Development and validation for prognostic nomogram of epithelial ovarian cancer recurrence based on circulating tumor cells and epithelial-mesenchymal transition

The correlation study between TOP2A gene expression in circulating tumor cells and chemotherapeutic drug resistance of patients with breast cancer

BACKGROUND

Patients with breast cancer (BC) at advanced stages have poor outcomes because of high rate of recurrence and metastasis. Biomarkers for predicting prognosis remain to be explored. This study aimed to evaluate the relationships between circulating tumor cells (CTCs) and outcomes of BC patients.

PATIENTS AND METHODS

A total of 50 female were enrolled in this study. Their diagnoses were determined by clinical characteristics, image data, and clinical pathology. CTC subtypes and TOP2A gene expression on CTCs were detected by CanPatrol™ technology and triple color in situ RNA hybridization (RNA-ISH), which divided into epithelial CTCs (eCTCs), mesenchymal CTCs (MCTCs), and hybrid CTCs (HCTCs) based on their surface markers. Hormone receptor, including estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER-2) expression, was measured by immunohistochemistry (IHC) method before treatment. The risk factors for predicting recurrence and metastasis were calculated by COX risk regression model. The progression-free survival (PFS) of patients was determined using Kaplan-Meier survival curve.

RESULTS

The patients with a large tumor size (≥ 3 cm) and advanced tumor node metastasis (TNM) stages had high total CTCs (TCTCs) (P < 0.05). These patients also had high TOP2A expression level. COX risk regression analysis indicated that TOP2A expression levels in TCTCs, ER + , HER-2 + , and TNM stages were critical risk factors for recurrence and metastasis of patients (P < 0.05). The PFS of patients with ≥ 5 TCTCs, ≥ 3 HCTCs, and positive TOP2A expression in ≥ 3 TCTCs was significantly longer than that in patient with < 5 TCTCs, < 3 HCTCs, and TOP2A expression in < 3 TCTCs (P < 0.05). In contrast, the PFS of patients with positive hormone receptors (ER + , PR + , HER-2 +) also was dramatically lived longer than that in patients with negative hormone receptor expression.

CONCLUSIONS

High TCTC, HCTCs, and positive TOP2A gene expression on CTCs were critical biomarkers for predicting outcomes of BC patients. Positive hormone receptor expression in BC patients has significant favor PFS.

Closing the diagnostic gap: Liquid biopsy potential to transform ovarian cancer outcomes in sub-Saharan Africa

Ovarian cancer presents a significant health challenge in sub-Saharan Africa (SSA), where late-stage diagnosis contributes to high mortality rates. This diagnostic gap arises from limited resources, poor healthcare infrastructure, and a lack of awareness about the disease. However, a potential game-changer is emerging in the form of liquid biopsy (LB), a minimally invasive diagnostic method. This paper analyses the current diagnostic gap in ovarian cancer in SSA, highlighting the socio-economic, cultural, and infrastructural factors that hinder early diagnosis and treatment. It discusses the challenges and potential of LB in the context of SSA, emphasizing its cost-effectiveness and adaptability to resource-limited settings. The transformative potential of LB in SSA is promising, offering a safer, more accessible, and cost-effective approach to ovarian cancer diagnosis. This paper provides recommendations for future directions, emphasizing the need for research, infrastructure development, stakeholder engagement, and international collaboration. By recognizing the transformative potential of LB and addressing the diagnostic gap, we can pave the way for early detection, improved treatment, and better outcomes for ovarian cancer patients in SSA. This paper sheds light on a path toward better healthcare access and equity in the region.

High-Grade Serous Ovarian Cancer-A Risk Factor Puzzle and Screening Fugitive

High-grade serous ovarian cancer (HGSOC) is the most lethal tumor of the female genital tract. Despite extensive studies and the identification of some precursor lesions like serous tubal intraepithelial cancer (STIC) or the deviated mutational status of the patients (BRCA germinal mutation), the pathophysiology of HGSOC and the existence of particular risk factors is still a puzzle. Moreover, a lack of screening programs results in delayed diagnosis, which is accompanied by a secondary chemo-resistance of the tumor and usually results in a high recurrence rate after the primary therapy. Therefore, there is an urgent need to identify the substantial risk factors for both predisposed and low-risk populations of women, as well as to create an economically and clinically justified screening program. This paper reviews the classic and novel risk factors for HGSOC and methods of diagnosis and prediction, including serum biomarkers, the liquid biopsy of circulating tumor cells or circulating tumor DNA, epigenetic markers, exosomes, and genomic and proteomic biomarkers. The novel future complex approach to ovarian cancer diagnosis should be devised based on these findings, and the general outcome of such an approach is proposed and discussed in the paper.

Purification of Circulating Tumor Cells Based on Multiantibody-Modified Magnetic Nanoparticles and Molecular Analysis toward Epithelial Ovarian Cancer Detection

Circulating tumor cells (CTCs) are valuable circulating biomarkers of cancer, which carry primary tumor information and may provide real-time assessment of tumor status as well as treatment response in cancer patients. Herein, we developed a novel assay for accurate diagnosis and dynamic monitoring of epithelial ovarian cancer (EOC) using CTC RNA analysis. Multiantibody-modified magnetic nanoparticles were prepared for purification of EOC CTCs from whole blood samples of clinical patients. Subsequently, nine EOC-specific mRNAs of purified CTCs were quantified using droplet digital PCR. The EOC CTC Score was generated using a multivariate logistic regression model for each sample based on the transcripts of the nine genes. This assay exhibited a distinguishing diagnostic performance for the detection of EOC (n = 17) from benign ovarian tumors (n = 30), with an area under the receiver operating characteristic curve (AUC) of 0.96 (95% CI = 0.91-1.00). Moreover, dynamic changes of the EOC CTC Score were observed in patients undergoing treatment, demonstrating the potential of the assay for monitoring EOC. In conclusion, we present an accurate assay for the diagnosis and monitoring of EOC via CTC RNA analysis, and the results suggest that it may provide a promising solution for the detection and treatment response assessment of EOC.

The Role of Circulating Tumor Cells in Ovarian Cancer Dissemination

Metastatic ovarian cancer is the main reason for treatment failures and consequent deaths. Ovarian cancer is predisposed to intraperitoneal dissemination. In comparison to the transcoelomic route, distant metastasis via lymph vessels and blood is less common. The mechanisms related to these two modes of cancer spread are poorly understood. Nevertheless, the presence of tumor cells circulating in the blood of OC patients is a well-established phenomenon confirming the significant role of lymphatic and hematogenous metastasis. Thus, the detection of CTCs may provide a minimally invasive tool for the identification of ovarian cancer, monitoring disease progression, and treatment effectiveness. This review focuses on the biology of ovarian CTCs and the role they may play in cancer diagnosis and therapy.

Molecular Management of High-Grade Serous Ovarian Carcinoma

High-grade serous ovarian carcinoma (HGSOC) represents the most common form of epithelial ovarian carcinoma. The absence of specific symptoms leads to late-stage diagnosis, making HGSOC one of the gynecological cancers with the worst prognosis. The cellular origin of HGSOC and the role of reproductive hormones, genetic traits (such as alterations in P53 and DNA-repair mechanisms), chromosomal instability, or dysregulation of crucial signaling pathways have been considered when evaluating prognosis and response to therapy in HGSOC patients. However, the detection of HGSOC is still based on traditional methods such as carbohydrate antigen 125 (CA125) detection and ultrasound, and the combined use of these methods has yet to support significant reductions in overall mortality rates. The current paradigm for HGSOC management has moved towards early diagnosis via the non-invasive detection of molecular markers through liquid biopsies. This review presents an integrated view of the relevant cellular and molecular aspects involved in the etiopathogenesis of HGSOC and brings together studies that consider new horizons for the possible early detection of this gynecological cancer.

Circulating tumor cell isolation for cancer diagnosis and prognosis

Circulating tumor cells (CTCs) are tumor cells that shed from the primary tumor and intravasate into the peripheral blood circulation system responsible for metastasis. Sensitive detection of CTCs from clinical samples can serve as an effective tool in cancer diagnosis and prognosis through liquid biopsy. Current CTC detection technologies mainly reply on the biomarker-mediated platforms including magnetic beads, microfluidic chips or size-sensitive microfiltration which can compromise detection sensitivity due to tumor heterogeneity. A more sensitive, biomarker independent CTCs isolation technique has been recently developed with the surface-charged superparamagnetic nanoprobe capable of different EMT subpopulation CTC capture from 1 mL clinical blood. In this review, this new strategy is compared with the conventional techniques on biomarker specificity, impact of protein corona, effect of glycolysis on cell surface charge, and accurate CTC identification. Correlations between CTC enumeration and molecular profiling in clinical blood and cancer prognosis are provided for clinical cancer management.

A meta-analysis of the prognostic value of circulating tumor cells in ovarian cancer

OBJECTIVE

To evaluate the prognostic value of circulating tumor cells (CTCs) in ovarian cancer.

METHODS

Chinese databases (Wanfang, Cqvip, CNKI) and English databases (PubMed, Web of Science, Embase, SinoMed, Cochrane Library) were retrieved to collect relevant studies on CTCs evaluation of ovarian cancer prognosis. Data were extracted to analyze the effect of CTCs on the overall survival (OS) and progression-free survival (PFS) of patients, and a meta-analysis was performed using Stata 15 software.

RESULTS

Nineteen studies were included in this meta-analysis. The results showed that ovarian cancer patients with positive CTCs had a shorter OS and higher death rate, (HR=1.57, 95% CI: 1.30, 1.84), a shorter PFS and an increased risk of disease progression (HR=1.29, 95% CI: 1.04, 1.54) compared with patients with negative CTCs. Subgroup analysis showed that the HRs for death and disease progression were higher in CTCs-positive patients after treatment than those patients with negative CTCs (P<0.05).

CONCLUSION

CTCs detection has a high application value in the prognosis assessment of ovarian cancer.

Identification and validation of a seven m6A-related lncRNAs signature predicting prognosis of ovarian cancer

Background

Long non-coding RNAs (lncRNAs) play an important role in angiogenesis, immune response, inflammatory response and tumor development and metastasis. m6 A (N6—methyladenosine) is one of the most common RNA modifications in eukaryotes. The aim of our research was to investigate the potential prognostic value of m6A-related lncRNAs in ovarian cancer (OC).

Methods

The data we need for our research was downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Pearson correlation analysis between 21 m6A regulators and lncRNAs was performed to identify m6A-related lncRNAs. Univariate Cox regression analysis was implemented to screen for lncRNAs with prognostic value. A least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression analyses was used to further reduct the lncRNAs with prognostic value and construct a m6A-related lncRNAs signature for predicting the prognosis of OC patients.

Results

Two hundred seventy-five m6A-related lncRNAs were obtained using pearson correlation analysis. 29 m6A-related lncRNAs with prognostic value was selected through univariate Cox regression analysis. Then, a seven m6A-related lncRNAs signature was identified by LASSO Cox regression. Each patient obtained a riskscore through multivariate Cox regression analyses and the patients were classified into high-and low-risk group using the median riskscore as a cutoff. Kaplan–Meier curve revealed that the patients in high-risk group have poor outcome. The receiver operating characteristic curve revealed that the predictive potential of the m6A-related lncRNAs signature for OC was powerful. The predictive potential of the m6A-related lncRNAs signature was successfully validated in the GSE9891, GSE26193 datasets and our clinical specimens. Multivariate analyses suggested that the m6A-related lncRNAs signature was an independent prognostic factor for OC patients. Moreover, a nomogram based on the expression level of the seven m6A-related lncRNAs was established to predict survival rate of patients with OC. Finally, a competing endogenous RNA (ceRNA) network associated with the seven m6A-related lncRNAs was constructed to understand the possible mechanisms of the m6A-related lncRNAs involed in the progression of OC.

Conclusions

In conclusion, our research revealed that the m6A-related lncRNAs may affect the prognosis of OC patients and identified a seven m6A-related lncRNAs signature to predict the prognosis of OC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09591-4.

Potential clinical utility of liquid biopsies in ovarian cancer

Ovarian cancer (OC) is the most lethal gynecologic malignancy worldwide. One of the main challenges in the management of OC is the late clinical presentation of disease that results in poor survival. Conventional tissue biopsy methods and serological biomarkers such as CA-125 have limited clinical applications. Liquid biopsy is a novel sampling method that analyzes distinctive tumour components released into the peripheral circulation, including circulating tumour DNA (ctDNA), circulating tumour cells (CTCs), cell-free RNA (cfRNA), tumour-educated platelets (TEPs) and exosomes. Increasing evidence suggests that liquid biopsy could enhance the clinical management of OC by improving early diagnosis, predicting prognosis, detecting recurrence, and monitoring response to treatment. Capturing the unique tumour genetic landscape can also guide treatment decisions and the selection of appropriate targeted therapies. Key advantages of liquid biopsy include its non-invasive nature and feasibility, which allow for serial sampling and longitudinal monitoring of dynamic tumour changes over time. In this review, we outline the evidence for the clinical utility of each liquid biopsy component and review the advantages and current limitations of applying liquid biopsy in managing ovarian cancer. We also highlight future directions considering the current challenges and explore areas where more studies are warranted to elucidate its emerging clinical potential.

Development and External Validation of a Novel Model for Predicting Postsurgical Recurrence and Overall Survival After Cytoreductive R0 Resection of Epithelial Ovarian Cancer

Purpose

Treatment of epithelial ovarian cancer is evolving towards personalization and precision, which require patient-specific estimates of overall survival (OS) and progression-free survival (PFS).

Patients and Methods

Medical records of 1173 patients who underwent debulking surgery in our center were comprehensively reviewed and randomly allocated into a derivation cohort of 879 patients and an internal validation cohort of 294 patients. Five hundred and seventy-seven patients from the other three cancer centers served as the external validation cohort. A novel nomogram model for PFS and OS was constructed based on independent predictors identified by multivariable Cox regression analysis. The predictive accuracy and discriminative ability of the model were measured using Harrell’s concordance index (C-index) and calibration curve.

Results

The C-index values were 0.82 (95% CI: 0.76–0.88) and 0.84 (95% CI: 0.78–0.90) for the PFS and OS models, respectively, substantially higher than those obtained with the FIGO staging system and most nomograms reported for use in epithelial ovarian cancer. The nomogram score could clearly classify the patients into subgroups with different risks of recurrence or postoperative mortality. The online versions of our nomograms are available at https://eocnomogram.shinyapps.io/eocpfs/ and https://eocnomogram.shinyapps.io/eocos/.

Conclusion

A externally validated nomogram predicting OS and PFS in patients after R0 reduction surgery was established using a propensity score matching model. This nomogram may be useful in estimating individual recurrence risk and guiding personalized surveillance programs for patients after surgery, and it could potentially aid clinical decision-making or stratification for clinical trials.

Circulating tumor cells as a "real-time liquid biopsy": Recent advances and the application in ovarian cancer

Even with the latest advances in technology, the treatment of ovarian cancer remains a big challenge because it is typically diagnosed at advanced stage, is prone to early relapse in spite of aggressive treatment and has an extremely poor prognosis. Circulating tumor cells (CTCs) can be used as a non-invasive "real-time liquid biopsy", which has shown the value of diagnosis, assessment of prognosis and chemoresistance, and detection of small residual tumors on ovarian cancer. This review article provides an overview on recent research on CTCs in ovarian cancer, with special focus on the clinical application of CTC tests.

Multi-Marker Immunofluorescent Staining and PD-L1 Detection on Circulating Tumour Cells from Ovarian Cancer Patients

Simple Summary

Circulating tumour cells (CTCs) have the potential to serve as a rich source of information for cancer diagnostic and therapeutic decisions. To fully exploit this minimally invasive diagnostic resource requires techniques that aid in enriching heterogenous populations of CTCs and markers to efficiently characterise these cells as tumour derived. In the present study we eva-luated the microfluidic enrichment of CTCs and a multi-marker staining methodology for the identification of heterogeneous CTCs in ovarian cancer (OC) patients and evaluation of PD-L1 expression. We showed, for the first time, the existence of hybrid CTCs with an epithelial/mesenchymal phenotype and their association with PD-L1 in OC. Incorporation of this method in future clinical trials can help predict immunotherapy responsiveness in OC patients.

Abstract

Detection of ovarian cancer (OC) circulating tumour cells (CTCs) is primarily based on targeting epithelial markers, thus failing to detect mesenchymal tumour cells. More importantly, the immune checkpoint inhibitor marker PD-L1 has not been demonstrated on CTCs from OC patients. An antibody staining protocol was developed and tested using SKOV-3 and OVCA432 OC cell lines. We targeted epithelial (cytokeratin (CK) and EpCAM), mesenchymal (vimentin), and OC-specific (PAX8) markers for detection of CTCs, and CD45/16 and CD31 were used for the exclusion of white blood and vascular endothelial cells, respectively. PD-L1 was used for CTC characterisation. CTCs were enriched using the Parsortix™ system from 16 OC patients. Results revealed the presence of CTCs in 10 (63%) cases. CTCs were heterogeneous, with 113/157 (72%) cells positive for CK/EpCAM (epithelial marker), 58/157 (37%) positive for vimentin (mesenchymal marker), and 17/157 (11%) for both (hybrid). PAX8 was only found in 11/157 (7%) CTCs. In addition, 62/157 (39%) CTCs were positive for PD-L1. Positivity for PD-L1 was significantly associated with the hybrid phenotype when compared with the epithelial (p = 0.007) and mesenchymal (p = 0.0009) expressing CTCs. Characterisation of CTC phenotypes in relation to clinical outcomes is needed to provide insight into the role that epithelial to mesenchymal plasticity plays in OC and its relationship with PD-L1.

Liquid biopsy for ovarian cancer using circulating tumor cells: Recent advances on the path to precision medicine

Ovarian cancer (OC) is the most lethal gynecologic malignance worldwide. Considering its metastasis nature, oncologists shift focus towards circulating tumor cells (CTCs), a progenitor that originates from primary tumor and undergoes morphologic/genetic alterations to enter bloodstream and invade nearby tissues. Mountains of evidence suggested that CTCs could provide deep insights into genomic, transcriptomic, and proteomic profiling of OC metastatic cascades. To pave the way for precision medicine, researchers exert great efforts to develop isolation/detection methodologies and construct CTCs-derived propagation platforms, including traditional cell cultures, patient-derived xenografts (PDXs), and organoids. From bench to bedside, CTCs provide minimally-invasive means to inform early diagnosis, predict prognosis, and guide treatment decisions. This review shined a spotlight on biology, detection technologies, and propagation platforms for CTCs. Of note, we also reviewed clinical applications of CTCs in liquid biopsy-based personalized cancer treatment and critically appraised limitations in routine clinical practice on the path to precision medicine.

Current methodologies to detect circulating tumor cells: a focus on ovarian cancer

Identification of circulating tumor cells (CTC) in liquid biopsies opens a window of opportunities for the optimization of clinical management of oncologic patients. In ovarian cancer (OC), which involves atypical routes of metastatic spread, CTC analyses may also offer novel insights about the mechanisms behind malignant progression of the disease. However, current methodologies struggle to precisely define CTC number in the peripheral blood of OC patients, and the isolation of viable cells for further characterization is still challenging. The biggest limitation is the lack of methodological standardization for OC CTC detection, preventing comprehensive definition of their clinical potential required for the transfer to practice. Here we describe and compare methods for CTC analysis that have been implemented for OC thus far, discussing pros, cons and improvements needed. We identify biophysical separation approaches as optimal for CTC enrichment. On the other hand, the identification of specific tumor antigens or gene transcripts, despite displaying drawbacks related to tumor heterogeneity, still remains the best approach for OC CTC detection.

Artificial Intelligence Based on Blood Biomarkers Including CTCs Predicts Outcomes in Epithelial Ovarian Cancer: A Prospective Study

Objective

We aimed to develop an ovarian cancer-specific predictive framework for clinical use platinum-sensitivity and prognosis using machine learning methods based on multiple biomarkers, including circulating tumor cells (CTCs).

Patients and Methods

We enrolled 156 epithelial ovarian cancer (EOC) patients, randomly assigned into the training and validation cohorts. Eight machine learning classifiers, including Random Forest (RF), Support Vector Machine, Gradient Boosting Machine, Conditional RF, Neural Network, Naive Bayes, Elastic Net, and Logistic Regression, were used to derive predictive information from 11 peripheral blood parameters, including CTCs. Through the advanced CanPatrol CTC-enrichment technique, we detect CTCs and classify them into subpopulations: epithelial, mesenchymal, and hybrids. Survival curves were generated by Kaplan–Meier method and calculated through the Log rank test.

Results

Machine learning techniques, especially the Random Forest classifier, were superior to conventional regression-based analyses in predicting multiple clinical parameters related to EOC. The values for the receiver operating characteristic (ROC) curve for segregating EOC with advanced clinical stages and platinum-sensitivity were 0.796 (95% CI, 0.727–0.866) and 0.809 (95% CI, 0.742–0.876), respectively. Stepwise, we used the unsupervised clustering analysis to identify EOC subgroups with significantly worse overall survival (OS), especially in the advanced-stage group with the p-value of 0.0018 (HR, 2.716; 95% CI, 1.602–4.605) for progression-free survival (PFS) and 0.0037 (HR, 2.359; 95% CI, 1.752–6.390) for overall survival (OS).

Conclusion

Machine learning systems could provide risk stratification for EOC patients before initial intervention through blood variables, including circulating tumor cells. The predictive algorithms could facilitate personalized treatment options through promising pre-treatment stratification of EOC patients.

Trial registration

ChiCTR-DDD-16009601 Registered 25 October 2016.