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

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.

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.