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 significant 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 
. 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 
. Gene expression based studies have been useful in predicting clinical phenotypes such as histologic types and stage for various tumor types 
, including breast 
and ovarian cancers 
Several groups have applied aCGH-based genomic technology to identify CNA patterns predictive of platinum resistance 
, and to identify potential driver genes contributing towards ovarian cancer pathogenesis 
. 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) () (Figures S4
). 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) (). 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) (). 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 (). 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). In cluster 3, 84–94% of tumors showed losses on 8p21.3 (Chr8:22388473–25606748) and 8p23.2 (Chr8:1422246–5781946) 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 
. 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-independent growth in a manner similar to a synthetic dominant-negative MAP2K4 previously reported 
. From a translational 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 benefit.
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 (). 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 nasopharyngeal 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 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 KrasG12D
conditional knock-in mouse model has been extensively used to study the mechanisms of Ras-induced tumor development 
. The conditional expression KrasG12D
in 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 
. 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: 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, angiogenesis, 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 
. 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 
is a BCL2-related gene and can function as a BCL2-independent regulator of programmed cell death 
. Both BCL2
are antiapoptotic and downstream targets of p53. Overexpression of BCL2L1
suppresses mitochondrial-mediated apoptosis and enhances cancer cell survival in cancer models 
. 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 
. In this study, we have identified a 2.8 Mb region including 61 genes. Among others, the likely candidates are MMP9
. 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 survival 
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
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 duplications 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.