Ovarian cancer biomarker discovery based on genomic approaches

The Role of Glutathione Transferase Omega-Class Variant Alleles in Individual Susceptibility to Ovarian Cancer

The tumor microenvironment is affected by reactive oxygen species and has been suggested to have an important role in ovarian cancer (OC) tumorigenesis. The role of glutathione transferases (GSTs) in the maintenance of redox balance is considered as an important contributing factor in cancer, including OC. Furthermore, GSTs are mostly encoded by highly polymorphic genes, which further highlights their potential role in OC, known to originate from accumulated genetic changes. Since the potential relevance of genetic variations in omega-class GSTs (GSTO1 and GSTO2), with somewhat different activities such as thioltransferase and dehydroascorbate reductase activity, has not been clarified as yet in terms of susceptibility to OC, we aimed to investigate whether the presence of different GSTO1 and GSTO2 genetic variants, individually or combined, might represent determinants of risk for OC development. Genotyping was performed in 110 OC patients and 129 matched controls using a PCR-based assay for genotyping single nucleotide polymorphisms. The results of our study show that homozygous carriers of the GSTO2 variant G allele are at an increased risk of OC development in comparison to the carriers of the referent genotype (OR1 = 2.16, 95% CI: 0.88-5.26, p = 0.08; OR2 = 2.49, 95% CI: 0.93-6.61, p = 0.06). Furthermore, individuals with GST omega haplotype H2, meaning the concomitant presence of the GSTO1*A and GSTO2*G alleles, are more susceptible to OC development, while carriers of the H4 (*A*A) haplotype exhibited lower risk of OC when crude and adjusted haplotype analysis was performed (OR1 = 0.29; 95% CI: 0.12-0.70; p = 0.007 and OR2 = 0.27; 95% CI: 0.11-0.67; p = 0.0054). Overall, our results suggest that GSTO locus variants may confer OC risk.

Novel proteomic technologies to address gaps in pre-clinical ovarian cancer biomarker discovery efforts

INTRODUCTION

An estimated 20,000 women in the United States will receive a diagnosis of ovarian cancer in 2023. Late-stage diagnosis is associated with poor prognosis. There is a need for novel diagnostic biomarkers for ovarian cancer to improve early-stage detection and novel prognostic biomarkers to improve patient treatment.

AREAS COVERED

This review provides an overview of the clinicopathological features of ovarian cancer and the currently available biomarkers and treatment options. Two affinity-based platforms using proximity extension assays (Olink) and DNA aptamers (SomaLogic) are described in the context of highly reproducible and sensitive multiplexed assays for biomarker discovery. Recent developments in ion mobility spectrometry are presented as novel techniques to apply to the biomarker discovery pipeline. Examples are provided of how these aforementioned methods are being applied to biomarker discovery efforts in various diseases, including ovarian cancer.

EXPERT OPINION

Translating novel ovarian cancer biomarkers from candidates in the discovery phase to bona fide biomarkers with regulatory approval will have significant benefits for patients. Multiplexed affinity-based assay platforms and novel mass spectrometry methods are capable of quantifying low abundance proteins to aid biomarker discovery efforts by enabling the robust analytical interrogation of the ovarian cancer proteome.

Social, Genetics and Histopathological Factors Related to Titin (TTN) Gene Mutation and Survival in Women with Ovarian Serous Cystadenocarcinoma: Bioinformatics Analysis

Several factors may increase the risk of development of ovarian cancer. In this study, we investigated the relationship between social, genetic, and histopathologic factors in women with ovarian serous cystadenocarcinoma and titin (TTN) mutations, whether the TTN gene mutation may be a predictor, and its impact on mortality and survival in these patients. A total of 585 samples from patients with ovarian serous cystadenocarcinoma were collected from The Cancer Genome Atlas and PanCancer Atlas through the cBioPortal for analysis of social, genetic, and histopathological factors. Logistic regression was used to investigate whether TTN mutation could be a predictor, and the Kaplan-Meier method was applied to analyze survival time. TTN mutation frequency did not differ between age at diagnosis, tumor stage, and race, and was related to increased Buffa hypoxia score (p = 0.004), mutation count (p < 0.0001), Winter hypoxia Score (p = 0.030), nonsynonymous tumor mutation burden (TMB) (p < 0.0001), and reduced microsatellite instability sensor score (p = 0.010). The number of mutations (p < 0.0001) and winter hypoxia score (p = 0.008) were positively associated with TTN mutations, and nonsynonymous TMB (p < 0.0001) proved to be a predictor. Mutated TTN affects the score of genetic variables involved in cancer cell metabolism in ovarian cystadenocarcinoma.

Through the Looking Glass: Updated Insights on Ovarian Cancer Diagnostics

Epithelial ovarian cancer (EOC) is the deadliest gynaecological malignancy and the eighth most prevalent cancer in women, with an abysmal mortality rate of two million worldwide. The existence of multiple overlapping symptoms with other gastrointestinal, genitourinary, and gynaecological maladies often leads to late-stage diagnosis and extensive extra-ovarian metastasis. Due to the absence of any clear early-stage symptoms, current tools only aid in the diagnosis of advanced-stage patients, wherein the 5-year survival plummets further to less than 30%. Therefore, there is a dire need for the identification of novel approaches that not only allow early diagnosis of the disease but also have a greater prognostic value. Toward this, biomarkers provide a gamut of powerful and dynamic tools to allow the identification of a spectrum of different malignancies. Both serum cancer antigen 125 (CA-125) and human epididymis 4 (HE4) are currently being used in clinics not only for EOC but also peritoneal and GI tract cancers. Screening of multiple biomarkers is gradually emerging as a beneficial strategy for early-stage diagnosis, proving instrumental in administration of first-line chemotherapy. These novel biomarkers seem to exhibit an enhanced potential as a diagnostic tool. This review summarizes existing knowledge of the ever-growing field of biomarker identification along with potential future ones, especially for ovarian cancer.

Applications of Proteomics in Ovarian Cancer: Dawn of a New Era

The ability to identify ovarian cancer (OC) at its earliest stages remains a challenge. The patients present an advanced stage at diagnosis. This heterogeneous disease has distinguishable etiology and molecular biology. Next-generation sequencing changed clinical diagnostic testing, allowing assessment of multiple genes, simultaneously, in a faster and cheaper manner than sequential single gene analysis. Technologies of proteomics, such as mass spectrometry (MS) and protein array analysis, have advanced the dissection of the underlying molecular signaling events and the proteomic characterization of OC. Proteomics analysis of OC, as well as their adaptive responses to therapy, can uncover new therapeutic choices, which can reduce the emergence of drug resistance and potentially improve patient outcomes. There is an urgent need to better understand how the genomic and epigenomic heterogeneity intrinsic to OC is reflected at the protein level, and how this information could potentially lead to prolonged survival.

Expression of CCL18 gene in ovarian cancer and its impact on the biologic function of ovarian cancer cells

The aim of the present study was to evaluate the expression of the chemokine ligand 18 (CCL18) gene in ovarian cancer and to investigate the effects of its overexpression or suppression on growth, invasion, and metastasis in an ovarian carcinoma cell line (SKOV3) in vitro. CCL18 mRNA expression in epithelial ovarian carcinoma (EOC), benign ovarian tumor and normal ovarian tissues was measured by fluorescence quantitative polymerase chain reaction. A CCL18 restructuring plasmid was constructed, and SKOV3 cells were transfected with the plasmid DNA in vitro. A restructuring interference vector was also transfected into CCL18-positive SKOV3 cells. The growth curves, cell cycle distribution, and invasive, migrative and adhesive capacities of SKOV3 cells following overexpression and suppression of CCL18 were evaluated by MTT assay, flow cytometry, Transwell assay, migration assay, and the fibronectin adhesion method, respectively. The positive expression rate of CCL18 in EOC was significantly higher than in benign ovarian tumor (P = 0.002) and normal ovarian tissues (P = 0.003). However, there was no statistical significance in the expression of CCL18 with regard to clinical pathology (including histological classification, pathological grade and surgical pathological stage), and the median survival times of CCL18-positive and CCL18-negative patients did not differ significantly. The invasive, migrative, and adhesive capacities of SKOV3-CCL18 cells were significantly higher than those of SKOV3 and SKOV3-vector cells (P < 0.05). However, there was no significant difference in cell proliferation between the SKOV3-CCL18 and negative control cells. The invasive, migrative, and adhesive capacities of the pSilencer4.1-CCL18-small interfering RNA127 group were significantly lower than those of non-transfected pSilencer4.1-negative and pSilencer4.1 groups (P < 0.05). In conclusion, the overexpression and silencing of CCL18 affected invasion, adhesion, and migration in EOC cells; thus CCL18 may have potential as a clinical marker for early diagnosis of malignant ovarian tumors, and as a target molecule in the treatment of ovarian cancer.

Radiomics: an Introductory Guide to What It May Foretell

PURPOSE OF REVIEW

To briefly review the radiomics concept, its applications, and challenges in oncology in the era of precision medicine.

RECENT FINDINGS

Over the last 5 years, more than 500 studies have evaluated the role of radiomics to predict tumor diagnosis, genetic pattern, tumor response to therapy, and survival in multiple cancers. This new post-processing method is aimed at extracting multiple quantitative features from the image and converting them into mineable data. Radiomics models developed have shown promising results and may play a role in the near future in the daily patient management especially to assess tumor heterogeneity acting as a whole tumor virtual biopsy. For now, radiomics is limited by its lack of standardization; future challenges will be to provide robust and reproducible metrics extracted from large multicenter databases.

Bayesian state-space modeling in gene expression data analysis: An application with biomarker prediction

Background and ObjectiveBayesian State Space models are recent advancement in stochastic modeling which capture the randomness of a hidden background process by scrutinizing the prior knowledge and likelihood of observed data. This article elucidate the scope of Bayesian state space modeling on predicting the future expression values of a longitudinal micro array data. MethodsThe study conveniently makes use of longitudinally collected clinical trial data (GSE30531) from NCBI Gene Expression Omnibus (GEO) data repository. Multiple testing methodology using t-test is used for selecting differentially expressed genes between groups for fitting the model. The parameter values of the predictive model and future expression levels are estimated by drawing samples from the posterior joint distribution using a stochastic Markov Chain Monte Carlo (MCMC) algorithm which relies on Gibbs Sampling. The study also made an attempt to get estimates and its 95% Credible Interval through assumptions of different covariance structures like Variance Components, First order Auto Regressive and Unstructured variance-covariance structure to showcase the flexibility of the algorithm. Results72 Distinct genes with significantly different expression levels where selected for model fitting. Parameter estimates showed almost similar trends under different covariance structure assumption. Cross tabulation of gene frequencies having minimum credible interval under each covariance structure and study group showed a significant P value of 0.02. ConclusionsPresent study reveals that Bayesian state space models can be effectively used to explain and predict a complex data like gene expression data.

Novel smac mimetic APG-1387 elicits ovarian cancer cell killing through TNF-alpha, Ripoptosome and autophagy mediated cell death pathway

Background

Ovarian cancer is a deadly disease. Inhibitors of apoptosis proteins (IAPs) are key regulators of apoptosis and are frequently dysregulated in ovarian cancer. Overexpression of IAPs proteins has been correlated with tumorigenesis, treatment resistance and poor prognosis. Reinstalling functional cell death machinery by pharmacological inhibition of IAPs proteins may represent an attractive therapeutic strategy for treatment of ovarian cancer.

Methods

CCK-8 and colony formation assay was performed to examine cytotoxic activity. Apoptosis was analyzed by fluorescence microscopy, flow cytometry and TUNEL assay. Elisa assay was used to determine TNFα protein. Caspase activity assay was used for caspase activation evaluation. Immunoprecipitation and siRNA interference were carried out for functional analysis. Western blotting analysis were carried out to test protein expression. Ovarian cancer cell xenograft nude mice model was used for in vivo efficacy evaluation.

Results

APG-1387 demonstrated potent inhibitory effect on ovarian cancer cell growth and clonogenic cell survival. APG-1387 induced RIP1- and TNFα-dependent apoptotic cell death in ovarian cancer through downregulation of IAPs proteins and induction of caspase-8/FADD/RIP1 complex, which drives caspase-8 activation. NF-κB signaling pathway was activated upon APG-1387 treatment and RIP1 contributed to NF-κB activation. APG-1387 induced cytoprotective autophagy while triggering apoptosis in ovarian cancer cells and inhibition of autophagy enhanced APG-1387-induced apoptotic cell death. APG-1387 exhibited potent antitumor activity against established human ovarian cancer xenografts.

Conclusions

Our results demonstrate that APG-1387 targets IAPs proteins to potently elicit apoptotic cell death in vitro and in vivo, and provide mechanistic and applicable rationale for future clinical evaluation of APG-1387 in ovarian cancer.

Elevated plasma fibrinogen levels and prognosis of epithelial ovarian cancer: a cohort study and meta-analysis

Objective

To evaluate the effect of elevated plasma fibrinogen levels on the prognosis of epithelial ovarian cancer (EOC).

Methods

We reviewed the data of 217 patients with advanced-stage EOC between 2000 and 2012, and investigated the prognostic role of elevated plasma fibrinogen levels compared with serum CA-125 levels, neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR). For further evaluation, we performed a meta-analysis using 5 cohort studies published to July 2015, including our cohort study after a literature review.

Results

Among the four biomarkers, only plasma fibrinogen levels >485.2 mg/dL were correlated with impaired progression-free survival (PFS) and overall survival (OS) (median, 13.9 vs. 20.3 months and 42.2 vs. 55.4 months; p<0.010). Elevated plasma fibrinogen levels were an independent factor for poor PFS with marginal significance and OS (adjusted hazard ratios [HRs]=1.389 and 1.581; 95% confidence intervals [CIs]=0.979–1.972 and 1.032–2.423, respectively). Furthermore, crude and subgroup meta-analyses demonstrated that elevated plasma fibrinogen levels were associated with impaired PFS and OS in patients with all stage EOC.

Conclusion

Elevated plasma fibrinogen levels be more important for predicting survival than serum CA-125 levels, NLR and PLR in patients with EOC, in particular, advanced-stage disease. Moreover, it may be related to poor prognosis of EOC.

ELF5 in epithelial ovarian carcinoma tissues and biological behavior in ovarian carcinoma cells

The expression of E74-like factor 5 (ELF5) in epithelial ovarian carcinoma tissues and its effects on biological behavior in ovarian carcinoma cells were assessed in search for a new approach for gene treatment of epithelial ovarian carcinoma. RT-PCR technology was applied to detect the expression of ELF5 mRNA in epithelial ovarian carcinoma (n=49), borderline ovarian epithelial tumor (n=19), benign ovarian epithelial tumor (n=31) and normal ovarian tissues (n=40). Then, we transfected recombinant plasmid pcDNA3.1‑ELF5+EGFP into human ovarian carcinoma SKOV3 cells (recombinant plasmid group) in vitro and screened out stably transfected cells to conduct multiplication culture. Western blot analysis was performed to detect the expression of ELF5 protein in the different groups. Flow cytometry was employed to detect cell apoptosis and cycles. ELF5 mRNA in epithelial ovarian carcinoma and borderline ovarian epithelial tumor tissues were significantly lower (P<0.05) than those in benign ovarian epithelial tumor and normal ovarian tissues. ELF5 protein expression in the cells of recombinant plasmid group was significantly higher compared with empty plasmid and blank control groups. The capacity of cell reproductive recombinant plasmid group at each time point decreased (P<0.05). Flow cytometry detection showed that 67.03% of cells in recombinant plasmid group was blocked in G0/G1 phase (P<0.05), compared with empty plasmid group (37.17%) and blank control group (38.24%). Apoptotic rate of recombinant plasmid group was significantly lower (31.4±1.9%; P<0.05), compared with that of empty plasmid group (9.1±2.2%) and blank control group (8.7±1.5%), and the differences were statistically significant. In conclusion, ELF5 interfered with cell cycle of human ovarian carcinoma SKOV3 cells and promoted apoptosis of human ovarian carcinoma SKOV3 cells inhibiting their growth and invasive capacity; and thus providing a new approach to gene treatment of ovarian carcinoma.

Cancer Salivary Biomarkers for Tumours Distant to the Oral Cavity

The analysis of saliva as a diagnostic approach for systemic diseases was proposed just two decades ago, but recently great interest in the field has emerged because of its revolutionary potential as a liquid biopsy and its usefulness as a non-invasive sampling method. Multiple molecules isolated in saliva have been proposed as cancer biomarkers for diagnosis, prognosis, drug monitoring and pharmacogenetic studies. In this review, we focus on the current status of the salivary diagnostic biomarkers for different cancers distant to the oral cavity, noting their potential use in the clinic and their applicability in personalising cancer therapies.

The serine protease prostasin (PRSS8) is a potential biomarker for early detection of ovarian cancer

Background

Ovarian cancer (OVC) is the deadliest of all gynecologic cancers, primarily as a consequence of asymptomatic progression. The complex nature of OVC creates challenges for early detection, and there is a lack of specific and sensitive biomarkers suitable for screening and detecting early stage OVC.

Methods

Potential OVC biomarkers were identified by bioinformatic analysis. Candidates were further screened for differential expression in a library of OVC cell lines. OVC-specific overexpression of a candidate gene, PRSS8, which encodes prostasin, was confirmed against 18 major human cancer types from 390 cancer samples by qRT-PCR. PRSS8 expression profiles stratified by OVC tumor stage-, grade- and subtype were generated using cDNA samples from 159 OVC samples. Cell-specific expression and localization of prostasin was determined by immunohistological tissue array analysis of more than 500 normal, benign, and cancerous ovarian tissues. The presence of prostasin in normal, benign, and OVC serum samples was also determined.

Results

Gene expression analysis indicated that PRSS8 was expressed in OVC at levels more than 100 fold greater than found in normal or benign ovarian lesions. This overexpression signature was found in early stages of OVC and was maintained in higher stages and grades of OVC. The PRSS8 overexpression signature was specific for OVC and urinary bladder cancer among 18 human cancer types. The majority of ovarian cell lines overexpressed PRSS8. In situ hybridization and histopathology studies of OVC tissues indicated that overexpression of prostasin was largely localized to tumor epithelium and was absent in neighboring stroma. Significantly higher levels of prostasin were found in early stage OVC serum samples compared to benign ovarian and normal donor samples.

Conclusions

The abundant amounts of secreted prostasin found in sera of early stage OVC can potentially be used as a minimally invasive screening biomarker for early stage OVC. Overexpression of PRSS8 mRNA and high levels of prostasin in multiple subtypes of early stage ovarian tumors may provide clinical biomarkers for early detection of OVC, which can potentially be used with CA125 and HE4.

Electronic supplementary material

The online version of this article (doi:10.1186/s13048-016-0228-9) contains supplementary material, which is available to authorized users.

Current state of biomarkers in ovarian cancer prognosis

High-grade serous ovarian cancer remains one of the most lethal malignancies in women. Despite recent advances in surgical and pharmaceutical therapies, survival rates remain poor. A major impediment in management of this disease, that continues to contribute to poor overall survival rates, is resistance to standard carboplatin-paclitaxel combination chemotherapies. In addition to tumor cell intrinsic mechanisms leading to drug resistance, there is increasing awareness of the crucial role of the tumor microenvironment in mediating natural immune defense mechanisms and selective pressures that appear to facilitate chemotherapy sensitivity. We provide an overview of some of the promising new genetic and immunological biomarkers in ovarian cancer and discuss their biology and their likely clinical utility in future ovarian cancer management.