
PLoS ONE | www.plosone.org 10 June 2008 | Volume 3 | Issue 7 | e2834

Region detection in Me-DIP-chip or ChIP-chip studies are
commonly performed by detecting enriched regions by applying a
chosen ‘‘cut off’’ for the IP/IN signal and subsequent comparison
of the enriched regions between cell lines [37–39,43]. This indirect
approach is limited to detection only enriched regions (i.e.
hypermethylated), and may be biased, based on the CpG density
for specific regions [38]. To address these limitations, the detection
of significant differentially enriched regions was based on the
application of the HMM algorithm on the fold differences between
IP/IN-normalized signal of the cancer (U2OS, MG63) versus
normal (human osteoblast) cells across each of the 4.2 milion
probes individually. Therefore, by making detection of significance
relative to the normal background, our approach was designed to
detect cancer-specific hypo- or hypermethylation in OS cells.
Cancer-specific changes in DNA methylation may play a role in
many cellular functions including destabilization of chromatin,
promotion of genomic instability, and hypomethylation of
repetitive elements, while some changes may play a functional
role in deregulation of gene expression [3,6,44]. Affymetrix
Promoter Tilling arrays are annotated to 25,500 human genes,
allowing integration of OS gene-specific methylation profiling with
the expression microarray data. VENN analysis of the lists of genes
with cancer-specific DNA methylation and gene expression
signatures narrowed down the list of genes of interest, to 106
genes that were hypermethylated and underexpressed in U2OS
(Table S7), and 34 in MG63 (Table S8). Genes common to both
cell lines (Table S9), included LHX9 that was interrogated and
confirmed in detail (Figure 3, and 4). Significant hypomethylation
and overexpression was evident in 76 genes in U2OS (Table S10),
and 92 genes in MG63 cells (Table S11). These data suggest that,
in addition to the hypermethylation and possible loss of gene
expression, increases in gene expression driven by hypomethyla-
tion may play a role in OS.
c-MYC was shown to be hypomethylated in acute leukemia
derived from myelodysplastic syndromes [45]. In a recent study it
was shown that hypomethylation of the LINE 1 retrotransposon,
as well as amplification of c-MYC can be used to predict tumour
stage in prostate cancer [46]. Our data show hypomethylation,
genomic gain, and overexpression of c-MYC-oncogene that is
particularly evident in MG63 cell line (Table S15) (Figure 8).
Furthermore, the IPA analysis of cancer-specific changes in gene
expression networks of MG63 cells revealed significant disruption
in the MYC-centered gene expression network, and revealed both
genetic and epigenetic abnormalities in a number of genes in this
pathway (Figure 7).
Majority of the genes that showed significant changes in DNA
methylation, genomic imbalance, and gene expression exhibited
DNA hypomethylation, genomic gain and overexpression in both
U2OS and MG63 cell lines (Figure 5C). Furthermore, majority of
these genes (Table S14 and S15) are located in the regions we and
others have previously shown to be amplified in OS, including
chromosomes 6p, 8q, 9p, and17p in OS tumours and cell lines
[18–25]. These data are consistent with the emerging evidence in
other cancers that links DNA hypomethylation and regions of
genomic imbalance and genomic instability [47–50].
Regions of hypo- and hypermehylation in U2OS and MG63
cells mapping close to larger repeat elements including segmental
duplications (.1000 nucleotide) were observed. For example we
identified a large region located at chromosome 8q21.2 spanning
genes REXO1L1 and REXO1L2P that exhibited DNA hypomethy-
lation in U2OS and not MG63 or osteoblast cells (Figure S4).
Interestingly, this hypomethylation spanned 8 CpG islands that
overlapped regions of segmental duplications, while outside CpG
islands were not affected. Alternatively, we also observed large
regions of hypermethylation including one at chromosome
5q31spanning nearly 2 Mb and including three Protocadherin
gene families, in both U2OS and MG63 cells (Figure S5).
Similarly, this hypermethylation was evident in the high-density
CpG island cluster that mapped at and around segmental
duplication hotspot. We validated one of these genes, PCDHB8
using real-time PCR (Figure 3) and quantitative DNA methylation
analysis (Figure 4). These observations indicate that in addition to
gene specific hypo- and hypermethylation changes, disruptions of
DNA methylation profiles may affect large regions of DNA which
in some cases may be associated with genomic repeats.
Genomic hypermethylation, especially in the context of tumour
suppressor inactivation may present an attractive target for the
chemotherapeutic intervention. In our previous study we have
performed microarray analysis of gene expression in U2OS cells
treated by the demethylating agent Decitabine and identified
genes with significant Decitabine-dependent overexpression [35].
Comparison of that dataset with the genes that were differentially
methylated in our Me-DIP-chip analysis of U2OS cells revealed
43 genes that were in common, majority of which (32) were
hypermethylated in U2OS relative to normal osteoblasts (Table
S17). One of those genes, PTGDR, exhibited 14-fold hypermethy-
lation in U2OS, and 6.5-fold overexpression after Decitabine
treatment of U2OS cells. Our validation of the enrichment
(Figure 3), and CpG methylation (Figure 4) have confirmed
extensive hypermethylation of PTGDR in U2OS cells specifically.
Interestingly, a recent paper by Sugino and coworkers showed
hypermethylation and Decitabine-induced overexpression of the
PTDGR and its’ alternative transcript PTDGR2 in neuroblastoma,
which is another common paediatric solid tumour [51].
In addition to gene hypermethylation, we observed promoter
related hypomethylation in 397 and 359 genes in MG63 and
U2OS cells respectively (Table S3, and S4), 19 and 25 percent of
which showed overexpression profiles relative to normal osteo-
blasts respectively (Table S7 and S8). Such correlation between
hypomethylation and overexpression is particularly evident in the
MG63 cell line (Figure 6B). Good agreement between hypo-
methylation and overexpression was also evident in U2OS cells, as
was hypermethylation with underexpression. A subset of genes also
exhibited hypomethylation with underexpression (Figure 6B). The
evidence of genes with concurrent hypomethylation and under-
expression in U2OS may be related to additional factors such as
presence/absence of specific transcription factors, epigenetic
histone modification, etc. In both U2OS and MG63 cells
hypomethylation seems to also strongly correlate with genomic
gain, while genomic gain also correlates with gene overexpression
(Figure 6B). Another important observation is that in U2OS cells
7.5% (93/1230) differentially methylated genes were in the regions
of copy number change, while in MG63 cells these genes comprise
17.1% (137/801) (Figure 6A). This marked difference between the
two cell lines is further evident in the analysis of the types of
methylation changes in the regions of genomic imbalance. While
majority of such genes in MG63 cells are hypomethylation events
in the regions of genomic gain, in U2OS cells both gene
hypomethylation and hypermethylation are evident in the regions
of genomic gain (Figure 6B). In both cell lines majority of
epigenetic changes in regions of genomic imbalance is regions of
genomic gain. One possible explanation for this observation is that
unlike in regions of genomic loss where gene expression or dosage
will be directly affected by the loss of genomic content, in the
regions of genomic gain DNA methylation may provide an
additional ‘‘layer’’ of control of the gene expression. As such
epigenetic changes in the regions of genomic gain may have an
additional selective advantage during tumour evolution. Further-
Integrative Epi/Genomics in OS
PLoS ONE | www.plosone.org 11 June 2008 | Volume 3 | Issue 7 | e2834

more, 3-way VENN analysis also shows that a subset of genes with
epigenetic, genetic and gene expression changes predominantly
display hypomethylation, genomic gain, and overexpression, in
both U2OS and MG63 cells (Figure 6C). This observation
suggests the possibility that during tumour evolution, hypomethy-
lation and gain of certain genomic regions may act cooperatively
to increase the gene expression/gene dosage levels. It is important
to note that although Affymetrix Promoter Arrays that were used
in this study provide detailed information of extensive promoter
regions of more than 25,500 genes, additional epigenetic changes
in intragenic regions and genomic repeats may account for
additional epigenetic influences in these cell lines.
To further assess the contribution of epigenetic and genetic
changes to the gene expression profiles in U2OS and MG63 cells
we performed Ingenuity Pathway Analysis of gene networks and
cellular functions with most significant changes in gene expression,
and compared it to the analyses of the DNA methylation and
genome imbalance in these cells. Although U2OS and MG63 cells
bear significant morphological and genetic differences (for
example U2OS is P53-positive, MG63 is P53-negative cell line),
top four out of five most significantly-affected cellular functions are
identical between the two cell lines (Figure S3). There is also a
significant epigenetic and genetic contribution to these cellular
functions. Such correlation is also seen in a 2-way VENN
comparison of epigenetic, genetic, and gene expression changes in
these two cell lines (Figure 5). Interestingly, in U2OS there was a
strong epigenetic contribution to disruption of cellular growth and
proliferation, while in MG63 cells there is a robust genetic
contribution to deregulation of these cellular functions, as well as
cellular development (Figure S3). An example of a gene network
with evidence of potential genetic and epigenetic contribution to
the cancer-specific gene expression profile is the c-MYC-related
network in MG63 cells (Figure 7 and 8). Collectively, these data
suggest that both epigenetic and copy number disruptions play a
cumulative role in deregulation of gene networks in OS cell lines.
Integration of genomic and epigenomic studies promises to
provide us with new insights in cancer aetiology. Recently,
genome-wide integrative studies of epigenetic and gene expression
profiles in Arabidopsis [52] and human leukemia [53] described
correlation between epigenomic alterations and gene expression
profiles. In this paper, we have used an integrative epigenetic,
genome imbalance, and gene expression analysis to provide first
genomic DNA methylation maps, and identified and validated
genes with aberrant DNA methylation in two osteosarcoma cell
lines. In conclusion, these data provide evidence of the cumulative
roles of epigenetic and genetic mechanisms in cancer-related
deregulation of gene expression networks.
Materials and Methods
Cell culture and DNA & RNA extraction
The human OS cell lines U2OS (ATCC # HTB-96) and
MG63 (ATCC # CRL-1427 ) were purchased from 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. Normal human osteoblasts are primary osteoblasts
from the hip bone of a normal male donor that were purchased
from PromoCell (Heidelberg, Germany, Catalogue # C-12760)
and maintained in medium provided by the manufacturer and
used at culture passage 3. Three days after plating (,80%
confluent), cells where harvested for DNA or RNA extractions.
DNA was extracted after harvesting the cells by trypsinization
followed by phenol-chloroform extraction and subsequent precip-
itation in 100% ethanol. DNA precipitate was washed with 70%
ethanol then eluted in DNAse free water. Total RNA from U2OS,
MG63 cells and osteoblasts was extracted using the TRIzol
reagent according to the manufacturer’s instructions (Invitrogen),
and analyzed for quantity and quality using bio-analyzer (Agilent
Technologies, USA).
Me-DIP-chip
Analysis of genomic methylation profiles across 25,500 gene
promoters (2.5 Kb 39, and 7.5–10 Kb 59 from the transcriptional
start site) at 35 nucleotide resolution was performed by methylated
DNA immunoprecipitation followed by the microarray hybridiza-
tion (Me-DIP-chip) using the Affymetrix Human Promoter 1.0R
Tilling Arrays using a modification of the Affymetrix chromatin
immunoprecipitation assay protocol. Genomic DNA from normal
human osteoblasts, U2OS and MG63 cells was sonicated (Sonic
Dismembrator Model 100, Fisher Scientific) to reduce the size of
DNA fragments to 200–1000 nucleotides and used as input (IN).
DNA (4 mg) was immunoprecipitated (IP) with 10 mlof5mC
Antibody (Eurogentec, BI-MECI-0500) using the Me-DIP proto-
col [37], with following modifications: The antibody-DNA
complexes were immunoprecipitated using Protein-A Agarose
Beads (Upstate, Massachusetts, USA , Catalogue # 16-125), and
the recovered DNA was purified using the Qiaquick PCR
purification kit (Qiagen, #28106, Maryland USA).
Random priming reactions of total 50 ng of IP and IN DNA,
followed by the genomic PCR, were performed using a
modification of the Affymetrix chromatin immunoprecipitation
assay protocol as previously described [39]. The uracil glycosilase
treatment, streptavidin/phycoerythrin labeling, hybridization and
microarray scanning were performed as per Affymetrix chromatin
immunoprecipitation assay protocol at The Centre for Applied
Genomics (The Hospital for Sick Children, Toronto, ON,
Canada). All microarray experiments were performed in duplicate
for both IP and IN fractions of each DNA sample starting with
initial sonication step; totalling 4 arrays per DNA sample (2 IP and
2 IN).
Array-CGH (a-CgH)
The Agilent Human Genome CGH microarray 44k and 244A
(Agilent Technologies, Inc., Palo Alto, USA) were used for the
MG63 and U2OS array-CGH experiments, respectively. Three
mg of Human Genomic DNA from multiple anonymous male
donors (Promega Corporation, Madison, USA) and 3 mg of test
genomic DNA sample were subject array-CGH as previously
described [29]. Arrays were washed according to the manufac-
turer’s recommendations; air dried, and scanned using an Agilent
2565AA DNA microarray scanner (Agilent Technologies, USA),
and processed using Agilent Feature Extraction software. Dye-
swapped duplicate experiments were carried out for both MG63
and U2OS cell lines.
Expression Arrays
Genomic RNA expression analysis was performed using the
Affymetrix Gene 1.0 ST arrays, where each of the 28,869 genes is
represented on the array by approximately 26 probes spread
across the full length of the gene, providing a more complete and
more accurate picture of gene expression than 39 based expression
array designs. RNA (200 ng) from normal human osteoblasts,
U2OS and MG63 cells was analyzed as per manufacturer’s
instructions at The Centre for Applied Genomics (The Hospital
for Sick Children, Toronto, ON, Canada). Each microarray
experiment was performed in duplicate.
Integrative Epi/Genomics in OS
PLoS ONE | www.plosone.org 12 June 2008 | Volume 3 | Issue 7 | e2834

Data Analysis and Integration
Data from Me-DIP-chip and RNA expression array experi-
ments in the form of .cel files (GCOS 1.3 software), and a-CGH
.txt files (Agilent Feature Extraction software) were imported into,
analysed and integrated using the Partek Genomic Suite Software
(PGS) (GEO accession numbers: GSE11416, GSE7077) (Figure 1).
The Me-DIP-chip .cel files for osteoblasts, U2OS and MG63 (2
IP and 2 IN) were log
2
transformed, normalized and imported into
PGS as previously described [39]. We baseline normalized the
signal using the matched-pair normalization tool in PGS, by
subtracting log
2
IN signal intensity at each of 4.2 million from cell
type-matched IP signals resulting in 6 corrected datasets (IP1 cor.
and IP2 cor. for each cell line). In order to determine the relative
enrichment of IP cor. signals in U2OS or MG63 relative to
osteoblasts, we used the PGS 1-way ANOVA tool and calculated
the fold change using the geometric mean (for log-transformed
data). Thus generated signals represented baseline-normalized,
osteoblast-relative, and cancer cell-specific enrichment levels for
each of 4.2 million probes. Significant region detection (both
enrichment/hypermethylation and depletion/hypomethylation)
was performed using Hidden Markov Model (HMM) tool in
PGS by applying it to the fold change data for both U2OS and
MG63. In order to capture significant differences in enrichment in
each cancer cell line vs. normal osteoblast across shorter genomic
regions (min. ,350 nucleotides) with robust enrichment differ-
ences as well as intermediate (min. ,500 nucleotides) and large
regions (min. 1400 nucleotides), three HMM algorithms were
applied (s-HMM, m-HMM, and l-HMM). Following cut-offs were
used: s-HMM (min. probes: 10, detection states: 25,5, ignore
state: 0, max. probability: 0.99, genomic decay: 10,000, sigma: 2),
m-HMM (min. probes: 15, detection states: 23,3, ignore state: 0,
max. probability: 0.99, genomic decay: 10,000, sigma: 1), and l-
HMM (min. probes: 40, detection states: 21.5,1.5, ignore state: 0,
max. probability: 0.99, genomic decay: 10,000, sigma: 1).
Significantly enriched/hypermethylated and depleted/hypo-
methylated HMM regions were annotated to the corresponding
genes present on the Affymetrix Gene 1.0 Array using the
HuGene-1_0-st-v1.na24.hg18.transcript.csv file. The visualization
of data using heat maps, .wig files for UCSC Genome Browser,
genome view files, dot plots, and VENN diagrams and
corresponding data tables/lists was performed using PGS as
previously described [39].
The expression array .cel files for osteoblasts, U2OS, and
MG63 (2 arrays each) were imported using PGS Gene Expression
Workflow tool (subject to RMA normalization and log
2
transfor-
mation). The significantly over- and under-expressed genes in
U2OS and in MG63 cell lines compared to normal osteoblasts
were detected using 1-way ANOVA tool at p,0.01 and +/2 2-
fold enrichment. Significantly over- and under-expressed regions
were annotated to the corresponding genes present on the
Affymetrix Gene 1.0 Array using the HuGene-1_0-st-
v1.na24.hg18.transcript.csv file. Visualizations and VENN analy-
sis were performed in PGS.
To analyze genomic imbalance in U2OS and MG63 (2 arrays
each) the processed R signal and processed G signal columns from
the Agilent Feature Extraction-generated a-CGH .txt files were
imported into PGS. The cancer cell line-specific signal across all
probes was normalized as a ratio to baseline using Normalise to
Baseline Tool in PGS, where baseline data corresponded to the
normal human DNA. The data was then log
2
transformed using
the PGS Normalization and Scaling Tool. In order to detect
regions of genomic gain and loss we applied the Genomic
Segmentation tool with segmentation parameters set at: min.
probes: 10 for MG63, and 50 for U2OS (due to 5-fold higher
probe density at respective arrays), p-value threshold: 0.01, and
signal to noise: 0.1. Region report was set at values bellow 21/+1
(log
2
) and p-value threshold of 0.05. Regions of significant gain or
loss were annotated to the corresponding genes present on the
Affymetrix Gene 1.0 Array using the HuGene-1_0-st-
v1.na24.hg18.transcript.csv file. Visualizations and VENN analy-
sis were performed in PGS.
The integration of significantly hypo- and hyper-methylated,
over- and under-expressed, and gained and lost gene lists was
performed using the VENN tool in PGS, and visualization to the
resulting tracks was performed as previously described [39].
Network identification and canonical pathway analysis
Functional identification of gene networks was performed using
Ingenuity Pathway Analysis program as previously described
[39].The tables representing the differentially expressed, methyl-
ated and genomicaly imbalanced genes from U2OS and MG63
cells as well as the corresponding expression (relative to
osteoblasts), methylation enrichment (mean of regions associated
with individual gene, relative to osteoblasts ), and gain (+1) and loss
(21) values were imported as individual experiments using the
Core Analysis tool. The analysis was performed using Ingenuity
Knowledge Database and was limited to direct interactions only.
Gene-specific real-time PCR validation of Me-DIP DNA
enrichment
In order to validate the enrichment status of genes detected by
Me-DIP-chip array we performed a gene specific real-time
expression analysis in a set of both common and cell-line specific
significantly enriched and depleted genes in MG63 and U2OS
cells. Real-time PCRs were performed in 1X SYBR Green PCR
mixture (Bio-Rad, USA), 10 ng of the original IP or IN DNA, and
1 mM gene-specific primers (Table S18) that were designed using
Primer 3 (http://frodo.wi.mit.edu/) software. The reactions were
performed in triplicate and relative enrichment determined as a
ratio of U2OS or Mg63 IP(Ct)/IN(Ct) over osteoblast IP(Ct)/
IN(Ct).
EpiTYPER quantitation of CpG Methylation
In order to validate the methylation status in a set of a genes
detected as enriched or depleted in the Me-DIP-chip experiment
we performed the quantitative analysis of CpG using the
EpiTYPER System for quantitative DNA methylation analysis
using the MassARRAY system (Sequenom, USA) at the Analytical
Genetics Technology Centre (Princess Margaret Hospital, Tor-
onto, ON, Canada), as per manufacturer’s instructions (http://
www.analyticalgenetics.ca/Function/Business/Service/Methylation.
aspx), using the original DNA samples for U2OS, MG63 and human
osteoblasts that were used in the Me-DIP-chip experiment. The gene-
specific bisulfite primers (Table S18) were designed to overlap HMM-
detected regions of differential enrichment using the MethPrimer
Software (http://www.urogene.org/methprimer/index1.html). Each
analysis was performed in triplicate and all resolvable CpG signals
were mapped and standard error bars are displayed.
Fluorescence In-Situ Hybridization (FISH)
Metaphase spreads for cytogenetics analysis were prepared from
the MG-63 and U-2 OS cultures using the conventional methods
[54]. A commercial probe for centromere 8 was used according to
the manufacturer’s instructions (CEP 8 SpectrumAqua Probe,
Abbott Molecular, Des Plaines, IL). BAC probe located within the
8q24.21 and 8q24.3 regions, were identified using the Resources
Integrative Epi/Genomics in OS
PLoS ONE | www.plosone.org 13 June 2008 | Volume 3 | Issue 7 | e2834

for Molecular Cytogenetics website (www.biologia.uniba.it/rmc/).
The BAC clones RP11-440N18 and RP11-349C2 were obtained
from the Centre for Applied Genomics (Toronto, ON, Canada)
and labeled by nick-translation (Nick-Translation Kit, Abbott
Molecular) using SpectrumOrange, or SpectrumGreen (Abbott
Molecular). Standard FISH procedures were followed [55]. Sides
were observed using an epifluorescence Zeiss Imager Z1
microscope equipped with a digital camera Axio Cam MRm
and AxioVision 4.3 capturing software (Zeiss, Toronto, ON,
Canada).
Supporting Information
Figure S1 Clustering analysis of the Me-DIP-chip array data.
(A) Raw, imported .cel file data of 4.2 million probes was subject
to Euclidean hierarchical clustering using PGS before (left panel)
and after background normalization. The replicate experiments
are numbered. Note the reproducibility of the signal in replicate
experiments, and separate clustering between IP and IN arrays, as
well as the separate clustering between cancer (U2OS, MG63) and
normal (osteoblast) cells. (B) Principal Component Analysis (PCA)
plot generated in PGS of the .cel file data revealing separate
clustering similar to hierarchical clustering, totalling 28%
variability across data
Found at: doi:10.1371/journal.pone.0002834.s001 (7.83 MB TIF)
Figure S2 Hidden Markov Model detection of significantly
enriched/depleted regions in Me-DIP-chip data. The heat-map
and profile images of the LHX9 gene promoter are as described in
Figure 2. LHX9 promoter in U2OS (A) and MG63 (B) exhibiting
significantly enriched regions (shaded boxes) detected by both l-
HMM algorithm (left), and s-HMM (algorithm). Note that l-HMM
detects longer regions with overall less robust enrichment, that
may include shorter regions with more robust enrichment detected
by s-HMM.
Found at: doi:10.1371/journal.pone.0002834.s002 (4.73 MB TIF)
Figure S3 Gene expression, epigenetic, and genetic contribution
to cellular function disruption in OS. Top 5 most significantly
affected biological functions in relation to the deregulation of gene
expression in U2OS and MG63 versus normal osteoblasts were
detected using the Ingenuity Pathway Analysis (dark blue bars),
and compared to IPA analysis of DNA methylation (light blue
bars) and genomic imbalance (turquoise bars) in these cells. The p-
value threshold is set at 0.05.
Found at: doi:10.1371/journal.pone.0002834.s003 (7.98 MB TIF)
Figure S4 Regional hypomethylation in U2OS cells. Top panel
is the PGS-generated region view of the hypomethylated genomic
region in U2OS cells located at 8q21.2, featuring the colour-coded
profile of the signal from each cell type, and the corresponding
heat-map of the replicate array experiments bellow (in log2).
Middle panel shows the PGS-generated .wig file of this region
imported into UCSC Genome Browser, displaying the corre-
sponding gene, CpG island, and segmental duplication tracks.
Found at: doi:10.1371/journal.pone.0002834.s004 (10.31 MB
TIF)
Figure S5 Hypermethylation of Protocadherin gene family in
U2OS and MG63 cells. Top panel is the PGS-generated region
view of the 2 Mb hypermethylated genomic region in U2OS and
MG63 cells located at 5q31.3, featuring the colour-coded profile of
the signal from each cell type, and the corresponding heat-map of
the replicate array experiments bellow (in log2). Middle panel
shows the PGS-generated .wig file of this region imported into
UCSC Genome Browser, displaying the corresponding gene, CpG
island, and segmental duplication tracks.
Found at: doi:10.1371/journal.pone.0002834.s005 (15.60 MB
TIF)
Table S1 Significant differentially-methylated regions in U2OS
cells. Enriched and depleted regions detected by the l-HMM, m-
HMM, and s-HMM algorithm in U2OS cells relative to the
normal osteoblast levels are shown. Table also includes identifiers
such as precise chromosome location of the region and the
annotated gene, gene assignment, mean enrichment level across
the region (fold enrichment), region length, PGS region ID, and
HMM state (l-HMM - +/2 1.5, m-HMM +/2 3, s-HMM +/2
5).
Found at: doi:10.1371/journal.pone.0002834.s006 (0.42 MB
XLS)
Table S2 Significant differentially-methylated regions in MG63
cells. Same as Table S1, for MG63 cells.
Found at: doi:10.1371/journal.pone.0002834.s007 (0.31 MB
XLS)
Table S3 Significant differentially-methylated genes in U2OS
cells. Tabulation of hyper- and hypomethylated genes generated
from Table S1. In case where more than one HMM algorithm is
detected in a specific gene promoter, the longest HMM region is
listed.
Found at: doi:10.1371/journal.pone.0002834.s008 (0.30 MB
XLS)
Table S4 Significant differentially-methylated genes in MG63
cells cells. Same as Table S3, for MG63 cells.
Found at: doi:10.1371/journal.pone.0002834.s009 (0.20 MB
XLS)
Table S5 Differentially expressed genes in U2OS cells. Genes
with significant changes in expression, both over and under,
relative to normal osteoblasts, and the corresponding p-value and
fold change are shown.
Found at: doi:10.1371/journal.pone.0002834.s010 (0.65 MB
XLS)
Table S6 Differentially expressed genes in MG63 cells. Same as
Table S5, for MG63 cells.
Found at: doi:10.1371/journal.pone.0002834.s011 (0.43 MB
XLS)
Table S7 Hypermethylated and underexpressed genes in U2OS
cells. Genes with significant hypermethylation and loss of
expression, relative to normal osteoblasts, and the corresponding
p-value and fold change underexpression and methylation mean of
region (fold enrichment) are shown.
Found at: doi:10.1371/journal.pone.0002834.s012 (0.03 MB
XLS)
Table S8 Hypermethylated and underexpressed genes in MG63
cells. Same as Table S7, for MG63 cells.
Found at: doi:10.1371/journal.pone.0002834.s013 (0.02 MB
XLS)
Table S9 Common hypermethylated and underexpressed genes
in U2OS and MG63 cells. Common genes with significant
hypermethylation and loss of expression in U2OS and MG63 cells,
relative to normal osteoblasts, and the corresponding p-value and
fold change underexpression and methylation mean of region (fold
enrichment) are shown.
Found at: doi:10.1371/journal.pone.0002834.s014 (0.02 MB
XLS)
Table S10 Hypomethylated and overexpressed genes in U2OS
cells. Genes with significant hypomethylation and gain of
expression, relative to normal osteoblasts, and the corresponding
Integrative Epi/Genomics in OS
PLoS ONE | www.plosone.org 14 June 2008 | Volume 3 | Issue 7 | e2834

p-value and fold change underexpression and methylation mean of
region (fold enrichment) are shown.
Found at: doi:10.1371/journal.pone.0002834.s015 (0.03 MB
XLS)
Table S11 Hypomethylated and overexpressed genes in MG63
cells. Same as table S10, for MG63 cells.
Found at: doi:10.1371/journal.pone.0002834.s016 (0.03 MB
XLS)
Table S12 Genes in regions of significant gain and loss in U2OS
cells. Precise chromosomal location and gene assignment for genes
detected in regions of significant gain or loss detected by the PGS
genomic segmentation algorithm in U2OS cells.
Found at: doi:10.1371/journal.pone.0002834.s017 (0.11 MB
XLS)
Table S13 Genes in regions of significant gain and loss in MG63
cells. Same as Table S12, for MG63 cells.
Found at: doi:10.1371/journal.pone.0002834.s018 (0.13 MB
XLS)
Table S14 U2OS genes with significant disruptions of DNA
methylation, gene expression, and genomic imbalance. The genes
from the 3-way intersect from DNA methylation, genomic
imbalance, and gene expression VENN diagram (Figure 6A),
and their corresponding expression and methylation values
(relative to osteoblasts), and genomic imbalance status (gain or
loss) are indicated.
Found at: doi:10.1371/journal.pone.0002834.s019 (0.02 MB
XLS)
Table S15 MG63 genes with significant disruptions of DNA
methylation, gene expression, and genomic imbalance. Same as
Table S14, for MG63 cells.
Found at: doi:10.1371/journal.pone.0002834.s020 (0.03 MB
XLS)
Table S16 Most significantly affected gene networks in U2OS
and MG63 cells. Ingenuity Pathway Analysis-generated networks
with most significant changes in gene expression in U2OS and
MG63 cells relative to osteoblasts.
Found at: doi:10.1371/journal.pone.0002834.s021 (0.03 MB PPT)
Table S17 Comparison of decitabine-induced re-expressed and
Me-DIP-chip detected genes. VENN analysis of the Me-DIP-chip
detected genes with significant changes in DNA methylation and
genes with decitabine-induced overexpression [35] in U2OS cells.
Fold enrichment in Me-DIP-chip (relative to osteoblasts), ford
overexpression (relative to osteoblasts), and expression p-value are
indicated.
Found at: doi:10.1371/journal.pone.0002834.s022 (0.03 MB
XLS)
Table S18 Real-time PCR and EpiTYPER validation primers.
F - forward primer, R - reverse primer.
Found at: doi:10.1371/journal.pone.0002834.s023 (0.03 MB
XLS)
Author Contributions
Conceived and designed the experiments: BS MZ JS. Performed the
experiments: BS MY KAR GM. Analyzed the data: BS. Wrote the paper:
BS MZ JS.
References
1. Hanahan D, Weinberg RA (2000) The hallmarks of cancer. Cell 100: 57–70.
2. Rodenhiser D, Mann M (2006) Epigenetics and human disease: translating basic
biology into clinical applications. Cmaj 174: 341–348.
3. Esteller M (2007) Cancer epigenomics: DNA methylomes and histone-
modification maps. Nat Rev Genet 8: 286–298.
4. Momparler RL, Bovenzi V (2000) DNA methylation and cancer. J Cell Physiol
183: 145–154.
5. Das PM, Singal R (2004) DNA methylation and cancer. J Clin Oncol 22:
4632–4642.
6. Ehrlich M (2002) DNA methylation in cancer: too much, but also too little.
Oncogene 21: 5400–5413.
7. Bedford MT, van Helden PD (1987) Hypomethylation of DNA in pathological
conditions of the human prostate. Cancer Res 47: 5274–5276.
8. Lin CH, Hsieh SY, Sheen IS, Lee WC, Chen TC, et al. (2001) Genome-wide
hypomethylation in hepatocellular carcinogenesis. Cancer Res 61: 4238–4243.
9. Kim YI, Giuliano A, Hatch KD, Schneider A, Nour MA, et al. (1994) Global
DNA hypomethylation increases progressively in cervical dysplasia and
carcinoma. Cancer 74: 893–899.
10. Wahlfors J, Hiltunen H, Heinonen K, Hamalainen E, Alhonen L, et al. (1992)
Genomic hypomethylation in human chronic lymphocytic leukemia. Blood 80:
2074–2080.
11. Feinberg AP, Vogelstein B (1983) Hypomethylation of ras oncogenes in primary
human cancers. Biochem Biophys Res Commun 111: 47–54.
12. Nishigaki M, Aoyagi K, Danjoh I, Fukaya M, Yanagihara K, et al. (2005)
Discovery of aberrant expression of R-RAS by cancer-linked DNA hypomethy-
lation in gastric cancer using microarrays. Cancer Res 65: 2115–2124.
13. Papaggeli PC, Kortsaris AC, Matsouka PT (2003) Aberrant methylation of c-
myc and c-fos protooncogenes and p53 tumor suppressor gene in myelodys-
plastic syndromes and acute non-lymphocytic leukemia. J Buon 8: 341–350.
14. Alves G, Tatro A, Fanning T (1996) Differential methylation of human LINE-1
retrotransposons in malignant cells. Gene 176: 39–44.
15. Singer MF, Krek V, McMillan JP, Swergold GD, Thayer RE (1993) LINE-1: a
human transposable element. Gene 135: 183–188.
16. Jurgens B, Schmitz-Drager BJ, Schulz WA (1996) Hypomethylation of L1 LINE
sequences prevailing in human urothelial carcinoma. Cancer Res 56: 5698–5703.
17. Tuck-Muller CM, Narayan A, Tsien F, Smeets DF, Sawyer J, et al. (2000) DNA
hypomethylation and unusual chromosome instability in cell lines from ICF
syndrome patients. Cytogenet Cell Genet 89: 121–128.
18. Batanian JR, Cavalli LR, Aldosari NM, Ma E, Sotelo-Avila C, et al. (2002)
Evaluation of paediatric osteosarcomas by classic cytogenetic and CGH
analyses. Mol Pathol 55: 389–393.
19. Forus A, Weghuis DO, Smeets D, Fodstad O, Myklebost O, et al. (1995)
Comparative genomic hybridization analysis of human sarcomas: II. Identifi-
cation of novel amplicons at 6p and 17p in osteosarcomas. Genes Chromosomes
Cancer 14: 15–21.
20. Lim G, Karaskova J, Beheshti B, Vukovic B, Bayani J, et al. (2005) An integrated
mBAND and submegabase resolution tiling set (SMRT) CGH array analysis of
focal amplification, microdeletions, and ladder structures consistent with
breakage-fusion-bridge cycle events in osteosarcoma. Genes Chromosomes
Cancer 42: 392–403.
21. Lim G, Karaskova J, Vukovic B, Bayani J, Beheshti B, et al. (2004) Combined
spectral karyotyping, multicolor banding, and microarray comparative genomic
hybridization analysis provides a detailed characterization of complex structural
chromosomal rearrangements associated with gene amplification in the
osteosarcoma cell line MG-63. Cancer Genet Cytogenet 153: 158–164.
22. Squire JA, Pei J, Marrano P, Beheshti B, Bayani J, et al. (2003) High-resolution
mapping of amplifications and deletions in pediatric osteosarcoma by use of
CGH analysis of cDNA microarrays. Genes Chromosomes Cancer 38: 215–225.
23. Tarkkanen M, Elomaa I, Blomqvist C, Kivioja AH, Kellokumpu-Lehtinen P, et
al. (1999) DNA sequence copy number increase at 8q: a potential new prognostic
marker in high-grade osteosarcoma. Int J Cancer 84: 114–121.
24. Zielenska M, Bayani J, Pandita A, Toledo S, Marrano P, et al. (2001) Comparative
genomic hybridization analysis identifies gains of 1p35 approximately p36 and
chromosome 19 in osteosarcoma. Cancer Genet Cytogenet 130: 14–21.
25. Zielenska M, Marrano P, Thorner P, Pei J, Beheshti B, et al. (2004) High-
resolution cDNA microarray CGH mapping of genomic imbalances in
osteosarcoma using formalin-fixed paraffin-embedded tissue. Cytogenet Ge-
nome Res 107: 77–82.
26. Bayani J, Zielenska M, Pandita A, Al-Romaih K, Karaskova J, et al. (2003)
Spectral karyotyping identifies recurrent complex rearrangements of chromo-
somes 8, 17, and 20 in osteosarcomas. Genes Chromosomes Cancer 36: 7–16.
27. Ozaki T, Neumann T, Wai D, Schafer KL, van Valen F, et al. (2003)
Chromosomal alterations in osteosarcoma cell lines revealed by comparative
genomic hybridization and multicolor karyotyping. Cancer Genet Cytogenet
140: 145–152.
28. Sandberg AA, Bridge JA (2003) Updates on the cytogenetics and molecular
genetics of bone and soft tissue tumors: osteosarcoma and related tumors.
Cancer Genet Cytogenet 145: 1–30.
29. Selvarajah S, Yoshimoto M, Maire G, Paderova J, Bayani J, et al. (2007)
Identification of cryptic microaberrations in osteosarcoma by high-definition
oligonucleotide array comparative genomic hybridization. Cancer Genet
Cytogenet 179: 52–61.
Integrative Epi/Genomics in OS
PLoS ONE | www.plosone.org 15 June 2008 | Volume 3 | Issue 7 | e2834

30. Locklin RM, Oreffo RO, Triffitt JT (1998) Modulation of osteogenic
differentiation in human skeletal cells in Vitro by 5-azacytidine. Cell Biol Int
22: 207–215.
31. Ryhanen S, Pirskanen A, Jaaskelainen T, Maenpaa PH (1997) State of
methylation of the human osteocalcin gene in bone-derived and other types of
cells. J Cell Biochem 66: 404–412.
32. Villagra A, Gutierrez J, Paredes R, Sierra J, Puchi M, et al. (2002) Reduced CpG
methylation is associated with transcriptional activation of the bone-specific rat
osteocalcin gene in osteoblasts. J Cell Biochem 85: 112–122.
33. Ulaner GA, Vu TH, Li T, Hu JF, Yao XM, et al. (2003) Loss of imprinting of
IGF2 and H19 in osteosarcoma is accompanied by reciprocal methylation
changes of a CTCF-binding site. Hum Mol Genet 12: 535–549.
34. Harada K, Toyooka S, Maitra A, Maruyama R, Toyooka KO, et al. (2002)
Aberrant promoter methylation and silencing of the RASSF1A gene in pediatric
tumors and cell lines. Oncogene 21: 4345–4349.
35. Al-Romaih K, Somers GR, Bayani J, Hughes S, Prasad M, et al. (2007)
Modulation by decitabine of gene expression and growth of osteosarcoma U2OS
cells in vitro and in xenografts: Identification of apoptotic genes as targets for
demethylation. Cancer Cell Int 7: 14.
36. Al-Romaih K, Sadikovic B, Yoshimoto M, Yuzhuo W, Zielenska M, et al. (2008)
Decitabine-induced demethylation of 59 CpG island in GADD45A leads to
apoptosis in osteosarcoma cells. Neoplasia 10: 471–480.
37. Weber M, Davies JJ, Wittig D, Oakeley EJ, Haase M, et al. (2005)
Chromosome-wide and promoter-specific analyses identify sites of differential
DNA methylation in normal and transformed human cells. Nat Genet 37:
853–862.
38. Weber M, Hellmann I, Stadler MB, Ramos L, Paabo S, et al. (2007)
Distribution, silencing potential and evolutionary impact of promoter DNA
methylation in the human genome. Nat Genet 39: 457–466.
39. Sadikovic B, Andrews J, Carter D, Robinson J, Rodenhiser DI (2008) Genome-
wide H3K9 histone acetylation profiles are altered in benzopyrene-treated
MCF7 breast cancer cells. J Biol Chem 283: 4051–4060.
40. DiNardo DN, Butcher DT, Robinson DP, Archer TK, Rodenhiser DI (2001)
Functional analysis of CpG methylation in the BRCA1 promoter region.
Oncogene 20: 5331–5340.
41. Mancini DN, Rodenhiser DI, Ainsworth PJ, O’Malley FP, Singh SM, et al.
(1998) CpG methylation within the 59 regulatory region of the BRCA1 gene is
tumor specific and includes a putative CREB binding site. Oncogene 16:
1161–1169.
42. Mancini DN, Singh SM, Archer TK, Rodenhiser DI (1999) Site-specific DNA
methylation in the neurofibromatosis (NF1) promoter interferes with binding of
CREB and SP1 transcription factors. Oncogene 18: 4108–4119.
43. Bernstein BE, Mikkelsen TS, Xie X, Kamal M, Huebert DJ, et al. (2006) A
bivalent chromatin structure marks key developmental genes in embryonic stem
cells. Cell 125: 315–326.
44. Jones PA, Baylin SB (2002) The fundamental role of epigenetic events in cancer.
Nat Rev Genet 3: 415–428.
45. Tsukamoto N, Morita K, Karasawa M, Omine M (1992) Methylation status of
c-myc oncogene in leukemic cells: hypomethylation in acute leukemia derived
from myelodysplastic syndromes. Exp Hematol 20: 1061–1064.
46. Kindich R, Florl AR, Kamradt J, Lehmann J, Muller M, et al. (2006)
Relationship of NKX3.1 and MYC gene copy number ratio and DNA
hypomethylation to prostate carcinoma stage. Eur Urol 49: 169–175. discussion
175.
47. Roman-Gomez J, Jimenez-Velasco A, Agirre X, Castillejo JA, Navarro G, et al.
(2008) Repetitive DNA hypomethylation in the advanced phase of chronic
myeloid leukemia. Leuk Res 32: 487–490.
48. Schulz WA, Elo JP, Florl AR, Pennanen S, Santourlidis S, et al. (2002)
Genomewide DNA hypomethylation is associated with alterations on chromo-
some 8 in prostate carcinoma. Genes Chromosomes Cancer 35: 58–65.
49. Shann YJ, Cheng C, Chiao CH, Chen DT, Li PH, et al. (2008) Genome-wide
mapping and characterization of hypomethylated sites in human tissues and
breast cancer cell lines. Genome Res.
50. Wong N, Lam WC, Lai PB, Pang E, Lau WY, et al. (2001) Hypomethylation of
chromosome 1 heterochromatin DNA correlates with q-arm copy gain in
human hepatocellular carcinoma. Am J Pathol 159: 465–471.
51. Sugino Y, Misawa A, Inoue J, Kitagawa M, Hosoi H, et al. (2007) Epigenetic
silencing of prostaglandin E receptor 2 (PTGER2) is associated with progression
of neuroblastomas. Oncogene 26: 7401–7413.
52. Zhang X, Shiu S, Cal A, Borevitz JO (2008) Global analysis of genetic,
epigenetic and transcriptional polymorphisms in Arabidopsis thaliana using
whole genome tiling arrays. PLoS Genet 4: e1000032.
53. Figueroa ME, Reimers M, Thompson RF, Ye K, Li Y, et al. (2008) An
integrative genomic and epigenomic approach for the study of transcriptional
regulation. PLoS ONE 3: e1882.
54. Bayani J, Zielenska M, Marrano P, Kwan Ng Y, Taylor MD, et al. (2000)
Molecular cytogenetic analysis of medulloblastomas and supratentorial primitive
neuroectodermal tumors by using conventional banding, comparative genomic
hybridization, and spectral karyotyping. J Neurosurg 93: 437–448.
55. Maire G, Forus A, Foa C, Bjerkehagen B, Mainguene C, et al. (2003) 11q13
alterations in two cases of hibernoma: large heterozygous deletions and
rearrangement breakpoints near GARP in 11q13.5. Genes Chromosomes
Cancer 37: 389–395.
Integrative Epi/Genomics in OS
PLoS ONE | www.plosone.org 16 June 2008 | Volume 3 | Issue 7 | e2834