Research plan
Ovarian adenocarcinoma is the ninth most common cancer among women and it accounts for more death than
any other types of cancer of the female reproductive system. Most of the women are usually asymptotic or
present with non-specific syndromes, making early diagnosis impossible. Despite surgical resection and
aggressive chemotherapy the majority of patients will suffer from recurrence of the disease and the 5-year
survival rate is less than 40%. Currently our understanding of the pathogenesis is still limited and early
diagnostic markers are yet to be identified. The cancer genome contains many irreversible (on the level of
nucleotides, copy number variations etc) and reversible aberrations (epigenetic changes that include changes
in the pattern of histone code, DNA methylation as well as regulation of microRNA expression). Epigenetic
changes in the genome are affected by genetic variations and in turn have an effect on gene and microRNA
expression. To our knowledge, the role of epigenetic mechanisms such as DNA methylation and microRNA
has not been fully explored for ovarian cancer. We hypothesize that changes in DNA methylation status
play an important role in the development of ovarian cancer as well as patient survival by altering gene
expression and the associated gene network states. The ovarian cancer dataset from The Cancer Genome
Atlas (TCGA) is comprised of genetic, epigenetic, and clinical traits, providing a unique opportunity to address
this hypothesis. Identifying ovarian cancer-specific DNA methylation aberrations as well as their relationship
with other types of molecular traits and clinical traits will not only help to understand the underlying molecular
mechanism of ovarian cancer but also help uncover novel biomarkers. The following specific aims are
proposed:
Specific Aim 1 - Investigate the role of DNA methylation in ovarian cancer by correlating methylation
changes with cancer status and other relevant clinical traits
A. Identify differential methylation loci between ovarian cancer cases and controls
B. Identify methylation loci that are correlated with traits related to cancer severity, survival, and
response to therapy
Specific Aim 2 – Determine the relationships between DNA methylation and other types of molecular
traits
A. Identify genes whose expression levels are correlated with differential DNA methylation loci
identified in Aim 1
B. Identify microRNAs whose expression levels are correlated with the differential DNA methylation
loci
C. Identify single nucleotide polymorphisms (SNPs) that are associated with DNA methylation
(mSNPs), gene expression (eSNPs), and microRNA (miSNPs)
D. Establish causal relationships between DNA methylation and gene/microRNA expression using
a conditional correlation-based causality test
Specific Aim 3 – Construction of co-expression and co-methylation networks to identify regulatory
mechanisms responsible for changes in gene expression and DNA methylation profiles.
A. Construct co-expression network and identify co-expression network modules that are
correlated with DNA methylation, candidate regulatory genes, and clinical traits
B. Construct co-methylation network and identify co-methylation network modules that are
correlated with expression of epigenes and clinical traits
Specific Aim 4 – Identify composite predictive markers of clinical phenotypes of ovarian cancer by
simultaneously considering expression, genetic and epigenetic data
A. Construct predictive models of survival using DNA methylation, microRNAs, SNPs, and copy
number variations (CNVs)
A. Experimental validation of the markers using tissue samples.
Significance of the proposed study
Ovarian cancer accounts for more death than any other types of cancer of the female reproductive
system. To date, our understanding of the disease pathogenesis, the availability of predictive markers, and
therapeutic strategies all remain limited. The proposed study will utilize an integrative genomics approach to
help understand the role of epigenetic factors in ovarian cancer development and patient survival. By directly
correlating DNA methylation data with cancer phenotypes (Specific Aim 1) and exploring the relationships
between DNA methylation and other types of molecular data including DNA variation, gene expression, and
microRNA expression (Specific Aim 2), we will be able to assess the contribution of DNA methylation status to
disease etiology as well as the underlying mechanisms; by incorporating the DNA methylation information into
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the network modules (Specific Aim 3), a more comprehensive disease map can be derived as well as
predictive biomarker models could be constructed (Specific Aim 4).
Background
Ovarian cancer. There are about 20 microscopically distinct types of ovarian cancer however 80% of them are
epithelial adenocarcinomas and the term “ovarian cancer” is generally used to refer to this type. It is a ninth
most common cancer among women and it accounts for more death than any cancer of the female
reproductive system. In 2010 about 3% (21,880 cases) of women will be diagnosed with ovarian cancer and
about 5% (13,850 cases) of women will die from it (American Cancer Society (ACS), http://www.cancer.org).
The median age of patients with ovarian cancer is 60 years and the average lifetime risk is 1.4%. The
symptoms of ovarian cancer are non-specific and most women are diagnosed mostly in advanced stages.
Stage identification is performed during surgery and the major path for treatment includes tumor resection
following platinum/taxane chemotherapy. About 75% of women live for one year after diagnosis and half of
women live for 5 years after the diagnosis. Survival is age and stage dependant. If the cancer is diagnosed
before it has spread to lower or upper abdomen (stage I), 93% of women will survive for 5 years. However,
only 20% of all ovarian cancer cases are identified at this stage (ACS). Despite tumor resection and aggressive
chemotherapy the majority of women die from recurrence of the disease.
DNA methylation in ovarian cancer. The cancer genome contains many irreversible genetic (on the level of
nucleotides, copy number variations etc) and reversible epigenetic aberrations (epigenetic changes that
include changes in the pattern of histone code, DNA methylation as well as regulation of microRNA
expression). DNA methylation is the most well studied epigenetic mechanism that is involved in regulation of
gene expression without changes in DNA sequence. In mammals it occurs in cytosines that precede guanines,
these are called dinucleotide CpGs. In the genome CpG-rich regions are called CpG islands and these span
the 5’ end of the regulatory region of many genes. Generally presence of DNA methylation inversely correlates
with gene expression. The cancer genome is characterized by overall hypomethylation that is associated with
a number of adverse outcomes such as inappropriate reactivation of tissue specific and germ line-specific
genes, loss of imprinting, chromosome instability, and activation of transposable elements. On the other hand,
many tumor suppressor, housekeeping and tissue specific genes become hypermethylated which leads to their
inactivation.
DNA methylation alterations have been widely reported in ovarian carcinomas, and there have been
many reports on target-specific cases of hypermethylation in ovarian cancer (Fiegl et al., 2008; Kikuchi et al.,
2007; 2008; Kwong, Ramalingam, Trieu, Desai, & Mok, 2009; Potapova, Hoffman, Godwin, Al-Saleem, &
Cairns, 2008; Staub et al., 2007; Watts, Futscher, Holtan, Degeest, Domann, & Rose, 2008a; Wei et al., 2006;
2002) as well as genome-wide studies (Houshdaran et al., 2010; Michaelson-Cohen et al., 2011; Teschendorff
et al., 2010). Analysis of 1500 CpG dinucleotides in 27 primary tumors and 15 ovarian cells lines showed that
DNA methylation patterns can help to identify different histological profiles of ovarian cancers (Houshdaran et
al., 2010). Analysis of 4 tumor and 2 normal tissues using MeDIP combined with Agilent CpG arrays has
identified that 2% of unmethylated CpGs in normal ovary become de-novo methylated in ovarian cancer while
9% of methylated CpGs in normal tissues undergo hypomethylation in ovarian cancer (Michaelson-Cohen et
al., 2011). Finally, a large UK based study using 261 patients has identified an age associated methylation
signature (Teschendorff et al., 2010). Correlation between DNA methylation patterns and the stage of ovarian
cancer (increased DNA methylation in higher stages) has also been identified (Watts, Futscher, Holtan,
Degeest, Domann, & Rose, 2008b). However, it has been shown that DNA methylation is an early but not
initial event in ovarian carcinogenesis (P. Cheng et al., 1997). Correlation between DNA methylation levels of
certain genes and response to chemotherapy has been observed. For instance, methylation analysis of hMLH1
(DNA mismatch repair) gene in ovarian cancer patients enrolled in SCOTROC1 Phase III clinical trial before
chemotherapy and at relapse showed that increased methylation of this gene predicts poor patient survival
regardless of age and tumor progression (Gifford, Paul, Vasey, Kaye, & R. Brown, 2004). Analysis of response
to chemotherapy of the patients in the late stages of cancer (III/IV) was highly correlated with methylation of
these three genes involved in DNA damage repair associated with chemotherapy: BRCA1, GSTP1 and MGMT
(Teodoridis et al., 2005).
Disadvantages of these preliminary studies are in small number of patients analyzed, analysis only a
single variable at the time (eg., only DNA methylation or only gene expression) and lack of network
approaches. We expect that our work will contribute to the field because of the large sample size of the dataset
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that we will use for our study, integrative approach (we will incorporate DNA methylation, gene expression,
SNPs, microRNA datasets and clinical information) as well as the use of the network approaches.
Available datasets and technical aspects. Several projects have been initiated to catalogue genetic, epigenetic,
and gene expression changes in cancer. One of these initiatives is The Cancer Genome Atlas (TCGA) which
was established in 2006. Currently TCGA hosts gene expression, microRNA expression, copy-number
variation, SNPs, DNA methylation and clinical datasets for 16 different cancer types, with ovarian cancer
dataset from 588 individuals being the largest (http://tcga-data.nci.nih.gov/tcga/).
DNA methylation profiling of primary tumor samples for this and other cancer types in TCGA has been
performed using the Illumina HumanMethylation27k arrays which covers ~27 thousand CpG dinucleotides in
14,495 genes. Limited analyses of DNA methylation data have been conducted using a subset of 188 TCGA
ovarian cancer subjects and a synergistic effect of DNA methylation and CNVs on gene expression for several
known oncogenes as well as novel candidate oncogenes has been reported (Louhimo & Hautaniemi, 2011). In
addition to CNVs, SNPs have been reported to influence DNA methylation status (Tycko, 2010; D. Zhang et al.,
2010) and microRNAs are often found to be silenced by DNA methylation in cancer (Melo & Esteller, 2010).
Therefore, an investigation of the relationships between DNA methylation and various types of molecular traits
(SNPs, microRNAs, expression) as well as phenotypic traits available in the full dataset can provide a more
comprehensive view of the molecular interactions and mechanisms underlying ovarian cancer.
Preliminary results
Phenotypic traits of TCGA ovarian cancer data
TCGA provides rich metadata information files about the patients whose samples were collected for the
analysis. We began by analyzing the clinical patient information available for ovarian cancer in TCGA and
grouping the information into categorical (Supplementary Figure 1 and 2) and quantitative traits
(Supplementary Figure 3). Based on the amount of information available for each trait (whether this trait was
measure for every patient) and the relevance to ovarian cancer pathophysiology, we decided to focus on the
following categorical traits: chemotherapy (this trait will be further broken down into the specific drugs that were
used for each patient), primary therapy outcome, tumor grade, tumor stage and tumor residual disease. The
following quantitative traits will be used: age at initial diagnosis, days to death and days to tumor recurrence.
Characteristics of the DNA methylation data
The Level 2 normalized methylation data for each CpG from TCGA is represented as a beta value: beta =
M/(M+U), where beta is the intensity ratio value, M is the intensity of the methylated bead type and U is the
intensity of the unmethylated bead type for the same locus. Beta values range from 0 to 1 and are easy to
interpret biologically: the value of 0 indicates a completely unmethylated locus, the value of 1 (or 100%)
indicates a completely methylated locus, while the value of 0.5 indicates 50% methylation. While the beta has
been used by many groups for inferring the DNA methylation status (Gibbs et al., 2010; Teschendorff et al.,
2010; D. Zhang et al., 2010), a main concern with the beta value is that it has severe heteroscedasticity in the
low and high methylation range which imposes serious challenges in applying many statistical models (Du, W.
A. Kibbe, & Lin, 2008). To address this, a different approach to measuring DNA methylation status has been
proposed (Du et al., 2010). The M value is a log2 ratio of the intensity of the methylated bead type versus
unmethylated bead type (M=log2(M/U)) at the corresponding DNA locus. While the level 2 DNA methylation
data from the Illumina HumanMethylation27k BeadChip arrays from TCGA uses beta values, for this proposal
we are planning to perform our analyses using the M value, as M value alleviates the problem associated with
the beta value and these measures can be converted one to each via a simple transformation (Du et al., 2010).
As a first step of our analyses we downloaded a small random sample of raw intensity values (50
patients) from TCGA for ovarian cancer and looked at the distributions of DNA methylation status represented
using both the M value and the beta value and confirmed that it is indeed easier to make methylated vs
unmethylated calls using the M value.
Experimental design and methods
Specific Aim 1 - Investigate the role of DNA methylation in ovarian cancer by correlating methylation
changes with cancer status and other relevant clinical traits
Rationale and Objectives: Understanding DNA methylation changes accompanying ovarian cancer status,
survival, and treatment response will provide mechanistic insight into the role of epigenetics in disease etiology.
The ovarian cancer-related hypermethylated and hypomethylated loci identified will not only serve as candidate
biomarkers but also help identify genes and microRNAs whose expression levels are regulated by DNA
methylation in downstream analysis.
3
Approach: Raw DNA methylation data (level 1) for ovarian tumor samples and controls will be downloaded
from TCGA. As an initial approach in the analysis we will use the M value as the measure for methylation and
Bioconductor lumi package in our analyses. In parallel we are planning to collaborate with other Sage
Bionetworks researchers on developing normalization methods appropriate for this platform that may utilize
only information about the methylated probes. Since not enough control patient DNA methylation data are
available from TCGA (currently only 8) we will collect DNA methylation datasets from age and gender-matched
normal individuals from other cancer types (such as kidney renal clear cell carcinoma (188 healthy individuals)
and other types that have less individuals but can be used as long as they are age and gender matched) as
well as from control healthy women will also be obtained from GEO GSE19711 (Teschendorff et al., 2010).
The difference in the overall methylation distribution patterns between cases and controls will be assessed
using the Kolmogorov-Smirnov test and the differences in mean will be assessed using the Mann-Whitney test.
To identify correlation between DNA methylation and categorical traits such as case/control and cancer stage,
we will use the non-parametric Kruskal-Wallis tests. For numeric traits such as survival, Cox proportional
hazards model will be used. FDR will be assessed using the Qvalue approach to determine the statistic
significance (Storey & Tibshirani, 2003).
Expected Results and Potential Problems: We expect to identify differential methylation loci that are
correlated with disease status, stage, survival and treatment response. Our main concern with the case/control
analysis is that not enough normal individuals for which DNA methylation has been profiled are available.
Since we are collecting normal patients from other cancer types we may expect a high variability in the control
group. We will compare distribution of the signal from the controls available for ovarian cancer with the
distribution in other healthy individuals from other datasets to ensure that they are similar.
Specific Aim 2 – Determine the relationships between DNA methylation and other types of molecular
traits
Rationale and Objectives: DNA methylation changes can be induced by genetic factors such as the presence
of local SNPs (Harlid et al., 2010; D. Zhang et al., 2010). DNA methylation status in turn can affect gene
expression and microRNA expression. Correlation of DNA methylation with other markers will provide
significant insight into the role of DNA methylation as the mechanism that drives changes in gene expression
and microRNA expression. On the other hand, correlation with SNP data will provide information on how
genetic changes influence the epigenome and the transcriptome of ovarian cancer. Understanding the
relationships and interactions among the molecular traits can help understand the molecular networks
underlying the genetic and epigenetic perturbations and thus provide insights into ovarian cancer biology.
Approach: The genetics of DNA methylation (mQTLs or mSNPs), gene expression (eQTLs or eSNPs), and
microRNA expression (miQTLs or miSNPs) will be evaluated in cancer and control groups separately using
non-parametric Mann-Whitney test (for two genotype groups) or Kruskal-Wallis tests (for three genotype
groups) as implemented in the 1dscan package. The correlation between DNA methylation and gene
expression or microRNA expression will be analyzed using Spearman correlation. The causal relationships
between DNA methylation and gene/microRNA expression when correlations are observed will be assessed
using a previously developed causality test (Millstein, B. Zhang, Zhu, & Schadt, 2009).
Expected Results and Potential Problems: We expect to 1) identify SNPs that affect DNA methylation,
gene expression, and microRNA expression, 2) identify DNA methylation loci that are correlated with gene
expression and microRNA expression, 3) identify differential methylation loci that account for the gene and
microRNAs expression changes. One potential problem is that the utility of causality test in human populations
of observational nature has not been well established and we may encounter issues related to statistical power
and interpretation the results. Therefore, we may not be able to identify causal methylation loci due to these
limitations.
Specific Aim 3 – Construction of co-expression and co-methylation networks to identify regulatory
mechanisms responsible for changes in gene expression and DNA methylation profiles.
Rationale and Objectives: Current network construction efforts mainly focus on other types of molecular data
such as gene expression and protein-protein interaction other than epigenetic data. As epigenetic changes are
an important aspect of human complex diseases, building a network framework that takes epigenetic data into
consideration will not only lead to methodological innovations but provide more comprehensive disease maps.
Approach: This work will be performed using the co-expression network analysis approach (B. Zhang &
Horvath, 2005). We are planning to construct two types of networks. First, we hypothesize that changes in
DNA methylation level of a few master transcriptional regulatory factors induce big cascades of gene
4
expression changes. In order to identify expression modules regulated by those transcriptional regulators we
will construct a co-expression network. To identify candidate regulators, we will 1) use the established key
driver analysis (KDA) at Sage which combines the co-expression network and Bayesian network and 2)
correlate the co-expression modules with gene expression data of a subset of genes comprised of known
transcription factors, epigenetic regulators, and microRNAs using Spearman correlation. These analyses will
provide us with a list of candidate regulators that might have induced the downstream transcriptional changes.
Finally we will evaluate methylation status of these genes by correlating it with DNA methylation data.
Alternatively, we hypothesize that changes in expression of epigenes (such as histone methyltransferase
(HMTs), histone acetylases (HATs), histone deacetylases (HDACs), histone demethylases and DNA
methyltransferases (DNMTs)) can induce substantial changes in DNA methylation that lead to cancer. Many
histone modifying proteins are found in the same complexes with DNA methyltransferases (Cedar & Bergman,
2009). Genetic variations can affect protein-protein interaction domains of these enzymes which will lead to
disruption of these complexes. Two recent articles identified mutations in chromatin remodeling enzyme gene
ARID1A in ovarian clear cells and endometrioid carcinomas (S. Jones et al., 2010; Kimberly C. Wiegand et al,
2010). Although it was not found to be mutated in ovarian serous carcinoma other chromatin remodeling
factors and histone modifying enzymes might have genetic variations in their coding sequence that are specific
to this type of cancer. We are proposing to construct a co-methylation network based on DNA methylation data
and identify co-methylated modules. We will then correlate the identified modules with expression of specific
epigenes (for example, see the list here: Berdasco & Esteller, 2010). Finally, we will correlate the list of
potential epigenes regulating co-methylation networks with SNP datasets to identify genetic variations. Other
potential future directions include 1) perform causality test between candidate regulators identified and the coexpression or co-methylation modules, 2) construct networks that incorporate both gene expression and DNA
methylation data to explore whether co-regulation of DNA methylation loci and groups of genes exist, and 3)
use the relationships between DNA methylation and other molecular traits as uncovered in Specific Aim 2 as
priors for the construction of Bayesian networks.
Expected Results and Potential Problems: Depending on primary regulatory mechanisms (hyper- or
hypomethylation of specific transcriptional factors vs genetic variations in epigenes) we will identify causal
transcriptional regulators which induce large changes in gene expression or epigenes that cause changes in
DNA methylation signature in ovarian cancer. We expect to find co-methylated modules regulated by epigenes
as previous studies have identified Polycomb group proteins regulated CpGs in ovarian cancer (Teschendorff
et al., 2010). We also expect that the list of transcriptional regulators identified in the first approach (building a
co-expression network) may contain some epigenes as those bind to DNA and regulate gene expression. In
this case we will consider an overlap between co-expression and co-methylation networks.
Specific Aim 4 – Identify composite predictive markers of clinical phenotypes of ovarian cancer by
simultaneously considering expression, genetic and epigenetic data
Rationale and Objectives: As both genetic and epigenetic variations are implicated in cancer development,
we reason that the combination of both factors will improve the predictive value of diagnostic markers.
Approach: As the result of analyses outlined in the specific aim 2 we will construct gene lists for each
molecular trait/clinical outcome relationship. We will further reduce these lists based on cis-regulation by DNA
methylation, SNPs and CNVs of gene and microRNA expression where possible. Additionally, we will select 2
lists of genes based on the analyses outlined in the specific aim 3. These lists will be used together for
identification of predictive markers using the ElasticNet regression model (Zou & Hastie, 2005), providing
feature selection in the “large p, small n” paradigm where features may be highly correlated. The top
biomarkers will be used for experimental validation in ovarian cancer and normal tissues as well as fallopian
tube tissues ((Karst, Levanon, & Drapkin, 2011; Khalil, Brewer, Neyarapally, & Runowicz, 2010)). As the first
stage experimental validation we propose to validate predicted biomarkers by analyzing SNPs and RNA
expression. This will allow us to increase the number of analyzed samples and regions as well as the
predictive power of the models. False positive and true positive fractions as well as predictive values will be
reported for each biomarker prediction model.
Expected Results and Potential Problems: One concern is data overfitting, which will lead the consequence
that the model fits the original TCGA dataset but may predict poorly for the samples analyzed. Another issue is
that a large number of samples are needed for such validation which may not be available to us. For this
reason we are focusing on the analysis of gene expression and SNPs first rather than profiling DNA
methylation.
5
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Appendix
Supplementary Figure 1. Distribution of categorical clinical traits
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Supplementary Figure 2. Distribution of categorical clinical traits.
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Supplementary Figure 3. Distribution of quantitative clinical traits.
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