Genome Wide DNA Copy Number Analysis of Serous Type Ovarian Carcinomas Identifies Genetic Markers Predictive of Clinical Outcome

Department of Statistics, Brigham Young University, Provo, Utah, United States of America.
PLoS ONE (Impact Factor: 3.23). 06/2012; 7(2):e30996. DOI: 10.1371/journal.pone.0030996
Source: PubMed


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.

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Available from: Gayatry Mohapatra,
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    • "In particular, loss at 6q24e27 has been extensively studied for its potential role in tumor suppression (Hayashi et al., 2012; Sun et al., 2003) and some candidate genes have been proposed such as PLAGL1 (Abdollahi et al., 2003), GRM1, SOD2 (Shridhar et al., 1999), SASH1 (Zeller et al., 2003) or Parkin (Denison et al., 2003). However, few studies so far have aimed to define specific DNA copy number markers that may have clinical relevance in predicting outcome in ovarian cancer (Baumbusch et al., 2013; Bruchim et al., 2009; Engler et al., 2012; Wang et al., 2012; Yamamoto et al., 2009). Most studies that have focused on the assessment of specific alterations have been limited by the absence of independent copy number replication datasets (Bruchim et al., 2009; Yamamoto et al., 2009). "
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    ABSTRACT: Standard treatments for advanced high-grade serous ovarian carcinomas (HGSOCs) show significant side-effects and provide only short-term survival benefits due to disease recurrence. Thus, identification of novel prognostic and predictive biomarkers is urgently needed. We have used 42 paraffin-embedded HGSOCs, to evaluate the utility of DNA copy number alterations, as potential predictors of clinical outcome. Copy number-based unsupervised clustering stratified HGSOCs into two clusters of different immunohistopathological features and survival outcome (HR = 0.15, 95%CI = 0.03–0.81; Padj = 0.03). We found that loss at 6q24.2–26 was significantly associated with the cluster of longer survival independently from other confounding factors (HR = 0.06, 95%CI = 0.01–0.43, Padj = 0.005). The prognostic value of this deletion was validated in two independent series, one consisting of 36 HGSOCs analyzed by fluorescent in situ hybridization (P = 0.04) and another comprised of 411 HGSOCs from the Cancer Genome Atlas study (TCGA) (HR = 0.67, 95%CI = 0.48–0.93, Padj = 0.019). In addition, we confirmed the association of low expression of the genes from the region with longer survival in 799 HGSOCs (HR = 0.74, 95%CI = 0.61–0.90, log-rank P = 0.002) and 675 high-FIGO stage HGSOCs (HR = 0.76, 95%CI = 0.61–0.96, log-rank P = 0.02) available from the online tool KM-plotter. Finally, by integrating copy number, RNAseq and survival data of 296 HGSOCs from TCGA we propose a few candidate genes that can potentially explain the association. Altogether our findings indicate that the 6q24.2–26 deletion is an independent marker of favorable outcome in HGSOCs with potential clinical value as it can be analyzed by FISH on tumor sections and guide the selection of patients towards more conservative therapeutic strategies in order to reduce side-effects and improve quality of life.
    Molecular Oncology 10/2014; 9(2). DOI:10.1016/j.molonc.2014.09.010 · 5.33 Impact Factor
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    • "Nakayama et al. demonstrated that amplification of CCNE1 is related to poor survival suggesting that CCNE1 can be a potential therapeutic target in the treatment of ovarian cancer [43]. MYC is a strong proto-oncogene that codes a transcription factor and is often found to be constitutively (persistently) expressed in many types of cancers [42]. This leads to the unregulated expression of many genes (presumably through DNA over-replication), some of which are involved in cell proliferation and result in cancer formation [44]. "
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    ABSTRACT: Motivation Understanding the molecular mechanisms underlying cancer is an important step for the effective diagnosis and treatment of cancer patients. With the huge volume of data from the large-scale cancer genomics projects, an open challenge is to distinguish driver mutations, pathways, and gene sets (or core modules) that contribute to cancer formation and progression from random passengers which accumulate in somatic cells but do not contribute to tumorigenesis. Due to mutational heterogeneity, current analyses are often restricted to known pathways and functional modules for enrichment of somatic mutations. Therefore, discovery of new pathways and functional modules is a pressing need. Results In this study, we propose a novel method to identify Mutated Core Modules in Cancer (iMCMC) without any prior information other than cancer genomic data from patients with tumors. This is a network-based approach in which three kinds of data are integrated: somatic mutations, copy number variations (CNVs), and gene expressions. Firstly, the first two datasets are merged to obtain a mutation matrix, based on which a weighted mutation network is constructed where the vertex weight corresponds to gene coverage and the edge weight corresponds to the mutual exclusivity between gene pairs. Similarly, a weighted expression network is generated from the expression matrix where the vertex and edge weights correspond to the influence of a gene mutation on other genes and the Pearson correlation of gene mutation-correlated expressions, respectively. Then an integrative network is obtained by further combining these two networks, and the most coherent subnetworks are identified by using an optimization model. Finally, we obtained the core modules for tumors by filtering with significance and exclusivity tests. We applied iMCMC to the Cancer Genome Atlas (TCGA) glioblastoma multiforme (GBM) and ovarian carcinoma data, and identified several mutated core modules, some of which are involved in known pathways. Most of the implicated genes are oncogenes or tumor suppressors previously reported to be related to carcinogenesis. As a comparison, we also performed iMCMC on two of the three kinds of data, i.e., the datasets combining somatic mutations with CNVs and secondly the datasets combining somatic mutations with gene expressions. The results indicate that gene expressions or CNVs indeed provide extra useful information to the original data for the identification of core modules in cancer. Conclusions This study demonstrates the utility of our iMCMC by integrating multiple data sources to identify mutated core modules in cancer. In addition to presenting a generally applicable methodology, our findings provide several candidate pathways or core modules recurrently perturbed in GBM or ovarian carcinoma for further studies.
    BMC Systems Biology 10/2013; 7(2). DOI:10.1186/1752-0509-7-S2-S4 · 2.44 Impact Factor
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    ABSTRACT: The SALL2 gene product and transcription factor p150 were first identified in a search for tumor suppressors targeted for inactivation by the oncogenic mouse polyoma virus. SALL2 has also been identified as a cellular quiescence factor, essential for cells to enter and remain in a state of growth arrest under conditions of serum deprivation. p150 is a transcriptional activator of p21(Cip1/Waf1) and BAX, sharing important growth arrest and proapoptotic properties with p53. It also acts as a repressor of c-myc. Restoration of SALL2 expression in cells derived from a human ovarian carcinoma (OVCA) suppresses growth of the cells in immunodeficient mice. Here we examine the pattern of p150 expression in the normal human ovary, in OVCA-derived cell lines and in primary ovarian carcinomas. Immunohistochemical staining showed that p150 is highly expressed in surface epithelial cells of the normal human ovary. Expression is exclusively from the P2 promoter governing the E1A splice variant of p150. The P2 promoter is CpG-rich and susceptible to methylation silencing. p150 expression was restored in OVCA cell lines following growth in the presence of 5-azacytidine. In a survey of 210 cases of OVCA, roughly 90% across major and minor histological types failed to show expression of the protein. Immunological and biochemical approaches were used to show hypermethylation of the SALL2 P2 promoter in OVCA-derived cell lines and in a majority of primary tumors. These results bring together molecular biological and clinical evidence in support of a role of SALL2 as a suppressor of ovarian cancers.
    Molecular oncology 12/2012; 7(3). DOI:10.1016/j.molonc.2012.11.005 · 5.33 Impact Factor
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