Abstract
Background
Current diagnostic tools for prostate cancer lack specificity and
sensitivity for detecting very early lesions. DNA methylation is a
stable genomic modification that is detectable in peripheral patient
fluids such as urine and blood plasma that could serve as a
non-invasive diagnostic biomarker for prostate cancer.
Methods
We measured genome-wide DNA methylation patterns in 73 clinically
annotated fresh-frozen prostate cancers and 63 benign-adjacent prostate
tissues using the Illumina Infinium HumanMethylation450 BeadChip array.
We overlaid the most significantly differentially methylated sites in
the genome with transcription factor binding sites measured by the
Encyclopedia of DNA Elements consortium. We used logistic regression
and receiver operating characteristic curves to assess the performance
of candidate diagnostic models.
Results
We identified methylation patterns that have a high predictive power
for distinguishing malignant prostate tissue from benign-adjacent
prostate tissue, and these methylation signatures were validated using
data from The Cancer Genome Atlas Project. Furthermore, by overlaying
ENCODE transcription factor binding data, we observed an enrichment of
enhancer of zeste homolog 2 binding in gene regulatory regions with
higher DNA methylation in malignant prostate tissues.
Conclusions
DNA methylation patterns are greatly altered in prostate cancer tissue
in comparison to benign-adjacent tissue. We have discovered patterns of
DNA methylation marks that can distinguish prostate cancers with high
specificity and sensitivity in multiple patient tissue cohorts, and we
have identified transcription factors binding in these differentially
methylated regions that may play important roles in prostate cancer
development.
Electronic supplementary material
The online version of this article (doi:10.1186/s12885-017-3252-2)
contains supplementary material, which is available to authorized
users.
Keywords: DNA methylation, Prostate cancer, EZH2, Biomarker, Diagnostic
Background
Currently, the most frequently used methods for detecting prostate
cancer are a digital rectal exam and a blood test to determine levels
of prostate-specific antigen (PSA) produced by the prostate gland
[[47]1]. However, these diagnostic tools can lack the sensitivity
required to detect very early prostate lesions [[48]2]. Furthermore,
PSA levels can increase for reasons unrelated to cancer or not increase
when cancer is present [[49]2]. If a prostate cancer is suspected,
prostate biopsies are performed. However, prostate biopsies are
invasive, and can lead to false-negatives and repeat biopsies, as they
do not sample the entire prostate. Recent developments in prostate
cancer detection include measuring the non-coding RNA prostate cancer
antigen 3 (PCA3) and transmembrane protease, serine 2 (TMPRSS2):v-ets
erythroblastosis virus E26 oncogene homolog (avian) (ERG) gene fusion
in urine to identify patients requiring repeat biopsies despite an
initial negative biopsy [[50]3–[51]5]. However, there is a clear need
to identify novel biomarkers for diagnostic purposes that are sensitive
and specific to prostate cancer.
Epigenetic patterns are known to be altered in several different cancer
types, including prostate cancer, and signatures of DNA methylation may
serve as potential diagnostic or prognostic biomarkers [[52]6].
Cancer-derived, methylated DNA has been identified and purified from
both patient serum and urine, making it a promising option for a
non-invasive biomarker [[53]7]. Previous studies investigating DNA
methylation patterns at select genomic loci in prostate cancer resulted
in discoveries of epigenetic differences between prostate cancer tissue
and benign-adjacent prostate in genes such as glutathione s-transferase
1 (GSTP1), Ras association domain family member 1 (RASSF1), and
adenomatous polyposis coli (APC), among others [[54]8–[55]10].
Recently, there have been studies using global approaches in prostate
cancer that have identified DNA methylation alterations in malignant
prostate tissue, including a previous study from our group
[[56]11–[57]17]. We sought to expand upon our previous discoveries by
performing genome-wide measurements of DNA methylation in 73 clinically
annotated fresh-frozen prostate cancers and 63 benign-adjacent prostate
tissues using the Illumina Infinium HumanMethylation450 BeadChip array,
which offers greater genomic coverage compared to the Methyl27 array
that we previously used [[58]11]. We present here novel DNA
methylation-based diagnostic models, and discuss transcription factors
whose binding sites are enriched in regions of differential methylation
in prostate cancer.
Methods
Tissue collection and nucleic acid extraction
We collected the prostate cancer and benign-adjacent tissues used in
this study at Stanford University Medical Center between 1999 and 2007
from patients undergoing radical prostatectomy with patient informed
consent under an IRB-approved protocol. The percentage of prostate
cancer epithelial cells in each sample was assessed by a pathologist
specializing in genitourinary cancers on hematoxylin and eosin (H & E)
stained frozen sections of the tissues from which the DNA was
extracted. We selected those samples in which at least 90% of the
epithelial cells were cancerous for nucleic acid extractions, and used
the QIAGEN AllPrep DNA/RNA mini kit (QIAGEN) to extract DNA and RNA.
DNA methylation analysis via Illumina Infinium HumanMethylation 450 K
We assayed DNA methylation levels by using the Illumina Infinium
HumanMethylation 450 K beadchip array (Illumina, San Diego, CA, USA)
[[59]18] and calculated the methylation beta score as:
b = Intensity[Methylated]/(Intensity[Methylated] + Intensity[Unmethylat
ed]). We converted data points that were not significant above
background intensity to NAs. We removed CpGs having greater than 10%
missing values prior to normalization. Data was normalized with the
ComBat R package [[60]19]. Post-ComBat normalization, we observed that
the Infinium I and II assays showed two distinct bimodal b-value
distributions, so we developed a regression method to convert the type
I and type II assays to a single bimodal b-distribution corresponding
to Reduced Representation Bisulfite Sequencing (RRBS) b-values
[[61]20]. After the Methylation 450 K data was converted to RRBS
b-values, any values less than zero were assigned zeros and values
greater than one were assigned ones. The equations for correction are
shown below:
Infinium I to RRBS:
[MATH: RRBSβ=0.00209+<
mn>0.4377×Methyl450β+0.6303×Methyl4502β :MATH]
Infinium II to RRBS:
[MATH: RRBSβ=‐0.01146<
mo>+0.2541×Methyl450β+0.9832×Methyl4502β :MATH]
Linear mixed model and logistic regression analysis
Linear mixed model analysis of the methylation data was performed using
the lme command in R, with patient as a random effect, and age and
ethnicity as fixed effects. Logistic regression was performed using the
glm command (family = binomial). The p-values were adjusted using the
Benjamini and Hochberg method [[62]21]. CpGs with a standard deviation
of less than 1% across samples were removed prior to analysis.
RNA-seq library construction and differential expression analysis
We constructed RNA sequencing libraries using a transposase-mediated
construction method described previously [[63]22]. Four RNA-seq
libraries were pooled into each lane and sequenced using Illumina HiSeq
2000 instruments to generate paired-end 50 sequencing reads (Illumina,
San Diego, CA, USA). Read-pairs were aligned to Gencode (version 9.0)
using TopHat (version 1.4.1), and the relative abundance of each
transcript was quantified using Cufflinks (version 1.3.0) and BEDTools
[[64]23–[65]26]. Differential expression analysis was conducted based
on tumor status using DESeq2 (version 1.8.1) with default settings in
likelihood ratio test (LRT) mode. Transcripts from the X and
Y-chromosomes were removed prior to differential expression analysis.
Pathway enrichment analysis
Chromosomal positions of significant CpGs were annotated using RefSeq
(hg19 assembly) [[66]27]. The Gene Set Enrichment Analysis (GSEA) tool
was used to analyze enriched cellular pathways [[67]28]. GSEA was run
with Kegg and Reactome selected, and used an FDR-corrected q-value
cutoff of 0.05.
Hierarchical clustering
Hierarchical clustering was performed using Cluster 3.0 [[68]29]. Data
was mean-centered and clustered by both gene and array using Euclidean
distance with average linkage. Clusters were visualized using TreeView
[[69]30].
TCGA data
TCGA DNA methylation (Illumina Methylation 450 k) datasets and
associated clinical data for prostate (PRAD_2013_09_07), lung
(LUAD_2013_09_07), breast (BRCA_2013_09_07) and pancreatic
(PAAD_2013_09_07) tissues were downloaded from the UCSC cancer genome
browser at time of manuscript preparation. Datasets were normalized
prior to validation analysis.
Transcription factor overlap
ENCODE transcription factor binding data was downloaded from
[70]http://genome.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=wgEncodeRegTfbsC
lusteredV3. We overlapped the CpGs found within gene regulatory regions
(promoter, first exon or first intron) from the top 10,000 most
significant CpGs from regression analysis with the ENCODE transcription
factor binding sites, and used a Fisher’s exact test to determine
transcription factor binding sites enriched for differential
methylation over background. For EZH2 binding site overlap, we
overlapped significant CpGs (FDR p-value < 0.05) with EZH2 binding data
previously published [[71]31]. For gene expression analysis, genes that
were differentially expressed between tumor and normal (DESeq2-based
FDR p-value < 0.05) were designated as overlapping a TF binding site if
greater than 50% the binding site fell within the transcript promoter
region. The promoter region was defined as 1000 bp upstream to 500 bp
downstream of the transcription start site. Transcription factors with
a Bonferroni-corrected p-value <0.05 were classified as significantly
enriched.
Results
Identification of differentially methylated cytosines in prostate cancer
To investigate differential DNA methylation associated with prostate
cancer, we used the Illumina Infinium HumanMethylation450 BeadChip
Methylation Assay, which covers more than 485,000 CpGs located
throughout the human genome [[72]18]. DNA methylation patterns were
measured in 73 patient prostate cancer tissues and 63 benign-adjacent
tissues, 52 of which are patient-matched (Table [73]1). Mixed model
linear regression analysis identified (LME) 226,235 CpGs with
significantly different methylation levels (LME, FDR-adjusted p-value
<0.05) in cancer tissues compared to benign-adjacent prostate tissues.
Of the 226,235 significant CpGs, ~67% had increased methylation and
~33% had decreased methylation in the cancer tissues compared to the
benign-adjacent tissues (Fig. [74]1a). CpGs with higher methylation
levels in tumor tissues were more likely to be within CpG islands
(Fisher’s Exact Test, p-value 3.44e–154, OR = 1.18, 95%
CI = 1.18–1.12), and statistically significant CpGs were also found in
greater proportion in gene regulatory regions (promoter, first exon, or
first intron) than in gene body regions (other exon, other intron, or
3′ proximal region), although this association did not reach
statistical significance. (Table [75]2A, B).
Table 1.
Clinical data for patients used in this study
Training cohort Testing cohort (TCGA)
Patients [n]
Tumor tissues 73 213
benign-adjacent tissues 63 49
patient-matched tissues 52 49
Age
Mean [years] 59.9 60.4
Median [years] 61 61
Range age [years] 43–73 43–75
Preoperative PSA [ng/mL]
Range 0.94–42 1.6–87
Mean 6.8 10.9
Median 5.62 7.4
< 4 [n] 15 19
4–10 [n] 44 100
> 10 [n] 9 55
unknown 5 39
Gleason Grade [n]
(<7) 16 15
(3 + 4) 40 84
(4 + 3) 10 50
8 4 25
(>8) 2 39
unknown 1 0
T Category
T2 2 NA
T2a 3 8
T2b 50 2
T2c NA 82
T3a 8 71
T3b 7 43
T4 1 5
unknown 2 2
Nodal Status
N0 66 160
N1 5 22
unknown 2 31
[76]Open in a new tab
Fig. 1.
Fig. 1
[77]Open in a new tab
a Histogram of differentially methylated CpGs (LME, FDR < 0.05). Blue
represents CpGs that have significantly higher methylation in
benign-adjacent prostate tissue when compared to prostate cancer
tissues (73,912 CpGs), and red represents CpGs that have significantly
higher methylation in prostate cancer tissues (152,324 CpGs). b Heatmap
of the top 10,000 CpGs with the most statistically significant DNA
methylation differences between unaffected prostate tissue and prostate
cancer tissue based on LME p-value. Color bar represents beta score
with 0.5 subtracted
Table 2.
Genomic regions of differentially methylated CpGs
Genomic location Sig. CpGs more methylated in tumor Sig. CpGs less
methylated in tumor Fisher’s exact p-value
A. Island versus non-island
CG Island 93,536 35,639 3.44E-154
Non-CG Island 58,787 38,273
B. Regulatory region versus gene body (All significant CpGs)
Regulatory Region 55,029 23,056 0.49
Gene Body 46,502 19,858
C. Regulatory Region Versus Gene Body (Top 10,000 most significant
CpGs)
Regulatory Region 3165 776 1.50E-02
Gene Body 1902 700
[78]Open in a new tab
Regulatory Region = promoter, first exon, first intron
Gene Body = other exon, other intron, 3′ proximal
CG Island = CG islands, CG shelves, and CG shores
A) Analysis of CpGs in islands versus non-islands. B) Analysis of CpGs
in gene bodies versus gene regulatory regions for all significant CpGs.
C) Analysis of CpGs in gene bodies versus gene regulatory regions for
the top 10,000 most significant CpGs
To explore the genes and cellular pathways found in differentially
methylated regions, we analyzed the top 10,000 most significant CpGs
between the prostate cancer tissue and unaffected prostate tissue (LME,
FDR p-value cutoff of <4.27e-13) (Fig. [79]1b). Of these, 75% had a
higher methylation level in the cancer tissues. We divided the top
10,000 CpGs that were uniquely annotated to one gene by whether they
resided in the gene regulatory region (promoter, first exon, and first
intron) or the gene body (other exon, other intron, and 3 prime
proximal region) and found that the CpGs with higher methylation in the
cancer compared to benign tissue were statistically more likely to be
associated with a gene regulatory region (Fisher’s Exact Test, p-value
0.015, OR = 1.10, 95% CI = 1.018–1.18) (Table [80]2C).
We used Gene Set Enrichment Analysis (GSEA) to determine which gene
pathways are represented in the top 10,000 most significant CpGs
[[81]28]. We observed 3165 CpGs in the regulatory regions of 1589 genes
with higher DNA methylation in the prostate cancer compared to benign
tissue. GSEA analysis of those 1589 genes showed a strong signature for
glycosaminoglycan metabolism, with five of the top 10 significantly
enriched pathways associated with heparan sulfate metabolism and
chondroitin sulfate metabolism (Additional file [82]1: Table S1). Other
pathways included focal adhesion, pathways in cancer, Wnt signaling
pathway, developmental biology and axon guidance. The enrichment for
glycosaminoglycan metabolism pathways was specific to CpGs in
regulatory regions with higher methylation in the cancer. Conversely,
there were 776 CpGs located in gene regulatory regions of 621 genes
with lower methylation in prostate cancer tissue. GSEA analysis of
these genes showed enrichment for olfactory signaling, G-protein
coupled receptor signaling, metabolism of carbohydrates, apoptosis,
immune system, neuronal growth factor signaling pathway, and hemostasis
(Additional file [83]2: Table S2).
Overlap of ENCODE transcription factor ChIP-seq data and differential DNA
methylation highlights the importance of EZH2 in prostate cancers
We compared the DNA methylation data with transcription factor
chromatin immunoprecipitation sequencing data (ChIP-seq) measured by
the Encyclopedia of DNA Elements (ENCODE) Consortium to test whether
there was an enrichment of transcription factor binding sites
coinciding with the top 10,000 most differentially methylated CpGs
between prostate cancer and benign-adjacent tissues. Enhancer of zeste
homolog 2 (EZH2) was the most significantly enriched TF overlapping
CpGs with higher methylation in the cancer tissues from our dataset
(Fisher’s Exact Test, Bonferroni adj. p-value 7.54e-172, OR = 3.4, 95%
CI = 3.14–3.68), and this observation was validated in The Cancer
Genome Atlas (TCGA) prostate methylation dataset (Fisher’s Exact Test,
Bonferroni adj. p-value 6.48e-120, OR = 2.48, 95% CI = 2.29–2.69) (Fig.
[84]2a and Additional file [85]3: Table S3A and B). ENCODE TF binding
data was generated from multiple types of cell lines. To determine
whether EZH2 binding enrichment occurs in prostate cancer specifically,
we compared the significant CpGs differentially methylated between
prostate cancer and benign-adjacent tissue (FDR p-value cutoff of
<0.05) with previously published EZH2 binding events from
androgen-dependent (AD) and androgen-independent (AI) cell line models
[[86]31]. EZH2 binding events were significantly enriched in both the
AD and AI contexts, although we observed a higher level of enrichment
in the AI context (Fisher’s Exact Test, AD enrichment p-value =0.01,
OR = 1.15, 95% CI = 1.01–1.30; AI enrichment p-value =0.00013,
OR = 1.18, 95% CI = 1.08–1.29) (Additional file [87]3: Table S3C).
Notably, in our tissue cohort, significant CpGs found in proximity to
EZH2-bound sites are mostly hypermethylated (Fig. [88]2b). We also
observed that a majority of transcripts that contain EZH2 binding sites
in the promoter region that are differentially expressed between
prostate cancer tissue and the benign-adjacent tissues have decreased
expression in the prostate cancer tissue (Fig. [89]2b).
Fig. 2.
Fig. 2
[90]Open in a new tab
Overlap of top 10,000 most significant (LME p-value) DNA methylation
sites in gene regulatory regions and higher methylation in prostate
cancer tissues with ENCODE transcription factor binding sites
highlights the role EZH2 plays in prostate cancer. a Barplot showing
the relative percent of ENCODE transcription factor binding sites
containing significant methylation changes. Dashed red lines represent
the upper and lower 95% confidence intervals generated from enrichment
values of randomly selected methylation sites. b Pie charts
demonstrating the directionality of significant DNA methylation sites
and gene expression levels within 1 kb of EZH2 binding sites
For CpGs with lower methylation in the prostate cancer tissues in
comparison with the adjacent-unaffected tissue, there were two TFs with
significant overlap that were bound in these regions: FOXA2 and SETDB1
(Additional file [91]3: Table S3D). However, we were unable to validate
the enrichment we observed for these TFs in the TCGA prostate
methylation dataset (Additional file [92]3: Table S3E).
Discovery and validation of most distinguishing DNA methylation sites in
prostate tissues
To discover DNA methylation patterns that best distinguish prostate
cancer tissue from benign-adjacent tissue, we performed logistic
regression on the 100 most statistically significant CpGs from the
linear mixed model regression (FDR p-value cutoff of <8.22e-15). We
tested each combination of three CpGs within the top 100 most
significant CpGs, as models containing three CpGs resulted in the
smallest Akaike Information Criterion (AIC) value. We calculated the
Area Under the Curve (AUC) for each Receiver Operating Characteristic
(ROC) curve to identify the model with a maximal AUC. The top DNA
methylation diagnostic model based on AUC from our analysis consists of
cg00054525, cg16794576, and cg24581650 (Fig. [93]3, Additional file
[94]4: Table S4). This DNA methylation model produces a ROC curve with
an AUC of 0.97 in our cohort of prostate tissues and has a specificity
of 98.4% and a sensitivity of 87.5%, indicating that DNA methylation
status at these three genomic positions has very high predictive power
for distinguishing malignant tissue from benign tissue (Fig. [95]4a).
The corresponding waterfall plot demonstrates the high accuracy our top
DNA model performs in classifying the prostate tissues (Fig. [96]4a).
Based on analysis of methylation data from other tissue types, these
methylation differences are unique to prostate cancer cells, as DNA
methylation levels at these sites performed poorly in distinguishing
lung, pancreatic, and breast cancer tissue from benign tissue
(Additional file [97]5: Figure S1). The top three diagnostic CpGs are
in close proximity to four total transcripts based on annotation,
including CYBA, ERGIC1, HLA-J, and NCRNA00171. Out of these four
transcripts, ERGIC1 has a statistically significant difference in mRNA
expression level between prostate malignant tissue and benign tissue
(DESeq2, adj. p-value 6.4e-06) (Additional file [98]6: Figure S2).
Fig. 3.
Fig. 3
[99]Open in a new tab
Boxplots of CpGs in the top diagnostic models. Normal data is from
benign-adjacent tissues and Tumor Data is from patient cancer tissues
Fig. 4.
Fig. 4
[100]Open in a new tab
ROC curve and waterfall plots for performance of the top 3 CpG
diagnostic model in a training and b validation datasets. The value of
the classifier is given by
6.52–17.04*cg00054525 + 24.18*cg16794576–13.82*cg24581650, where the
intercept and coefficients have been regressed by a binomial
generalized linear model. A threshold value of this classifier was
chosen to yield maximal non-unity specificity in the training set. The
red dot on the ROC curve corresponds to the sensitivity and specificity
of the classifier at the chosen threshold. The dashed line on the
waterfall plots is drawn at the chosen threshold value of the
classifier
We utilized prostate data from TCGA as a validation cohort for our DNA
methylation signature (Table [101]1). The TCGA methylation data was
also measured using the Human Methylation 450 BeadChip and included 213
prostate cancer tissues and 49 normal tissues. Our model, based on 3
CpGs, could distinguish normal from malignant prostate tissues with a
sensitivity of 84.5% and a specificity of 91.7% in the TCGA dataset,
resulting in a ROC curve with an AUC of 0.920 (Fig. [102]4b, Additional
file [103]4: Table S4). To determine how our top diagnostic model
performs in the context of benign prostate hyperplasia (BPH), we used a
previously published cohort (GEO accession: [104]GSE55599) to see if
our top DNA methylation model could distinguish prostate cancer tissue
from prostate tissue obtained from patients with benign-hyperplasia and
found that our model could perfectly discriminate these two types of
tissues (Additional file [105]7: Figure S3) [[106]32].
Additionally, we investigated prostate diagnostic markers from
significant CpGs from the linear mixed model analysis that exclusively
demonstrated an increase in methylation in cancers, as biomarkers that
are hypermethylated in the cancer tissues are potentially more easily
translatable to the clinic. In this context, the top model consists of
cg15338327, cg00054525, and cg14781281 (Additional file [107]8: Figure
S4), resulting in a ROC curve with an AUC of 0.97 in our dataset and an
AUC of 0.92 in the TCGA prostate validation dataset (Additional file
[108]9: Figure S5A-B). This hypermethylated diagnostic model also
performed well at distinguishing benign-hyperplasia prostate tissue
from prostate cancer tissue, with an AUC of 0.85 (Additional file
[109]9: Figure S5C-D).
Discussion
Shifts in epigenetics play a large role in cancer formation and
maintenance, and DNA methylation is a stable modification that can be
detected non-invasively in fluids such as urine, blood and saliva. For
these reasons, DNA methylation is an attractive cancer biomarker
candidate. In our study, we identified a large number of CpG loci with
statistically significant DNA methylation levels between our cohort of
prostate cancer tissues and the adjacent, unaffected prostate tissues.
More than half of the significant CpGs were found to be hypermethylated
in the prostate tumor tissues. Our previous work strongly suggests that
these methylation changes are the result of dysregulation of the DNA
methyltransferases DNMT3A2 and DNMT3B [[110]11].
Global DNA methylation changes implicate genes associated with the
stroma and tumor microenvironment as being enriched targets for
methylation changes. We observed an overwhelming signature of
glycosaminoglycan (GAG) metabolism in the regulatory regions of
transcripts with higher methylation in malignant tissues. GAGs are long
polysaccharides that have both structural and signaling roles within
the extracellular matrix and cellular membranes and have a documented
role in many cancers [[111]33]. In prostate cancer, altered expression
of GAGs has been observed in early stage prostate cancer and correlates
with malignant progression. A large body of literature documents
numerous ways that altered proteoglycan metabolism can influence
prostate cancer development and progression, including altering
prostate cancer cell growth, motility, survival, local diffusion of
growth factors, and cell signaling [[112]34]. The enrichment of GAG
metabolism, and specifically heparan sulfate and chondroitin sulfate
metabolism, in regions with lower DNA methylation in benign-adjacent
prostate tissues likely points to the structural changes occurring in
the extracellular space surrounding the cancer, and we confirmed that
the majority of these genes have higher expression in the
benign-adjacent tissues (Additional file [113]10: Table S5A). A recent
study investigating transcriptional activity of genes involved in
heparan sulfate biosynthesis in prostate tissues found that these genes
have lower expression in prostate cancer tissues compared to prostate
tissue from individuals with no prostate cancer, and findings from our
study suggest that the expression of these genes is down-regulated in
prostate cancer, at least in part, due to epigenetic changes [[114]35].
Regions of the genome with reduced DNA methylation in the prostate
cancer tissue were enriched for a diverse collection of cellular
pathways. Olfactory signaling was represented among the enriched
pathways. We observed a large number of odorant receptor genes had less
methylation in their gene regulatory region in the prostate cancer
tissue in comparison with benign-adjacent prostate tissue, and their
gene expression levels were mostly higher in the cancer tissues
(Additional file [115]10: Table S5B). A recent study demonstrated that
activation of odorant receptors increases cell invasion into collagen
gel [[116]36].
We overlaid ENCODE TF ChIP-seq data with sites of differential
methylation and observed that EZH2 was the most highly enriched TF
binding in these regions. EZH2 is part of the polycomb repressive
complex that is known to regulate chromatin structure during
development primarily through repression of expression of a large and
diverse set of genes [[117]37]. EZH2 functions to repress gene
expression through methylation of histone H3 at lysine 27 (H3K27
methylation), and EZH2 can also recruit DNA methyltransferases to
EZH2-target promoters [[118]31, [119]38, [120]39]. EZH2 expression
increases throughout prostate cancer progression and EZH2 expression
levels are associated with methylation level in prostate cancer
[[121]11, [122]40]. Our data suggest that EZH2-directed methylation
alterations are critical for the formation and maintenance of prostate
cancer, in addition to roles EZH2 plays in castration-resistant
prostate cancer.
A practical application of genome wide DNA methylation profiling is the
identification of candidate diagnostic biomarkers. We have demonstrated
that as few as three CpGs can be used to distinguish benign-adjacent
from malignant prostate tissues with high sensitivity (92.6%) and
specificity (87.8%). Methylation biomarkers have been identified in
prostate cancer previously, including promoter segments of GSTP1,
RASSF1, and APC, which are used in commercial tissue-based test to
identify patients needing repeat biopsies after an initial negative
biopsy [[123]41]. Clinical validation studies of this commercial
methylation-based assay obtained a sensitivity level of 68% and a
specificity level of 64% [[124]42, [125]43]. We also investigated
candidate diagnostic models developed from CpGs that have higher
methylation in prostate cancer tissue. Hypermethylated CpGs appear to
retained throughout all stages of prostate cancer, likely due to
selection pressures, whereas CpGs that become hypomethylated in
prostate cancer are less likely to be preserved [[126]44]. In this
context, we tested a 3-CpG model that provided a sensitivity of 90% and
a specificity of 82% that again, exceed those reported for DNA
methylation markers currently in use. One of the diagnostic the
diagnostic CpGs (cg00054525) falls within the regulatory region of the
CYBA gene. Methylation of CYBA has been previously associated with the
progression of melanoma [[127]45, [128]46]. However, other genes
associated with our diagnostic CpGs, such as HLA-J and a non-coding
RNA, have not yet been associated with cancer, to our knowledge, and
thus, introduce new biological aspects to explore. Our model’s
diagnostic performance is relatively poor in lung, breast and pancreas
adenocarcinomas, suggesting it has some specificity to prostate cancer.
This is a characteristic that could hold value in future studies
pursuing a non-invasive, peripheral fluids-based assay.
It is important to note that currently available prostate cancer
patient cohorts, including our own, are limited in numbers of samples,
and future studies will elucidate the value of our DNA methylation
signatures across larger cohorts of prostate cancer patients.
Furthermore, the full utility of these DNA methylation-based diagnostic
biomarkers will be realized when they can be measured in peripheral
fluids from patients. Thus, an important future direction of our study
is to determine whether these DNA methylation signatures can be
detected in patient urine or blood. To definitively determine their
clinical relevance, it will be important to directly compare these
diagnostic biomarkers to clinically established markers, such as PSA.
Finally, given the recent identification of molecular subtypes of
prostate cancer, it will be important to determine DNA methylation
patterns that not only distinguish tumor tissue from benign tissue, but
also can inform about the molecular subtype of the tumor [[129]17].
Conclusions
Our results indicate that DNA methylation can be used to successfully
distinguish prostate cancer tissue from benign-adjacent tissue and that
our 3-CpG DNA methylation signatures are not common to other cancers.
Sites of differential methylation point to a role for odorant receptors
and GAG metabolism and integration of ENCODE transcription factor
binding data demonstrates EZH2 enrichment at the sites of altered DNA
methylation. These data have the potential to impact both diagnosis and
treatment of prostate cancer.
Additional files
[130]Additional file 1: Table S1.^ (9.4KB, xlsx)
Gene Set Enrichment Analysis. Top 10 enriched pathways from 1589 genes
with high methylation in cancer tissue. (XLSX 9 kb)
[131]Additional file 2: Table S2.^ (9.4KB, xlsx)
Gene Set Enrichment Analysis. Top 10 enriched pathways from 621 genes
with low methylation in cancer. (XLSX 9 kb)
[132]Additional file 3: Table S3.^ (45.9KB, xlsx)
ENCODE and EZH2 transcription factor overlay significantly associated
with regions of differential methylation in cancer tissue. A)
Transcription factors enriched in the top 10,000 most significant CpGs
found in gene regulatory regions with higher methylation levels in the
tumor tissues. B) TCGA validation of transcription factors enriched in
the top 10,000 most significant CpGs found within gene regulatory
regions with higher methylation levels in the tumor tissues. C)
Analysis of significant methylation sites overlaid with EZH2 binding
data from an androgen-dependent (LNCaP) and an androgen-independent
(LNCaP-abl) prostate cancer cell line. D) Transcription factors
enriched in the top 10,000 most significant CpGs within gene regulatory
regions with higher methylation in the benign-adjacent tissue. E) TCGA
validation of transcription factors enriched in the top 10,000 most
significant CpGs found within gene regulatory regions with higher
methylation in the benign-adjacent tissue. (XLSX 45 kb)
[133]Additional file 4: Table S4.^ (46.6KB, xlsx)
Coefficients, standard errors and performance statistics for the top
diagnostic model and top hypermethylated CpG model. (XLSX 46 kb)
[134]Additional file 5: Figure S1.^ (1.1MB, pdf)
ROC curve and waterfall plots for diagnostic model applied to a) lung
b) pancreatic and c) breast cancer TCGA datasets. (PDF 1091 kb)
[135]Additional file 6: Figure S2.^ (118.9KB, pdf)
Gene expression of genes in close proximity to the top diagnostic
model. (PDF 118 kb)
[136]Additional file 7: Figure S3.^ (775.3KB, pdf)
ROC curve and waterfall plot for diagnostic model applied to an
independent cohort of prostate cancer and benign prostate hyperplasia
samples. (PDF 775 kb)
[137]Additional file 8: Figure S4.^ (129.6KB, pdf)
Boxplots of CpGs in the top diagnostic model from hypermethylated CpGs.
Normal data is from benign-adjacent tissues and Tumor data is from
patient cancer tissues. (PDF 129 kb)
[138]Additional file 9: Figure S5.^ (842.5KB, pdf)
ROC curve and waterfall plot for the top hypermethylated CpG model in
the TCGA validation cohort (a-b) and BPH cohort (c-d). (PDF 842 kb)
[139]Additional file 10: Table S5.^ (51.4KB, xlsx)
DESeq2 results for gene expression differences between prostate cancer
tissue and benign-adjacent tissue for select transcripts with
methylation differences that drive GSEA enrichments. A) DESeq2 results
for gene expression of glycosaminoglycan metabolism genes with higher
methylation in prostate cancer tissue. B) DESeq2 results for gene
expression of odorant receptor genes with reduced methylation in
prostate cancer tissue. (XLSX 51 kb)
Acknowledgements