Biomarker testing for ovarian cancer: clinical utility of multiplex assays

Mass Spectrometry-Based Proteomics for Next-Generation Precision Oncology

Cancer is the leading cause of death worldwide characterized by patient heterogeneity and complex tumor microenvironment. While the genomics-based testing has transformed modern medicine, the challenge of diverse clinical outcomes highlights unmet needs for precision oncology. As functional molecules regulating cellular processes, proteins hold great promise as biomarkers and drug targets. Mass spectrometry (MS)-based clinical proteomics has illuminated the molecular features of cancers and facilitated discovery of biomarkers or therapeutic targets, paving the way for innovative strategies that enhance the precision of personalized treatment. In this article, we introduced the tools and current achievements of MS-based proteomics, choice of discovery and targeted MS from discovery to validation phases, profiling sensitivity from bulk samples to single-cell level and tissue to liquid biopsy specimens, current regulatory landscape of MS-based protein laboratory-developed tests (LDTs). The challenges, success and future perspectives in translating research MS assay into clinical applications are also discussed. With well-designed validation studies to demonstrate clinical benefits and meet the regulatory requirements for both analytical and clinical performance, the future of MS-based assays is promising with numerous opportunities to improve cancer diagnosis, treatment, and monitoring.

Evaluation of Serum Proteome Sample Preparation Methods to Support Clinical Proteomics Applications

Serum contains several proteins that are associated with disease-related processes. Mass spectrometry (MS)-based proteomics approaches greatly facilitate serum protein biomarker development. However, the serum proteome complexity presents a technical challenge for the accurate, sensitive, and reproducible quantification of proteins by MS. Thus, efficient sample preparation methods are of critical importance for serum proteome analyses. In this study, we evaluated the technical performance of two serum proteome sample preparation methods using sera from patients with high-grade serous ovarian cancer and patients with benign nongynecological conditions with a goal of providing insight into their compatibility with clinical proteomics workflows. One method entailed the use of immobilized trypsin (SMART Digest Trypsin) with RapiGest SF, an acid-labile surfactant designed to enhance the in-solution enzymatic digestion of proteins. The other method incorporated a commercially available sample preparation kit, iST-BCT, which contains standardized reagents. Significantly higher protein sequence coverage, albeit with lower digestion efficiency, was obtained with the immobilized trypsin + RapiGest SF workflow, whereas the iST-BCT workflow was quicker and had marginally better reproducibility. Protein relative abundance analysis revealed that the serum proteomes clustered primarily by the sample processing workflow and secondarily by disease state. We conducted a time course study to determine whether differences in the relative abundance of diagnostic high-grade serous ovarian cancer serum protein biomarker candidates were biased according to the duration of enzymatic digestion. Our results highlight the importance of optimizing enzymatic digestion kinetics according to the peptide targets of interest while considering the sensitivity of the downstream analytical method utilized in clinical proteomics workflows designed to measure biomarkers.

Delineating protein biomarkers for gastric cancers: A catalogue of mass spectrometry-based markers and assessment of their suitability for targeted proteomics applications

Gastric cancer (GC) is a global health concern. To facilitate improved management of GCs, protein biomarkers have been identified through mass spectrometry-based proteomics platforms. In order to exhibit clinical utility of such data, we congregated over 6800 differentially regulated proteins in GCs from proteomics studies and recorded the mass spectrometry platforms, association of the protein with infectious agents, protein identifiers, sample size and clinical characters of samples used with details on validation. Development of targeted proteomics methods is the cornerstone for pursuing these markers into clinical utility. Therefore, we developed Protein Biomarker Matrix for Gastric Cancer (PBMGC), a simple catalogue of robustness of each protein. This analysis yielded the identification of robust tissue, serum, and urine diagnostic and prognostic protein biomarker panels which can be further tested for their clinical utility. We also ascertained proteotypic tryptic peptides of 5631 proteins suitable for developing multiple reaction monitoring (MRM) assays. Extensive characterization of these peptides was carried out to record peptide ions, mass/charge and enhanced specific peptide features. With the vision of catering to proteomics researchers, the data generated through this analysis has been catalogued at Gastric Cancer Proteomics DataBase (GCPDB) (https://ciods.in/gcpdb/). Users can browse and download the data and improve GCPDB by submitting recently published data. SIGNIFICANCE: Mass spectrometry-based proteomics platforms have accumulated substantial data on proteins differentially regulated in gastric cancer (GC) clinical samples. The utility of such data in clinical applications is limited by search for suitable biomarker panels for assessment of GCs. We assembled over 6800 differentially regulated proteins in GCs from proteomics studies and recorded the corresponding details including mass spectrometry platforms, status on the association of the protein with infectious agents, protein identifiers from different databases, sample size and clinical characters of samples used in test and control conditions along with details on their validation. Towards the vision of utilizing these markers in clinical assays, Protein Biomarker Matrix for Gastric Cancer (PBMGC) was developed and clinically relevant multi-protein panels were identified. We also demonstrated identification and characterization of tryptic proteotypic tryptic peptides of 5631 proteins biomarkers of GCs which are suitable for development of MRM assays in a SCIEX QTRAP instrument. Aimed to caterproteomics researchers, the data generated through this analysis has been catalogued at Gastric Cancer Proteomics DataBase (GCPDB) (https://ciods.in/gcpdb/). The users can browse and download details on different markers and improve GCPDB by submitting recently published data. Such an analysis could lay a cornerstone for building more such resources or conduct such analysis in different clinical conditions to uptake and develop targeted proteomics as the method of choice for clinical applications.

Extracellular vesicles as a potential delivery platform for CRISPR-Cas based therapy in epithelial ovarian cancer

Ovarian Cancer (OC) is the most common gynecological malignancy and the eighth most diagnosed cancer in females worldwide. Presently, it ranks as the fifth leading cause of cancer-related mortality among patients globally. Major factors contributing to the lethality of OC worldwide include delayed diagnosis, chemotherapy resistance, high metastatic rates, and the heterogeneity of subtypes. Despite continuous efforts to develop novel targeted therapies and chemotherapeutic agents, challenges persist in the form of OC resistance and recurrence. In the last decade, CRISPR-Cas-based genome editing has emerged as a powerful tool for modifying genetic and epigenetic mechanisms, holding potential for treating numerous diseases. However, a significant challenge for therapeutic applications of CRISPR-Cas technology is the absence of an optimal vehicle for delivering CRISPR molecular machinery into targeted cells or tissues. Recently, extracellular vesicles (EVs) have gained traction as potential delivery vehicles for various therapeutic agents. These heterogeneous, membrane-derived vesicles are released by nearly all cells into extracellular spaces. They carry a molecular cargo of proteins and nucleic acids within their intraluminal space, encased by a cholesterol-rich phospholipid bilayer membrane. EVs actively engage in cell-to-cell communication by delivering cargo to both neighboring and distant cells. Their inherent ability to shield molecular cargo from degradation and cross biological barriers positions them ideally for delivering CRISPR-Cas ribonucleoproteins (RNP) to target cells. Furthermore, they exhibit higher biocompatibility, lower immunogenicity, and reduced toxicity compared to classical delivery platforms such as adeno-associated virus, lentiviruses, and synthetic nanoparticles. This review explores the potential of employing different CRISPR-Cas systems to target specific genes in OC, while also discussing various methods for engineering EVs to load CRISPR components and enhance their targeting capabilities.

Mass spectrometry-based proteomics as an emerging tool in clinical laboratories

Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.

VCAM-1 complements CA-125 in detecting recurrent ovarian cancer

BACKGROUND

Close to three-quarters of ovarian cancer cases are frequently diagnosed at an advanced stage, with more than 70% of them failing to respond to primary therapy and relapsing within 5 years. There is an urgent need to identify strategies for early detection of ovarian cancer recurrence, which may lead to earlier intervention and better outcomes.

METHODS

A customized magnetic bead-based 8-plex immunoassay was evaluated using a Bio-Plex 200 Suspension Array System. Target protein levels were analyzed in sera from 58 patients diagnosed with advanced ovarian cancer (including 34 primary and 24 recurrent tumors) and 46 healthy controls. The clinical performance of these biomarkers was evaluated individually and in combination for their ability to detect recurrent ovarian cancer.

RESULTS

An 8-plex immunoassay was evaluated with high analytical performance suitable for biomarker validation studies. Logistic regression modeling selected a two-marker panel of CA-125 and VCAM-1 that improved the performance of CA-125 alone in detecting recurrent ovarian cancer (AUC: 0.813 versus 0.700). At a fixed specificity of 83%, the two-marker panel significantly improved sensitivity in separating primary from recurrent tumors (70.8% versus 37.5%, P = 0.004), demonstrating that VCAM-1 was significantly complementary to CA-125 in detecting recurrent ovarian cancer.

CONCLUSIONS

A two-marker panel of CA-125 and VCAM-1 showed strong diagnostic performance and improvement over the use of CA-125 alone in detecting recurrent ovarian cancer. The experimental results warrant further clinical validation to determine their role in the early detection of recurrent ovarian cancer.

Recent Trends in Biosensing and Diagnostic Methods for Novel Cancer Biomarkers

Cancer is one of the major public health issues in the world. It has become the second leading cause of death, with approximately 75% of cancer deaths transpiring in low- or middle-income countries. It causes a heavy global economic cost estimated at more than a trillion dollars per year. The most common cancers are breast, colon, rectum, prostate, and lung cancers. Many of these cancers can be treated effectively and cured if detected at the primary stage. Nowadays, around 50% of cancers are detected at late stages, leading to serious health complications and death. Early diagnosis of cancer diseases substantially increases the efficient treatment and high chances of survival. Biosensors are one of the potential screening methodologies useful in the early screening of cancer biomarkers. This review summarizes the recent findings about novel cancer biomarkers and their advantages over traditional biomarkers, and novel biosensing and diagnostic methods for them; thus, this review may be helpful in the early recognition and monitoring of treatment response of various human cancers.

Mucin 13 (MUC13) as a candidate biomarker for ovarian cancer detection: potential to complement CA125 in detecting non-serous subtypes

OBJECTIVES

Ovarian cancer is the most lethal gynecological malignancy in developed countries. One of the key associations with the high mortality rate is diagnosis at late stages. This clinical limitation is primarily due to a lack of distinct symptoms and detection at the early stages. The ovarian cancer biomarker, CA125, is mainly effective for identifying serous ovarian carcinomas, leaving a gap in non-serous ovarian cancer detection. Mucin 13 (MUC13) is a transmembrane, glycosylated protein with aberrant expression in malignancies, including ovarian cancer. We explored the potential of MUC13 to complement CA125 as an ovarian cancer biomarker, by evaluating its ability to discriminate serous and non-serous subtypes of ovarian cancer at FIGO stages I-IV from benign conditions.

METHODS

We used our newly developed, high sensitivity ELISA to measure MUC13 protein in a large, well-defined cohort of 389 serum samples from patients with ovarian cancer and benign conditions.

RESULTS

MUC13 and CA125 serum levels were elevated in malignant compared to benign cases (p<0.0001). Receiver-operating characteristic (ROC) curve analysis showed similar area under the curve (AUC) of 0.74 (MUC13) and 0.76 (CA125). MUC13 concentrations were significantly higher in mucinous adenocarcinomas compared to benign controls (p=0.0005), with AUC of 0.80. MUC13 and CA125 showed significant elevation in early-stage cases (stage I-II) in relation to benign controls (p=0.0012 and p=0.014, respectively).

CONCLUSIONS

We report the novel role of MUC13 as a serum ovarian cancer biomarker, where it could complement CA125 for detecting some subtypes of non-serous ovarian carcinoma and early-stage disease.

Proteomic Applications and Considerations: From Research to Patient Care

Despite technological advancements in the field of proteomics, the rate at which serum and plasma biomarkers identified using proteomic approaches are translated into clinical use remains extremely low. In this chapter, we describe recent technological advancements and analytical strategies in proteomic methods. We also describe the progress of proteomic blood-based biomarkers to date and discuss what the future of proteomics might entail with the use of multi-omic approaches and implementing machine learning on large proteomic datasets. Lastly, we provide several key considerations for biomarker studies, ranging from sample type to the use of reference samples, in order to achieve progress from bench to bedside, ultimately improving patient diagnosis, disease, and/or therapeutic monitoring and care.

Current and emerging biomarkers in ovarian cancer diagnosis; CA125 and beyond

Ovarian cancer (OC) is one of the most common causes of cancer-related death in women worldwide. Its five-year survival rates are worse than the two most common gynecological cancers, cervical and endometrial. This is because it is asymptomatic in the early stages and usually detected in the advanced metastasized stage. Thus, survival is increasingly dependent on timely diagnosis. The delay in detection is contributed partly by the occurrence of non-specific clinical symptoms in the early stages and the lack of effective biomarkers and detection approaches. This underlines the need for biomarker identification and clinical validation, enabling earlier diagnosis, effective prognosis, and response to therapy. Apart from the traditional diagnostic biomarkers for OC, several new biomarkers have been delineated using advanced high-throughput molecular approaches in recent years. They are currently being clinically evaluated for their true diagnostic potential. In this chapter, we document the commonly utilized traditional screening markers and recently identified emerging biomarkers in OC diagnosis, focusing on secretory and protein biomarkers. We also briefly reviewed the recent advances and prospects in OC diagnosis.

Analysis of CXCL8 and its receptors CXCR1/CXCR2 at the mRNA level in neoplastic tissue, as well as in serum and peritoneal fluid in patients with ovarian cance

Understanding the relationship between the coexistence of inflammatory and neoplastic processes in ovarian cancer, particularly those involving chemokines and their receptors, may help to elucidate the involvement of the studied parameters in tumor pathogenesis and could lead to improved clinical applications. Therefore, the present study aimed to analyze the levels of C-X-C motif chemokine ligand 8 (CXCL8), and its receptors C-X-C chemokine receptor (CXCR)1 and CXCR2, in the serum and peritoneal fluid of women with ovarian cancer, and to evaluate the association between the expression of these parameters in tumor tissue and patient characteristics, particularly the degree of histological differentiation. The study group included women with ovarian cancer diagnosed with serous cystadenocarcinoma International Federation of Gynecology and Obstetrics stage IIIc and a control group, which consisted of women who were diagnosed with a benign lesion (serous cystadenoma). The transcript levels of CXCL8, CXCR1 and CXCR2 were evaluated using reverse transcription-quantitative PCR (RT-qPCR). The quantitative analysis was carried out using the LightCycler^® 480 System and GoTaq^® 1-Step RT-qPCR System, according to the manufacturers' instructions. The concentration of CXCL8 in serum and peritoneal fluid was determined using a Human Interleukin-8 ELISA kit, and the concentrations of CXCR1 and CXCR2 were determined using the CLOUD-CLONE ELISA kit. Local and systemic disturbances in immune and inflammatory responses involving the CXCL8 chemokine and its receptors indicated the involvement of these studied parameters in the pathogenesis of ovarian cancer. Immunoregulation of the CXCL8-CXCR1 system may influence the course of the inflammatory process accompanying ovarian cancer development, which may result in the identification of novel clinical applications; however, further studies are required.

Novel Diagnostic Biomarkers for High-Grade Serous Ovarian Cancer Uncovered by Data-Independent Acquisition Mass Spectrometry

High-grade serous ovarian cancer (HGSOC) represents the major histological type of ovarian cancer, and the lack of effective screening tools and early detection methods significantly contributes to the poor prognosis of HGSOC. Currently, there are no reliable diagnostic biomarkers for HGSOC. In this study, we performed liquid chromatography data-independent acquisition tandem mass spectrometry (MS) on depleted serum samples from 26 HGSOC cases and 24 healthy controls (HCs) to discover potential HGSOC diagnostic biomarkers. A total of 1,847 proteins were identified across all samples, among which 116 proteins showed differential expressions between HGSOC patients and HCs. Network modeling showed activations of coagulation and complement cascades, platelet activation and aggregation, neutrophil extracellular trap formation, toll-like receptor 4, insulin-like growth factor, and transforming growth factor β signaling, as well as suppression of lipoprotein assembly and Fc gamma receptor activation in HGSOC. Based on the network model, we prioritized 28 biomarker candidates and validated 18 of them using targeted MS assays in an independent cohort. Predictive modeling showed a sensitivity of 1 and a specificity of 0.91 in the validation cohort. Finally, in vitro functional assays on four potential biomarkers (FGA, VWF, ARHGDIB, and SERPINF2) suggested that they may play an important role in cancer cell proliferation and migration in HGSOC. All raw data were deposited in PRIDE (PXD033169).

Biomarkers for Early Diagnosis of Ovarian Carcinoma

The leading cause of gynecological cancer-related morbidity and mortality is ovarian cancer (OC), which is dubbed a silent killer. Currently, OC is a target of intense biomarker research, because it is often not discovered until the disease is advanced. The goal of OC research is to develop effective tests using biomarkers that can detect the disease at the earliest stages, which would eventually decrease the mortality, thereby preventing recurrence. Therefore, there is a pressing need to revisit the existing biomarkers to recognize the potential biomarkers that can lead to efficient predictors for the OC diagnosis. This Perspective covers an update on the currently available biomarkers used in the triaging of OC to gain certain insights into the potential role of these biomarkers and their estimation that are crucial to the understanding of neoplasm progression, diagnostics, and therapy.

Stacking Machine Learning Algorithms for Biomarker-Based Preoperative Diagnosis of a Pelvic Mass

Simple Summary

It is critical for women who are diagnosed with a pelvic mass, or an ovarian cyst to be accurately assessed for their risk of having an ovarian malignancy. Accurate risk stratification for these women will allow for appropriate triage and referral to centers best equipped to treat women diagnosed with ovarian cancer. In this study, machine learning (ML) algorithms were used to determine the optimal combination of biomarkers for prediction of malignancy in women presenting with a pelvic mass. Nine unique ML algorithms were employed to evaluate age, menopausal status, race, and levels of 67 biomarkers from serum, urine, and plasma samples prospectively collected in a cohort 140 women with a variety of pelvic mass diagnoses benign and malignant. A complex statistical algorithm using serum levels of CA125, HE4 and transferrin provided greater than 93% sensitivity and specificity for the preoperative prediction of malignancy in women presenting with a pelvic mass.

Abstract

Objective: To identify the most predictive parameters of ovarian malignancy and develop a machine learning (ML) based algorithm to preoperatively distinguish between a benign and malignant pelvic mass. Methods: Retrospective study of 70 predictive parameters collected from 140 women with a pelvic mass. The women were split into a 3:1 “training” to “testing” dataset. Feature selection was performed using Gini impurity through an embedded random forest model and principal component analysis. Nine unique ML classifiers were assessed across a variety of model-specific hyperparameters using 25 bootstrap resamples of the training data. Model predictions were then combined into an ensemble stack by LASSO regression. The final ensemble stack and individual classifiers were then applied to the testing dataset to assess model performance. Results: Feature selection identified HE4, CA125, and transferrin as three predictive parameters of malignancy. Assessment of the ensemble stack on the testing dataset outperformed all individual ML classifiers in predicting malignancy. The ensemble stack demonstrated an accuracy of 97.1%, a receiver operating characteristic (ROC) area under the curve (AUC) of 0.951, and a sensitivity of 93.3% with a specificity of 100%. Conclusions: Combining the measurement of three distinct biomarkers with the stacking of multiple ML classifiers into an ensemble can provide valuable preoperative diagnostic predictions for patients with a pelvic mass.

Quantitative global lipidomics analysis of patients with ovarian cancer versus benign adnexal mass

Altered lipid metabolism has emerged as an important feature of ovarian cancer (OC), yet the translational potential of lipid metabolites to aid in diagnosis and triage remains unproven. We conducted a multi-level interrogation of lipid metabolic phenotypes in patients with adnexal masses, integrating quantitative lipidomics profiling of plasma and ascites with publicly-available tumor transcriptome data. Using Sciex Lipidyzer, we assessed concentrations of > 500 plasma lipids in two patient cohorts-(i) a pilot set of 100 women with OC (50) or benign tumor (50), and (ii) an independent set of 118 women with malignant (60) or benign (58) adnexal mass. 249 lipid species and several lipid classes were significantly reduced in cases versus controls in both cohorts (FDR < 0.05). 23 metabolites-triacylglycerols, phosphatidylcholines, cholesterol esters-were validated at Bonferroni significance (P < 9.16 × 10-5). Certain lipids exhibited greater alterations in early- (diacylglycerols) or late-stage (lysophospholipids) cases, and multiple lipids in plasma and ascites were positively correlated. Lipoprotein receptor gene expression differed markedly in OC versus benign tumors. Importantly, several plasma lipid species, such as DAG(16:1/18:1), improved the accuracy of CA125 in differentiating early-stage OC cases from benign controls, and conferred a 15-20% increase in specificity at 90% sensitivity in multivariate models adjusted for age and BMI. This study provides novel insight into systemic and local lipid metabolic differences between OC and benign disease, further implicating altered lipid uptake in OC biology, and advancing plasma lipid metabolites as a complementary class of circulating biomarkers for OC diagnosis and triage.

Serum proteome modulations upon treatment provides biological insight on response to treatment in relapsed mantle cell lymphoma

Background

The possibility to monitor patient's serum proteome during treatment can provide deepened understanding of the biology associated with response to specific drugs. Non‐invasive serum sampling provides an opportunity for sustainable repetitive sampling of patients, which allows for more frequent evaluation of the biological response and enhanced flexibility in treatment selection in contrast to tissue biopsies.

Aim

To pin‐point biologically relevant changes in pre‐ and on‐treatment serum proteome samples in relapsed mantle cell lymphoma (MCL) patients, leading to insight into mechanisms behind response to treatment in sub‐groups of patients.

Methods

Pre‐ and on‐treatment serum samples from relapsed MCL patients treated with a triple combination therapy of rituximab, ibrutinib and lenalidomide were available for the study, together with detailed clinicopathological information. A microarray technology targeting 158 serum proteins using 371 antibody‐fragments was used to compare the serum proteome at the two time‐points.

Results

Proteins modulated by the treatment were shown to be associated to a MCL sub‐group with ATM/TP53 alterations, which emphasizes the importance of treatment stratification. Absolute values of serum protein levels in on‐treatment samples were highly variable and showed no correlation to outcome. To circumvent the challenge of variability in absolute serum protein levels, the velocity of change of individual serum proteins was used to identify proteins associated with clinical response. Increased values of TGF‐β1, CD40 and complement component 4 comparing pre‐ and on‐treatment samples were associated with remaining minimal residual disease (MRD) and increased BTK was associated with short progression‐free survival (PFS).

Conclusion

We show that the genetic sub‐type of MCL affects the biological response to treatment in serum and that the change in defined serum proteins reveals the biology associated with clinical response.

The Molecular Landscape Influencing Prognoses of Epithelial Ovarian Cancer

Epithelial ovarian cancer (EOC) is one of the major increasing lethal malignancies of the gynecological tract, mostly due to delayed diagnosis and chemoresistance, as well as its very heterogeneous genetic makeup. Application of high-throughput molecular technologies, gene expression microarrays, and powerful preclinical models has provided a deeper understanding of the molecular characteristics of EOC. Therefore, molecular markers have become a potent tool in EOC management, including prediction of aggressiveness, prognosis, and recurrence, and identification of novel therapeutic targets. In addition, biomarkers derived from genomic/epigenomic alterations (e.g., gene mutations, copy number aberrations, and DNA methylation) enable targeted treatment of affected signaling pathways in advanced EOC, thereby improving the effectiveness of traditional treatments. This review outlines the molecular landscape and discusses the impacts of biomarkers on the detection, diagnosis, surveillance, and therapeutic targets of EOC. These findings focus on the necessity to translate these potential biomarkers into clinical practice.

Sub-millimeter endoscope demonstrates feasibility of in vivo reflectance imaging, fluorescence imaging, and cell collection in the fallopian tubes

SIGNIFICANCE

Most cases of high-grade serous ovarian carcinoma originate as serous tubal intraepithelial carcinoma (STIC) lesions in the fallopian tube epithelium (FTE), enabling early endoscopic detection.

AIM

The cell-acquiring fallopian endoscope (CAFE) was built to meet requirements for locating potentially pathological tissue indicated by an alteration in autofluorescence or presence of a targeted fluorophore. A channel was included for directed scrape biopsy of cells from regions of interest.

APPROACH

Imaging resolution and fluorescence sensitivity were measured using a standard resolution target and fluorescence standards, respectively. A prototype was tested in ex vivo tissue, and collected cells were counted and processed.

RESULTS

Measured imaging resolution was 88  μm at a 5-mm distance, and full field of view was ∼45  deg in air. Reflectance and fluorescence images in ex vivo porcine reproductive tracts were captured, and fit through human tracts was verified. Hemocytometry counts showed that on the order of 105  cells per scrape biopsy could be collected from ex vivo porcine tissue.

CONCLUSIONS

All requirements for viewing STIC in the FTE were met, and collected cell counts exceeded input requirements for relevant analyses. Our benchtop findings suggest the potential utility of the CAFE device for in vivo imaging and cell collection in future clinical trials.

Glycomic-Based Biomarkers for Ovarian Cancer: Advances and Challenges

Ovarian cancer remains one of the most common causes of death among gynecological malignancies afflicting women worldwide. Among the gynecological cancers, cervical and endometrial cancers confer the greatest burden to the developing and the developed world, respectively; however, the overall survival rates for patients with ovarian cancer are worse than the two aforementioned. The majority of patients with ovarian cancer are diagnosed at an advanced stage when cancer has metastasized to different body sites and the cure rates, including the five-year survival, are significantly diminished. The delay in diagnosis is due to the absence of or unspecific symptoms at the initial stages of cancer as well as a lack of effective screening and diagnostic biomarkers that can detect cancer at the early stages. This, therefore, provides an imperative to prospect for new biomarkers that will provide early diagnostic strategies allowing timely mitigative interventions. Glycosylation is a protein post-translational modification that is modified in cancer patients. In the current review, we document the state-of-the-art of blood-based glycomic biomarkers for early diagnosis of ovarian cancer and the technologies currently used in this endeavor.

MicroRNA as Biomarker in Ovarian Cancer Management: Advantages and Challenges

Ovarian cancer is the most prevalent gynecological malignancy affecting women throughout the globe. Ovarian cancer has several subtypes, including epithelial ovarian cancer (EOC) with a whopping incidence rate of 239,000 per year, making it the sixth most common gynecological malignancy worldwide. Despite advancement of detection and therapeutics, death rate accounts for 152,000 per annum. Several protein-based biomarkers such as CA125 and HE4 are currently being used for diagnosis, but their sensitivity and specificity for early detection of ovarian cancer are under question. MicroRNA (a small noncoding RNA molecule that participates in post-transcription regulation of gene expression) and its functional deregulation in most cancers have been discovered in the previous two decades. Studies support that miRNA deregulation has an epigenetic component as well. Aberrant miRNA expression is often correlated with the form of EOC tumor, histological grade, prognosis, and FIGO stage. In this review, we addressed epigenetic regulation of miRNAs, the latest research on miRs as a biomarker in the detection of EOC, and tailored assays to use miRNAs as a biomarker in ovarian cancer diagnosis.

A multiplex biomarker assay improves the diagnostic performance of HE4 and CA125 in ovarian tumor patients

OBJECTIVE

Survival in epithelial ovarian cancer (EOC) remains poor. Most patients are diagnosed in late stages. Early diagnosis increases the chance of survival. We used the proximity extension assay from Olink Proteomics to search for new protein biomarkers with the potential to improve the diagnostic performance of CA125 and HE4 in patients with ovarian tumors.

MATERIAL AND METHODS

Plasma samples were obtained from 180 women with ovarian tumors; 30 cases of benign tumor, 28 cases with borderline tumors, 25 early EOC cases (FIGO stage I) and 97 advanced EOC cases (FIGO stages II-IV). Proteins were measured using the Olink® Oncology II and Inflammation panels. For statistical analyses, patients were categorized into benign tumors versus cancer and benign tumors versus borderline + cancer, respectively.

RESULTS

We analyzed 177 biomarkers. Thirty-four proteins had ROC AUC > 0.7 for discrimination between benign tumors and cancer. Fifteen proteins had ROC AUC > 0.7 for discrimination between benign tumors and borderline tumors + cancer. HE4 ranked highest for both comparisons. A reference model with HE4, CA125 and age (AUC 0.838 for benign tumors vs. cancer and AUC 0.770 for benign tumors vs. borderline tumors + cancer) was compared to the reference model with the addition of each of the remaining proteins with AUC > 0.7. ITGAV was the only individual biomarker found to improve diagnostic performance of the reference model, to AUC 0.874 for benign tumors vs. cancer and AUC 0.818 for benign tumors vs. borderline tumors + cancer (p < 0.05). Cross-validation and LASSO regression was combined to select multiple biomarker combinations. The best performing model for discrimination between benign tumors and borderline tumors + cancer was a 6-biomarker combination (HE4, CA125, ITGAV, CXCL1, CEACAM1, IL-10RB) and age (AUC 0.868, sensitivity 0.86 and specificity 0.82, p = 0.016 for comparison with the reference model).

CONCLUSION

HE4 was the best performing individual biomarker for discrimination between benign ovarian tumors and EOC including borderline tumors. The addition of other carcinogenesis-related biomarkers in a multiplex biomarker panel can improve the diagnostic performance of the established biomarkers HE4 and CA125.

Prediction of the treatment response in ovarian cancer: a ctDNA approach

Ovarian cancer is the eighth most commonly occurring cancer in women. Clinically, the limitation of conventional screening and monitoring approaches inhibits high throughput analysis of the tumor molecular markers toward prediction of treatment response. Recently, analysis of liquid biopsies including circulating tumor DNA (ctDNA) open new way toward cancer diagnosis and treatment in a personalized manner in various types of solid tumors. In the case of ovarian carcinoma, growing pre-clinical and clinical studies underscored promising application of ctDNA in diagnosis, prognosis, and prediction of treatment response. In this review, we accumulate and highlight recent molecular findings of ctDNA analysis and its associations with treatment response and patient outcome. Additionally, we discussed the potential application of ctDNA in the personalized treatment of ovarian carcinoma. ctDNA-monitoring usage during the ovarian cancer treatments procedures.

An Improved Prediction Model for Ovarian Cancer Using Urinary Biomarkers and a Novel Validation Strategy

This study was designed to analyze urinary proteins associated with ovarian cancer (OC) and investigate the potential urinary biomarker panel to predict malignancy in women with pelvic masses. We analyzed 23 biomarkers in urine samples obtained from 295 patients with pelvic masses scheduled for surgery. The concentration of urinary biomarkers was quantitatively assessed by the xMAP bead-based multiplexed immunoassay. To identify the performance of each biomarker in predicting cancer over benign tumors, we used a repeated leave-group-out cross-validation strategy. The prediction models using multimarkers were evaluated to develop a urinary ovarian cancer panel. After the exclusion of 12 borderline tumors, the urinary concentration of 17 biomarkers exhibited significant differences between 158 OCs and 125 benign tumors. Human epididymis protein 4 (HE4), vascular cell adhesion molecule (VCAM), and transthyretin (TTR) were the top three biomarkers representing a higher concentration in OC. HE4 demonstrated the highest performance in all samples with OC (mean area under the receiver operating characteristic curve (AUC) 0.822, 95% CI: 0.772–0.869), whereas TTR showed the highest efficacy in early-stage OC (AUC 0.789, 95% CI: 0.714–0.856). Overall, HE4 was the most informative biomarker, followed by creatinine, carcinoembryonic antigen (CEA), neural cell adhesion molecule (NCAM), and TTR using the least absolute shrinkage and selection operator (LASSO) regression models. A multimarker panel consisting of HE4, creatinine, CEA, and TTR presented the best performance with 93.7% sensitivity (SN) at 70.6% specificity (SP) to predict OC over the benign tumor. This panel performed well regardless of disease status and demonstrated an improved performance by including menopausal status. In conclusion, the urinary biomarker panel with HE4, creatinine, CEA, and TTR provided promising efficacy in predicting OC over benign tumors in women with pelvic masses. It was also a non-invasive and easily available diagnostic tool.

The contribution and perspectives of proteomics to uncover ovarian cancer tumor markers

Despite all the advances in understanding the mechanisms involved in ovarian cancer (OC) development, many aspects still need to be unraveled and understood. Tumor markers (TMs) are of special interest in this disease. Some aspects of clinical management of OC might be improved by the use of validated TMs, such as differentiating subtypes, defining the most appropriate treatment, monitoring the course of the disease, or predicting clinical outcome. The Food and Drug Administration (FDA) has approved a few TMs for OC: CA125 (cancer antigen 125; monitoring), HE4 (Human epididymis protein; monitoring), ROMA (Risk Of Malignancy Algorithm; HE4+CA125; prediction of malignancy) and OVA1 (Vermillion's first-generation Multivariate Index Assay [MIA]; prediction of malignancy). Proteomics can help advance the research in the field of TMs for OC. A variety of biological materials are being used in proteomic analysis, among them tumor tissues, interstitial fluids, tumor fluids, ascites, plasma, and ovarian cancer cell lines. However, the discovery and validation of new TMs for OC is still very challenging. The enormous heterogeneity of histological types of samples and the individual variability of patients (lifestyle, comorbidities, drug use, and family history) are difficult to overcome in research protocols. In this work, we sought to gather relevant information regarding TMs, OC, biological samples for proteomic analysis, as well as markers and algorithms approved by the FDA for use in clinical routine.

Urine Biomarkers for the Early Detection of Ovarian Cancer - Are We There Yet?

Ovarian cancer affects around 7500 women in the United Kingdom every year. Despite this, there is no effective screening strategy or standard treatment for ovarian cancer. If diagnosed during stage I, ovarian cancer has a 90% 5-year survival rate; however, there is usually a masking of symptoms which leads to an often late-stage diagnosis and correspondingly poor survival rate. Current diagnostic methods are invasive and consist of a pelvic examination, transvaginal ultrasonography, and blood tests to detect cancer antigen 125 (CA125). Unfortunately, surgery is often still required to make a positive diagnosis. To address the need for accurate, specific, and non-invasive diagnostic methods, there has been an increased interest in biomarkers identified through non-invasive tests as tools for the earlier diagnosis of ovarian cancer. Although most studies have focused on the identification of biomarkers in blood, the ease of availability of urine and the high patient compliance rates suggest that it could provide a promising resource for the screening of patients for ovarian cancer.

Simultaneous Measurement of 92 Serum Protein Biomarkers for the Development of a Multiprotein Classifier for Ovarian Cancer Detection

The best known ovarian cancer biomarker, CA125, is neither adequately sensitive nor specific for screening the general population. By using a combination of proteins for screening, it may be possible to increase the sensitivity and specificity over CA125 alone. In this study, we used Proseek Multiplex Oncology II plates to simultaneously measure the expression of 92 cancer-related proteins in serum using proximity extension assays. This technology combines the sensitivity of the PCR with the specificity of antibody-based detection methods, allowing multiplex biomarker detection and high-throughput quantification. We analyzed 1 μL of sera from each of 61 women with ovarian cancer and compared the values obtained with those from 88 age-matched healthy women. Principle component analysis and unsupervised hierarchical clustering separated the ovarian cancer patients from the healthy, with minimal misclassification. Data from the Proseek plates for CA125 levels exhibited a strong correlation with clinical values for CA125. We identified 52 proteins that differed significantly (P < 0.006) between ovarian cancer and healthy samples, several of which are novel serum biomarkers for ovarian cancer. In total, 40 proteins had an estimated area under the ROC curve of 0.70 or greater, suggesting their potential to serve as biomarkers for ovarian cancer. CA125 alone achieved a sensitivity of 93.4% at a specificity of 98%. By adding the Oncology II values for five proteins to CA125 in a multiprotein classifier, we increased the assay sensitivity to 98.4% at a specificity of 98%, thereby improving the sensitivity and specificity of CA125 alone.

Detecting ovarian cancer using extracellular vesicles: progress and possibilities

Ovarian cancer (OC) is the deadliest gynecological malignancy. Most patients are diagnosed when they are already in the later stages of the disease. Earlier detection of OC dramatically improves the overall survival, but this is rarely achieved as there is a lack of clinically implemented biomarkers of early disease. Extracellular vesicles (EVs) are small cell-derived vesicles that have been extensively studied in recent years. They contribute to various aspects of cancer pathology, including tumor growth, angiogenesis and metastasis. EVs are released from all cell types and the macromolecular cargo they carry reflects the content of the cells from which they were derived. Cancer cells release EVs with altered cargo into biofluids, and so, they represent an excellent potential source of novel biomarkers for the disease. In this review, we describe the latest developments in EVs as potential biomarkers for earlier detection of OC. The field is still relatively young, but many studies have shown that EVs and the cargo they carry, including miRNAs and proteins, can be used to detect OC. They could also give insights into the stage of the disease and predict the likely therapeutic outcome. There remain many challenges to the use of EVs as biomarkers, but, through ongoing research and innovation in this exciting field, there is great potential for the development of diagnostic assays in the clinic that could improve patient outcome.

Anti-tumorigenic and Platinum-Sensitizing Effects of Apolipoprotein A1 and Apolipoprotein A1 Mimetic Peptides in Ovarian Cancer

Objective: Apolipoprotein A1 (ApoA1) is remarkably decreased in serum and ovarian tissues of ovarian cancer patients. ApoA1 and ApoA1 mimetic peptides can sequestrate pro-inflammatory phospholipids, some of which are known to activate a variety of oncogenic pathways. Besides, more intrinsic anti-tumorigenic properties, independent from interaction with lipids, have also been described for ApoA1. We aimed to disclose the effects of ApoA1 and a mimetic peptide on the malignant phenotype of ovarian cancer cells, particularly regarding cell viability, invasiveness and platinum sensitization. Methods: Cells viability was assessed by MTS assay. Extracellular matrix invasion was assessed by transwell and spheroid invasion assays. Western blotting was performed to evaluate the effect of test compounds on intracellular pathways. Sensitization assays were performed in vitro and in the biologically relevant in ovo chorioallantoic membrane model. Results: Both ApoA1 and the mimetic peptide, at a concentration of 100 μg/mL, were able to decrease the viability of SKOV3, CAOV3, and OVCAR3 cells (p < 0.05). The peptide at this concentration was not able to affect the viability of immortalized non-neoplastic ovarian cells (p > 0.05). ApoA1 decreased SKOV3 cells invasiveness at 300 μg/mL after 72 and 96 h of exposure (p < 0.05), while the ApoA1 mimetic peptide prevented cell invasion at 50 and 100 μg/mL (p < 0.01). Treatment with 100 μg/mL of ApoA1 mimetic peptide decreased Akt phosphorylation in SKOV3 cells (p < 0.01). Accordingly, treatment with increasing concentrations of the peptide sensitized SKOV3, OVCAR3 and CAOV3 cells to cisplatin. This synergistic effect was observed both in vitro and in ovo. Conclusions: These results support the role of ApoA1 and ApoA1 mimetics as suppressors of ovarian tumorigenesis and as chemo-sensitising agents.

Serum Biomarker Based Algorithms in Diagnosis of Ovarian Cancer: A Review

Epithelial ovarian cancer accounts for more than 90% of ovarian tumours and continues as a leading cause of death from gynaecological malignancies. It is often difficult to differentiate a benign ovarian mass from malignant ones. Invasive histopathological biopsy is used as the gold standard diagnostic tool to diagnose cancer in patients with ovarian mass. A wide spectrum of Biomarkers were tried in various studies to develop a non invasive diagnostic tool, out of which HE4 and CA 125 remain the only clinically useful biomarker. Consequently various Biomarker based algorithms i.e. Risk of Malignancy Index, risk of ovarian cancer algorithm, OVA1, risk of malignancy algorithm were generated that have been developed to assess the risk of a mass being malignant. These algorithms help in timely triage of patients. Recently in 2016 FDA cleared Ova1 test (OVERA) with CA 125-II, HE4, apolipoprotein A-1, FSH, and transferring (Sensitivity 91% and Specificity 69%) as a referral or Triage test in patients presenting with ovarian mass. Combination of protein and circulating Micro RNA analysis in blood, could provide a comprehensive screening and diagnostic panel, in management of patients presenting with ovarian mass in one clinical setting.

The Clearance of Serum Human Epididymis Protein 4 Following Primary Cytoreductive Surgery for Ovarian Carcinoma

OBJECTIVE

The aim of this study was to examine the clearance of serum human epididymis protein 4 (HE4) in the immediate postoperative period in patients undergoing maximal effort cytoreductive surgery for ovarian carcinoma.

METHODS

The study was performed at a tertiary gynecologic oncology center. The surgery was performed by accredited gynecological oncologists.

RESULTS

Preoperative and serial postoperative venous blood samples at 4, 8, 24, 48, 72, 96, and 120 hours were taken from 10 sequential patients. Pretreatment HE4 is considered elevated at greater than 70 pmol/L. Human epididymis protein 4 was greater than 70 pmol/L in 7 patients, including all patients with high-grade serous carcinoma. Patients with preoperative elevation of serum HE4 and complete cytoreduction cleared more than 80% of serum HE4 in the first 4 hours and more than 88% within 5 days of surgery. One patient with incomplete cytoreduction of high-grade serous carcinoma had 66% clearance at 4 hours and a plateau thereafter.

CONCLUSIONS

Human epididymis protein 4 derived from ovarian carcinoma had a short half-life of less than 4 hours in the circulation when cytoreductive surgery was complete. Sustained low HE4 following surgery could be a useful indicator of the completeness of cytoreduction. Plateau or rise in serum HE4 could suggest persistent disease. Comparison of values on day 1 and day 4 or 5 might have value in assessing the completeness of cytoreduction.

Advanced architectures in the design of responsive polymers for cancer nanomedicine

In recent decades, nanoparticles have shown significant promise as an oncology treatment modality. Responsive polymers represent a promising class of nanoparticles that can trigger delivery through the exploitation of a specific stimuli. Response to a stimulus is one of the most basic processes found in living systems. As such, the desire to engineer dynamic and functional materials is becoming more prevalent in an effort to achieve precise control over our environment. The combination of controlled radical polymerization and high yielding chemistry strategies provide an excellent basis for the development of the next generation of drug delivery systems. The versatility of polymer chemistries available enables the synthesis of increasingly complex architectures with enhanced delivery specificity and control over the desired properties to interface with biological systems. This tutorial review highlights recent developments in polymer-based approaches to internally responsive nanoparticles for oncology. Presented are concise overviews of the current challenges and opportunities in cancer nanomedicine, common polymer-based architectures, and the basis for internally triggered stimuli-response relationships commonly employed in oncology applications. Examples of the chemistry used in the design of environmentally labile nanomaterials are discussed, and we outline recent advances in creating advanced bioresponsive drug delivery architectures.

Translational -omics: Future potential and current challenges in precision medicine

Rapid advances in computational science and biotechnology are paving the way for precision medicine - a vision in next-generation healthcare that promises to provide a care package uniquely tailored to each individual's molecular make-up. Until relatively recently, the focus has been firmly centred around the genome; however, over the past two decades there has been a surge in the study of molecular activity within other biological domains (proteome/transcriptome/metabolome) involved in health and pathogenesis. The term '-omics' is broadly applied to these disciplines and 'translational -omics' refers to clinical utilisation of data derived from these scientific approaches. Translational -omics represents the cornerstone of the precision medicine initiative and offers positively disruptive solutions in global healthcare from a humanitarian, scientific and economic standpoint. However, there are unique challenges anticipated for all stakeholders within the precision medicine community, and addressing these early on in the adoption of precision approaches is critical. Herein, we outline the potential for translational -omics in precision medicine, highlight key roadblocks to successful implementation and propose potential solutions to current and expected problems.

Does the Risk of Ovarian Malignancy Algorithm Provide Better Diagnostic Performance Than HE4 and CA125 in the Presurgical Differentiation of Adnexal Tumors in Polish Women?

Aim

This study compared the diagnostic performance of the Risk of Ovarian Malignancy Algorithm (ROMA) and HE4 and CA125 for the presurgical differentiation of adnexal tumors.

Material and Methods

This prospective study included 302 patients admitted for surgical treatment due to adnexal tumors. The ROMA was calculated depending on CA125, HE4, and menopausal status.

Results

Fifty patients were diagnosed with malignant disease. In the differentiation of malignant from nonmalignant adnexal tumors, the area under curve (AUC) was higher for ROMA and HE4 than that for CA125 in both the premenopausal and postmenopausal subgroups. In the differentiation of stage I FIGO malignancies and epithelial ovarian cancer from nonmalignant pathologies, the AUC of HE4 and ROMA was higher than that of CA125. The ROMA performed significantly better than CA125 in the differentiation of all malignancies and differentiation of stage I FIGO malignancies from nonmalignant pathologies (p = 0.043 and p = 0.025, resp.). There were no significant differences between the ROMA and the tumor markers for any other variants.

Conclusions

The ROMA is more useful than CA125 for the differentiation of malignant (including stage I FIGO) from nonmalignant adnexal tumors. It is also as useful as HE4 and CA125 for the differentiation of epithelial ovarian cancer from nonmalignant adnexal tumors.

Candidate genes and pathways downstream of PAX8 involved in ovarian high-grade serous carcinoma

Understanding the biology and molecular pathogenesis of ovarian epithelial cancer (EOC) is key to developing improved diagnostic and prognostic indicators and effective therapies. Although research has traditionally focused on the hypothesis that high-grade serous carcinoma (HGSC) arises from the ovarian surface epithelium (OSE), recent studies suggest that additional sites of origin exist and a substantial proportion of cases may arise from precursor lesions located in the Fallopian tubal epithelium (FTE). In FTE cells, the transcription factor PAX8 is a marker of the secretory cell lineage and its expression is retained in 96% of EOC. We have recently reported that PAX8 is involved in the tumorigenic phenotype of ovarian cancer cells. In this study, to uncover genes and pathways downstream of PAX8 involved in ovarian carcinoma we have determined the molecular profiles of ovarian cancer cells and in parallel of Fallopian tube epithelial cells by means of a silencing approach followed by an RNA-seq analysis. Interestingly, we highlighted the involvement of pathways like WNT signaling, epithelial-mesenchymal transition, p53 and apoptosis. We believe that our analysis has led to the identification of candidate genes and pathways regulated by PAX8 that could be additional targets for the therapy of ovarian carcinoma.

Point-of-Care Testing and Personalized Medicine for Metabolic Disorders

This chapter describes innovations in biomarker testing that can facilitate earlier and better treatment of patients who suffer from metabolic disorders. The use of new microfluidic devices along with miniaturized biosensors and transducers enables analysis of a single drop of a blood within the time frame of a typical visit to a doctor's office. Steps are underway so that these approaches will incorporate both biochemical and clinical data, resulting in unique bioprofiles for each patient. This will allow earlier, personalized, and more effective therapeutic options. In addition, smartphone apps for self-monitoring will be used increasingly for the best possible patient outcomes.

The Diagnostic and Prognostic Potential of microRNAs in Epithelial Ovarian Carcinoma

Ovarian cancer causes more than 100,000 deaths globally per year. Despite intensive research efforts, there has been little improvement in the overall survival of patients over the past three decades. Most patients are not diagnosed until the cancer is at an advanced stage, by which time their chances of still being alive after 5 years are appallingly low. Attempts to extend life in these patients have been, for the most part, unsuccessful. This owes partly to the lack of suitable biomarkers for stratifying patients at the molecular level, into responders and non-responders. This would lead to more drugs being shown to have a clinical benefit and being approved for use in subgroups of patients. There is also a desperate need for improved biomarkers for earlier detection of ovarian cancer; if the disease is detected sooner there is a significantly improved outlook. In this review, we outline the evidence that microRNAs are deregulated in ovarian cancer, what this can tell us about tumour progression and how it could be used to improve patient stratification in clinical trials. We also describe the potential for circulating microRNAs, both associated with proteins or carried in vesicles, to be used as diagnostics for earlier detection or as biomarkers for informing clinicians on the prognosis and best treatment of ovarian cancer.

Development of a tool for prediction of ovarian cancer in patients with adnexal masses: Value of plasma fibrinogen

Objective

To develop a tool for individualized risk estimation of presence of cancer in women with adnexal masses, and to assess the added value of plasma fibrinogen.

Study design

We performed a retrospective analysis of a prospectively maintained database of 906 patients with adnexal masses who underwent cystectomy or oophorectomy. Uni- and multivariate logistic regression analyses including pre-operative plasma fibrinogen levels and established predictors were performed. A nomogram was generated to predict the probability of ovarian cancer. Internal validation with split-sample analysis was performed. Decision curve analysis (DCA) was then used to evaluate the clinical net benefit of the prediction model.

Results

Ovarian cancer including borderline tumours was found in 241 (26.6%) patients. In multivariate analysis, elevated plasma fibrinogen, elevated CA-125, suspicion for malignancy on ultrasound, and postmenopausal status were associated with ovarian cancer and formed the basis for the nomogram. The overall predictive accuracy of the model, as measured by AUC, was 0.91 (95% CI 0.87–0.94). DCA revealed a net benefit for using this model for predicting ovarian cancer presence compared to a strategy of treat all or treat none.

Conclusion

We confirmed the value of plasma fibrinogen as a strong predictor for ovarian cancer in a large cohort of patients with adnexal masses. We developed a highly accurate multivariable model to help in the clinical decision-making regarding the presence of ovarian cancer. This model provided net benefit for a wide range of threshold probabilities. External validation is needed before a recommendation for its use in routine practice can be given.

VSV based virotherapy in ovarian cancer: the past, the present and …future?

The standard approach to treating patients with advanced epithelial ovarian cancer (EOC) after primary debulking surgery remains taxane and platinum-based chemotherapy. Despite treatment with this strategy, the vast majority of patients relapse and develop drug-resistant metastatic disease that may be driven by cancer stem cells (CSCs) or cancer initiating cells (CICs). Oncolytic viruses circumvent typical drug-resistance mechanisms, therefore they may provide a safe and effective alternative treatment for chemotherapy-resistant CSCs/CICs. Among oncolytic viruses vesicular stomatitis virus (VSV) has demonstrated oncolysis and preferential replication in cancer cells. In this review, we summarize the recent findings regarding existing knowledge on biology of the ovarian cancer and the role of ovarian CSCs (OCSCs) in tumor dissemination and chemoresistance. In addition we also present an overview of recent advances in ovarian cancer therapies with oncolytic viruses (OV). We focus particularly on key genetic or immune response pathways involved in tumorigenesis in ovarian cancer which facilitate oncolytic activity of vesicular stomatitis virus (VSV). We highlight the prospects of targeting OCSCs with VSV. The importance of testing an emerging ovarian cancer animal models and ovarian cancer cell culture conditions influencing oncolytic efficacy of VSV is also addressed.

Emerging diagnostic, prognostic and therapeutic biomarkers for ovarian cancer

BACKGROUND

In spite of various treatment options currently available, ovarian cancer (OC) still remains a leading cause of death in women world-wide. Diagnosis at an early stage is one of the most important factors that determines survival. Current clinical diagnostic tools have, however, a limited efficacy in early OC detection. Therefore, there is a critical need for new (early) diagnostic biomarkers and tools. Through advances in genomic, proteomic and metabolomic techniques, several novel molecular OC biomarkers have recently been identified. These biomarkers are currently subject to validation. In addition, integration of genomic, proteomic and metabolomic data, in conjunction with epidemiologic and clinical data, is considered essential for obtaining useful results. Interesting recent work has already shown that specific diagnostic biomarkers, such as BRCA mutations, may have profound therapeutic implications. Here, we review the current state of OC research through literature and database searches, with a focus on various recently identified biomarkers via different technologies for the (early) diagnosis, prognosis and treatment of OC.

CONCLUSIONS

Multi-biomarker panels accompanied by a meticulous determination of their sensitivity and specificity, as well their validation, using multivariate analyses will be critical for its clinical application, including early OC detection and tailor-made OC treatment.

Emerging diagnostic, prognostic and therapeutic biomarkers for ovarian cancer

BACKGROUND

In spite of various treatment options currently available, ovarian cancer (OC) still remains a leading cause of death in women world-wide. Diagnosis at an early stage is one of the most important factors that determines survival. Current clinical diagnostic tools have, however, a limited efficacy in early OC detection. Therefore, there is a critical need for new (early) diagnostic biomarkers and tools. Through advances in genomic, proteomic and metabolomic techniques, several novel molecular OC biomarkers have recently been identified. These biomarkers are currently subject to validation. In addition, integration of genomic, proteomic and metabolomic data, in conjunction with epidemiologic and clinical data, is considered essential for obtaining useful results. Interesting recent work has already shown that specific diagnostic biomarkers, such as BRCA mutations, may have profound therapeutic implications. Here, we review the current state of OC research through literature and database searches, with a focus on various recently identified biomarkers via different technologies for the (early) diagnosis, prognosis and treatment of OC.

CONCLUSIONS

Multi-biomarker panels accompanied by a meticulous determination of their sensitivity and specificity, as well their validation, using multivariate analyses will be critical for its clinical application, including early OC detection and tailor-made OC treatment.

Emerging diagnostic, prognostic and therapeutic biomarkers for ovarian cancer

BACKGROUND

In spite of various treatment options currently available, ovarian cancer (OC) still remains a leading cause of death in women world-wide. Diagnosis at an early stage is one of the most important factors that determines survival. Current clinical diagnostic tools have, however, a limited efficacy in early OC detection. Therefore, there is a critical need for new (early) diagnostic biomarkers and tools. Through advances in genomic, proteomic and metabolomic techniques, several novel molecular OC biomarkers have recently been identified. These biomarkers are currently subject to validation. In addition, integration of genomic, proteomic and metabolomic data, in conjunction with epidemiologic and clinical data, is considered essential for obtaining useful results. Interesting recent work has already shown that specific diagnostic biomarkers, such as BRCA mutations, may have profound therapeutic implications. Here, we review the current state of OC research through literature and database searches, with a focus on various recently identified biomarkers via different technologies for the (early) diagnosis, prognosis and treatment of OC.

CONCLUSIONS

Multi-biomarker panels accompanied by a meticulous determination of their sensitivity and specificity, as well their validation, using multivariate analyses will be critical for its clinical application, including early OC detection and tailor-made OC treatment.

Blood Plasma-Derived Anti-Glycan Antibodies to Sialylated and Sulfated Glycans Identify Ovarian Cancer Patients

Altered levels of naturally occurring anti-glycan antibodies (AGA) circulating in human blood plasma are found in different pathologies including cancer. Here the levels of AGA directed against 22 negatively charged (sialylated and sulfated) glycans were assessed in high-grade serous ovarian cancer (HGSOC, n = 22) patients and benign controls (n = 31) using our previously developed suspension glycan array (SGA). Specifically, the ability of AGA to differentiate between controls and HGSOC, the most common and aggressive type of ovarian cancer with a poor outcome was determined. Results were compared to CA125, the commonly used ovarian cancer biomarker. AGA to seven glycans that significantly (P<0.05) differentiated between HGSOC and control were identified: AGA to top candidates SiaTn and 6-OSulfo-TF (both IgM) differentiated comparably to CA125. The area under the curve (AUC) of a panel of AGA to 5 glycans (SiaTn, 6-OSulfo-TF, 6-OSulfo-LN, SiaLea, and GM2) (0.878) was comparable to CA125 (0.864), but it markedly increased (0.985) when combined with CA125. AGA to SiaTn and 6-OSulfo-TF were also valuable predictors for HGSOC when CA125 values appeared inconclusive, i.e. were below a certain threshold. AGA-glycan binding was in some cases isotype-dependent and sensitive to glycosidic linkage switch (α2-6 vs. α2-3), to sialylation, and to sulfation of the glycans. In conclusion, plasma-derived AGA to sialylated and sulfated glycans including SiaTn and 6-OSulfo-TF detected by SGA present a valuable alternative to CA125 for differentiating controls from HGSOC patients and for predicting the likelihood of HGSOC, and may be potential HGSOC tumor markers.

A microfluidic ExoSearch chip for multiplexed exosome detection towards blood-based ovarian cancer diagnosis

Tumor-derived circulating exosomes, enriched with a group of tumor antigens, have been recognized as a promising biomarker source for cancer diagnosis via a less invasive procedure. Quantitatively pinpointing exosome tumor markers is appealing, yet challenging. In this study, we developed a simple microfluidic approach (ExoSearch) which provides enriched preparation of blood plasma exosomes for in situ, multiplexed detection using immunomagnetic beads. The ExoSearch chip offers a robust, continuous-flow design for quantitative isolation and release of blood plasma exosomes in a wide range of preparation volumes (10 μL to 10 mL). We employed the ExoSearch chip for blood-based diagnosis of ovarian cancer by multiplexed measurement of three exosomal tumor markers (CA-125, EpCAM, CD24) using a training set of ovarian cancer patient plasma, which showed significant diagnostic power (a.u.c. = 1.0, p = 0.001) and was comparable with the standard Bradford assay. This work provides an essentially needed platform for utilization of exosomes in clinical cancer diagnosis, as well as fundamental exosome research.

MicroRNA: a new and promising potential biomarker for diagnosis and prognosis of ovarian cancer

Epithelial ovarian cancer (EOC) is the leading cause of death among all gynecological malignancies. Despite the technological and medical advances over the past four decades, such as the development of several biological markers (mRNA and proteins biomarkers), the mortality rate of ovarian cancer remains a challenge because of its late diagnosis, which is specifically attributed to low specificities and sensitivities. Under this compulsive scenario, recent advances in expression biology have shifted in identifying and developing specific and sensitive biomarkers, such as microRNAs (miRNAs) for cancer diagnosis and prognosis. MiRNAs are a novel class of small non-coding RNAs that deregulate gene expression at the posttranscriptional level, either by translational repression or by mRNA degradation. These mechanisms may be involved in a complex cascade of cellular events associated with the pathophysiology of many types of cancer. MiRNAs are easily detectable in tissue and blood samples of cancer patients. Therefore, miRNAs hold good promise as potential biomarkers in ovarian cancer. In this review, we attempted to provide a comprehensive profile of key miRNAs involved in ovarian carcinoma to establish miRNAs as more reliable non-invasive clinical biomarkers for early detection of ovarian cancer compared with protein and DNA biomarkers.

Translational Research and Plasma Proteomic in Cancer

Proteomics is a recent field of research in molecular biology that can help in the fight against cancer through the search for biomarkers that can detect this disease in the early stages of its development. Proteomic is a speedily growing technology, also thanks to the development of even more sensitive and fast mass spectrometry analysis. Although this technique is the most widespread for the discovery of new cancer biomarkers, it still suffers of a poor sensitivity and insufficient reproducibility, essentially due to the tumor heterogeneity. Common technical shortcomings include limitations in the sensitivity of detecting low abundant biomarkers and possible systematic biases in the observed data. Current research attempts are trying to develop high-resolution proteomic instrumentation for high-throughput monitoring of protein changes that occur in cancer. In this review, we describe the basic features of the proteomic tools which have proven to be useful in cancer research, showing their advantages and disadvantages. The application of these proteomic tools could provide early biomarkers detection in various cancer types and could improve the understanding the mechanisms of tumor growth and dissemination.

A joint model based on longitudinal CA125 in ovarian cancer to predict recurrence

AIMS

To develop a new package of joint model to fit longitudinal CA125 in epithelial ovarian cancer relapse.

PATIENTS & METHODS

Included were 305 epithelial ovarian cancer patients who reached complete remission after cytoreductive surgery and first-line chemotherapy. Univariate and multivariate analysis with a joint model was performed to select independent risk factors, which were subsequently combined to predict recurrence.

RESULTS

Independent factors were longitudinal CA125, age, stage and residual tumor size (p < 0.05). Prediction of recurrence with these factors had an average of 80.7% accuracy, 5.6-10.7% better than kinetic factors.

CONCLUSION

The new package of joint model fits longitudinal CA125 well. Potential application can be extended to other biomarkers.

Identification of novel candidate plasma metabolite biomarkers for distinguishing serous ovarian carcinoma and benign serous ovarian tumors

OBJECTIVE

Serous ovarian carcinoma (OC) represents a leading cause of cancer-related death among U.S. women. Non-invasive tools have recently emerged for discriminating benign from malignant ovarian masses, but evaluation remains ongoing, without widespread implementation. In the last decade, metabolomics has matured into a new avenue for cancer biomarker development. Here, we sought to identify novel plasma metabolite biomarkers to distinguish serous ovarian carcinoma and benign serous ovarian tumor.

METHODS

Using liquid chromatography-mass spectrometry, we conducted global and targeted metabolite profiling of plasma isolated at the time of surgery from 50 serous OC cases and 50 serous benign controls.

RESULTS

Global lipidomics analysis identified 34 metabolites (of 372 assessed) differing significantly (P<0.05) between cases and controls in both training and testing sets, with 17 candidates satisfying FDR q<0.05, and two reaching Bonferroni significance. Targeted profiling of ~150 aqueous metabolites identified a single amino acid, alanine, as differentially abundant (P<0.05). A multivariate classification model built using the top four lipid metabolites achieved an estimated AUC of 0.85 (SD=0.07) based on Monte Carlo cross validation. Evaluation of a hybrid model incorporating both CA125 and lipid metabolites was suggestive of increased classification accuracy (AUC=0.91, SD=0.05) relative to CA125 alone (AUC=0.87, SD=0.07), particularly at high fixed levels of sensitivity, without reaching significance.

CONCLUSIONS

Our results provide insight into metabolic changes potentially correlated with the presence of serous OC versus benign ovarian tumor and suggest that plasma metabolites may help differentiate these two conditions.