Role of common genetic variants in ovarian cancer susceptibility and outcome: progress to date from the Ovarian Cancer Association Consortium (OCAC)

RIPK1 polymorphisms and expression levels: impact on genetic susceptibility and clinical outcome of epithelial ovarian cancer

BACKGROUND

The aim of this study was to explore the associations of RIPK1 polymorphisms, plasma levels and mRNA expression with susceptibility to epithelial ovarian cancer (EOC) and clinical outcome.

METHODS

Three hundred and nineteen EOC patients included in a 60-month follow-up program and 376 controls were enrolled. Two tag SNPs (rs6907943 and rs9392453) of RIPK1 were genotyped using polymerase chain reaction (PCR)-restriction fragment length polymorphism (RFLP) method. Plasma levels of RIPK1 and RIPK1 mRNA expression in white blood cells were determined by ELISA and qPCR, respectively.

RESULTS

For rs9392453, significantly increased EOC risk was found to be associated with C allele (P = 0.002, OR = 1.49, 95%CI  1.15-1.92), and with CT/CC genotypes in the dominant genetic model (P = 0.006, OR = 1.54, 95%CI  1.12-2.08). CC haplotype (rs6907943-rs9392453) was associated with increased EOC susceptibility. CC genotype of rs6907943 and CT/CC genotypes of rs9392453 were associated with early onset (age ≤ 50 years) of EOC (OR = 2.5, 95%CI  1.03-5.88, and OR = 1.64, 95%CI  1.04-2.63, respectively). AC genotype of rs6907943 was associated with better overall survival of EOC patients in the over-dominant genetic model (P = 0.035, HR = 0.41, 95%CI  0.18-0.94). Multivariate survival analysis identified the AC genotype of rs6907943 as an independent protective factor for survival of early onset patients (P = 0.044, HR = 0.12, 95%CI  0.02-0.95). Compared to controls, significantly increased plasma levels of RIPK1 and reduced RIPK1 mRNA expression were observed in patients.

CONCLUSIONS

Our results suggest that tag SNPs of RIPK1, increased plasma levels of RIPK1 protein and reduced RIPK1 mRNA expression in white blood cells, may influence the susceptibility to EOC. SNP rs6907943 may be a useful marker to distinguish EOC patients with high risk of death.

Association between tooth agenesis and cancer: a systematic review

The congenital absence of multiple teeth may share the same genetic background of the development of some types of cancer. Objective: This systematic review aimed to investigate the possible association between dental agenesis and cancer, and the perspective of agenesis as an early predictor for cancer risk. Methodology: The electronic databases PubMed, Scopus, Web of Science, Cochrane Library, LILACS, and OpenGrey were searched and the risk of bias was evaluated using the Newcastle-Ottawa tool. The GRADE tool was used to evaluate the certainty of the evidence. Results: Six studies met the eligibility criteria. A positive co-occurrence between ovarian cancer and hypodontia was found in two articles. Three studies evaluated the association between dental agenesis and colorectal cancer and only one showed common genes for these conditions. One paper found individuals with hypodontia had a higher risk of family history of cancer. Five studies had a fair quality and one a good quality. The certainty of evidence was classified as very low. Conclusion: Notwithstanding the limited scientific evidence, there may be a possible association between dental agenesis and cancer due to genes involved in both conditions. Agenesis of multiple teeth could be an early indicator of cancer risk. Nevertheless, studies with a better level of evidence are needed to confirm this possible association.

Unraveling a difficult diagnosis: the tricks for early recognition of ovarian cancer

Epithelial ovarian cancer (EOC) is the predominant type of ovarian cancer (OC). The 5-year survival of patients has improved over the last three decades, although the overall cure rate of OC if about 30%. Despite high response rates after initial chemotherapy, most patients with advanced ovarian cancer ultimately develop the recurrent disease because of resistance to chemotherapy. A proper early diagnosis and treatment of patients with ovarian cancer are urgently needed. Nowadays the diagnosis is performed by means of clinical symptoms and signs, often indicators of a disease already at an advanced stage, tumor markers (CA125 and HE4), transvaginal ultrasonography and imaging, very useful in distinguishing adnexal masses. Understand the nature of an adnexal mass is the primary point to begin the diagnosis of OC. Validated different model to approach and characterize adnexal pathology preoperatively are described, such as the International Ovarian Tumor Analysis (IOTA) and the Assessment of Different NEoplasias in the AdneXa (ADNEX) model. New tumor markers, such as PRSS8, FOLR1, KLK6/7, GSTT1, and miRNAs, are getting ahead and are worth noting for early detection of ovarian cancer. Despite the development of numerous ultrasound models for the diagnosis of adnexal masses and the analysis of different tumor markers, the early diagnosis of ovarian cancer is still difficult to practice. Moreover, identifying genetic risk alleles, such as germline BRCA1 and BRCA2 mutations, for ovarian cancer has had a significant impact on disease prevention strategies.

A comprehensive gene-environment interaction analysis in Ovarian Cancer using genome-wide significant common variants

As a follow-up to genome-wide association analysis of common variants associated with ovarian carcinoma (cancer), our study considers seven well-known ovarian cancer risk factors and their interactions with 28 genome-wide significant common genetic variants. The interaction analyses were based on data from 9971 ovarian cancer cases and 15,566 controls from 17 case-control studies. Likelihood ratio and Wald tests for multiplicative interaction and for relative excess risk due to additive interaction were used. The top multiplicative interaction was noted between oral contraceptive pill (OCP) use (ever vs. never) and rs13255292 (p value = 3.48 × 10-4 ). Among women with the TT genotype for this variant, the odds ratio for OCP use was 0.53 (95% CI = 0.46-0.60) compared to 0.71 (95%CI = 0.66-0.77) for women with the CC genotype. When stratified by duration of OCP use, women with 1-5 years of OCP use exhibited differential protective benefit across genotypes. However, no interaction on either the multiplicative or additive scale was found to be statistically significant after multiple testing correction. The results suggest that OCP use may offer increased benefit for women who are carriers of the T allele in rs13255292. On the other hand, for women carrying the C allele in this variant, longer (5+ years) use of OCP may reduce the impact of carrying the risk allele of this SNP. Replication of this finding is needed. The study presents a comprehensive analytic framework for conducting gene-environment analysis in ovarian cancer.

Making Sense of the Epigenome Using Data Integration Approaches

Epigenetic research involves examining the mitotically heritable processes that regulate gene expression, independent of changes in the DNA sequence. Recent technical advances such as whole-genome bisulfite sequencing and affordable epigenomic array-based technologies, allow researchers to measure epigenetic profiles of large cohorts at a genome-wide level, generating comprehensive high-dimensional datasets that may contain important information for disease development and treatment opportunities. The epigenomic profile for a certain disease is often a result of the complex interplay between multiple genetic and environmental factors, which poses an enormous challenge to visualize and interpret these data. Furthermore, due to the dynamic nature of the epigenome, it is critical to determine causal relationships from the many correlated associations. In this review we provide an overview of recent data analysis approaches to integrate various omics layers to understand epigenetic mechanisms of complex diseases, such as obesity and cancer. We discuss the following topics: (i) advantages and limitations of major epigenetic profiling techniques, (ii) resources for standardization, annotation and harmonization of epigenetic data, and (iii) statistical methods and machine learning methods for establishing data-driven hypotheses of key regulatory mechanisms. Finally, we discuss the future directions for data integration that shall facilitate the discovery of epigenetic-based biomarkers and therapies.

Genome-wide association study (GWAS) of ovarian cancer in Japanese predicted regulatory variants in 22q13.1

Genome-wide association studies (GWAS) have identified greater than 30 variants associated with ovarian cancer, but most of these variants were investigated in European populations. Here, we integrated GWAS and subsequent functional analyses to identify the genetic variants with potential regulatory effects. We conducted GWAS for ovarian cancer using 681 Japanese cases and 17,492 controls and found that rs137672 on 22q13.1 exhibited a strong association with a P-value of 1.05 × 10⁻⁷ and an odds ratio of 0.573 with a 95% confidence interval of 0.466–0.703. In addition, three previously reported SNPs, i.e., rs10088218, rs9870207 and rs1400482, were validated in the Japanese population (P < 0.05) with the same risk allele as noted in previous studies. Functional studies including regulatory feature analysis and electrophoretic mobility shift assay (EMSA) revealed two regulatory SNPs in 22q13.1, rs2072872 and rs6509, that affect the binding affinity to some nuclear proteins in ovarian cancer cells. The plausible regulatory proteins whose motifs could be affected by the allele changes of these two SNPs were also proposed. Moreover, the protective G allele of rs6509 was associated with a decreased SYNGR1 expression level in normal ovarian tissues. Our findings elucidated the regulatory variants in 22q13.1 that are associated with ovarian cancer risk.

Oleanolic acid 3-acetate, a minor element of ginsenosides, induces apoptotic cell death in ovarian carcinoma and endometrial carcinoma cells via the involvement of a reactive oxygen species-independent mitochondrial pathway

Objectives

Oleanolic acid, a minor element of ginsenosides, and its derivatives have been shown to have cytotoxicity against some tumor cells. The impact of cytotoxic effect of oleanolic acid 3-acetate on ovarian cancer SKOV3 cells and endometrial cancer HEC-1A cells were examined both in vivo and in vitro to explore the underlying mechanisms.

Methods

Cytotoxic effects of oleanolic acid 3-acetate were assessed by cell viability, phosphatidylserine exposure on the cell surface, mitochondrial release of cytochrome C, nuclear translocation of apoptosis-inducing factor, depolarization of mitochondrial transmembrane potential (ΔΨ_m), and generation of reactive oxygen species (ROS). In vivo inhibition of tumor growth was also assessed with xenografts in immunocompromised mice.

Results

Oleanolic acid 3-acetate exhibited potent cytotoxicity toward SKOV3 and HEC-1A cells by decreasing cell viability in a concentration-dependent manner. Importantly, oleanolic acid 3-acetate effectively suppressed the growth of SKOV3 cell tumor xenografts in immunocompromised mice. Furthermore, oleanolic acid 3-acetate induced apoptotic cell death as revealed by loss of ΔΨ_m, release of cytochrome c, and nuclear translocation of apoptosis-inducing factor with a concomitant activation of many proapoptotic cellular components including poly(ADP-ribose) polymerase, Bcl-2, and caspases-8, caspase-3, and caspase-7. Oleanolic acid 3-acetate, however, caused a decrease in ROS production, suggesting the involvement of an ROS-independent pathway in oleanolic acid 3-acetate–induced apoptosis in SKOV3 and HEC-1A cells.

Conclusion

These findings support the notion that oleanolic acid 3-acetate could be used as a potent anticancer supplementary agent against ovarian and endometrial cancer. Oleanolic acid 3-acetate exerts its proapoptotic effects through a rather unique molecular mechanism that involves an unconventional ROS-independent but mitochondria-mediated pathway.

Common Genetic Variation and Susceptibility to Ovarian Cancer: Current Insights and Future Directions

In this review, we summarize current progress in the genetic epidemiology of epithelial ovarian cancer (EOC), focusing exclusively on elucidating the role of common germline genetic variation in conferring susceptibility to EOC. We provide an overview of the more than 30 EOC risk loci identified to date by genome-wide association studies (GWAS) and describe the contribution of large-scale, cross-cancer type, custom genotyping projects, such as the OncoArray and the Collaborative Oncological Gene-Environment Study, to locus discovery and replication. We discuss the histotype-specific nature of these EOC risk loci, pleiotropy, or overlapping genetic effects between EOC and other hormone-related cancer types, and the application of findings to polygenic risk prediction for EOC. The second part of the article offers a concise review of primarily laboratory-based studies that have led to the identification of several putative EOC susceptibility genes using common variants at the known EOC risk loci as starting points. More global biological insights emerging from network- and pathway-based analyses of GWAS for EOC susceptibility are also highlighted. Finally, we delve into potential future directions, including the need to identify EOC risk loci in non-European populations and the next generation of GWAS functional studies that are likely to involve genome editing to establish the cell type-specific carcinogenic effects of EOC risk variants Cancer Epidemiol Biomarkers Prev; 27(4); 395-404. ©2018 AACRSee all articles in this CEBP Focus section, "Genome-Wide Association Studies in Cancer."

Searching for new biomarkers in ovarian cancer patients: Rationale and design of a retrospective study under the Mermaid III project

Ovarian cancer is a silent killer and, due to late diagnosis, the primary cause of death amongst gynecological cancers, killing approximately 376 women annually in Denmark. The discovery of a specific and sensitive biomarker for ovarian cancer could improve early diagnosis, but also treatment, by predicting which patients will benefit from specific treatment strategies. The Mermaid III project is consisting of 3 parts including "Early detection, screening and long-term survival," "Biomarkers and/or prognostic markers" and "The infection theory." The present paper gives an overview of the part regarding biomarkers and/or prognostic markers, with a focus on rationale and design. The study described has 3 major branches: microRNAs, epigenetics and Next Generation Sequencing. Tissue and blood from ovarian cancer patients, already enrolled in the prospective ongoing pelvic mass cohort, will be examined. Relevant microRNAs and DNA methylation patterns will be investigated using array technology. Patient exomes will be fully sequenced, and identified genetic variations will be validated with Next Generation Sequencing. In all cases, data will be correlated with clinical information on the patient, in order to identify possible biomarkers. A thorough investigation of biomarkers in ovarian cancer, including large numbers of different markers, has never been done before. Besides from improving diagnosis and treatment, other outcomes could be markers for screening, knowledge of the molecular aspects of cancer and the discovery of new drugs. Moreover, biomarkers are a prerequisite for the development of precision medicine. This study will attack the ovarian cancer problem from several angles, thereby increasing the chance of successfully contributing to saving lives.

Can 9q34.2 rs633862 polymorphism predict survival in epithelial ovarian cancer?

Objective

Our previous genome-wide association study (GWAS) identified that the ABO rs633862 variant in chromosome 9q34.2 was associated with the risk of epithelial ovarian cancer (EOC) in Chinese Han women. The aim of the present study was to evaluate its prognostic effect on EOC.

Methods

A total of 669 EOC patients were enrolled for the genotyping of rs633862 variant in 9q34.2. We used Kaplan–Meier survival curves, univariate and multivariate Cox proportional hazard models to evaluate the association of rs633862 with overall survival (OS) in EOC patients.

Results

We found that rs633862 variant AG/GG genotypes were significantly associated with a longer OS by using univariate Cox proportional hazards regression analysis, compared with the rs633862 AA genotype (HR = 0.69, 95% CI [0.49–0.98], p = 0.035), albeit with a boardline significance in the multivariate analysis. Similar findings were observed in the subgroup of high-grade serous ovarian carcinoma. Further expression quantitative trait loci (eQTL) analysis indicated that the rs633862 AA genotype was associated with an increased level of ABO mRNA expression (p = 1.8 × 10⁻¹¹).

Conclusions

Supplementary to the previous GWAS, our study provides additional evidence on the prognostic value of the 9q34.2 rs633862 variant in EOC patients, and this variant may function by regulating the ABO mRNA expression.

Rethinking ovarian cancer genomics: where genome-wide association studies stand?

Genome-wide association studies (GWAS) allow the finding of genetic variants associated with several traits. Regarding ovarian cancer (OC), 15 GWAS have been conducted since 2009, with the discovery of 49 SNPs associated with disease susceptibility and 46 with impact in the clinical outcome of patients (p < 5.00 × 10-2). Among them, 14 variants reached the genome-wide significance (p < 5.00 × 10−8). Despite the results obtained, they should be validated in independent sets. So far, five validation studies have been conducted which could confirm the association of 12 OC susceptibility SNPs. Consequently, post-GWAS studies are crucial unravel the biological plausibility of GWAS’ findings and the allelic spectrum of OC.

Assessment of Multifactor Gene-Environment Interactions and Ovarian Cancer Risk: Candidate Genes, Obesity, and Hormone-Related Risk Factors

BACKGROUND

Many epithelial ovarian cancer (EOC) risk factors relate to hormone exposure and elevated estrogen levels are associated with obesity in postmenopausal women. Therefore, we hypothesized that gene-environment interactions related to hormone-related risk factors could differ between obese and non-obese women.

METHODS

We considered interactions between 11,441 SNPs within 80 candidate genes related to hormone biosynthesis and metabolism and insulin-like growth factors with six hormone-related factors (oral contraceptive use, parity, endometriosis, tubal ligation, hormone replacement therapy, and estrogen use) and assessed whether these interactions differed between obese and non-obese women. Interactions were assessed using logistic regression models and data from 14 case-control studies (6,247 cases; 10,379 controls). Histotype-specific analyses were also completed.

RESULTS

SNPs in the following candidate genes showed notable interaction: IGF1R (rs41497346, estrogen plus progesterone hormone therapy, histology = all, P = 4.9 × 10(-6)) and ESR1 (rs12661437, endometriosis, histology = all, P = 1.5 × 10(-5)). The most notable obesity-gene-hormone risk factor interaction was within INSR (rs113759408, parity, histology = endometrioid, P = 8.8 × 10(-6)).

CONCLUSIONS

We have demonstrated the feasibility of assessing multifactor interactions in large genetic epidemiology studies. Follow-up studies are necessary to assess the robustness of our findings for ESR1, CYP11A1, IGF1R, CYP11B1, INSR, and IGFBP2 Future work is needed to develop powerful statistical methods able to detect these complex interactions.

IMPACT

Assessment of multifactor interaction is feasible, and, here, suggests that the relationship between genetic variants within candidate genes and hormone-related risk factors may vary EOC susceptibility. Cancer Epidemiol Biomarkers Prev; 25(5); 780-90. ©2016 AACR.

Estimation of sparse directed acyclic graphs for multivariate counts data

The next-generation sequencing data, called high-throughput sequencing data, are recorded as count data, which are generally far from normal distribution. Under the assumption that the count data follow the Poisson log-normal distribution, this article provides an L1-penalized likelihood framework and an efficient search algorithm to estimate the structure of sparse directed acyclic graphs (DAGs) for multivariate counts data. In searching for the solution, we use iterative optimization procedures to estimate the adjacency matrix and the variance matrix of the latent variables. The simulation result shows that our proposed method outperforms the approach which assumes multivariate normal distributions, and the log-transformation approach. It also shows that the proposed method outperforms the rank-based PC method under sparse network or hub network structures. As a real data example, we demonstrate the efficiency of the proposed method in estimating the gene regulatory networks of the ovarian cancer study.

Evidence of a genetic link between endometriosis and ovarian cancer

OBJECTIVE

To evaluate whether endometriosis-associated genetic variation affects risk of ovarian cancer.

DESIGN

Pooled genetic analysis.

SETTING

University hospital.

PATIENT(S)

Genetic data from 46,176 participants (15,361 ovarian cancer cases and 30,815 controls) from 41 ovarian cancer studies.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

Endometriosis-associated genetic variation and ovarian cancer.

RESULT(S)

There was significant evidence of an association between endometriosis-related genetic variation and ovarian cancer risk, especially for the high-grade serous and clear cell histotypes. Overall we observed 15 significant burden statistics, which was three times more than expected.

CONCLUSION(S)

By focusing on candidate regions from a phenotype associated with ovarian cancer, we have shown a clear genetic link between endometriosis and ovarian cancer that warrants further follow-up. The functional significance of the identified regions and SNPs is presently uncertain, though future fine mapping and histotype-specific functional analyses may shed light on the etiologies of both gynecologic conditions.

FXYD5 is a Marker for Poor Prognosis and a Potential Driver for Metastasis in Ovarian Carcinomas

Ovarian cancer (OC) is a leading cause of cancer mortality, but aside from a few well-studied mutations, very little is known about its underlying causes. As such, we performed survival analysis on ovarian copy number amplifications and gene expression datasets presented by The Cancer Genome Atlas in order to identify potential drivers and markers of aggressive OC. Additionally, two independent datasets from the Gene Expression Omnibus web platform were used to validate the identified markers. Based on our analysis, we identified FXYD5, a glycoprotein known to reduce cell adhesion, as a potential driver of metastasis and a significant predictor of mortality in OC. As a marker of poor outcome, the protein has effective antibodies against it for use in tissue arrays. FXYD5 bridges together a wide variety of cancers, including ovarian, breast cancer stage II, thyroid, colorectal, pancreatic, and head and neck cancers for metastasis studies.

Hypodontia phenotype in patients with epithelial ovarian cancer

BACKGROUND

Ovarian cancer is usually diagnosed in an advanced stage and the present clinical and diagnostic molecular markers for early OC screening are insufficient. The aim of this study was to identify potential relationship between the hypodontia and epithelial ovarian cancer (EOC).

PATIENTS AND METHODS

A retrospective study was conducted on 120 patients with EOC treated at the Department of Gynaecologic and Breast Oncology at the University Clinical Centre and 120 gynaecological healthy women (control group) of the same mean age. Women in both groups were reviewed for the presence of hypodontia and the patients with EOC also for clinicopathological characteristics of EOC according to hypodontia phenotype.

RESULTS

Hypodontia was diagnosed in 23 (19.2%) of patients with EOC and 8 (6.7%) controls (p = 0.004; odds ratio [OR] = 3.32; confidence interval [CI], 1.42-7.76). There was no statistically significant difference in patients with EOC with or without hypodontia regarding histological subtype (p = 0.220); they differed in regard to FIGO stage (p = 0.014; OR =3.26; CI, 1.23-8.64) and tumour differentiation grade (p = 0.042; OR = 3.1; CI, 1.01-9.53). Also, bilateral occurrence of EOC was more common than unilateral occurrence in women with hypodontia (p = 0.021; OR = 2.9; CI, 1.15-7.36). We also found statistically significant difference between the ovarian cancer group and control group in presence of other malignant tumours in subjects (p < 0.001).

CONCLUSIONS

The results of the study suggest a statistical association between EOC and hypodontia phenotype. Hypodontia might serve as a risk factor for EOC detection.

Identification of six new susceptibility loci for invasive epithelial ovarian cancer

Genome-wide association studies (GWAS) have identified 12 epithelial ovarian cancer (EOC) susceptibility alleles. The pattern of association at these loci is consistent in BRCA1 and BRCA2 mutation carriers who are at high risk of EOC. After imputation to 1000 Genomes Project data, we assessed associations of 11 million genetic variants with EOC risk from 15,437 cases unselected for family history and 30,845 controls and from 15,252 BRCA1 mutation carriers and 8,211 BRCA2 mutation carriers (3,096 with ovarian cancer), and we combined the results in a meta-analysis. This new study design yielded increased statistical power, leading to the discovery of six new EOC susceptibility loci. Variants at 1p36 (nearest gene, WNT4), 4q26 (SYNPO2), 9q34.2 (ABO) and 17q11.2 (ATAD5) were associated with EOC risk, and at 1p34.3 (RSPO1) and 6p22.1 (GPX6) variants were specifically associated with the serous EOC subtype, all with P < 5 × 10(-8). Incorporating these variants into risk assessment tools will improve clinical risk predictions for BRCA1 and BRCA2 mutation carriers.

Hypodontia prevalence and pattern in women with epithelial ovarian cancer

OBJECTIVE

To analyze the possible association between hypodontia and epithelial ovarian cancer (EOC), with the special interest in hypodontia pattern.

MATERIALS AND METHODS

One hundred twenty women with EOC treated at the Department of Gynecologic and Breast Oncology at the University Clinical Centre and 120 gynecologically healthy women of the same average age were reviewed for the presence and pattern of hypodontia. Collected data were analyzed for frequency, tooth type, location per jaw and side, number of missing teeth per person, and family history of hypodontia.

RESULTS

The results of the study showed prevalence of hypodontia in 19.2% of women with EOC and in 6.7% of women in the control group (P  =  .004). The most frequently missing teeth for women with EOC and women in the control group were maxillary second premolars and maxillary lateral incisors, respectively. Unilateral occurrence of hypodontia was more common than bilateral occurrence in women with EOC (P  =  .034). Of women with EOC and hypodontia, 21.7% reported a positive family history of hypodontia compared with no report in the control group of women with hypodontia (P  =  .150).

CONCLUSIONS

The results statistically support possible association between EOC and hypodontia. Because hypdontia can be recognized early in life, this finding could possibly help in earlier detection of EOC, resulting in better prognosis and treatment in earlier stages of the disease. Earlier EOC diagnosis and treatment could save many lives.

In 2014, can we do better than CA125 in the early detection of ovarian cancer?

Ovarian cancer is a lethal gynecologic malignancy with greater than 70% of women presenting with advanced stage disease. Despite new treatments, long term outcomes have not significantly changed in the past 30 years with the five-year overall survival remaining between 20% and 40% for stage III and IV disease. In contrast patients with stage I disease have a greater than 90% five-year overall survival. Detection of ovarian cancer at an early stage would likely have significant impact on mortality rate. Screening biomarkers discovered at the bench have not translated to success in clinical trials. Existing screening modalities have not demonstrated survival benefit in completed prospective trials. Advances in high throughput screening are making it possible to evaluate the development of ovarian cancer in ways never before imagined. Data in the form of human “-omes” including the proteome, genome, metabolome, and transcriptome are now available in various packaged forms. With the correct pooling of resources including prospective collection of patient specimens, integration of high throughput screening, and use of molecular heterogeneity in biomarker discovery, we are poised to make progress in ovarian cancer screening. This review will summarize current biomarkers, imaging, and multimodality screening strategies in the context of emerging technologies.

Approaches to integrating germline and tumor genomic data in cancer research

Cancer is characterized by a diversity of genetic and epigenetic alterations occurring in both the germline and somatic (tumor) genomes. Hundreds of germline variants associated with cancer risk have been identified, and large amounts of data identifying mutations in the tumor genome that participate in tumorigenesis have been generated. Increasingly, these two genomes are being explored jointly to better understand how cancer risk alleles contribute to carcinogenesis and whether they influence development of specific tumor types or mutation profiles. To understand how data from germline risk studies and tumor genome profiling is being integrated, we reviewed 160 articles describing research that incorporated data from both genomes, published between January 2009 and December 2012, and summarized the current state of the field. We identified three principle types of research questions being addressed using these data: (i) use of tumor data to determine the putative function of germline risk variants; (ii) identification and analysis of relationships between host genetic background and particular tumor mutations or types; and (iii) use of tumor molecular profiling data to reduce genetic heterogeneity or refine phenotypes for germline association studies. We also found descriptive studies that compared germline and tumor genomic variation in a gene or gene family, and papers describing research methods, data sources, or analytical tools. We identified a large set of tools and data resources that can be used to analyze and integrate data from both genomes. Finally, we discuss opportunities and challenges for cancer research that integrates germline and tumor genomics data.

CD24 and Nanog identify stem cells signature of ovarian epithelium and cysts that may develop to ovarian cancer

Ovarian cancer is the most lethal gynecological cancer. There is a general debate whether ovarian cancer is an intrinsic or an imported disease. We investigated whether in normal morphological appearance and in early stages of ovarian tumorgenesis typical cancer cell markers such as CD24 and Nanog are expressed. In 25% of normal appearing ovaries of post-menopausal women there was co-localization of CD24 and Nanog in the walls of the ovarian cysts, leaving the epithelial cells on the surface of these ovaries free of Nanog or CD24 expression. In benign ovarian tumors 37% of specimens were positive to CD24 and Nanog labeling while 26% of them were localized in the cyst walls. In contrast, in serous borderline tumors 79% specimens were labeled with CD24, 42% of them were localized in cysts and in 32% of them showed co-localization with CD24 and Nanog was evident: the rest were labeled in the ovarian epithelial cells. In serous ovarian carcinomas 81% specimens were labeled with CD24 antibodies. In 45% of them co-localization with Nanog was evident in the bulk of the cancerous tissue. In mucinous carcinomas no labeling with CD24 or Nanog was evident. In view of the synergistic effect of CD24 and Nanog expressed in malignant cancer development in other systems, it is suggested that such an analysis can be valuable for early detection of ovarian cancer. Moreover, the abundance of these markers in cysts in the development of ovarian cancer may suggest that they present an intrinsic source of the development of the highly malignant disease. Finally, since CD24 is exposed on the surface of the cancer cells, it may be highly beneficial to target these cells with antibodies to CD24 conjugated to cytotoxic drugs for more efficient treatment of this malignant disease.

The VDR gene FokI polymorphism and ovarian cancer risk

The polymorphism of vitamin D receptor (VDR) gene is demonstrated to affect the activity of its encoding protein and the subsequent downstream effects mediated by vitamin D. Mutations in VDR gene FokI have been suggested in the development of various cancers. Whether the polymorphism of the VDR gene FokI confers risk to ovarian cancer still remains controversial across the published studies in different ethnicity. The aim of this meta-analysis was to determine the role of VDR gene FokI variant in the susceptibility to ovarian cancer. Six publications with 14 individual case-control studies involving a total of 10,964 subjects were finally included into our study after a comprehensive literature search of the PubMed, Embase, Web of Science, and Wanfang databases. The strength of the association between the VDR gene FokI polymorphism and ovarian cancer risk was estimated under the allelic (T vs. C), homozygous (TT vs. CC), additive (CT vs. CC), recessive (TT vs. CC + CT), and dominant (CT + TT vs. CC) gene models. The overall odds ratios (ORs) for the contrast models of T vs. C, TT vs. CC, CT vs. CC, and CT + TT vs. CC indicated that the VDR gene FokI variant was related to an increased risk of ovarian cancer (OR(T vs. C) = 1.09, 95 % confidence interval (CI) 1.03-1.15, P(OR) = 0.004; OR(TT vs. CC) = 1.17, 95 % CI 1.04-1.32, P(OR) = 0.011; OR(CT vs. CC) = 1.10, 95 % CI 1.01-1.20, P(OR) = 0.027; OR(CT + TT vs. CC) = 1.12, 95 % CI 1.03-1.21, P(OR) = 0.007). The stratified analysis among the Caucasians also identified a significant association between the VDR gene FokI polymorphism and the susceptibility to ovarian cancer. The present meta-analysis with large available published data has revealed that the VDR gene FokI polymorphism confers susceptibility to ovarian cancer, particularly among the Caucasian population.

Gene-gene interaction network analysis of ovarian cancer using TCGA data

BACKGROUND

The Cancer Genome Atlas (TCGA) Data portal provides a platform for researchers to search, download, and analysis data generated by TCGA. The objective of this study was to explore the molecular mechanism of ovarian cancer pathogenesis.

METHODS

Microarray data of ovarian cancer were downloaded from TCGA database, and Limma package in R language was used to identify the differentially expressed genes (DEGs) between ovarian cancer and normal samples, followed by the function and pathway annotations of the DEGs. Next, NetBox software was used to for the gene-gene interaction (GGI) network construction and the corresponding modules identification, and functions of genes in the modules were screened using DAVID.

RESULTS

Our studies identified 332 DEGs, including 146 up-regulated genes which mainly involved in the cell cycle related functions and cell cycle pathway, and 186 down-regulated genes which were enriched in extracellular region par function, and Ether lipid metabolism pathway. GGI network was constructed by 127 DEGs and their significantly interacted 209 genes (LINKERs). In the top 10 nodes ranked by degrees in the network, 5 were LINKERs. Totally, 7 functional modules in the network were selected, and they were enriched in different functions and pathways, such as mitosis process, DNA replication and DNA double-strand synthesis, lipid synthesis processes and metabolic pathways. AR, BRCA1, TFDP1, FOXM1, CDK2, and DBF4 were identified as the transcript factors of the 7 modules.

CONCLUSION

our data provides a comprehensive bioinformatics analysis of genes, functions, and pathways which may be involved in the pathogenesis of ovarian cancer.

Genetic variants in the fibroblast growth factor pathway as potential markers of ovarian cancer risk, therapeutic response, and clinical outcome

BACKGROUND

The fibroblast growth factor (FGF) and FGF receptor (FGFR) axis plays a critical role in tumorigenesis, but little is known of its influence in ovarian cancer. We sought to determine the association of genetic variants in the FGF pathway with risk, therapeutic response, and survival of patients with ovarian cancer.

METHODS

We matched 339 non-Hispanic white ovarian cancer cases with 349 healthy controls and genotyped them for 183 single-nucleotide polymorphisms (SNPs) from 24 FGF (fibroblast growth factor) and FGFR (fibroblast growth factor receptor) genes. Genetic associations for the main effect, gene-gene interactions, and the cumulative effect were determined.

RESULTS

Multiple SNPs in the FGF-FGFR axis were associated with an increased risk of ovarian cancer. In particular, FGF1 [fibroblast growth factor 1 (acidic)] SNP rs7727832 showed the most significant association with ovarian cancer (odds ratio, 2.27; 95% CI, 1.31-3.95). Ten SNPs were associated with a reduced risk of ovarian cancer. FGF18 (fibroblast growth factor 18) SNP rs3806929, FGF7 (fibroblast growth factor 7) SNP rs9920722, FGF23 (fibroblast growth factor 23) SNP rs12812339, and FGF5 (fibroblast growth factor 5) SNP rs3733336 were significantly associated with a favorable treatment response, with a reduction of risk of nonresponse of 40% to 60%. Eleven SNPs were significantly associated with overall survival. Of these SNPs, FGF23 rs7961824 was the most significantly associated with improved prognosis (hazard ratio, 0.55; 95% CI, 0.39-0.78) and was associated with significantly longer survival durations, compared with individuals with the common genotype at this locus (58.1 months vs. 38.0 months, P = 0.005). Survival tree analysis revealed FGF2 rs167428 as the primary factor contributing to overall survival.

CONCLUSIONS

Significant associations of genetic variants in the FGF pathway were associated with ovarian cancer risk, therapeutic response, and survival. The discovery of multiple SNPs in the FGF-FGFR pathway provides a molecular approach for risk assessment, monitoring therapeutic response, and prognosis.

mEH Tyr113His polymorphism and the risk of ovarian cancer development

Background

The causes of ovarian cancer are complex and may be influenced by many factors, including polymorphism in the microsomal epoxide hydrolase (mEH) gene. Previous work suggests an association between the Tyr113His mEH polymorphism rs1051740 and susceptibility to ovarian cancer, but the results have been inconsistent.

Methods

PubMed, EMBASE, Google Scholar, and Chinese National Knowledge Infrastructure databases were systematically searched to identify relevant studies. A meta-analysis was performed to examine the association between Tyr113His mEH polymorphism and susceptibility to ovarian cancer. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated.

Results

Five studies involving 2,566 cases and 2,839 controls were included. Although the polymorphism did not affect ovarian cancer risk in the allelic contrast model (OR = 0.99, 95% CI = 0.83-1.17, P = 0.86), the mutant CC genotype was significantly associated with increased risk in the homozygote comparison (OR = 1.20, 95% CI = 1.01-1.43, P = 0.04) and recessive genetic models (OR = 1.20, 95% CI = 1.01-1.41, P = 0.03). The wild-type TT genotype was not associated with higher or lower ovarian cancer risk in the dominant genetic model (OR = 1.04, 95% CI = 0.83-1.29, P = 0.74). These results were robust to sensitivity analysis.

Conclusions

The CC genotype of Tyr113His mEH may confer increased risk of ovarian cancer. These conclusions should be verified in large and well-designed studies.

Analysis of over 10,000 Cases finds no association between previously reported candidate polymorphisms and ovarian cancer outcome

BACKGROUND

Ovarian cancer is a leading cause of cancer-related death among women. In an effort to understand contributors to disease outcome, we evaluated single-nucleotide polymorphisms (SNP) previously associated with ovarian cancer recurrence or survival, specifically in angiogenesis, inflammation, mitosis, and drug disposition genes.

METHODS

Twenty-seven SNPs in VHL, HGF, IL18, PRKACB, ABCB1, CYP2C8, ERCC2, and ERCC1 previously associated with ovarian cancer outcome were genotyped in 10,084 invasive cases from 28 studies from the Ovarian Cancer Association Consortium with over 37,000-observed person-years and 4,478 deaths. Cox proportional hazards models were used to examine the association between candidate SNPs and ovarian cancer recurrence or survival with and without adjustment for key covariates.

RESULTS

We observed no association between genotype and ovarian cancer recurrence or survival for any of the SNPs examined.

CONCLUSIONS

These results refute prior associations between these SNPs and ovarian cancer outcome and underscore the importance of maximally powered genetic association studies.

IMPACT

These variants should not be used in prognostic models. Alternate approaches to uncovering inherited prognostic factors, if they exist, are needed.

Localization of the stem cell markers LGR5 and Nanog in the normal and the cancerous human ovary and their inter-relationship

LGR5 and Nanog were recently characterized as stem cell markers in various embryonic, adult and cancer stem cells. However, there are no data on their precise localization in the normal adult ovary, which may be important for the initial steps of development of ovarian cancer, the most lethal gynecological cancer. We analyzed by immunocytochemistry the precise localization of these markers in normal ovary (11 specimens, age range 43-76), in borderline specimens (12 specimens), and in serous ovarian cancer (12 specimens of stage II) which comprises the vast majority (80%) of all ovarian cancer. Surprisingly, we revealed that both Nanog and LGR5 are clearly localized in the epithelial cells of the normal ovary. However, in 5 of 12 ovaries there was no labeling at all, while in 3 ovaries staining of Nanog was more prominent with only weak labeling of LGR5. In addition, we found in 3 of 11 ovaries clear labeling in foci of both LGR5 and Nanog antibodies, with partial overlapping. Occasionally, we also found in the stroma foci labeled by either Nanog or LGR5 antibodies. In general, the stroma area of tissue sections labeled with LGR5 was much greater than that labeled with Nanog. In borderline tumors a significant portion of the specimens (7 of 12) was labeled exclusively with Nanog and not with LGR5. In ovarian carcinomas almost 100% of the cells were exclusively labeled only with Nanog (6 of 12 of the specimens) with no labeling of LGR5. These data may suggest the potential of ovaries from postmenopausal women, which express Nanog, to undergo transformation, since Nanog was shown to be oncogenic. We conclude that Nanog, which probably plays an important role in ovarian embryonic development, may be partially silenced in fertile and post-menopausal women, but is re-expressed in ovarian cancer, probably by epigenetic activation of Nanog gene expression. Expression of Nanog and LGR5 in normal ovaries and in borderline tumors may assist in the early detection and improved prognosis of ovarian cancer. Moreover, targeting of Nanog by inhibitory miRNA or other means may assist in treating this disease.

Epigenome-wide ovarian cancer analysis identifies a methylation profile differentiating clear-cell histology with epigenetic silencing of the HERG K+ channel

Ovarian cancer remains the leading cause of death in women with gynecologic malignancies, despite surgical advances and the development of more effective chemotherapeutics. As increasing evidence indicates that clear-cell ovarian cancer may have unique pathogenesis, further understanding of molecular features may enable us to begin to understand the underlying biology and histology-specific information for improved outcomes. To study epigenetics in clear-cell ovarian cancer, fresh frozen tumor DNA (n = 485) was assayed on Illumina Infinium HumanMethylation450 BeadChips. We identified a clear-cell ovarian cancer tumor methylation profile (n = 163) which we validated in two independent replication sets (set 1, n = 163; set 2, n = 159), highlighting 22 CpG loci associated with nine genes (VWA1, FOXP1, FGFRL1, LINC00340, KCNH2, ANK1, ATXN2, NDRG21 and SLC16A11). Nearly all of the differentially methylated CpGs showed a propensity toward hypermethylation among clear-cell cases. Several loci methylation inversely correlated with tumor gene expression, most notably KCNH2 (HERG, a potassium channel) (P = 9.5 × 10(-7)), indicating epigenetic silencing. In addition, a predicted methylation class mainly represented by the clear-cell cases (20 clear cell out of 23 cases) had improved survival time. Although these analyses included only 30 clear-cell carcinomas, results suggest that loss of expression of KCNH2 (HERG) by methylation could be a good prognostic marker, given that overexpression of the potassium (K(+)) channel Eag family members promotes increased proliferation and results in poor prognosis. Validation in a bigger cohort of clear-cell tumors of the ovary is warranted.

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.

Fertility medications and the risk of cancer

Cancer is among the leading causes of death, and malignant diseases of the female genital tract are among the most common sites. Reproductive factors that are also associated with infertility have been identified as risk factors. Hormones are thought to play a role in the induction and promotion of some type of cancers. Hormonal preparations affect the endocrine milieu and, by acting on the reproductive organs themselves, could even have a direct effect on carcinogenesis. Numerous case-control and cohort studies have tried to determine whether fertility medication use increases cancer risk. Overall, the current data is reassuring but most research groups drew conclusions based on a small number of cases and after a relatively short follow-up. Proper counseling prior to fertility medication use is important and further studies on even larger patient groups with more cancer cases are needed to support widespread use of fertility medications.

The Wnt gatekeeper SFRP4 modulates EMT, cell migration and downstream Wnt signalling in serous ovarian cancer cells

Aberrant Wnt signalling is implicated in numerous human cancers, and understanding the effects of modulation of pathway members may lead to the development of novel therapeutics. Expression of secreted frizzled related protein 4 (SFRP4), an extracellular modulator of the Wnt signalling pathway, is progressively lost in more aggressive ovarian cancer phenotypes. Here we show that recombinant SFRP4 (rSFRP4) treatment of a serous ovarian cancer cell line results in inhibition of β-catenin dependent Wnt signalling as measured by TOP/FOP Wnt reporter assay and decreased transcription of Wnt target genes, Axin2, CyclinD1 and Myc. In addition, rSFRP4 treatment significantly increased the ability of ovarian cancer cells to adhere to collagen and fibronectin, and decreased their ability to migrate across an inflicted wound. We conclude that these changes in cell behaviour may be mediated via mesenchymal to epithelial transition (MET), as rSFRP4 treatment also resulted in increased expression of the epithelial marker E-cadherin, and reduced expression of Vimentin and Twist. Combined, these results indicate that modulation of a single upstream gatekeeper of Wnt signalling can have effects on downstream Wnt signalling and ovarian cancer cell behaviour, as mediated through epithelial to mesenchymal plasticity (EMP). This raises the possibility that SFRP4 may be used both diagnostically and therapeutically in epithelial ovarian cancer.

Epigenetic analysis leads to identification of HNF1B as a subtype-specific susceptibility gene for ovarian cancer

HNF1B is overexpressed in clear cell epithelial ovarian cancer, and we observed epigenetic silencing in serous epithelial ovarian cancer, leading us to hypothesize that variation in this gene differentially associates with epithelial ovarian cancer risk according to histological subtype. Here we comprehensively map variation in HNF1B with respect to epithelial ovarian cancer risk and analyse DNA methylation and expression profiles across histological subtypes. Different single-nucleotide polymorphisms associate with invasive serous (rs7405776 odds ratio (OR)=1.13, P=3.1 × 10(-10)) and clear cell (rs11651755 OR=0.77, P=1.6 × 10(-8)) epithelial ovarian cancer. Risk alleles for the serous subtype associate with higher HNF1B-promoter methylation in these tumours. Unmethylated, expressed HNF1B, primarily present in clear cell tumours, coincides with a CpG island methylator phenotype affecting numerous other promoters throughout the genome. Different variants in HNF1B associate with risk of serous and clear cell epithelial ovarian cancer; DNA methylation and expression patterns are also notably distinct between these subtypes. These findings underscore distinct mechanisms driving different epithelial ovarian cancer histological subtypes.

Ovarian cancer genome

Ovarian cancer (OC) is a relatively frequent malignant disease with a lifetime risk approaching to approximately 1 in 70. As many as 15-25 % OC arise due to known heterozygous germ-line mutations in DNA repair genes, such as BRCA1, BRCA2, RAD51C, NBN (NBS1), BRIP, and PALB2. Sporadic ovarian cancers often phenocopy the features of BRCA1-related hereditary disease (so-called BRCAness), i.e., show biallelic somatic inactivation of the BRCA1 gene. Tumor-specific BRCA1 deficiency renders selective sensitivity of transformed cells to platinating compounds and several other anticancer drugs, which explains high response rates of OC to systemic therapies. High-throughput molecular profiling of OC is instrumental for further progress in identification of novel OC diagnostic markers as well as for the development of new OC-specific treatments. However, interpretation of the huge bulk of incoming data may present a challenge. There is a critical need in the development of bioinformatic tools capable to integrate the multiplicity of available data sets into biologically and medically meaningful pieces of knowledge.