Genome Wide DNA Copy Number Analysis of Serous
Type Ovarian Carcinomas Identifies Genetic Markers
Predictive of Clinical Outcome
David A. Engler2., Sumeet Gupta3., Whitfield B. Growdon4, Ronny I. Drapkin5, Mai Nitta1, Petra A.
Sergent4, Serena F. Allred2, Jenny Gross7, Michael T. Deavers8, Wen-Lin Kuo6, Beth Y. Karlan7, Bo R.
Rueda4, Sandra Orsulic7, David M. Gershenson8, Michael J. Birrer9, Joe W. Gray6, Gayatry Mohapatra1*
1Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts, United States of America, 2Department of Statistics, Brigham Young University,
Provo, Utah, United States of America, 3Whitehead Institute of Biomedical Research, Cambridge, Massachusetts, United States of America, 4Department of Vincent
Obstetrics and Gynecology, Vincent Center for Reproductive Biology, Massachusetts General Hospital, Boston, Massachusetts, United States of America, 5Department of
Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America, 6Life Sciences Division, Lawrence Berkeley National Laboratory,
Berkeley, California, United States of America, 7Women’s Cancer Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, United States of America,
8Department of Pathology and Gynecology Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America, 9Center for Cancer
Research, Massachusetts General Hospital, Boston, Massachusetts, United States of America
Ovarian cancer is the fifth leading cause of cancer death in women. Ovarian cancers display a high degree of complex
genetic alterations involving many oncogenes and tumor suppressor genes. Analysis of the association between genetic
alterations and clinical endpoints such as survival will lead to improved patient management via genetic stratification of
patients into clinically relevant subgroups. In this study, we aim to define subgroups of high-grade serous ovarian
carcinomas that differ with respect to prognosis and overall survival. Genome-wide DNA copy number alterations (CNAs)
were measured in 72 clinically annotated, high-grade serous tumors using high-resolution oligonucleotide arrays. Two
clinically annotated, independent cohorts were used for validation. Unsupervised hierarchical clustering of copy number
data derived from the 72 patient cohort resulted in two clusters with significant difference in progression free survival (PFS)
and a marginal difference in overall survival (OS). GISTIC analysis of the two clusters identified altered regions unique to
each cluster. Supervised clustering of two independent large cohorts of high-grade serous tumors using the classification
scheme derived from the two initial clusters validated our results and identified 8 genomic regions that are distinctly
different among the subgroups. These 8 regions map to 8p21.3, 8p23.2, 12p12.1, 17p11.2, 17p12, 19q12, 20q11.21 and
20q13.12; and harbor potential oncogenes and tumor suppressor genes that are likely to be involved in the pathogenesis of
ovarian carcinoma. We have identified a set of genetic alterations that could be used for stratification of high-grade serous
tumors into clinically relevant treatment subgroups.
Citation: Engler DA, Gupta S, Growdon WB, Drapkin RI, Nitta M, et al. (2012) Genome Wide DNA Copy Number Analysis of Serous Type Ovarian Carcinomas
Identifies Genetic Markers Predictive of Clinical Outcome. PLoS ONE 7(2): e30996. doi:10.1371/journal.pone.0030996
Editor: Amanda Ewart Toland, Ohio State University Medical Center, United States of America
Received September 7, 2011; Accepted December 28, 2011; Published February 15, 2012
Copyright: ? 2012 Engler 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 supported by the Director, Office of Science, Office of Biological & Environmental Research, of the U.S. Department of Energy under
Contract No. DE-AC02-05CH11231, the National Cancer Institute grant P50 CA 83639 (JWG), the National Cancer Institute grant RC4 CA 156551 (MJB), Dana
Farber/Harvard Cancer Center Ovarian SPORE P50 CA105009 (SO and BRR), Ovarian Cancer Research Fund (SO and BRR), and by the Michael Wall Gynecologic
Oncology Research Fund (GM). The GOG component was supported by the National Cancer Institute grants to the Gynecologic Oncology Group (GOG)
Administrative Office (Philip DiSaia U10 CA027469), GOG Tissue Bank (Philip DiSaia U10CA027469 and U24 CA011479), and the GOG Statistical Data Center (John
Blessing U10 CA037517). 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: email@example.com
. These authors contributed equally to this work.
Epithelial ovarian carcinoma represents the fifth leading cause
of cancer death among women in the United States [1,2]. It is
estimated that there will be 21,550 cases of invasive ovarian cancer
diagnosed and 14,660 deaths attributed to ovarian cancer in 2009
. The five year survival rate of ovarian cancer ranges from 30 to
92%, depending on the spread of the disease at the time of
diagnosis . While early-stage ovarian cancers are highly curable,
over 70% of ovarian cancer patients are diagnosed with the
advanced disease with lower cure rates and are associated with
significant morbidity and mortality . Over the past decades
there have been significant advances in ovarian cancer treatment
as a result of improved surgical techniques and chemotherapy
regimens through multiple clinical trials [5,6]. Debulking surgery
has become the standard treatment for advanced stage ovarian
carcinoma; a residual tumor size of greater than 2 cm is associated
with a survival of 12–16 months, compared with 40–45 months if
the tumor is less than 2 cm [7,8]. Adjuvant chemotherapy with
platinum and taxane based regimens improves both disease free
survival and overall survival in all patient subgroups; however, the
longest survival periods are observed in optimally debulked
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patients. Up to 80% of patients with advanced stage disease
experience an initial response to chemotherapy but eventually
relapse with a median progression free survival of 18 months
[9,10,11,12,13]. A number of resistance mechanisms have been
defined in vitro [14,15,16]. However, the importance of these
resistance mechanisms in patients remains unclear. Thus, there is
a need for improvement in the understanding of the underlying
genetic alterations involved in the pathogenesis of ovarian cancer.
Identification of prognostic/predictive markers can improve
patient management and allow development of molecularly
The serous type ovarian carcinoma accounts for approximately
70% of ovarian cancer cases and is one of the clinically aggressive
subtypes . High-grade serous tumors differ from all other
ovarian carcinomas in terms of their pathology, pathogenesis,
prognosis and underlying genetic alterations [18,19]. The most
frequently documented mutation is in the TP53 tumor suppressor
Expression profiling-based studies have also shown that high-
grade tumors cluster separately from low grade carcinomas and
borderline tumors [20,21]. Several expression profiling based
studies have identified gene expression signatures associated with
response to chemotherapy [22,23] and to different subtypes of
ovarian cancer [21,24]. High-level amplifications of ERBB2, MYC,
PIK3CA, EVI1, RAB25, AKT2, CCNE1, NOTCH3, FGFR2, CCND1,
PAK1, EMSY, ZNF217, NCOA3 [23,25,26,27,28,29,30,31,32] and
homozygous deletion, mutation, reduced expression and/or
hypermethylation of TP53, KRAS, LOT1, DOC2, NOEY2, OVCA1,
SPARC, CDKN2A, RB1, PTEN [33,34,35,36,37,38,39] genes have
also been reported. However, little consensus or overlap between
all these studies has emerged.
Array-based comparative genomic hybridization (aCGH) allows
detection of DNA copy number alterations (CNA) and provides a
global assessment of molecular events in the genome . Several
studies have been reported utilizing either conventional metaphase
chromosome-based CGH [41,42,43] or array-based high resolu-
tion genomic technologies for identifying genome wide CNAs in
ovarian cancer [23,44,45,46,47]. The above mentioned studies
have identified frequent regions of increased copy number along
1q, 3q26, 7q32–q36, 8q24, 17q32 and 20q13; and regions of
decreased copy number along 1p36, 4q, 13q, 16q, 18q and Xq12.
However, specific genetic markers that are predictive of clinical
outcome are yet to be identified for high-grade ovarian cancers.
The rationale for our study is based on the idea that genetic
alterations are the cause of tumor development and progression.
Therefore, it is likely that combination of specific genetic
alterations will be predictive of clinical behavior [48,49]. In this
study, using high-resolution aCGH, we sought to identify
potentially useful DNA-based prognostic marker/s to delineate
high-grade serous type ovarian cancer patients into molecularly
defined clinically relevant subgroups.
Materials and Methods
Tumor samples and clinical data
The study group included tumor samples from 72 patients
identified within prospectively collected MGH Gynecological
Tissue Repository and Cedars-Sinai Women’s Cancer Research
Program Tissue Bank under IRB approved protocols at Massa-
chusetts General Hospital and Cedars-Sinai Medical Center from
1991 to 2008 (Table 1). Under these protocols, patients with
suspected ovarian cancer are consented in writing for tissue
collection and prospective clinical data collection prior to surgical
exploration. Frozen tumor tissues were collected, catalogued and
anonymized. In each case, a small piece of tissue adjacent to the
tissue that was used for DNA extraction, was paraffin embedded
and H&E stained for histological validation. All samples were
reviewed by a pathologist to confirm the presence of viable tumor
cells in the tissue sample. Only samples with more than 70–80%
viable tumor tissue were chosen for this study. Clinical data were
then paired to the assigned catalogue number of each sample.
Clinical factors including age at diagnosis, stage of disease, grade
of tumor, origin of tumor (ovary, peritoneum, fallopian tube),
specific surgical therapy, specific chemotherapy, platinum sensi-
tivity, recurrence, progression free survival (PFS), overall survival
(OS) were recorded and paired with the molecular data for
For reference DNA, buffy coats from 5 anonymous donors were
purchased from the Massachusetts General Hospital Blood Bank.
Table 1. Patient characterisitics.
Range 36.9, 90.5
2 5 (6.9%)
II 5 (6.9%)
IV 24 (33.3%)
Primary tumor site
ovary 56 (77.7%)
fallopian tube4 (5.6%)
No 67 (93.0%)
Yes 64 (88.9%)
No 8 (11.1%)
Not available6 (8.3%)
% censoring 59.2
median (months) 8.0
% censoring 28.2
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Two independent datasets were used for validation. The first
dataset included a panel of 160 high-grade serous tumors from
UCSF and the Gynecology Oncology Group (UCSF-GOG).
These samples were analyzed using a 1 Mb BAC array platform.
For these patients, overall survival information was available. The
second dataset was obtained from The Cancer Genome Atlas
(TCGA) project, included 246 high-grade serous tumors that were
analyzed using a custom designed 415 k oligonucleotide array
from Agilent. Clinical information for these samples was obtained
with permission from the TCGA data committee.
Oligonucleotide array CGH
High molecular weight genomic DNA was isolated from 72
primary ovarian tumor samples and normal whole blood from 5
anonymous female donors using routine protocol. Array CGH was
performed to determine DNA copy number changes using Agilent
Human 105 K oligonucleotide microarrays (014698_D_20070820)
following the manufacturer’s instructions (http://www.home.
agilent.com/agilent/home.jspx). Genomic coordinates for this
array are based on the NCBI build 36, March 2006 freeze of the
assembled human genome (UCSC hg18), available through the
UCSC Genome Browser. This array includes a comprehensive
probe coverage spanning both coding and non-coding regions, with
emphasis on well-known genes, promoters, micro RNAs, and
telomeric regions and provides an average spatial resolution of
21.7 kb. Array hybridization, washing and image processing were
performed following the protocol described in Gabeau-Lacet et al
aCGH data analysis methods
All 5 normal reference DNA samples were hybridized one at a
time to identify the common polymorphisms (CNVs) . These
CNVs were flagged during image analysis and were eliminated
from subsequent analysis. DNA copy number alteration (CNA)
was identified through dynamic thresholding of segmented aCGH
data. Circular binary segmentation (CBS) was used to segment
each hybridization into regions of common mean . For each
hybridization, the median absolute deviation (MAD) across all
segments was then obtained. Probes assigned to segments with
mean value greater than a scaled MAD were identified as gain.
Likewise, probes corresponding to segments with mean value less
than a scaled MAD were identified as loss. A default MAD scaling
factor of 1.11 was utilized for both gains and losses . Both
UCSF-GOG and TCGA data sets were subjected to CBS-MAD
algorithms followed by GISTIC analysis to identify amplifications
and deletions. Following segmentation and classification, data
were further reduced, without compromising the continuity and
breakpoints, to facilitate downstream analyses . This reduced
dataset was used for all subsequent analyses.
To identify minimal regions of common alteration across all
hybridizations, the Genomic Identification of Significant Targets
in Cancer (GISTIC) approach  was utilized on each data set.
Threshold selection for the GISTIC procedure was based,
conservatively, on the maximum threshold for alteration (across
all hybridizations) identified under the MAD approach described
above; 0.4 was selected as the gain and loss threshold and 0.25 was
selected as the significance threshold. Each analyzed CBS segment
consisted of at least four markers. Segments that contained fewer
than four markers were combined with the adjacent segment
closest in segment value. A q-value was then obtained for each
region. Each peak (i.e., region associated with a low q-value) was
tested to determine whether the signal was primarily due to broad
events, focal events or overlapping events of both types.
Identification of markers associated with survival (PFS and OS)
was conducted through utilization of cluster analysis. Unsuper-
vised clustering was first conducted on the set of log2 ratios from
the reduced data set described above. Markers on the X
chromosome were excluded from the analysis. The Euclidean
distance metric was employed in conjunction with the Ward
approach for agglomerative clustering. Resultant clusters were
then assessed for differences in survival under the Cox propor-
tional hazards model. Because significant differences were
identified, GISTIC was performed to identify makers uniquely
associated with each subgroup.
To validate the identified set of discriminating markers,
supervised clustering was then conducted separately on the
UCSF-GOG and TCGA data sets through use of Support Vector
Machines ; genomic regions in each of the two validation data
sets corresponding to the identified discriminating markers were
utilized to guide the clustering. For each data set, resultant clusters
were then assessed for differences with regard to both overall and
Clinical Characteristics of OVCA patients
The median age at the time of diagnosis of the 72 patient cohort
was 60 years (range 37–90) (Table 1). Mean follow up time was 37
months (range 1–212). The majority (93%) of the population
presented with advanced stage disease. Surgical staging was
utilized as upfront therapy for all patients in the cohort, and this
intervention was described as optimal with less than 1 cm of
residual disease in 67 patients (88%). Extensive surgical cyto-
reduction including peritoneal stripping and bowel resection were
utilized in 64% of the cohort in order to achieve an optimal
debulking. Only 1 patient did not receive a taxane and platinum-
containing regimen as adjuvant therapy after surgery. Six patients
were lost to follow up less than 2 months after surgical exploration.
Platinum sensitivity defined as a progression free survival of
greater than 6 months following the last dose of adjuvant
chemotherapy was observed in 42 of 70 (60%) patients, with 12
patients (17%) demonstrating progressive disease despite chemo-
therapy. Median progression free survival was 8 months, with a
median overall survival of 38 months. Univariate survival analysis
identified platinum sensitive disease (p,0.0001), optimal cytor-
eduction (p,0.0001), lack of recurrence or progression (p,0.001)
and presenting CA-125,500 U/mL (p,0.04) as prognostic
clinical factors predicting an overall survival advantage. A Cox
proportional hazards model incorporating these clinical factors
adjusted for age revealed that platinum sensitive disease (hazard
ratio 0.06), and optimal cytoreduction (0.12) were independent
prognostic factors associated with an improved survival.
Global DNA copy number alterations
Genomic copy number for each probe was determined by
calculating the log2 ratio of median signal intensities of the tumor
and normal reference DNA. High signal to noise ratios were
observed in all samples due to good quality tumor DNA.
Representative profiles for five different tumors are shown in
Figure 1. A large number of tumors showed some degree of
genetic heterogeneity in the background along with distinct
increase and decrease of DNA copy numbers involving large
portions of chromosome arms (Figure 1A, C, and D). High-level
amplifications of regions including 3q26.2 and 8q24.2 were
frequently observed (Figure 1B–D). Some tumors displayed more
than 10 regions of high-level amplifications (Figure 1E). A
genome-wide view of the CNAs in the 72 tumors is shown in
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Figure 1F and the frequency of amplification and deletion is shown
in Figure 1G. In order to identify frequent regions of copy-number
alterations, and to define the minimal regions of gains and losses,
the statistical method Genomic Identification of Significant
Targets In Cancer (GISTIC) was applied to the entire dataset
(Figure 1H and I).
GISTIC analysis identified 19 regions of gains along 18
chromosome arms (Figure 1H) and 18 regions of losses along 17
chromosome arms (Figure 1I) distributed throughout the genome.
Several chromosomal arms had more than one minimal region of
gain and loss. For each alteration, the peak region (i.e., the highest
frequency and amplitude of events) was selected as the region most
likely to contain a cancer gene. Several oncogenes and tumor
suppressor genes previously known to have copy number changes
in human ovarian cancer, such as MYCL1, EVI1, BRAF, MYC,
KRAS, CCNE1, TP73, RB1, and MN1, were readily identified by
GISTIC. Chromosomal locations, frequencies, genomic intervals,
gene contents and candidate cancer genes of these changes are
highlighted in Table 2. There were 19 regions each of gains and
18 regions of losses (with significant q values) identified with the
number of genes ranging from 2–61. The size of deletions ranged
from 400 kb to 3 Mb and the number of genes mapping to these
regions ranged from 6–106 respectively. In addition, gain and loss
of entire chromosome arms were frequently observed. Genes with
known or possible function in cancer are highlighted in figure 1H
Amplification of 3q26.2 including EVI1 gene and 8q24.12
including MYC oncogene were the most frequent alterations
occurring in 72–75% of tumors suggesting a role for these genes in
tumor maintenance or dissemination process. The most frequently
deleted regions (78%) were located on 16q24.2 including FBXO31
and BANP genes and on 22q13.33 (Table 2). Other amplified
regions were observed in 28–58% of tumors and deleted regions
were observed in 30–70% of tumors respectively. In addition to
the identification of regions of gain and loss common to the entire
set of tumors, it was also of interest to identify regions of copy
number alteration significantly associated with differences in OS
and PFS which was assessed using clustering algorithms.
In order to identify a robust genomic signature and to define
clinically relevant genetic subgroups among the high-grade
tumors, we applied unsupervised hierarchical clustering algorithm
to unfiltered aCGH data from 72 serous type tumors. Figure 2A
illustrates the two subgroups that resulted from unsupervised
clustering. The two primary subgroups were shown to differ
significantly with regard to progression free survival (PFS)
(p=0.0008) and a marginal difference in OS (p=0.07); figure 2B
shows the PFS Kaplan-Meier plot for the two groups. Figure 2C
illustrates differences between clusters with regard to clinical
covariates. Formal comparison under the Cox proportional
hazards model revealed a significant difference between the two
subgroups with regards to platinum sensitivity (p=0.016) and
peritoneal stripping (p=0.011).
To identify CNAs associated with each subgroup, and to
determine whether these markers predict outcome independent of
grade, we conducted a separate GISTIC analysis of grade 3
tumors only from each cluster. Figure 3A and B show
amplifications and deletions identified by GISTIC for tumors in
cluster 1 (worse prognosis) and 3C and D for tumors in cluster 2
(better prognosis) respectively. Amplification and deletion peaks
unique to each group were readily identified by GISTIC and are
indicated by green stars. We used these unique probe sets, listed in
Supplementary Table S1, to build a prediction model for
conducting supervised clustering. We then evaluated the model
against our tumor panel, including grades 2 and 3, using leave-
one-out cross validation method. This resulted in 80% accuracy
rate in classifying the tumors into good and poor outcome
Validation of independent datasets
Two independent datasets of high-grade serous tumors with
clinical follow up information were used for validation. The
UCSF-GOG dataset included 160 high-grade tumors, with overall
survival information, randomly selected from the Gynecology
Oncology Group. Copy number information for this dataset was
generated using a 1 Mb BAC array. Data were analyzed using
CBS-MAD followed by GISTIC (Figure S3). In order to perform a
proper comparison, we pulled targets from the BAC array
corresponding to unique probe sets identified from our analysis
as described in methods (Table S2 1ists BACs used for clustering).
Supervised clustering using our discriminating markers resulted in
two subgroups with a statistically significant difference in overall
survival (p=0.028) (Figure 4). Since validation datasets were
generated using different array formats, frequency of amplifica-
tions and deletions were compared in all three datasets prior to
analysis (Table S3).
The second dataset included 246 high-grade serous tumors from
the TCGA project that were analyzed by a custom made Agilent
415 K oligonucleotide array. Supervised clustering using our
discriminating markers resulted in three subgroups with significant
difference in PFS (p=0.0017) and in OS (p=0.0098) (Figure 5A
and B) (Figure S1). Further analysis of the subgroups showed a
difference in PFS (p,0.001) and OS (p=0.0028) between
subgroup 2 and combined subgroups 1 and 3 (Figure 5A1 and
B1) suggesting that cluster 2 includes patients with worst outcome.
Results from the GISTIC analysis of TCGA clusters are shown in
Figure S2 A–F. Note that the amplification and deletion peaks of
original cluster 1 resembled the amplification and deletion peaks of
TCGA cluster 2. To identify genetic alterations specific to each
group, we compared CNAs in each cluster (Figure 5C–E1). The
TCGA clusters were distinctly different at 8 genomic regions along
8p21.3, 8p23.2, 12p12.1, 17p11.2, 17p12, 19q12, 20q11.21, and
In this study, we first evaluated global DNA copy number
alterations in a panel of 72 clinically annotated high-grade serous
ovarian carcinomas to identify specific genetic alterations
associated with clinical outcome. Unsupervised hierarchical
clustering identified two distinct genomic subgroups with signif-
icant difference in clinical outcome. Unique genomic regions
identified from each group were then able to successfully divide
two independent datasets into clinically distinct subgroups with a
significant difference in survival.
Previous studies that attempted to identify the molecular
determinants of clinical outcome have focused on single genes
because of the frequent involvement of these genes/pathways in
serous type ovarian cancers [57,58]. However, these genes,
although frequently associated in ovarian carcinomas, failed to
predict outcome compared to the conventional clinical indicator
such as the extent of surgery [59,60]. Gene expression based
studies have been useful in predicting clinical phenotypes such as
histologic types and stage for various tumor types , including
breast [62,63] and ovarian cancers [64,65,66,67,68].
Several groups have applied aCGH-based genomic technology
to identify CNA patterns predictive of platinum resistance [23,45],
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and to identify potential driver genes contributing towards ovarian
cancer pathogenesis [29,30,39]. However, these studies have not
established a correlation between CNA pattern and clinical
endpoints such as PFS and OS. Some limitations that could have
affected the outcome of these studies are sample size, a
heterogeneous mixture of samples from different histology/grades,
difficulty in combining data from various platforms due to minimal
overlap of the results, and lack of a robust dataset for validation.
To our knowledge, our study is the first to link a distinct set of
CNAs to clinically relevant patient subgroups of high-grade serous
ovarian cancers with a significant difference in PFS and OS.
Based on GISTIC analysis, we identified a set of discriminating
markers from a cohort a 72 high-grade serous ovarian cancer.
Next, we applied those discriminating markers on a dataset
generated from a cohort of 160 high-grade serous cancers that
were analyzed using a 1 Mb BAC array and identified three
clusters which is likely due to larger sample size. Analysis of the
three resulting clusters showed a significant difference in overall
survival between cluster 1 and combined clusters 2 and 3
(p=0.028) (Figure 4) (Figures S4 and S5). We then used a cohort
of 246 tumors from TCGA that were analyzed using Agilent 415 k
oligonucleotide arrays. Using the same discriminating markers, we
identified three clusters with a significant difference in both PFS
(p=0.0017) and OS (p=0.0098) (Figure 5). To further define the
groups, we compared the groups in combination. Combination of
clusters 1 and 2 versus cluster 3 showed a marginally significant p
value of 0.048 for PFS and 0.077 for OS. However, comparison of
cluster 2 versus clusters 1 and 3 resulted in a significant difference
both in PFS (p,0.001) and OS (p=0.0028) (Figure 5). Of note,
alterations in the cluster 1 of our dataset resembled the alterations
in the cluster 1 of UCSF-GOG dataset and cluster 2 of TCGA
dataset further confirming our initial results.
In order to identify markers specific for each group, we utilized
TCGA dataset since it provided the highest resolution and larger
sample size. First, we compared the frequency of losses, gains and
high-level amplifications and deletions in each cluster (Figure 5 C–
E1). The three TCGA clusters were distinctly different at 8
genomic regions along 8p21.3, 8p23.2, 12p12.1, 17p11.2, 17p12,
19q12, 20q11.21, and 20q13.2. In Cluster 1, 70–76% of samples
showed loss of 17p11.2 (Chr17:17646236–21720090) and 17p12
(Chr17:10689461–16833125). In cluster 2, 65–70% of samples
had amplifications on 12p12.1 (Chr12:16803022–25998952),
19q12 (Chr19:34794890–35592893), 20q11.21 (Chr20:29363673–
29773184)and 20q13.12 (Chr20:42510865–45356897).
cluster 3, 84–94% of tumors showed losses on 8p21.3
5781946) regions respectively. Furthermore, in all three datasets
the poor outcome subgroups had amplifications along 12p12.1,
19q12 and 20q. In the UCSF-GOG dataset, the 12p12.1 in
cluster 1 was distinctly visible compared to the 19q and 20q
amplifications. This is likely due to the lower resolution of the
array used for these samples. Similarly, the deletions along 8p
and 17p were also present in high frequencies in the other two
clusters (Supplementary Figure S4).
The minimal region of deletions including homozygous
deletions along 17p included the mitogen-activated protain
kinase 3 (MAP2K3) and mitogen-activated protein kinase 4
(MAP2K4) genes. MAP2K3 is activated by mitogenic and
environmental stress, and participates in the MAP kinase-
mediated signaling cascade. MAP2K4 is a central mediator in
the stress activated protein kinase signaling pathway that
responds to a number of cellular and environmental stress factors
. By phosphorylating MAP kinases such as JNK, MAP2K4
can ultimately transmit stress signals to nuclear transcription
factors that mediate various processes including proliferation,
apoptosis and differentiation. The majority of metastatic ovarian
cancers show significantly reduced expression suggesting that
MAP2K4 protein levels are down regulated when cells acquire
the ability to grow at a metastatic site . Analysis of a number
human ovarian cancer cell lines showed that MAP2K4 expression
is not detectable in 3 cell lines (SHOV3ip.1, SKOV-3 and HEY-
A8) known to be metastatic in vivo while other members of the
MAP2K4 pathway are intact including MEKK1, MKK7, JNK
and c-JUN. In addition, key members of the p38 pathway
including MKK6, MKK3 and p38 were also present. These
results implicate dysregulation of the stress-activated protein
kinase signaling cascade in ovarian cancer metastasis and support
the hypothesis that MAP2K4 regulates metastatic colonization in
ovarian cancer. Several studies have reported somatic mutations
in the MAP2K4 gene in multiple cancer types including ovarian
cancer [71,72,73]. Kan et al. 2010 stably expressed MAP2K4
mutants in mammalian cells to test their transforming activity.
They found that several of the mutants promoted anchorage-
independent growth. However, a majority of the MAP2K4
mutants showed reduced activity compared with wild-type kinase.
These results suggest that the MAP2K4 mutants may function in
a dominant-negative manner and promote anchorage-indepen-
dent growth in a manner similar to a synthetic dominant-negative
perspective, this finding suggests that modulation of the MAP2K4
pathway, either by restoration of MAP2K4 function alone or in
combination with therapeutic agents, could have a clinical
Figure 1. A–E. Representative aCGH profiles of 5 ovarian carcinomas. Log2 ratios (y axis) are plotted along the chromosomes (x axis). Each tumor
showing many CNAs including gain and loss of entire chromosome and/or chromosome arms, interstitial deletions, and high-level amplifications
(indicated in red arrows). Some tumors had more than 10 high-level amplifications. F. Genomic profiles of 72 primary ovarian carcinomas generated
by oligonucleotide array CGH. Each column in the left panel represents a tumor sample and rows represent losses and gains of DNA sequences along
the length of chromosomes 1 through X as determined by the segmentation analysis of normalized log2 ratios. The color scale ranges from blue
(loss) through white (two copies) to red (gain). The right panel indicates the frequencies of gain and loss of oligonucleotide probes on a probe-by-
probe basis for all autosomes and the X chromosome. The color scale ranges from white (no changes) to blue (frequent changes). Amplification of
3q26.2 and 8q24.12 including the EVI1 and MYC oncogenes and deletion of 16q24.2 and 22q13.33 were the most frequent alterations observed in
75% and 78% of the ovarian carcinomas respectively. G. Overall frequency of CNAs in 72 high-grade serous ovarian carcinomas. H and I. GISTIC
analysis of copy number gains (H) and losses (I) in ovarian carcinomas. The statistical significance of the aberrations identified by GISTIC are displayed
as false discovery rate q values to account for multiple hypothesis testing (q values; green line is 0.25 cut-off for significance). Scores for each
alteration are plotted along the x-axis and the genomic positions are plotted along the y-axis; dotted lines indicate the centromeres. H) GISTIC
revealed twenty broad and focal regions of gain (copy number threshold=log2 ratio $0.4). I) Loss of both broad and focal regions were identified by
GISTIC (copy number threshold=log2 ratio#0.4 for broad and #0.1 for focal events). Twenty broad and focal regions of losses, including seven focal
events, were identified in the background of broad regions. Candidate genes for some broad and focal events are noted. Green stars indicate known
or presumed copy number polymorphisms.
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PLoS ONE | www.plosone.org6 February 2012 | Volume 7 | Issue 2 | e30996
The second cluster included the worse outcome subgroup. In
this cluster, four regions along 12p12.1, 19q12, 20q11.21, and
20q13.12 were amplified in significantly high proportion of
samples (Figure 5). The peak region on 12p12.1 included 4 genes:
SRY (sex determining region Y)-box 5 isoform b (SOX5),
(branched chain aminotransferase 1, cytosolic) BCAT1, cancer
susceptibility candidate 1 isoform a (CASC1), and c-K-ras2 protein
isoform a precursor (KRAS). The SOX5 gene encodes a member of
the SOX (SRY-related HMG-box) family of transcription factors
involved in the regulation of embryonic development and in the
determination of the cell fate. The encoded protein may act as a
transcriptional regulator after forming a protein complex with
other proteins . The functional consequence of SOX5
amplification in human cancers has not been explored. One
report suggests that over expression of SOX5 enhances nasopha-
ryngeal carcinoma progression and correlates with poor survival
. However, its role in ovarian cancer is unexplored.
The Bcat1 gene was isolated in mouse by a subtraction/
coexpression strategy with Myc-induced tumors of transgenic
mice, and was shown that Bcat1 is a direct genetic target for Myc
regulation in mouse . The Bcat1 gene is highly expressed early
in embryogenesis, and during organogenesis its expression is
Table 2. Amplifications and deletion peaks identified by GISTIC.
Focal Peak Limits q values
genes in peak
1p34.2Amp Peak 1focalchr1:39685801–40370914(probes 1602:1635) 1.58E-0547 10 MYCL1
1q21.2 Amp Peak 2broad chr1:148088286–149154002(probes 4428:4494)1.08E-0554 19
1q42.3 Amp Peak 3both chr1:232669917–234247146(probes 7525:7574)3.59E-08557
2p21 Amp Peak 4broad chr2:44361420–47866370(probes 9494:9634)0.0008159 4721
2q31.1 Amp Peak 5focal chr2:175187074–177201863(probes 13436:13501)0.00028984116
3q26.2 Amp Peak 6 both chr3:170088444–170608075(probes 21389:21408)7.20E-28 752 EVI1
5p15.33Amp Peak 7broad chr5:763495–848743(probes 28334:28422) 0.001127739 23 TERT
6p22.3 Amp Peak 8 broad chr6:18594470–21251395(probes 34246:34314)1.13E-0755 21DEK
7q34 Amp Peak 9 broadchr7:138546566–139329889(probes 43855:43891)9.41E-0648 61 HIPK2
8q24.21Amp Peak 10 both chr8:128870582–129868380(probes 48692:48710)4.80E-31726 MYC
9p24.2 Amp Peak 11broad chr9:2454035–3357700(probes 49382:49413)0.0059345 287
10p15.1 Amp Peak 12broadchr10:5337351–6259241(probes 53434:53478)1.40E-05 51 18
10q22.3Amp Peak 13 focalchr10:80077917–80824746(probes 55808:55827) 0.02584730 19
11q14.1Amp Peak 14 focalchr11:76347688–79590923(probes 60715:60818)8.53E-05 34 27 PAK1
12p12.1Amp Peak 15 broadchr12:24100724–24946002(probes 63600:63632)8.34E-06 42 25 SOX5
19p13.11 Amp Peak 17 broadchr19:16413980–16621934(probes 86569:86580) 0.0043441 39 27NOTCH3
19q12 Amp Peak 18focal chr19:34887276–35388638(probes 86927:86937) 3.99E-11453 CCNE1
20p13Amp Peak 19broad chr20:2081797–3588124(probes 88375:88451)1.06E-06 5026
20q13.12 Amp Peak 20focal chr20:43063207–44606609(probes 89762:89861)1.34E-10582 ZMYND8
1p36.33Del Peak 1 focalchr1:823965–2511264(probes 10:101) 0.000207146 56 TP73
4q34.1 Del Peak 2both chr4:174091952–174549004(probes 27547:27566)1.28E-1155 10
5q13.2 Del Peak 3broad chr5:72832600–75235131(probes 30065:30149) 2.93E-07 5329
6q26 Del Peak 4broad chr6:162719313–165363813(probes 38878:38948)1.84E-08 5228
7p22.3Del Peak 5 focalchr7:902447–1887560(probes 39172:39224)0.0046966 39 23 MAD1L1
8p23.2 Del Peak 6 broadchr8:1422246–3652163(probes 44761:44852) 1.14E-11606
9q34.11Del Peak 7 broadchr9:130311520–131652310(probes 52720:52791)6.85E-064631
11p15.5Del Peak 8 both chr11:1–562228(probes 57848:57879)2.58E-1156 22 HRAS
13q14.12Del Peak 9 broadchr13:39671016–49044112(probes 68180:68577)3.87E-054953RB1
15q13.1Del Peak 10 broad chr15:26364997–27222402(probes 74201:74215)0.002452930 10
16p13.3Del Peak 11focalchr16:479088–756440(probes 77335:77357) 2.93E-0751 22
16q24.2 Del Peak 12broadchr16:86172468–87009930(probes 80015:80041) 3.65E-1578 12FBXO31, BANP
17p11.2 Del Peak 13broad chr17:17622694–18869071(probes 80901:80951)1.14E-12 65 35
18q23 Del Peak 14broad chr18:71478691–74906480(probes 85605:85715)1.12E-08 5512
19p13.3Del Peak 15both chr19:353214–3505632(probes 85779:85940)3.99E-16 70106
19q13.32Del Peak 16broad chr19:52180116–52242321(probes 87658:87660) 8.45E-08 50 57
22q12.1Del Peak 17 broadchr22:26250112–26828858(probes 92440:92459) 1.86E-095517MN1
22q13.33Del Peak 18bothchr22:48814623–49204003(probes 93575:93600) 3.40E-21 7830
DNA Copy Number in Ovarian Carcinoma
PLoS ONE | www.plosone.org7February 2012 | Volume 7 | Issue 2 | e30996
localized to the neural tube, the somites, and the mesonephric
tubules. The gene is also expressed in several MYC-based tumors.
As in mouse, the BCAT1 gene is a target for MYC activity in the
oncogenesis process in human . Using expression profiling, Ju
et al. 2009 reported differential expression of BCAT1 gene in
chemoresistant ovarian cancer compared to chemosensitive
tumors . Depletion of BCAT1 by RNA interference in
nasopharyngeal cancer cells effectively blocked the proliferation of
cells suggesting a role for BCAT1 in tumorigenesis . In
colorectal cancer immuno-histochemical analysis of BCAT1
protein showed significantly higher levels of expression in tumor
tissues with distant metastasis compared to those without and was
shown to be highly predictive of distant metastasis . The Casc1
gene was identified as a strong candidate lung tumor susceptibility
gene through whole genome analyses in inbred mice .
About 20–40% of human tumors carry mutation in KRAS .
The KrasG12Dconditional knock-in mouse model has been
extensively used to study the mechanisms of Ras-induced tumor
development [83,84]. The conditional expression KrasG12Din
mice, when combined with other mutations, leads to malignant
tumorigenesis in various tissues, including ovarian surface
epithelium (OSE). The responses of cells to RAS activation
appear to be context dependent such that cells may either undergo
oncogenic transformation or become senescent . Although
there are rare documented cases of RAS mutations in serous
carcinomas, the amplification of this gene may ultimately activate
the same pathways that mutant RAS turns on. A better
understanding of the molecular targets of RAS in OSE will help
identify potential therapeutic targets.
The region on 19q12 included focal amplification of the cyclin
E1 (CCNE1) gene. High-levels of CCNE1 protein, an activating
subunit of the cyclin dependent kinase 2 (CDK2), are often
observed in patients with ovarian cancer . Deregulation of cell
cycle control is thought to be a prerequisite for tumor
development, and several studies have shown an accelerated entry
into S phase because of constitutive expression of CCNE1 [87,88].
Furthermore, CCNE1 is able to induce chromosome instability by
inappropriate initiation of DNA replication, and centrosome
duplication . Amplification of CCNE1 in ovarian cancer
correlates with drug resistance  and poor clinical outcome
. Our finding confirmed the above-mentioned studies and
identified amplification of CCNE1 as a marker of poor outcome
and a possible therapeutic target.
Amplification of two distinct regions on 20q11.21, and
20q13.12 were associated with the poor outcome subgroup. The
region on 20q11.21 included two notable genes among others:
Figure 2. Unsupervised hierarchical clustering of CNAs identifies distinct patient subgroups. A) Unsupervised hierarchical clustering of
raw log2 ratios derived from 72 serous type ovarian cancers. Copy number values are color coded as follows: blue (loss), white (normal) and magenta
(gain). The pattern of dendrogram suggests two major genomic subgroups within the grade 3 tumors. B) PFS Kaplan-Meier plot for the two
subgroups. C) Comparison of clinical characteristics between the patient subgroups. Histology: red=serous; Grade: orange=grade 2, yellow=grade
3; Stage: red=Ic, blue=II, green=IIc, yellow=IIIa, orange=IIIb, brown=IIIc, pink=IV, dark gray=IVa; Status: red=evidence of disease, blue=no
evidence of disease; Outcome: green=complete remission, orange=progression, yellow=partial remission, brown=lost to follow up, pink=benign;
6 month progression: red=yes, blue=no, green=P (progression); Recurrence: brown=yes, orange=persistent disease, yellow=no; Platinum
response: red=sensitive, black=resistant; Drug: blue=yes, light blue=no; Ascites: red=yes, black=no; Chemo: orange=yes, brown=no; Radiation:
red=yes, black=no; General: white=n/a and/or blank.
DNA Copy Number in Ovarian Carcinoma
PLoS ONE | www.plosone.org8 February 2012 | Volume 7 | Issue 2 | e30996
inhibitor of DNA binding 1 (ID1) and BCL2-like 1 (BCL2L1). ID1
is a member of a family of 4 proteins (ID1-4) known to inhibit the
activity of basic helix loop helix transcription factors by blocking
their ability to bind DNA. ID1 has been implicated in a variety of
cellular processes including cell growth, differentiation, angiogen-
esis, and neoplastic transformation. It has been shown that ID1 is
de-regulated in multiple cancers and up-regulation of ID1 is
correlated with high-grades and poor prognosis in human cancers
[91,92]. ID1 has also been shown to be an effector of the p53-
dependent DNA damage response pathway . In ovarian
cancer, the level of Id1 protein expression correlates with
malignant potential, associated with poor differentiation and
aggressive behavior of tumor leading to poor clinical outcome
. BCL2L1 is a BCL2-related gene and can function as a BCL2-
independent regulator of programmed cell death . Both BCL2
and BCL2L1 are antiapoptotic and downstream targets of p53.
Overexpression of BCL2L1 suppresses mitochondrial-mediated
apoptosis and enhances cancer cell survival in cancer models .
Figure 3. GISTIC analysis of patient subgroups. A–B) Cluster 1; C–D) Cluster 2 amplification and deletion peaks defined by GISTIC in two
patient subgroups show clear difference in the location of peaks. Green stars indicate major differences between the two subgroups. Probes from
these regions were used to build the model for training.
DNA Copy Number in Ovarian Carcinoma
PLoS ONE | www.plosone.org9 February 2012 | Volume 7 | Issue 2 | e30996
Several studies report the expression of BCL2L1 in 60–70% of
ovarian cancer and that BCL2L1 expression is associated with
chemoresistant and recurrent disease .
Previous studies using conventional CGH have reported
consistent high-level amplification of the 20q13.12 region
encompassing many genes that may play causal role in ovarian
cancer pathogenesis [42,98,99]. In this study, we have identified
a 2.8 Mb region including 61 genes. Among others, the likely
[100,101,102,103,104]. Based on integrated analysis of DNA
copy number and expression profiling results, 20q11.22–q13.12
region has been reported to be associated with poor response to
primary treatment . More recently, another study using
tissue microarray composed of late stage, high-grade serous
ovarian carcinomas correlated PI3 expression with poor overall
Finally, cluster 3 samples predominantly showed losses on
8p21.3 and 8p23.2 regions. Several candidate tumor suppressor
genes that are less known to be implicated in human cancers
include DOCK5  and CSMD1  map to this region. Based
on the available literature, the above mentioned genes are likely to
play important roles but future studies are required to define their
roles in the pathogenesis of serous type ovarian carcinomas.
Whether expressions of all candidate genes described above are
altered in high grade serous ovarian cancer is not yet known and is
currently under investigation in our laboratory. Our study may
also have missed rare copy number variants, including duplica-
tions and deletions, in predisposing cancer susceptibility genes
since the normal reference DNA was made from healthy donors
but not matched normal DNA from each patient. However, it is
less likely given the very large deletions and amplifications we
identified in these tumors.
In summary, the results from this study illustrate the unique
molecular landscape of the genetic subgroups that exist within the
high-grade tumors. In the future, using these genomic markers, the
high-grade serous tumors can be stratified into clinically relevant
subgroups, help develop new diagnostic strategies and eventually
lead to targeted therapy.
Figure 4. Validation of classification accuracy in UCSF-GOG
dataset. Kaplan-Meier plot for UCSF-GOG subgroups identified
through supervised clustering. Subgroups are clinically distinct with
regard to overall survival (p=0.028).
Figure 5. Validation of classification accuracy in TCGA dataset. Kaplan-Meier plots for TCGA subgroups identified through supervised
clustering. Subgroups are clinically distinct with regard to both (A) progression-free survival (p=0.0017) and (B) overall survival (p=0.0098). The
combined cluster of subgroup 1 and 3 is clinically distinct from subgroup 2 with regard to both (A1) progression-free survival (p,0.001) and (B1)
overall survival (p=0.0028). C–E) Frequencies of genome copy number gain and loss plotted as a function of genome location from 1pter to 22qter
in the three clusters identified in the TCGA dataset. Vertical lines indicate chromosome boundaries, and vertical dashed lines indicate position of
centromeres along the chromosomes. Positive and negative values indicate frequencies of tumors showing copy number increases (gain shown in
red) and decreases (loss shown in green). C1–E1) Frequencies of tumors showing high-level amplifications and homozygous deletions in the three
TCGA clusters. Data are displayed as described in C–E. Arrows indicate genomic regions where the three clusters differ significantly.
DNA Copy Number in Ovarian Carcinoma
PLoS ONE | www.plosone.org10February 2012 | Volume 7 | Issue 2 | e30996
Supervised clustering of TCGA samples.
GISTIC analysis of TCGA clusters.
A. Summary of CNAs in UCSF-GOG dataset. B–C. GISTIC
analysis of UCSF-GOG dataset.
DNA copy number analysis of UCSF-GOG samples.
Supervised clustering of UCSF-GOG dataset.
Kaplan-Meier analysis of UCSF-GOG clusters.
peaks used for supervised clustering.
Probe set corresponding to amplification and deletion
List of BACs used for clustering GOG data.
Amplifications and deletion peaks identified by
We would like to thank the staff in the GOG Statistical and Data Center
and the GOG Tissue Bank for their efforts in selecting and distributing
frozen tumor samples to the Gray laboratory.
Conceived and designed the experiments: GM DAE JWG. Performed the
experiments: GM MN PAS. Analyzed the data: GM DAE SG SFA.
Contributed reagents/materials/analysis tools: RID MJB WBG BRR SO
WLK JG BYK MTD DMG. Wrote the paper: GM DAE.
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