Liquid biopsy in ovarian cancer: Catching the silent killer before it strikes

Nano biosensor unlocks tumor derived immune signals for the early detection of ovarian cancer

Ovarian cancer is a critical health issue for women nowadays. Its impact is significant because of its high mortality rate (324,603 worldwide), late-stage diagnosis and poor survival rate. Lack of screening tests, vague symptoms, misdiagnosis, and age factor makes it even more difficult to detect. Neutrophils, a subset of immune cells, undergo tumor-specific changes as ovarian cancer progresses inside ovarian tumour microenvironment. Therefore, monitoring the time-specific activity of neutrophils in circulation has the potential to aid in the diagnosis of ovarian cancer. Most ovarian tumor-specific antigens are unknown, making it difficult to identify neutrophils associated with ovarian tumor. We present ovarian tumor-associated circulating neutrophil cell profiling as a stand-alone cancer diagnostic method using a liquid biopsy. Using a SERS-functionalized nano probe, the metabolic profiles of neutrophils from ovarian tumor interaction are detected. We demonstrate that neutrophils associated with cancer stem cells have a distinct metabolic profile and are useful in the diagnosis of early ovarian cancer. Using 5 μL of peripheral blood and an artificial neural network, the characteristics of neutrophil profiles in patient blood could distinguish cancer cohort from non-cancer (healthy) with a 90 % sensitivity and 100 % specificity. Our results demonstrate the viability of using circulating neutrophils for non-invasive cancer diagnostics.

Development of a radiomic model to predict CEACAM1 expression and prognosis in ovarian cancer

We aimed to investigate the prognostic role of CEACAM1 and to construct a radiomic model to predict CEACAM1 expression and prognosis in ovary cancer (OC). Sequencing data and CT scans in OC were sourced from TCGA and TCIA databases. CEACAM1 expression was assessed by Cox regression analyses, Kaplan-Meier curves and GSVA enrichment analysis. Furthermore, radiomic features were extracted from CT scans and selected by LASSO and ROC. The selected radiomic features were used to construct a radiomic model to predict CEACAM1 expression. In addition, the radiomic score (RS) and its relationship with OC survival were investigated by Kaplan-Meier and ROC curves. At last, RS and clinical features were included into LASSO, using nomogram to predict OC prognosis. Cox regression analyses showed that CEACAM1 expression was an independent prognostic factor and associated with immune cell infiltration in OC. By LASSO and ROC, six radiomic features were selected and used to construct a radiomic model. The PR, calibration, DCA and ROC curves revealed the good performance and clinical utility of the radiomic model to predict CEACAM1 expression. In addition, RS based on radiomic features was found to be associated with OC survival. At last, a nomogram based on RS, age, chemotherapy and tumor residual disease was constructed and was found to have high accuracy in predicting OC prognosis. For the first time, our study constructed a radiomic model to predict CEACAM1 expression and prognosis of OC patients. Those findings may guide novel diagnosis and treatment for OC patients.

An assessment of the prevalence of drug therapy problems and its associated factors among ovarian cancer patients at Kenyatta National Hospital

IntroductionDespite several studies in various cancers reporting a high prevalence of drug therapy problems (DTPs), there is a notable scarcity of comprehensive data addressing DTPs among patients with an ovarian cancer diagnosis. This study aimed to assess DTPs among those patients at Kenyatta National Hospital.MethodsA retrospective cohort study was used to assess the prevalence of DTPs among all 100 eligible patients with an ovarian cancer diagnosis. This research used a data abstraction tool to gather ovarian patients' data on drug therapy problems. The statistical package for Social Science version 20.0 software was used for data entry and analysis. The findings were presented using frequency tables, percentages and figures.ResultsThe mean age of the study population was 51.2 ± 15.1 with a bigger population being above 50 years (57.0%). The prevalent histological type of ovarian cancer was epithelial ovarian cancer (79.0%) followed by sex cord-stromal ovarian cancer (13%) and germ cell ovarian cancer (8%) which was least prevalent. A total of 175 DTPs were identified with the most prevalent being adverse drug reactions (40.6%) and drug non-adherence (17.1%). The number of medications used and stage of ovarian cancer as statistically significant predictors of DTP.ConclusionThe prevalence of DTPs was high and the most prevalent DTP was adverse drug reactions. The number of medications used and the stage of ovarian cancer were statistically significant predictors of DTP. Hence, regular medication reviews and robust pharmacovigilance systems should be implemented to detect and manage DTPs effectively.

A pilot study evaluating the feasibility of enriching and detecting circulating tumour cells from peripheral and ovarian veins in rare epithelial ovarian carcinomas

INTRODUCTION

Studies on circulating tumour cells (CTCs) in rare epithelial ovarian carcinomas (EOC) are limited, despite their potential as a minimally invasive biomarker for monitoring cancer progression and predicting outcomes. This pilot study aimed to assess the feasibility of enriching and detecting CTCs from both peripheral and ovarian vein blood samples in rare EOC subtypes.

MATERIALS AND METHODS

Blood samples were collected from the peripheral and ovarian veins of 20 patients with rare EOC. Among the 20 patients, 12 had early-stage disease (I-II), while 8 had advanced disease (III-IV). CTCs were enriched using the Parsortix® system and immunophenotyped via immunofluorescence targeting epithelial markers (EpCAM/pan-cytokeratin) and Hoechst for positive selection, and CD45 for negative selection. CTC status (positive versus negative) was correlated with clinicopathological data.

RESULTS

CTCs were successfully detected in 45 % (1-19 CTCs) of baseline peripheral samples and 55 % (1-4776 CTCs) of ovarian vein samples. CTC doublets and clusters were detected in ovarian vein samples (3/11), but not in peripheral samples (0/20). A higher proportion of deaths were observed in CTC+ patients compared to CTC- patients (p = 0.0088).

CONCLUSION

Here we demonstrate the feasibility of enriching and detecting CTCs from both peripheral and ovarian vein blood in patients with rare EOC. The higher CTC yield in ovarian vein blood suggests that tumour-draining blood may play a role in improving CTC detection. This pilot study paves the way for larger studies to investigate the prognostic utility of CTCs and refine their clinical value in these rare understudied EOC.

Opportunities for predictive proteogenomic biomarkers of drug treatment sensitivity in epithelial ovarian cancer

Genomic analysis has played a significant role in the identification of driver mutations that are linked to disease progression and response to drug treatment in ovarian cancer. A prominent example is the stratification of epithelial ovarian cancer (EOC) patients with homologous recombination deficiency (HRD) characterized by mutations in DNA damage repair genes such as BRCA1/2 for treatment with PARP inhibitors. However, recent studies have shown that some epithelial ovarian tumors respond to PARP inhibitors irrespective of their HRD or BRCA mutation status. An exclusive focus on the genome overlooks the significant insight that can be gained from other biological analytes, including proteins, which carry out cellular functions. Proteogenomics is the integration of genomics, transcriptomics, epigenomics and proteomics data. This review paper provides novel insight into the role of proteogenomics as an analytical approach to identify predictive biomarkers of drug treatment response in epithelial ovarian cancer. Proteogenomic analysis can facilitate the identification of predictive biomarkers of drug treatment response, consequently greatly improving the stratification of patients with EOC for treatment towards a goal of personalized medicine.

Noble Metal Nanoparticle Assisted Mass Spectrometry for Metabolite-Based In Vitro Diagnostics

In vitro diagnostics (IVD) makes clinical diagnosis rapid, simple, and noninvasive to patients, playing a crucial role in the early diagnosis and monitoring of diseases. Metabolic biomarkers are closely correlated to the phenotype of diseases. However, most IVD platforms are constrained by the sensitivity and throughput of assay. In recent years, noble-metal-nanoparticle (NMNP)-assisted laser desorption/ionization mass spectrometry (LDI MS) has generated major advances in metabolite analysis, significantly improving the sensitivity, accuracy, and throughput of IVD due to the unique optical and electrical properties of NMNPs. This review systematically assesses the development of NMNPs as LDI MS matrices in the detection of metabolites for IVD application. The analysis of several NMNP structures, such as core-shell, porous, and 2D nanoparticles, elucidates their significant contribution to the enhancement of MS performance. Furthermore, the recent advancements in the application of NMNPs for diagnosing various systemic diseases are summarized. Finally, the prospects and challenges of NMNP-assisted MS for IVD are discussed. This review elucidates the roles of NMNPs' structure in enhancing MS-based metabolic detection and provides an overview of various IVD applications, consequently offering comprehensive insights for researchers and developers in this field.

Evolving landscape of detection and targeting miRNA/epigenetics for therapeutic strategies in ovarian cancer

Ovarian cancer (OC) accounts for the highest mortality rates among all gynecologic malignancies. The high mortality of OC is often associated with delayed detection, prolonged latency, enhanced metastatic potential, acquired drug resistance, and frequent recurrence. This review comprehensively explores key aspects of OC, including cancer diagnosis, mechanisms of disease resistance, and the pivotal role of epigenetic regulation, particularly by microRNAs (miRs) in cancer progression. We highlight the intricate regulatory mechanisms governing miR expression within the context of OC and the current status of epigenetic advancement in the therapeutic development and clinical trial progression. Through network analysis we elucidate the regulatory interactions between dysregulated miRs in OC and their targets which are involved in different signaling pathways. By exploring these interconnected facets and critical analysis, we endeavor to provide a nuanced understanding of the molecular dynamics underlying OC, its detection and shedding light on potential avenues for miRs and epigenetics-based therapeutic intervention and management strategies.

Unravelling the biological and clinical challenges of circulating tumour cells in epithelial ovarian carcinoma

Epithelial ovarian carcinoma (EOC) is the eighth most common cancer in women and the leading cause of gynaecological cancer death, predominantly due to the absence of effective screening tools, advanced stage at diagnosis, and high rates of recurrence. Circulating tumour cells (CTCs), a rare subset of tumour cells that disseminate from a tumour and migrate into the circulation, play a pivotal role in the metastatic cascade, and therefore hold promise as biomarkers for disease monitoring and prognostication. Exploring CTCs from liquid biopsies is an appealing approach for research and clinical practice, given it is minimally invasive, facilitates serial sampling and enables the capture of the entire spectrum of cancer cells circulating in the blood. The prognostic utility of CTC enumeration has been FDA-approved for clinical use in metastatic breast, prostate, and colorectal cancers. However, the unique biology of EOC, discussed herein, compounds the detection and characterisation complexities already inherent in CTC research, consequently hindering progress towards clinical applications. The aim of this review is to provide an overview of both the biological and clinical challenges encountered in harnessing the power of CTCs in EOC.

Biomarkers in high grade serous ovarian cancer

High-grade serous ovarian cancer (HGSC) is the most common subtype of ovarian cancer. HGSC patients typically present with advanced disease, which is often resistant to chemotherapy and recurs despite initial responses to therapy, resulting in the poor prognosis associated with this disease. There is a need to utilise biomarkers to manage the various aspects of HGSC patient care. In this review we discuss the current state of biomarkers in HGSC, focusing on the various available immunohistochemical (IHC) and blood-based biomarkers, which have been examined for their diagnostic, prognostic and theranostic potential in HGSC. These include various routine clinical IHC biomarkers such as p53, WT1, keratins, PAX8, Ki67 and p16 and clinical blood-borne markers and algorithms such as CA125, HE4, ROMA, RMI, ROCA, and others. We also discuss various components of the liquid biopsy as well as a number of novel IHC biomarkers and non-routine blood-borne biomarkers, which have been examined in various ovarian cancer studies. We also discuss the future of ovarian cancer biomarker research and highlight some of the challenges currently facing the field.

Platinum-based chemotherapy promotes antigen presenting potential in monocytes of patients with high-grade serous ovarian carcinoma

Ovarian cancer (OC) is the most lethal gynecologic malignancy worldwide. The major clinical challenge includes the asymptomatic state of the disease, making diagnosis possible only at advanced stages. Another OC complication is the high relapse rate and poor prognosis following the standard first-line treatment with platinum-based chemotherapy. At present, numerous clinical trials are being conducted focusing on immunotherapy in OC; nevertheless, there are still no FDA-approved indications. Personalized decision regarding the immunotherapy, including immune checkpoint blockade and immune cell-based immunotherapies, can depend on the effective antigen presentation required for the cytotoxic immune response. The major aim of our study was to uncover tumor-specific transcriptional and epigenetic changes in peripheral blood monocytes in patients with high-grade serous ovarian cancer (HGSOC). Another key point was to elucidate how chemotherapy can reprogram monocytes and how that relates to changes in other immune subpopulations in the blood. To this end, we performed single-cell RNA sequencing of peripheral blood mononuclear cells (PBMCs) from patients with HGSOC who underwent neoadjuvant chemotherapeutic treatment (NACT) and in treatment-naïve patients. Monocyte cluster was significantly affected by tumor-derived factors as well as by chemotherapeutic treatment. Bioinformatical analysis revealed three distinct monocyte subpopulations within PBMCs based on feature gene expression - CD14.Mn.S100A8.9hi, CD14.Mn.MHC2hi and CD16.Mn subsets. The intriguing result was that NACT induced antigen presentation in monocytes by the transcriptional upregulation of MHC class II molecules, but not by epigenetic changes. Increased MHC class II gene expression was a feature observed across all three monocyte subpopulations after chemotherapy. Our data also demonstrated that chemotherapy inhibited interferon-dependent signaling pathways, but activated some TGFb-related genes. Our results can enable personalized decision regarding the necessity to systemically re-educate immune cells to prime ovarian cancer to respond to anti-cancer therapy or to improve personalized prescription of existing immunotherapy in either combination with chemotherapy or a monotherapy regimen.

Machine Learning-Enhanced Extraction of Biomarkers for High-Grade Serous Ovarian Cancer from Proteomics Data

Comprehensive biomedical proteomic datasets are accumulating exponentially, warranting robust analytics to deconvolute them for identifying novel biological insights. Here, we report a strategic machine learning (ML)-based feature extraction workflow that was applied to unveil high-performing protein markers for high-grade serous ovarian carcinoma (HGSOC) from publicly available ovarian cancer tissue and serum proteomics datasets. Diagnosis of HGSOC, an aggressive form of ovarian cancer, currently relies on diagnostic methods based on tissue biopsy and/or non-specific biomarkers such as the cancer antigen 125 (CA125) and human epididymis protein 4 (HE4). Our newly developed ML-based approach enabled the identification of new serum proteomic biomarkers for HGSOC. The performance verification of these marker combinations using two independent cohorts affirmed their outperformance against known biomarkers for ovarian cancer including clinically used serum markers with >97% AUC. Our analysis also added novel biological insights such as enriched cancer-related processes associated with HGSOC.

MR Relaxometry for Discriminating Malignant Ovarian Cystic Tumors: A Prospective Multicenter Cohort Study

BACKGROUND

Endometriosis-associated ovarian cancer (EAOC) is a well-known type of cancer that arises from ovarian endometrioma (OE). OE contains iron-rich fluid in its cysts due to repeated hemorrhages in the ovaries. However, distinguishing between benign and malignant tumors can be challenging. We conducted a retrospective study on magnetic resonance (MR) relaxometry of cyst fluid to distinguish EAOC from OE and reported that this method showed good accuracy. The purpose of this study is to evaluate the accuracy of a non-invasive method in re-evaluating pre-surgical diagnosis of malignancy by a prospective multicenter cohort study.

METHODS

After the standard diagnosis process, the R2 values were obtained using a 3T system. Data on the patients were then collected through the Case Report Form (CRF). Between December 2018 and March 2023, six hospitals enrolled 109 patients. Out of these, 81 patients met the criteria required for the study.

RESULTS

The R2 values calculated using MR relaxometry showed good discriminating ability with a cut-off of 15.74 (sensitivity 80.6%, specificity 75.0%, AUC = 0.750, p < 0.001) when considering atypical or borderline tumors as EAOC. When atypical and borderline cases were grouped as OE, EAOC could be distinguished with a cut-off of 16.87 (sensitivity 87.0%, specificity 61.1%).

CONCLUSIONS

MR relaxometry has proven to be an effective tool for discriminating EAOC from OE. Regular use of this method is expected to provide significant insights for clinical practice.

Sindbis Virus Vaccine Platform: A Promising Oncolytic Virus-Mediated Approach for Ovarian Cancer Treatment

This review article provides a comprehensive overview of a novel Sindbis virus vaccine platform as potential immunotherapy for ovarian cancer patients. Ovarian cancer is the most lethal of all gynecological malignancies. The majority of high-grade serous ovarian cancer (HGSOC) patients are diagnosed with advanced disease. Current treatment options are very aggressive and limited, resulting in tumor recurrences and 50-60% patient mortality within 5 years. The unique properties of armed oncolytic Sindbis virus vectors (SV) in vivo have garnered significant interest in recent years to potently target and treat ovarian cancer. We discuss the molecular biology of Sindbis virus, its mechanisms of action against ovarian cancer cells, preclinical in vivo studies, and future perspectives. The potential of Sindbis virus-based therapies for ovarian cancer treatment holds great promise and warrants further investigation. Investigations using other oncolytic viruses in preclinical studies and clinical trials are also presented.

Investigation of Polymorphisms in Global Genome Repair Genes in Patients With Ovarian Cancer in the Turkish Population

INTRODUCTION

Ovarian cancer (OC) poses significant challenges due to its high mortality rate, particularly in advanced stages where symptoms may not be evident. DNA repair mechanisms, including nucleotide excision repair (NER), are crucial in maintaining genomic stability and preventing cancer. This study focuses on exploring the role of two NER-related genes, Xeroderma Pigmentosum Complementation Group C (XPC) and DNA Damage Binding Protein 2 (DDB2), in OC susceptibility.

OBJECTIVES

This study aims to investigate the association between variations in two NER-related genes, XPC rs2228001 and DDB2 rs830083, among a cohort of Turkish individuals with OC and control subjects.

METHODS

Genotyping of XPC rs2228001 and DDB2 rs830083 was performed on 103 OC patients and 104 control subjects from the Turkish population using the Fast Real-Time 7500 PCR platform from Applied Biosystems.

RESULTS

Individuals with the homozygous AA genotype of XPC rs2228001 exhibited a reduced likelihood of developing OC (OR 0.511; 95% CI 0.261 - 1.003; P-value 0.049), whereas those with the CC variant faced an elevated risk (OR = 2.32, 95% CI = 1.75-3.08; P-value 0.035). The presence of the A allele was associated with decreased OC occurrence (P-value = 0.035). Similarly, for DDB2 rs830083, individuals with the homozygous CG genotype had a diminished risk of OC (P-value 0.036), compared to those with the GG polymorphism (OR 1.895; 95% CI 1.033 - 3.476; P-value 0.038). Furthermore, the presence of the C allele was associated with a 1.89-fold decrease in the likelihood of OC.

CONCLUSION

These findings shed light on the genetic factors influencing OC susceptibility, emphasizing the importance of DNA repair systems in disease. Further research in larger and more diverse populations is warranted to validate these findings, facilitating precise risk assessment, and potentially guiding tailored treatment strategies for OC patients.

Meta-analysis of microarray data to determine gene indicators involved in the cisplatin resistance in ovarian cancer

BACKGROUND

Significant miss-expressed gene indicators contributing to cisplatin resistance in ovarian cancer have not been completely understood. It seems that several regulatory genes and signaling pathways are associated with the emergence of the chemo-resistant phenotype.

AIMS

Here, a meta-analysis approach was adopted to assess deregulated genes involved in relapse after the first line of chemotherapy (cisplatin).

METHODS AND RESULTS

To do so, six ovarian cancer libraries were gathered from GEO repository. Batch effect removal and quality assessment, and boxplots and PCA were performed using SVA and ggplot2 packages in R, respectively. Cisplatin-resistant and -sensitive ovarian cancer groups were compared with find genes with significant expression changes using linear regression models in the LIMMA R package. The significance threshold for DEGs was taken as adj p-value < .05 and - 1 > logFC > 1. A total of 261 genes were identified to have significant differential expression levels in the cisplatin-resistant versus cisplatin-sensitive group. Among the 10 top up-regulated and down-regulated genes, PITX2, SNCA, and EPHA7 (up), as well as TMEM98 (down) are indirect upstream regulators of PI3K/AKT signaling pathway, contributing greatly to the development of chemo-resistance in cancer via promoting cell proliferation, survival, and cell cycle progression as well as inhibiting apoptosis. Moreover, a comprehensive assessment of DEGs revealed the dysregulation of not only membrane ion channels KCa1.1, Kv4, and CACNB4, affecting cell excitability, proliferation, and apoptosis but also cell adhesion proteins COL4A6, EPHA3, and CD9, affecting the attachment of normal cells to ECM and apoptosis, introducing good options to reverse cisplatin resistance.

CONCLUSION

Our results predict and suggest that upstream regulators of PI3K/AKT signaling pathway, ion channels, and cell adhesion proteins play important roles in cisplatin resistance development in ovarian cancer.

Prognostic impact of early ctDNA dynamics during chemotherapy of metastatic cancer

Aim: Clinical utility of the dynamics of ctDNA is sparse. This study aimed at evaluating the prognostic impact of early ctDNA dynamics in patients with metastatic cancer treated with chemotherapy. Materials & methods: The ctDNA dynamics were evaluated in 595 patients with metastatic cancer using droplet digital PCR. Results: Patients with an increase in ctDNA after one treatment cycle (n = 73; 12.2%) had an overall survival of 5.6 months compared with 8.6 months in patients with stable or decreasing ctDNA (n = 328; 55.1%) and 21.0 months in patients with undetectable ctDNA (p < 0.001; hazard ratio: 0.47; 95% CI: 0.41-0.53). Conclusion: Early ctDNA dynamics hold important prognostic information and have great implications for evaluation with the perspective of a more individualized treatment strategy.

The Prognosis Predictive Score around Neo Adjuvant Chemotherapy (PPSN) Improves Diagnostic Efficacy in Predicting the Prognosis of Epithelial Ovarian Cancer Patients

BACKGROUND

Recent studies have shown that pretreatment inflammatory responses can predict prognosis. However, no reports have analyzed the combined effect of the inflammatory response with pre-treatment and post-neo adjuvant chemotherapy (NACT). This retrospective study aims to identify factors predicting prognosis and create a novel predictive scoring system.

METHODS

The study was conducted at our institution between June 2006 and March 2020. Demographic and clinicopathological data were collected from patients with advanced epithelial ovarian cancer who underwent neoadjuvant chemotherapy after sample collection by laparoscopic or laparotomy surgery, followed by interval debulking surgery. We created a scoring system, called the Predictive Prognosis Score around NACT (PPSN), using factors extracted from a receiver operating characteristic curve analysis. Univariate and multivariate analyses were conducted to assess the efficacy of PPSN in predicting progression-free survival and overall survival. Kaplan-Meier and log-rank tests were used to compare the PFS or OS rate.

RESULTS

Our study included 72 patients, with a cut-off value of four for the scoring system. Our analysis showed that high PPSN (≥4) significantly predicts poor prognosis. Moreover, CD3+ and CD8+ tumor-infiltrating lymphocytes with low PPSN (<4) showed higher aggregation than those with high PPSN (≥4) cases.

CONCLUSION

Our study shows that PPSN could be a useful prognostic tool for advanced EOC patients who undergo NACT followed by IDS.

Epidemiology and risk factors for ovarian cancer

Ovarian cancer is a complex disease, mostly observed in postmenopausal women, and is associated with poor survival rates. It is the sixth most common cancer and the fifth most common cause of death due to cancer among women in developed countries. Thus, despite representing less than one third of all gynaecologic cancers, deaths due to ovarian cancer account for more than two thirds of deaths due to gynaecologic cancers. Its prevalence is higher in Western Europe and Northern America than Asia and Africa. In sub-Saharan Africa, there is a considerably lower prevalence of ovarian cancer than other parts of Africa. Ovarian cancer is multifaceted, involving many factors, complex biological processes and unpredictable consequences. Unlike other female cancers that have early warning symptoms, ovarian cancer's symptoms are non-specific. As a result, ovarian cancers are normally undetected until advanced stages (III or IV). The major risk factors for ovarian cancer include older age, genetics, family history, hormone replacement therapy, nulliparity, and dietary fat. Controversial factors include obesity, infertility, talc powder, radiation exposure, fertility medications and in vitro fertilization. The current review discusses the aetiology, epidemiology and risk factors for ovarian cancer. Nevertheless, identification of the main risk factors for ovarian cancer may increase the awareness among women of the general population. This should help to decrease the incidence rate of ovarian cancer and increase the five-year survival rate.

Uterine Cavity Lavage Mutation Analysis in Lithuanian Ovarian Cancer Patients

BACKGROUND

Type II ovarian cancer (OC) is generally diagnosed at an advanced stage, translating into a poor survival rate. Current screening methods for OC have failed to demonstrate a reduction in mortality. The uterine lavage technique has been used to detect tumor-specific TP53 mutations from cells presumably shed from high-grade serous ovarian cancer (HGSOC). We aimed to pilot whether the detection of TP53 mutation in uterine cavity lavage can be used as a diagnostic method for HGSOC using an expanded gene panel.

METHODS

In this study 90, uterine lavage and 46 paired biopsy samples were analyzed using next-generation sequencing (NGS) targeting TP53 as well as five additional OC-related genes: BRCA1, BRCA2, PI3KCA, PTEN, and KRAS.

RESULTS

Uterine lavage was successfully applied to all patients, and 56 mutations were detected overall. TP53 mutations were detected in 27% (10/37) of cases of type HGSOC; BRCA1 and BRCA2 mutations were also frequent in this group (46%; 17/37). Overall concordance between tissue and liquid biopsy samples was 65.2%.

CONCLUSION

Uterine lavage TP53 mutations in combination with other biomarkers could be a useful tool for the detection of lowly invasive HGSOC.

The prognosis predictive score around primary debulking surgery (PPSP) improves diagnostic efficacy in predicting the prognosis of ovarian cancer

In recent years, the pretreatment inflammatory responses have proven to predict the prognosis, but no report exists analyzing the combined inflammatory response of the pre- and postsurgical treatment. The current study aims to extract the factors predicting the recurrence and create novel predictive scoring. This retrospective study was conducted at our institution between November 2006 and December 2020, with follow-up until September 2022. Demographic and clinicopathological data were collected from women who underwent primary debulking surgery. We created the scoring system named the prognosis predictive score around primary debulking surgery(PPSP) for progression-free survival(PFS). Univariate and multivariate analyses were performed to assess its efficacy in predicting PFS and overall survival(OS). Cox regression analyses were used to assess its time-dependent efficacy. Kaplan-Meier and the log-rank test were used to compare the survival rate. A total of 235 patients were included in the current study. The cut-off value of the scoring system was six. Multivariate analyses revealed that an advanced International Federation of Gynecology and Obstetrics(FIGO) stage (p < 0.001 for PFS; p = 0.038 for OS), the decreased white blood cell count difference (p = 0.026 for PFS) and the high-PPSP (p = 0.004 for PFS; p = 0.002 for OS) were the independent prognostic factors. Cox regression analysis also supported the above results. The PPSP showed good prognostic efficacy not only in predicting the PFS but also OS of ovarian cancer patients comparable to FIGO staging.

The application of circulating tumor cell and cell-free DNA liquid biopsies in ovarian cancer

Ovarian cancer is the deadliest gynecological cancer. 70% of the cases are diagnosed at late stages with already developed metastases due to the absence of easily noticeable symptoms. Early-stage ovarian cancer has a good prognosis with a 5-year survival rate reaching 95%, hence the identification of effective biomarkers for early diagnosis is important. Advances in liquid biopsy-based methods can have a significant impact not just on the development of an efficient screening strategy, but also in clinical decision-making with additional molecular profiling and genetic alterations linked to therapy resistance. Despite the well-known advantages of liquid biopsy, there are still challenges that need to be addressed before its routine use in clinical practice. Various liquid biopsy-based biomarkers have been investigated in ovarian cancer; however, in this review, we are concentrating on the current use of cell-free DNA (cfDNA) and circulating tumor cells (CTCs) in disease management, focusing on their emerging importance in clinical practice. We also discuss the technical aspects of these workflows. The analysis of cfDNA is often chosen for the detection of mutations, copy number aberrations, and DNA methylation changes, whereas CTC analysis provides a unique opportunity to study whole cells, thus allowing DNA, RNA, and protein-based molecular profiling as well as in vivo studies. Combined solutions which merge the strengths of cfDNA and CTC approaches should be developed to maximize the potential of liquid biopsy technology.

The Endometriotic Neoplasm Algorithm for Risk Assessment (e-NARA) Index Sheds Light on the Discrimination of Endometriosis-Associated Ovarian Cancer from Ovarian Endometrioma

BACKGROUND

Magnetic resonance (MR) relaxometry provides a noninvasive tool to discriminate endometriosis-associated ovarian cancer (EAOC) from ovarian endometrioma (OE) with high accuracy. However, this method has a limitation in discriminating malignancy in clinical use because the R2 value depends on the device manufacturer and repeated imaging is unrealistic. The current study aimed to reassess the diagnostic accuracy of MR relaxometry and investigate a more powerful tool to distinguish EAOC from OE.

METHODS

This retrospective study was conducted at our institution from December, 2012, to May, 2022. A total of 150 patients were included in this study. Patients with benign ovarian tumors (n = 108) mainly received laparoscopic surgery, and cases with suspected malignancy (n = 42) underwent laparotomy. Information from a chart review of the patients' medical records was collected.

RESULTS

A multiple regression analysis revealed that the age, the tumor diameter, and the R2 value were independent malignant predicting factors. The endometriotic neoplasm algorithm for risk assessment (e-NARA) index provided high accuracy (sensitivity, 85.7%; specificity, 87.0%) to discriminate EAOC from OE.

CONCLUSIONS

The e-NARA index is a reliable tool to assess the probability of malignant transformation of endometrioma.

Detection of ESR1 Mutations in Primary Tumors and Plasma Cell-Free DNA in High-Grade Serous Ovarian Carcinoma Patients

Simple Summary

In the present study we evaluated the frequency and the clinical relevance of ESR1 mutations in high-grade serous ovarian cancer (HGSOC). Drop-off droplet digital PCR (ddPCR) was first used to screen for ESR1 mutations in primary tumors (formalin-fixed paraffin-embedded, FFPEs) from HGSOC patients and plasma cell-free DNA (cfDNA) samples from advanced and metastatic ovarian cancer patients. We further used the recently developed ESR1-NAPA assay to detect individual ESR1 mutations in drop-off ddPCR-positive samples. We report for the first time the presence of ESR1 mutations in 15% of FFPEs and in 13.8% of plasma cfDNA samples from advanced and metastatic ovarian cancer patients.

Abstract

ESR1 mutations have been recently associated with resistance to endocrine therapy in metastatic breast cancer and their detection has led to the development and current evaluation of novel, highly promising therapeutic strategies. In ovarian cancer there have been just a few reports on the presence of ESR1 mutations. The aim of our study was to evaluate the frequency and the clinical relevance of ESR1 mutations in high-grade serous ovarian cancer (HGSOC). Drop-off droplet digital PCR (ddPCR) was first used to screen for ESR1 mutations in 60 primary tumors (FFPEs) from HGSOC patients and in 80 plasma cell-free DNA (cfDNA) samples from advanced and metastatic ovarian cancer patients. We further used our recently developed ESR1-NAPA assay to identify individual ESR1 mutations in drop-off ddPCR-positive samples. We report for the first time the presence of ESR1 mutations in 15% of FFPEs and in 13.8% of plasma cfDNA samples from advanced and metastatic ovarian cancer patients. To define the clinical significance of this finding, our results should be further validated in a large and well-defined cohort of ovarian cancer patients.

A bespoke target selection tool to guide biomarker discovery in tubo-ovarian cancer

Introduction

Cancers presenting at advanced stages inherently have poor prognosis. High grade serous carcinoma (HGSC) is the most common and aggressive form of tubo-ovarian cancer. Clinical tests to accurately diagnose and monitor this condition are lacking. Hence, development of disease-specific tests are urgently required.

Methods

The molecular profile of HGSC during disease progression was investigated in a unique patient cohort. A bespoke data browser was developed to analyse gene expression and DNA methylation datasets for biomarker discovery. The Ovarian Cancer Data Browser (OCDB) is built in C# with a.NET framework using an integrated development environment of Microsoft Visual Studio and fast access files (.faf). The graphical user interface is easy to navigate between four analytical modes (gene expression; methylation; combined gene expression and methylation data; methylation clusters), with a rapid query response time. A user should first define a disease progression trend for prioritising results. Single or multiomics data are then mined to identify probes, genes and methylation clusters that exhibit the desired trend. A unique scoring system based on the percentage change in expression/methylation between disease stages is used. Results are filtered and ranked using weighting and penalties.

Results

The OCDB’s utility for biomarker discovery is demonstrated with the identified target OSR2. Trends in OSR2 repression and hypermethylation with HGSC disease progression were confirmed in the browser samples and an independent cohort using bioassays. The OSR2 methylation biomarker could discriminate HGSC with high specificity (95%) and sensitivity (93.18%).

Conclusions

The OCDB has been refined and validated to be an integral part of a unique biomarker discovery pipeline. It may also be used independently to aid identification of novel targets. It carries the potential to identify further biomarker assays that can reduce type I and II errors within clinical diagnostics.

The Comparison of Three Predictive Indexes to Discriminate Malignant Ovarian Tumors from Benign Ovarian Endometrioma: The Characteristics and Efficacy

This study aimed to evaluate the prediction efficacy of malignant transformation of ovarian endometrioma (OE) using the Copenhagen Index (CPH-I), the risk of ovarian malignancy algorithm (ROMA), and the R2 predictive index. This retrospective study was conducted at the Department of Gynecology, Nara Medical University Hospital, from January 2008 to July 2021. A total of 171 patients were included in the study. In the current study, cases were divided into three cohorts: pre-menopausal, post-menopausal, and a combined cohort. Patients with benign ovarian tumor mainly received laparoscopic surgery, and patients with suspected malignant tumors underwent laparotomy. Information from a review chart of the patients’ medical records was collected. In the combined cohort, a multivariate analysis confirmed that the ROMA index, the R2 predictive index, and tumor laterality were extracted as independent factors for predicting malignant tumors (hazard ratio (HR): 222.14, 95% confidence interval (CI): 22.27−2215.50, p < 0.001; HR: 9.80, 95% CI: 2.90−33.13, p < 0.001; HR: 0.15, 95% CI: 0.03−0.75, p = 0.021, respectively). In the pre-menopausal cohort, a multivariate analysis confirmed that the CPH index and the R2 predictive index were extracted as independent factors for predicting malignant tumors (HR: 6.45, 95% CI: 1.47−28.22, p = 0.013; HR: 31.19, 95% CI: 8.48−114.74, p < 0.001, respectively). Moreover, the R2 predictive index was only extracted as an independent factor for predicting borderline tumors (HR: 45.00, 95% CI: 7.43−272.52, p < 0.001) in the combined cohort. In pre-menopausal cases or borderline cases, the R2 predictive index is useful; while, in post-menopausal cases, the ROMA index is better than the other indexes.

Diagnostic potential of nanoparticle aided assays for MUC16 and MUC1 glycovariants in ovarian cancer

Our study reports the discovery and evaluation of nanoparticle aided sensitive assays for glycovariants of MUC16 and MUC1 in a unique collection of paired ovarian cyst fluids and serum samples obtained at or prior to surgery for ovarian carcinoma suspicion. Selected glycovariants and the immunoassays for CA125, CA15‐3 and HE4 were compared and validated in 347 cyst fluid and serum samples. Whereas CA125 and CA15‐3 performed poorly in cyst fluid to separate carcinoma and controls, four glycovariants including MUC16^MGL, MUC16^STn, MUC1^STn and MUC1^Tn provided highly improved separations. In serum, the two STn glycovariants outperformed conventional CA125, CA15‐3 and HE4 assays in all subcategories analyzed with main benefits obtained at high specificities and at postmenopausal and early‐stage disease. Serum MUC16^STn performed best at high specificity (90%‐99%), but sensitivity was also improved by the other glycovariants and CA15‐3. The highly improved specificity, excellent analytical sensitivity and robustness of the nanoparticle assisted glycovariant assays carry great promise for improved identification and early detection of ovarian carcinoma in routine differential diagnostics.