Genome wide DNA copy number analysis of serous type ovarian carcinomas identifies genetic markers predictive of clinical outcome

Recurrent Somatic Copy Number Alterations and Their Association with Oncogene Expression Levels in High-Grade Ovarian Serous Carcinoma

Somatic copy number alterations (SCNAs) are frequently observed in high-grade ovarian serous carcinoma (HGOSC). However, their impact on gene expression levels has not been systematically assessed. In this study, we explored the relationship between recurrent SCNA and gene expression using The Cancer Genome Atlas Pan Cancer dataset (OSC, TCGA, PanCancer Atlas) to identify cancer-related genes in HGOSC. We then investigated any association between highly correlated cancer genes and clinicopathological parameters, including age of diagnosis, disease stage, overall survival (OS), and progression-free survival (PFS). A total of 772 genes with recurrent SCNAs were observed. SCNA and mRNA expression levels were highly correlated for 274 genes; 24 genes were classified as a Tier 1 gene in the Cancer Gene Census in the Catalogue of Somatic Mutations in Cancer (CGC-COSMIC). Of these, 11 Tier 1 genes had highly correlated SCNA and mRNA expression levels: TBL1XR1, PIK3CA, UBR5, EIF3E, RAD21, EXT1, RECQL4, KRAS, PRKACA, BRD4, and TPM4. There was no association between gene amplification and disease stage or PFS. EIF3E, RAD21, and EXT1 were more frequently amplified in younger patients, specifically those under the age of 55 years. Patients with tumors carrying PRKACA, BRD4, or TPM4 amplification were associated with a significantly shorter OS. RECQL4 amplification was more frequent in younger patients, and tumors with this amplification were associated with a significantly better OS.

Genomic analyses of germline and somatic variation in high-grade serous ovarian cancer

BACKGROUND

High-grade serous ovarian cancers (HGSCs) display a high degree of complex genetic alterations. In this study, we identified germline and somatic genetic alterations in HGSC and their association with relapse-free and overall survival. Using a targeted capture of 557 genes involved in DNA damage response and PI3K/AKT/mTOR pathways, we conducted next-generation sequencing of DNA from matched blood and tumor tissue from 71 HGSC participants. In addition, we performed the OncoScan assay on tumor DNA from 61 participants to examine somatic copy number alterations (SCNA).

RESULTS

Approximately one-third of tumors had loss-of-function (LOF) germline (18/71, 25.4%) or somatic (7/71, 9.9%) variants in the DNA homologous recombination repair pathway genes BRCA1, BRCA2, CHEK2, MRE11A, BLM, and PALB2. LOF germline variants also were identified in other Fanconi anemia genes and in MAPK and PI3K/AKT/mTOR pathway genes. Most tumors harbored somatic TP53 variants (65/71, 91.5%). Using the OncoScan assay on tumor DNA from 61 participants, we identified focal homozygous deletions in BRCA1, BRCA2, MAP2K4, PTEN, RB1, SLX4, STK11, CREBBP, and NF1. In total, 38% (27/71) of HGSC patients harbored pathogenic variants in DNA homologous recombination repair genes. For patients with multiple tissues from the primary debulking or from multiple surgeries, the somatic mutations were maintained with few newly acquired point mutations suggesting that tumor evolution was not through somatic mutations. There was a significant association of LOF variants in homologous recombination repair pathway genes and high-amplitude somatic copy number alterations. Using GISTIC analysis, we identified NOTCH3, ZNF536, and PIK3R2 in these regions that were significantly associated with an increase in cancer recurrence and a reduction in overall survival.

CONCLUSIONS

From 71 patients with HGCS, we performed targeted germline and tumor sequencing and provided a comprehensive analysis of these 557 genes. We identified germline and somatic genetic alterations including somatic copy number alterations and analyzed their associations with relapse-free and overall survival. This single-site long-term follow-up study provides additional information on genetic alterations related to occurrence and outcome of HGSC. Our findings suggest that targeted treatments based on both variant and SCNA profile potentially could improve relapse-free and overall survival.

Genomic Analyses of Germline and Somatic Variation in High-Grade Serous Ovarian Cancer

Background High-grade serous ovarian cancers (HGSCs) display a high degree of complex genetic alterations. In this study, we identified germline and somatic genetic alterations in HGSC and their association with relapse-free and overall survival. Using a targeted capture of 577 genes involved in DNA damage response and PI3K/AKT/mTOR pathways, we conducted next-generation sequencing of DNA from matched blood and tumor tissue from 71 HGSC participants. In addition, we performed the OncoScan assay on tumor DNA from 61 participants to examine somatic copy number alterations. Results Approximately one-third of tumors had loss-of-function germline (18/71, 25.4%) or somatic (7/71, 9.9%) variants in the DNA homologous recombination repair pathway genes BRCA1, BRCA2, CHEK2, MRE11A, BLM , and PALB2 . Loss-of-function germline variants also were identified in other Fanconi anemia genes and in MAPK and PI3K/AKT/mTOR pathway genes. Most tumors harbored somatic TP53 variants (65/71, 91.5%). Using the OncoScan assay on tumor DNA from 61 participants, we identified focal homozygous deletions in BRCA1, BRCA2, MAP2K4, PTEN, RB1, SLX4, STK11, CREBBP , and NF1 . In total, 38% (27/71) of HGSC patients harbored pathogenic variants in DNA homologous recombination repair genes. For patients with multiple tissues from the primary debulking or from multiple surgeries, the somatic mutations were maintained with few newly acquired point mutations suggesting that tumor evolution was not through somatic mutations. There was a significant association of loss-of-function variants in homologous recombination repair pathway genes and high-amplitude somatic copy number alterations. Using GISTIC analysis, we identified NOTCH3, ZNF536 , and PIK3R2 in these regions that were significantly associated with an increase in cancer recurrence and a reduction in overall survival. Conclusions From 71 patients with HGCS, we performed targeted germline and tumor sequencing and provided a comprehensive analysis of these 577 genes. We identified germline and somatic genetic alterations including somatic copy number alterations and analyzed their associations with relapse-free and overall survival. This single-site long-term follow-up study provides additional information on genetic alterations related to occurrence and outcome of HGSC. Our findings suggest that targeted treatments based on both variant and SCNA profile potentially could improve relapse-free and overall survival.

A model and algorithm for identifying driver pathways based on weighted non-binary mutation matrix

It is generally acknowledged that driver pathway plays a decisive role in the occurrence and progress of tumors, and the identification of driver pathways has become imperative for precision medicine or personalized medicine. Due to the inevitable sequencing error, the noise contained in single omics cancer data usually plays a negative effect on identification. It is a feasible approach to take advantage of multi-omics cancer data rather than a single one now that large amounts of multi-omics cancer data have become available. The identification of driver pathways by integrating multi-omics cancer data has attracted attention of researchers in bioinformatics recently. In this paper, a weighted non-binary mutation matrix is constructed by integrating copy number variations, somatic mutations and gene expressions. Based on the weighted non-binary mutation matrix, a new identification model is proposed through defining new measurements of coverage and exclusivity. Then, a cooperative coevolutionary algorithm CGA-MWS is put forward for solving the presented model. Both real cancer data and simulated one were used to conduct comparisons among methods Dendrix, GA, iMCMC, MOGA, PGA-MWS and CGA-MWS. Compared with the pathways identified by the other five methods, more genes, belonging to the pathway identified by the CGA-MWS method, are enriched in a known signaling pathway in most cases. Simultaneously, the high efficiency of method CGA-MWS makes it practical in realistic applications. All of which have been verified through a number of experiments.

Genome-wide analysis of copy number alterations led to the characterisation of PDCD10 as oncogene in ovarian cancer

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We have identified 201 altered chromosomal bands and 3300 altered genes in human ovarian cancer samples.

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The gene encoding for PDCD10 was selected for further studies.

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PDCD10 was found to be over-expressed in primary cancer samples and in the corresponding metastatic lesions.

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High PDCD10 expression correlates with grade, nodal involvement or advanced FIGO stage.

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PDCD10 is involved in the control of cell growth and motility in vitro as well as tumorigenicity in vivo.

Copy Number Alterations (CNAs) represent the most common genetic alterations identified in ovarian cancer cells, being responsible for the extensive genomic instability observed in this cancer. Here we report the identification of CNAs in a cohort of Italian patients affected by ovarian cancer performed by SNP-based array.

Our analysis allowed the identification of 201 significantly altered chromosomal bands (70 copy number gains; 131 copy number losses). The 3300 genes subjected to CNA identified here were compared to those present in the TCGA dataset. The analysis allowed the identification of 11 genes with increased CN and mRNA expression (PDCD10, EBAG9, NUDCD1, ENY2, CSNK2A1, TBC1D20, ZCCHC3, STARD3, C19orf12, POP4, UQCRFS1). PDCD10 was selected for further studies because of the highest frequency of CNA.

PDCD10 was found, by immunostaining of three different Tissue Micro Arrays, to be over-expressed in the majority of ovarian primary cancer samples and in metastatic lesions. Moreover, significant correlations were found in specific subsets of patients, between increased PDCD10 expression and grade (p < 0.005), nodal involvement (p < 0.05) or advanced FIGO stage (p < 0.01).

Finally, manipulation of PDCD10 expression by shRNA in ovarian cancer cells (OVCAR-5 and OVCA429) demonstrated a positive role for PDCD10 in the control of cell growth and motility in vitro and tumorigenicity in vivo.

In conclusion, this study allowed the identification of novel genes subjected to copy number alterations in ovarian cancer. In particular, the results reported here point to a prominent role of PDCD10 as a bona fide oncogene.

Single-cell analysis of copy-number alterations in serous ovarian cancer reveals substantial heterogeneity in both low- and high-grade tumors

Unusually high aneuploidy is a hallmark of epithelial serous ovarian cancer (SOC). Previous analyses have focused on aneuploidy on average across all tumor cells. With the expansion of single-cell sequencing technologies, however, an analysis of copy number heterogeneity cell-to-cell is now technically feasible. Here, we describe an analysis of single-cell RNA sequencing (scRNA-seq) data to infer arm-level aneuploidy in individual serous ovarian cancer cells. By first clustering high-quality sequenced epithelial versus non-epithelial cells, high-confidence tumor cell populations were identified. InferCNV was used to predict segmented copy-number alterations (CNAs), which were then used to determine arm-level aneuploidy at the single-cell level. Control comparisons of normal cells to normal cells showed zero arm-level aneuploidy, whereas a median of four aneuploid events were detectable in cancer cells. A heterogeneity analysis of high-grade tumor cells compared to low-grade tumor cells showed similar levels of cell-to-cell variation between cancer grades. Metastatic tumors potentially showed selection pressure with reduced cell-to-cell variation compared to cells from primary tumors. Minor cell populations with CNAs similar to metastatic cells were identified within the matched primary tumors. Taken together, these results provide a minimum estimate for single-cell aneuploidy in serous ovarian cancer and demonstrate the utility of single-cell sequencing for CNA analysis.

Genome-wide DNA copy number profiling and bioinformatics analysis of ovarian cancer reveals key genes and pathways associated with distinct invasive/migratory capabilities

Ovarian cancer (OC) metastasis presents major hurdles that must be overcome to improve patient outcomes. Recent studies have demonstrated copy number variations (CNVs) frequently contribute to alterations in oncogenic drivers. The present study used a CytoScan HD Array to analyse CNVs and loss of heterozygosity (LOH) in the entire genomes of 6 OC patients and human OC cell lines to determine the genetic target events leading to the distinct invasive/migratory capacities of OC. The results showed that LOH at Xq11.1 and Xp21.1 and gains at 8q21.13 were novel, specific CNVs. Ovarian cancer-related CNVs were then screened by bioinformatics analysis. In addition, transcription factors-target gene interactions were predicted with information from PASTAA analysis. As a result, six genes (i.e., GAB2, AKT1, EGFR, COL6A3, UGT1A1 and UGT1A8) were identified as strong candidates by integrating the above data with gene expression and clinical outcome data. In the transcriptional regulatory network, 4 known cancer-related transcription factors (TFs) interacted with 6 CNV-driven genes. The protein/DNA arrays revealed 3 of these 4 TFs as potential candidate gene-related transcription factors in OC. We then demonstrated that these six genes can serve as potential biomarkers for OC. Further studies are required to elucidate the pathogenesis of OC.

ZC3H18 specifically binds and activates the BRCA1 promoter to facilitate homologous recombination in ovarian cancer

Reduced BRCA1 expression causes homologous recombination (HR) repair defects in high-grade serous ovarian cancers (HGSOCs). Here, we demonstrate that BRCA1 is transcriptionally activated by a previously unknown function of ZC3H18. We show that ZC3H18 is a DNA-binding protein that interacts with an E2F site in the BRCA1 promoter where it facilitates recruitment of E2F4 to an adjacent E2F site to promote BRCA1 transcription. Consistent with ZC3H18 role in activating BRCA1 expression, ZC3H18 depletion induces BRCA1 promoter methylation, reduces BRCA1 expression, disrupts HR, and sensitizes cells to DNA crosslinkers and poly(ADP-ribose) polymerase inhibitors. Moreover, in patient-derived xenografts and primary HGSOC tumors, ZC3H18 and E2F4 mRNA levels are positively correlated with BRCA1 mRNA levels, further supporting ZC3H18 role in regulating BRCA1. Given that ZC3H18 lies within 16q24.2, a region with frequent copy number loss in HGSOC, these findings suggest that ZC3H18 copy number losses could contribute to HR defects in HGSOC.

Integrative Analysis of Multi-Genomic Data for Kidney Renal Cell Carcinoma

Accounting for nine out of ten kidney cancers, kidney renal cell carcinoma (KIRC) is by far the most common type of kidney cancer. In view of limited and ineffective available therapies, understanding the genetic basis of disease becomes important for better diagnosis and treatment. The present studies are based on a single type of genomic data. These studies do not consider interactions between genomic data types and their underlying biological relationships in the disease. However, the current availability of multiple genomic data and the possibility of combining it have facilitated a better understanding of the cancer's characterization. But high dimensionality and the existence of complex interactions (within and between genomic data types) are the two main challenges of integrative methods to analyze cancer effectively. In this paper, we propose a method to build an integrative model based on Bayesian model averaging procedure for improved prediction of clinical outcome in cancer survival. The proposed method initially uses dimensionality reduction techniques to generate low-dimensional latent features for the predictive models and then incorporates interactions between them. It defines the latent features using principal components and their sparse version. It compares the predictive performance of models based on these two latent features on real data. These models also validate several ccRCC-specific cancer biomarkers previously reported in the literature. Applied on kidney renal cell carcinoma (KIRC) dataset of The Cancer Genome Atlas (TCGA), the method achieves better prediction with sparse principal components model by including latent feature interactions as compared to without including them.

Identifying mutated driver pathways in cancer by integrating multi-omics data

Since the driver pathway in cancer plays a crucial role in the formation and progression of cancer, it is very imperative to identify driver pathways, which will offer important information for precision medicine or personalized medicine. In this paper, an improved maximum weight submatrix problem model is proposed by integrating such three kinds of omics data as somatic mutations, copy number variations, and gene expressions. The model tries to adjust coverage and mutual exclusivity with the average weight of genes in a pathway, and simultaneously considers the correlation among genes, so that the pathway having high coverage but moderate mutual exclusivity can be identified. By introducing a kind of short chromosome code and a greedy based recombination operator, a parthenogenetic algorithm PGA-MWS is presented to solve the model. Experimental comparisons among algorithms GA, MOGA, iMCMC and PGA-MWS were performed on biological and simulated data sets. The experimental results show that, compared with the other three algorithms, the PGA-MWS one based on the improved model can identify the gene sets with high coverage but moderate mutual exclusivity and scales well. Many of the identified gene sets are involved in known signaling pathways, most of the implicated genes are oncogenes or tumor suppressors previously reported in literatures. The experimental results indicate that the proposed approach may become a useful complementary tool for detecting cancer pathways.

Genome-wide Analysis of Common Copy Number Variation and Epithelial Ovarian Cancer Risk

BACKGROUND

Germline DNA copy number variation (CNV) is a ubiquitous source of genetic variation and remains largely unexplored in association with epithelial ovarian cancer (EOC) risk.

METHODS

CNV was quantified in the DNA of approximately 3,500 cases and controls genotyped with the Illumina 610k and HumanOmni2.5M arrays. We performed a genome-wide association study of common (>1%) CNV regions (CNVRs) with EOC and high-grade serous (HGSOC) risk and, using The Cancer Genome Atlas (TCGA), performed in silico analyses of tumor-gene expression.

RESULTS

Three CNVRs were associated (P < 0.01) with EOC risk: two large (∼100 kb) regions within the 610k set and one small (<5 kb) region with the higher resolution 2.5M data. Large CNVRs included a duplication at LILRA6 (OR = 2.57; P = 0.001) and a deletion at CYP2A7 (OR = 1.90; P = 0.007) that were strongly associated with HGSOC risk (OR = 3.02; P = 8.98 × 10^-5). Somatic CYP2A7 alterations correlated with EGLN2 expression in tumors (P = 2.94 × 10^-47). An intronic ERBB4/HER4 deletion was associated with reduced EOC risk (OR = 0.33; P = 9.5 × 10^-2), and somatic deletions correlated with ERBB4 downregulation (P = 7.05 × 10^-5). Five CNVRs were associated with HGSOC, including two reduced-risk deletions: one at 1p36.33 (OR = 0.28; P = 0.001) that correlated with lower CDKIIA expression in TCGA tumors (P = 2.7 × 10^-7), and another at 8p21.2 (OR = 0.52; P = 0.002) that was present somatically where it correlated with lower GNRH1 expression (P = 5.9 × 10^-5).

CONCLUSIONS

Though CNV appears to not contribute largely to EOC susceptibility, a number of low-to-common frequency variants may influence the risk of EOC and tumor-gene expression.

IMPACT

Further research on CNV and EOC susceptibility is warranted, particularly with CNVs estimated from high-density arrays.

Massively parallel sequencing of cell-free DNA in plasma for detecting gynaecological tumour-associated copy number alteration

The discovery of circulating tumour DNA molecules created a paradigm shift in tumour biomarkers as predictors of recurrence. Non-invasive prenatal testing (NIPT) to detect circulating cell-free foetal DNA in maternal plasma is increasingly recognised as a valuable substitute to perceive foetal copy number variation (CNV). This study aimed to determine whether the copy number detection in plasma samples using NIPT platform could be used as a prognostic biomarker in patients with gynaecological cancer. We conducted a prospective study using samples containing preoperative plasma from 100 women with gynaecological cancers. Samples were randomly rearranged and blindly sequenced using a low-coverage whole-genome sequencing plasma DNA, NIPT platform. The NIPT pipeline identified copy number alterations (CNAs) were counted in plasma as a gain or loss if they exceeded 10 Mb from the expected diploid coverage. Progression-free survival (PFS) and overall survival (OS) were analysed according to the presence of CNA in plasma using Kaplan-Meier analyses. The NIPT pipeline detected 19/100 cases of all gynaecological cancers, including 6/36 ovarian cancers, 3/11 cervical cancers, and 10/53 endometrial cancers. Patients with CNA in plasma had a significantly poorer prognosis in all stages concerning PFS and OS. Therefore, low-coverage sequencing NIPT platform could serve as a predictive marker of patient outcome.

Genome-wide DNA copy number analysis in clonally expanded human ovarian cancer cells with distinct invasive/migratory capacities

Ovarian cancer has the worst prognosis of any gynecological malignancy, and generally presents with metastasis at advanced stages. Copy number variation (CNV) frequently contributes to the alteration of oncogenic drivers. In this study, we sought to identify genetic targets in heterogeneous clones from human ovarian cancers cells. We used array-based technology to systematically assess all the genes with CNVs in cell models clonally expanded from A2780 and SKOV3 ovarian cancer cell lines with distinct highly and minimally invasive/migratory capacities. We found that copy number alterations differed between matched highly and minimally invasive/migratory subclones, differentially affecting specific functional processes including immune response processes, DNA damage repair, cell cycle and cell proliferation. We also identified seven genes as strong candidates, including DDB1, ERCC1, ERCC2, PRPF19, BCAT1, CDKN1B and MARK4, by integrating the above data with gene expression and clinical outcome data. Thus, by determining the molecular signatures of heterogeneous invasive/migratory ovarian cancer cells, we identified genes that could be specifically targeted for the treatment and prognosis of advanced ovarian cancers.

Integrated genomic analysis of clear cell ovarian cancers identified PRKCI as a potential therapeutic target

Clear cell ovarian cancer (CCOC) is an epithelial ovarian cancer histotype with unique pathologic, biologic and clinical features. Despite its worse prognosis than serous ovarian cancer (SOC), the genomic landscape of CCOC is less well defined. Integrated genomic analysis of CCOC allows the identification of potential therapeutic targets to improve the treatment of this tumor. Using comparative genomic hybridization and gene expression profiling, we have screened 12 CCOC cell lines and 40 tumors to identify 45 amplified and overexpressed genes. Pathways analysis of these genes identified 19 genes with cancer-related functions. Of these, PRKCI is one of the most frequently amplified and overexpressed genes and its expression induced cancer cell proliferation and migration/invasion in vitro as well as tumor growth in vivo. Targeting PRKCI by small molecule inhibitor, sodium aurothiomalate (ATM), significantly reduced the in vivo tumor growth and may be a new therapeutic strategy to improve the treatment of CCOC.

A systematic comparison of copy number alterations in four types of female cancer

Background

Detection and localization of genomic alterations and breakpoints are crucial in cancer research. The purpose of this study was to investigate, in a methodological and biological perspective, different female, hormone-dependent cancers to identify common and diverse DNA aberrations, genes, and pathways.

Methods

In this work, we analyzed tissue samples from patients with breast (n = 112), ovarian (n = 74), endometrial (n = 84), or cervical (n = 76) cancer. To identify genomic aberrations, the Circular Binary Segmentation (CBS) and Piecewise Constant Fitting (PCF) algorithms were used and segmentation thresholds optimized. The Genomic Identification of Significant Targets in Cancer (GISTIC) algorithm was applied to the segmented data to identify significantly altered regions and the associated genes were analyzed by Ingenuity Pathway Analysis (IPA) to detect over-represented pathways and functions within the identified gene sets.

Results and Discussion

Analyses of high-resolution copy number alterations in four different female cancer types are presented. For appropriately adjusted segmentation parameters the two segmentation algorithms CBS and PCF performed similarly. We identified one region at 8q24.3 with focal aberrations that was altered at significant frequency across all four cancer types. Considering both, broad regions and focal peaks, three additional regions with gains at significant frequency were revealed at 1p21.1, 8p22, and 13q21.33, respectively. Several of these events involve known cancer-related genes, like PPP2R2A, PSCA, PTP4A3, and PTK2. In the female reproductive system (ovarian, endometrial, and cervix [OEC]), we discovered three common events: copy number gains at 5p15.33 and 15q11.2, further a copy number loss at 8p21.2. Interestingly, as many as 75% of the aberrations (75% amplifications and 86% deletions) identified by GISTIC were specific for just one cancer type and represented distinct molecular pathways.

Conclusions

Our results disclose that some prominent copy number changes are shared in the four examined female, hormone-dependent cancer whereas others are definitive to specific cancer types.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-016-2899-4) contains supplementary material, which is available to authorized users.

Molecular diagnosis and molecular profiling to detect treatment-resistant ovarian cancer

INTRODUCTION

Epithelial ovarian cancer remains the gynecologic tumor with the highest rate of recurrence after initial optimal cytoreductive surgery followed by adjuvant chemotherapy. Unfortunately, with the development of recurrent ovarian cancer often comes the discovery of chemo-resistant disease. The absence of improvement in long term survival, notwithstanding the use of newer agents as is seen in other cancers, emphasizes the need for improved understanding of the processes that lead to chemo-resistant disease.

AREAS COVERED

This review will cover the following topics: 1. Molecular and cellular mechanisms in platinum and paclitaxel resistance 2. Other molecular mediators of chemo-resistance 3. Expression of stem cell markers in ovarian cancer and relationship to chemo-resistance 4. MicroRNA and long non-coding RNA expression in chemo-resistant ovarian cancer 5. Determination of chromosomal aberrations as markers of chemo-resistance 6. Molecular profiling in chemo-resistant disease. A standard MEDLINE search was performed using the key words; ovarian cancer, chemo-resistant disease, molecular profiling, cancer stem cells and chemotherapy. Expert Commentary: Over the next few years the challenge remains to precisely determine the mechanisms responsible for the onset and maintenance of chemo-resistance and to effectively target these mechanisms.

Identification of recurrent focal copy number variations and their putative targeted driver genes in ovarian cancer

BACKGROUND

Genomic regions with recurrent DNA copy number variations (CNVs) are generally believed to encode oncogenes and tumor suppressor genes (TSGs) that drive cancer growth. However, it remains a challenge to delineate the key cancer driver genes from the regions encoding a large number of genes.

RESULTS

In this study, we developed a new approach to CNV analysis based on spectral decomposition of CNV profiles into focal CNVs and broad CNVs. We performed an analysis of CNV data of 587 serous ovarian cancer samples on multiple platforms. We identified a number of novel focal regions, such as focal gain of ESR1, focal loss of LSAMP, prognostic site at 3q26.2 and losses of sub-telomere regions in multiple chromosomes. Furthermore, we performed network modularity analysis to examine the relationships among genes encoded in the focal CNV regions. Our results also showed that the recurrent focal gains were significantly associated with the known oncogenes and recurrent losses associated with TSGs and the CNVs had a greater effect on the mRNA expression of the driver genes than that of the non-driver genes.

CONCLUSIONS

Our results demonstrate that spectral decomposition of CNV profiles offers a new way of understanding the role of CNVs in cancer.

Tensor GSVD of patient- and platform-matched tumor and normal DNA copy-number profiles uncovers chromosome arm-wide patterns of tumor-exclusive platform-consistent alterations encoding for cell transformation and predicting ovarian cancer survival

The number of large-scale high-dimensional datasets recording different aspects of a single disease is growing, accompanied by a need for frameworks that can create one coherent model from multiple tensors of matched columns, e.g., patients and platforms, but independent rows, e.g., probes. We define and prove the mathematical properties of a novel tensor generalized singular value decomposition (GSVD), which can simultaneously find the similarities and dissimilarities, i.e., patterns of varying relative significance, between any two such tensors. We demonstrate the tensor GSVD in comparative modeling of patient- and platform-matched but probe-independent ovarian serous cystadenocarcinoma (OV) tumor, mostly high-grade, and normal DNA copy-number profiles, across each chromosome arm, and combination of two arms, separately. The modeling uncovers previously unrecognized patterns of tumor-exclusive platform-consistent co-occurring copy-number alterations (CNAs). We find, first, and validate that each of the patterns across only 7p and Xq, and the combination of 6p+12p, is correlated with a patient’s prognosis, is independent of the tumor’s stage, the best predictor of OV survival to date, and together with stage makes a better predictor than stage alone. Second, these patterns include most known OV-associated CNAs that map to these chromosome arms, as well as several previously unreported, yet frequent focal CNAs. Third, differential mRNA, microRNA, and protein expression consistently map to the DNA CNAs. A coherent picture emerges for each pattern, suggesting roles for the CNAs in OV pathogenesis and personalized therapy. In 6p+12p, deletion of the p21-encoding CDKN1A and p38-encoding MAPK14 and amplification of RAD51AP1 and KRAS encode for human cell transformation, and are correlated with a cell’s immortality, and a patient’s shorter survival time. In 7p, RPA3 deletion and POLD2 amplification are correlated with DNA stability, and a longer survival. In Xq, PABPC5 deletion and BCAP31 amplification are correlated with a cellular immune response, and a longer survival.

Somatic mutations favorable to patient survival are predominant in ovarian carcinomas

Somatic mutation accumulation is a major cause of abnormal cell growth. However, some mutations in cancer cells may be deleterious to the survival and proliferation of the cancer cells, thus offering a protective effect to the patients. We investigated this hypothesis via a unique analysis of the clinical and somatic mutation datasets of ovarian carcinomas published by the Cancer Genome Atlas. We defined and screened 562 macro mutation signatures (MMSs) for their associations with the overall survival of 320 ovarian cancer patients. Each MMS measures the number of mutations present on the member genes (except for TP53) covered by a specific Gene Ontology (GO) term in each tumor. We found that somatic mutations favorable to the patient survival are predominant in ovarian carcinomas compared to those indicating poor clinical outcomes. Specially, we identified 19 (3) predictive MMSs that are, usually by a nonlinear dose-dependent effect, associated with good (poor) patient survival. The false discovery rate for the 19 "positive" predictors is at the level of 0.15. The GO terms corresponding to these MMSs include "lysosomal membrane" and "response to hypoxia", each of which is relevant to the progression and therapy of cancer. Using these MMSs as features, we established a classification tree model which can effectively partition the training samples into three prognosis groups regarding the survival time. We validated this model on an independent dataset of the same disease (Log-rank p-value < 2.3 × 10(-4)) and a dataset of breast cancer (Log-rank p-value < 9.3 × 10(-3)). We compared the GO terms corresponding to these MMSs and those enriched with expression-based predictive genes. The analysis showed that the GO term pairs with large similarity are mainly pertinent to the proteins located on the cell organelles responsible for material transport and waste disposal, suggesting the crucial role of these proteins in cancer mortality.

Deletion at 6q24.2-26 predicts longer survival of high-grade serous epithelial ovarian cancer patients

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.

Somatic copy number alterations predict response to platinum therapy in epithelial ovarian cancer

OBJECTIVE

Platinum resistance remains an obstacle in the treatment of epithelial ovarian cancer (EOC). The goal of this study was to profile EOCs for somatic copy number alterations (SCNAs) as predictive markers of platinum response.

METHODS

SCNAs were assessed in a discovery (n=86) and validation cohort (n=115) of high risk stage I or stage II-IV EOCs using high-resolution SNP arrays. ASCAT and GISTIC identified all significantly overrepresented amplified or deleted chromosomal regions. Cox regression and univariate analysis assessed which SCNAs correlated with overall survival (OS), progression-free survival (PFS), platinum-free interval (PFI) and platinum response. Relevant SCNAs were also assessed in a pooled analysis involving both cohorts and published SCNA data from The Cancer Genome Atlas (TCGA; n=227).

RESULTS

We identified 53 regions to be significantly overrepresented in EOC. Of these, 6 were associated with OS, PFS or PFI in the discovery cohort at P<0.05. In the validation cohort, amplifications of chromosomal region 14q32.33, which contains AKT1 as a potential driver gene, also correlated with OS (OR=1.670; P=0.018). In a pooled analysis of 428 tumors, involving the discovery, validation and TCGA cohorts, 14q32.33 amplifications significantly reduced OS, PFS and PFI (HR=2.69, P=1.7×10(-4); HR=1.82, P=1.9×10(-2) and HR=1.80, P=2.2×10(-2) respectively). Moreover, AKT1 mRNA expression correlated with the number of chromosomal copies of the 14q32.33 region (P=2.8×10(-11);R(2)=0.26).

CONCLUSIONS

We established that amplifications in 14q32.33 were associated with reduced OS, PFS, PFI and platinum resistance in three independent cohorts, suggesting that AKT1 amplifications act as a potentially predictive marker for EOC treated with platinum-based chemotherapy.

Ovarian cancer: genomic analysis

OBJECTIVES

Despite improvements in the management of ovarian cancer patients over the last 30 years, there has been only a minimal improvement in overall survival. While targeted therapeutic approaches for the treatment of cancer have evolved, major challenges in ovarian cancer research persist, including the identification of predictive biomarkers with clinical relevance, so that empirical drug selection can be avoided. In this article, we review published genomic analysis studies including data generated in our laboratory and how they have been incorporated into modern clinical trials in a rational and effective way.

METHODS

Multiple published genomic analysis studies were collected for review and discussion with emphasis on their potential clinical applicability.

RESULTS

Genomic analysis has been shown to be a powerful tool to identify dysregulated genes, aberrantly activated pathways and to uncover uniqueness of subclasses of ovarian tumors. The application of this technology has provided a solid molecular basis for different clinical behaviors associated with tumor histology and grade. Genomic signatures have been obtained to predict clinical end points for patients with cancer, including response rates, progression-free survival, and overall survival. In addition, genomic analysis has provided opportunities to identify biomarkers, which either result in a modification of existing clinical management or to stratification of patients to novel therapeutic approaches designed as clinical trials.

CONCLUSIONS

Genomic analyses have accelerated the identification of relevant biomarkers and extended our understanding of the molecular biology of ovarian cancer. This in turn, will hopefully lead to a paradigm shift from empirical, uniform treatment to a more rational, personalized treatment of ovarian cancers. However, validation of potential biomarkers on both the statistical and biological levels is needed to confirm they are of clinical relevance, in order to increase the likelihood that the desired outcome can be predicted and achieved.

Cyclin E1 deregulation occurs early in secretory cell transformation to promote formation of fallopian tube-derived high-grade serous ovarian cancers

The fallopian tube is now generally considered the dominant site of origin for high-grade serous ovarian carcinoma. However, the molecular pathogenesis of fallopian tube-derived serous carcinomas is poorly understood and there are few experimental studies examining the transformation of human fallopian tube cells. Prompted by recent genomic analyses that identified cyclin E1 (CCNE1) gene amplification as a candidate oncogenic driver in high-grade serous ovarian carcinoma, we evaluated the functional role of cyclin E1 in serous carcinogenesis. Cyclin E1 was expressed in early- and late-stage human tumor samples. In primary human fallopian tube secretory epithelial cells, cyclin E1 expression imparted malignant characteristics to untransformed cells if p53 was compromised, promoting an accumulation of DNA damage and altered transcription of DNA damage response genes related to DNA replication stress. Together, our findings corroborate the hypothesis that cyclin E1 dysregulation acts to drive malignant transformation in fallopian tube secretory cells that are the site of origin of high-grade serous ovarian carcinomas.

A distinctive ovarian cancer molecular subgroup characterized by poor prognosis and somatic focal copy number amplifications at chromosome 19

OBJECTIVE

High-grade serous ovarian cancer (HGS-OvCa), the most common epithelial ovarian cancer, is very complex and heterogeneous at the molecular level. The identification of intrinsic HGS-OvCa subgroups characterized by specific molecular alterations and aggressive behavior could improve patient treatment.

METHODS

High-resolution copy number data for 560 HGS-OvCa patients and gene expression data obtained from the TCGA database were analyzed to identify distinct molecular subgroups based on significant focal somatic copy number alterations (SCNAs).

RESULTS

Using unsupervised consensus clustering, a subgroup accounting for 26.8% of the patients (150/560 patients) characterized by focal somatic copy number amplification at chromosome 19 was identified. The subgroup was independently associated by multivariate Cox regression analysis with poor overall (HR, 1.61; P = 0.001) and progression-free survival (HR, 1.36; P = 0.036). The specific focal SCNA locations were 19p13.2, 19p13.12, 19p13.11, 19q12, 19q13.12, and 19q13.2. The differential gene expression signature of the subgroup compared with that of the remaining patients also suggested that chromosome 19 was the mainly amplified region. The clinical significances of subgroup 2 were validated in independent data sets using the gene expression signature characteristics. In addition, the subgroup had a tendency toward mutual exclusivity with patients with BRCA1/2 mutations. The most significantly altered pathway of the subgroup was the cyclin and cell cycle regulation pathway.

CONCLUSION

A unique molecular subgroup associated with poor survival was identified based on focal SCNAs and could aid the further molecular classification of ovarian cancers.

Loss of LKB1 and PTEN tumor suppressor genes in the ovarian surface epithelium induces papillary serous ovarian cancer

Epithelial ovarian cancer presents mostly with serous, endometrioid or mucinous histology but is treated as a single disease. The development of histotype-specific therapy has been challenging because of the relative lack of studies attributing disrupted pathways to a distinct histotype differentiation. mTOR activation is frequently associated with poor prognosis in serous ovarian cancer, which is the most common and most deadly histotype. However, the mechanisms dysregulating mTOR in the pathogenesis of ovarian cancer are unknown. We detected copy number loss and correlated lower expression levels of LKB1, TSC1, TSC2 and PTEN tumor suppressor genes for upstream regulators of mTOR activity in up to 80% in primary ovarian serous tumor databases, with LKB1 allelic loss-predominant. Reduced LKB1 protein was usually associated with increased mTOR activity in both serous ovarian cancer cell lines and primary tumors. Conditional deletion of Lkb1 in murine ovarian surface epithelial (OSE) cells caused papillary hyperplasia and shedding but not tumors. Simultaneous deletion of Lkb1 and Pten, however, led to development of high-grade ovarian serous histotype tumors with 100% penetrance that expressed WT1, ERα, PAX8, TP53 and cytokeratin 8, typical markers used in the differential diagnosis of serous ovarian cancer. Neither hysterectomy nor salpingectomy interfered with progression of ovarian tumorigenesis, suggesting that neither uterine nor Fallopian tube epithelial cells were contributing to tumorigenesis. These results implicate LKB1 loss in the OSE in the pathogenesis of serous ovarian cancer and provide a compelling rationale for investigating the therapeutic potential of targeting LKB1 signaling in patients with this deadly disease.

Identification of mutated core cancer modules by integrating somatic mutation, copy number variation, and gene expression data

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.

FGF18 as a prognostic and therapeutic biomarker in ovarian cancer

High-throughput genomic technologies have identified biomarkers and potential therapeutic targets for ovarian cancer. Comprehensive functional validation studies of the biological and clinical implications of these biomarkers are needed to advance them toward clinical use. Amplification of chromosomal region 5q31-5q35.3 has been used to predict poor prognosis in patients with advanced stage, high-grade serous ovarian cancer. In this study, we further dissected this large amplicon and identified the overexpression of FGF18 as an independent predictive marker for poor clinical outcome in this patient population. Using cell culture and xenograft models, we show that FGF18 signaling promoted tumor progression by modulating the ovarian tumor aggressiveness and microenvironment. FGF18 controlled migration, invasion, and tumorigenicity of ovarian cancer cells through NF-κB activation, which increased the production of oncogenic cytokines and chemokines. This resulted in a tumor microenvironment characterized by enhanced angiogenesis and augmented tumor-associated macrophage infiltration and M2 polarization. Tumors from ovarian cancer patients had increased FGF18 expression levels with microvessel density and M2 macrophage infiltration, confirming our in vitro results. These findings demonstrate that FGF18 is important for a subset of ovarian cancers and may serve as a therapeutic target.

Discovery analysis of TCGA data reveals association between germline genotype and survival in ovarian cancer patients

BACKGROUND

Ovarian cancer remains a significant public health burden, with the highest mortality rate of all the gynecological cancers. This is attributable to the late stage at which the majority of ovarian cancers are diagnosed, coupled with the low and variable response of advanced tumors to standard chemotherapies. To date, clinically useful predictors of treatment response remain lacking. Identifying the genetic determinants of ovarian cancer survival and treatment response is crucial to the development of prognostic biomarkers and personalized therapies that may improve outcomes for the late-stage patients who comprise the majority of cases.

METHODS

To identify constitutional genetic variations contributing to ovarian cancer mortality, we systematically investigated associations between germline polymorphisms and ovarian cancer survival using data from The Cancer Genome Atlas Project (TCGA). Using stage-stratified Cox proportional hazards regression, we examined >650,000 SNP loci for association with survival. We additionally examined whether the association of significant SNPs with survival was modified by somatic alterations.

RESULTS

Germline polymorphisms at rs4934282 (AGAP11/C10orf116) and rs1857623 (DNAH14) were associated with stage-adjusted survival (p= 1.12e-07 and 1.80e-07, FDR q= 1.2e-04 and 2.4e-04, respectively). A third SNP, rs4869 (C10orf116), was additionally identified as significant in the exome sequencing data; it is in near-perfect LD with rs4934282. The associations with survival remained significant when somatic alterations.

CONCLUSIONS

Discovery analysis of TCGA data reveals germline genetic variations that may play a role in ovarian cancer survival even among late-stage cases. The significant loci are located near genes previously reported as having a possible relationship to platinum and taxol response. Because the variant alleles at the significant loci are common (frequencies for rs4934282 A/C alleles = 0.54/0.46, respectively; rs1857623 A/G alleles = 0.55/0.45, respectively) and germline variants can be assayed noninvasively, our findings provide potential targets for further exploration as prognostic biomarkers and individualized therapies.

Ovarian cancer: in search of better marker systems based on DNA repair defects

Ovarian cancer is the fifth most common female cancer in the Western world, and the deadliest gynecological malignancy. The overall poor prognosis for ovarian cancer patients is a consequence of aggressive biological behavior and a lack of adequate diagnostic tools for early detection. In fact, approximately 70% of all patients with epithelial ovarian cancer are diagnosed at advanced tumor stages. These facts highlight a significant clinical need for reliable and accurate detection methods for ovarian cancer, especially for patients at high risk. Because CA125 has not achieved satisfactory sensitivity and specificity in detecting ovarian cancer, numerous efforts, including those based on single and combined molecule detection and “omics” approaches, have been made to identify new biomarkers. Intriguingly, more than 10% of all ovarian cancer cases are of familial origin. BRCA1 and BRCA2 germline mutations are the most common genetic defects underlying hereditary ovarian cancer, which is why ovarian cancer risk assessment in developed countries, aside from pedigree analysis, relies on genetic testing of BRCA1 and BRCA2. Because not only BRCA1 and BRCA2 but also other susceptibility genes are tightly linked with ovarian cancer-specific DNA repair defects, another possible approach for defining susceptibility might be patient cell-based functional testing, a concept for which support came from a recent case-control study. This principle would be applicable to risk assessment and the prediction of responsiveness to conventional regimens involving platinum-based drugs and targeted therapies involving poly (ADP-ribose) polymerase (PARP) inhibitors.

Promoter methylation of the SALL2 tumor suppressor gene in ovarian cancers

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.