Clinical Use of Cancer Biomarkers in Epithelial Ovarian Cancer: Updated Guidelines From the European Group on Tumor Markers

The diagnostic performance of CA-125 for the detection of ovarian cancer in women from different ethnic groups: a cohort study of English primary care data

BACKGROUND

CA-125 testing is a recommended first line investigation for women presenting with possible symptoms of ovarian cancer in English primary care, to help determine whether further investigation for ovarian cancer is needed. It is currently not known how well the CA-125 test performs in ovarian cancer detection for patients from different ethnic groups.

METHODS

A retrospective cohort study utilising English primary care data linked to the national cancer registry was undertaken. Women aged ≥ 40 years with a CA-125 test between 2010 and 2017 were included. Logistic regression predicted one-year ovarian cancer incidence by ethnicity, adjusting for age, deprivation status, and comorbidity score. The estimated incidence of ovarian cancer by CA-125 level was modelled for each ethnic group using restricted cubic splines.

RESULTS

The diagnostic performance of CA-125 differed for women from different ethnicities. In an unadjusted analysis, predicted CA-125 levels for Asian and Black women were higher than White women at corresponding probabilities of ovarian cancer. The higher PPVs for White women compared to Asian or Black women were eliminated by inclusion of covariates.

CONCLUSION

The introduction of ethnicity-specific thresholds may increase the specificity and PPVs of CA-125 in ovarian cancer detection at the expense of sensitivity, particularly for Asian and Black women. As such, we cannot recommend the use of ethnicity-specific thresholds for CA-125.

Serum miRNA improves the accuracy of a multivariate index assay for triage of an adnexal mass

OBJECTIVE

To determine whether a multimodal assay combining serum microRNA with protein biomarkers and metadata improves triage assessment of an adnexal mass.

METHODS

Serum samples from 468 training subjects (191 cancer cases and 277 benign adnexal mass controls or healthy controls) were analyzed for seven protein biomarkers and 180 miRNA. Circulating analyte data were combined with age and menopausal status (metadata) into a neural network model to classify samples as cases or controls. Forward regression with ten-fold cross-validation minimized the dimensionality of the model while maximizing linear separation between cases and controls. Model validation proceeded using both internal (44 cases and 56 controls) and external validation sets (51 cases and 59 controls).

RESULTS

The total study population comprised 678 subjects, including 286 cases and 392 controls. Overall, 290 (43%) of the subjects were premenopausal. A panel of 10 miRNA delivered optimal performance when combined with protein and metadata features. The combined model improved the Receiver Operator Characteristic Area Under the Curve (ROC AUC) on the internal (AUC = 0.9; 95% CI 0.81-0.95) and external validation sets (AUC = 0.95; 95% CI 0.90-0.98) compared to miRNA alone or proteins plus metadata (without miRNA). On external validation, the combined model offered 92% sensitivity at 80% specificity overall, with 80% and 100% sensitivity for early and late-stage cancers, respectively, including 78% sensitivity for early-stage, serous ovarian cancers and 82% sensitivity for early-stage, non-serous cancers.

CONCLUSIONS

A multimodal assay combining miRNA with protein biomarkers, age, and menopausal status improves surgical triage of an adnexal mass.

Diagnostic performance of a modified O-RADS classification system for adnexal lesions incorporating clinical features

PURPOSE

To compare the diagnostic efficacy of the Ovarian-Adnexal Reporting and Data System (O-RADS) MRI score with that of the modified O-RADS score on the basis of a simplified contrast-enhanced (CE) MRI protocol in characterizing adnexal masses with solid tissue. The added value of clinical features was evaluated to improve the ability of the scoring system to classify adnexal masses.

METHODS

A total of 124 patients with 124 adnexal lesions containing solid tissue were included in this two-center retrospective study. Among them, there were 40 benign lesions (40/124, 32.3%) and 84 were malignant lesions (84/124, 67.7%). Three radiologists independently reviewed the images and assigned the O-RADS MRI score and the modified O-RADS score for each adnexal mass. Histopathology was used as the reference standard. The diagnostic efficacy of the two scoring methods was compared. Univariate and multivariate logistic regression were performed to evaluate the value of significant features in the prediction of malignant tumors.

RESULTS

The O-RADS MRI score and modified O-RADS score showed sensitivity at 100.0% (95% CI, 95.7-100.0%) and 71.4% (95% CI, 60.5-80.8%), specificity at 12.5% (95% CI, 4.2-26.8%) and 75.0% (95% CI, 58.8-87.3%), respectively. The area under the curve of the modified O-RADS score was higher than the O-RADS score (0.732 [95% CI, 0.645-0.808] vs 0.575 [95% CI, 0.483-0.663]; p < 0.001). Multivariate analysis showed that the modified O-RADS score 4b or 5 combined with patient age > 38.5 years, nullipara, maximum diameter > 40.5 mm and HE4 > 78.9 pmol/L significantly improved the diagnostic efficacy up to 0.954 (95% CI, 0.901-0.984) (p < 0.001).

CONCLUSION

A modified O-RADS score combined with certain clinical features can significantly improve the diagnostic efficacy in predicting malignant tumors.

Large-scale proteomics reveals precise biomarkers for detection of ovarian cancer in symptomatic women

Ovarian cancer is the 8th most common cancer among women and has a 5-year survival of only 30-50%. While the survival is close to 90% for stage I tumours it is only 20% for stage IV. Current biomarkers are not sensitive nor specific enough, and novel biomarkers are urgently needed. We used the Explore PEA technology for large-scale analysis of 2943 plasma proteins to search for new biomarkers using two independent clinical cohorts. The discovery analysis using the first cohort identified 296 proteins that had significantly different levels in malign tumours as compared to benign and for 269 (91%) of these, the association was replicated in the second cohort. Multivariate modelling, including all proteins independent of their association in the univariate analysis, identified a model for separating benign conditions from malign tumours (stage I-IV) consisting of three proteins; WFDC2, KRT19 and RBFOX3. This model achieved an AUC of 0.92 in the replication cohort and a sensitivity and specificity of 0.93 and 0.77 at a cut-off developed in the discovery cohort. There was no statistical difference of the performance in the replication cohort compared to the discovery cohort. WFDC2 and KRT19 have previously been associated with ovarian cancer but RBFOX3 has not previously been identified as a potential biomarker. Our results demonstrate the ability of using high-throughput precision proteomics for identification of novel plasma protein biomarker for ovarian cancer detection.

Evaluation of Serum Cancer Antigen (CA)-125 Levels as a Biomarker for Ovarian Lesions: Correlation With Histopathological Diagnosis and Clinical Outcomes

BACKGROUND

 Ovarian cancer is a significant cause of morbidity and mortality among women worldwide. Early detection and accurate diagnosis are crucial for improving patient outcomes. Serum biomarkers, such as cancer antigen 125 (CA-125), have shown promise in aiding the diagnosis and monitoring of ovarian lesions.

OBJECTIVE

 This study aimed to assess the utility of serum CA-125 levels as a biomarker for ovarian lesions, correlating with histopathological diagnosis and clinical outcomes.

METHODS

 A prospective observational study was conducted, enrolling 144 female patients presenting with suspected ovarian lesions at a hospital or clinic. Demographic data, physical examination findings, imaging results, and serum CA-125 levels were collected at baseline. Patients underwent laparoscopic or surgical intervention for tissue biopsy or resection, and a histopathological examination was performed to confirm the diagnosis. Clinical outcomes, including response to treatment and disease recurrence, were monitored during follow-up visits.

RESULTS

 The baseline characteristics of the study population showed significant differences between participants with and without ovarian lesions. Older age (mean age 54.8 vs. 45.6) years; p < 0.001) and higher serum CA-125 levels (65.9 vs. 28.6 U/mL, p < 0.001) were associated with ovarian pathology. Histopathological analysis revealed benign cystadenoma as the most prevalent subtype (31.8%), followed by serous carcinoma (27.3%) and borderline tumors (22.7%). Clinical outcomes indicated favorable treatment responses in most patients, with 77.3% achieving complete remission and 15.9% experiencing recurrence. However, elevated CA-125 levels were significantly associated with poorer treatment response (p < 0.001) and higher rates of recurrence, suggesting its potential as a prognostic biomarker for ovarian lesions.

CONCLUSION

 Serum CA-125 levels serve as a valuable biomarker for ovarian lesions, aiding in the diagnosis and monitoring of ovarian cancer. However, its utility is limited by its lack of specificity, particularly in differentiating between benign and malignant ovarian lesions. Integrating CA-125 with other clinical parameters and imaging modalities may enhance diagnostic accuracy and improve patient outcomes in ovarian cancer management.

Clinical relevance of circulating tumor DNA in ovarian cancer: current issues and future opportunities

Ovarian cancer (OC) is the most lethal gynecologic malignancy worldwide. Due to the lack of effective screening and early detection strategies, many patients with OC are diagnosed with advanced disease, where treatment is rarely curative. Moreover, OC is characterized by high intratumor heterogeneity, which represents a major barrier to the development of effective treatments. Conventional tumor biopsy and blood-based biomarkers, such as cancer antigen 125 (CA125), have different limitations. Liquid biopsy has recently emerged as an attractive and promising area of investigation in oncology, due to its minimally invasive, safe, comprehensive, and real-time dynamic nature. Preliminary evidence suggests a potential role of liquid biopsy to refine OC management, by improving screening, early diagnosis, assessment of response to treatment, detection, and profiling of drug resistance. The current knowledge and the potential clinical value of liquid biopsy in OC is discussed in this review to provide an overview of the clinical settings in which its use might support and improve diagnosis and treatment.

Development and validation of radiomics nomogram for metastatic status of epithelial ovarian cancer

To develop and validate an enhanced CT-based radiomics nomogram for evaluating preoperative metastasis risk of epithelial ovarian cancer (EOC). One hundred and nine patients with histologically confirmed EOC were retrospectively enrolled. The volume of interest (VOI) was delineated in preoperative enhanced CT images, and 851 radiomics features were extracted. The radiomics features were selected by the least absolute shrinkage and selection operator (LASSO), and the rad-score was calculated using the formula of the radiomics label. A clinical model, radiomics model, and combined model were constructed using the logistic regression classification algorithm. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the diagnostic performance of the models. Seventy-five patients (68.8%) were histologically confirmed to have metastasis. Eleven optimal radiomics features were retained by the LASSO algorithm to develop the radiomic model. The combined model for evaluating metastasis of EOC achieved area under the curve (AUC) values of 0.929 (95% CI 0.8593-0.9996) in the training cohort and 0.909 (95% CI 0.7921-1.0000) in the test cohort. To facilitate clinical use, a radiomic nomogram was built by combining the clinical characteristics with rad-score. The DCA indicated that the nomogram had the most significant net benefit when the threshold probability exceeded 15%, surpassing the benefits of both the treat-all and treat-none strategies. Compared with clinical model and radiomics model, the radiomics nomogram has the best diagnostic performance in evaluating EOC metastasis. The nomogram is a useful and convenient tool for clinical doctors to develop personalized treatment plans for EOC patients.

CT-Based Radiomics for the Preoperative Prediction of Occult Peritoneal Metastasis in Epithelial Ovarian Cancers

RATIONALE AND OBJECTIVES

The objective of this study was to develop a comprehensive combined model for predicting occult peritoneal metastasis (OPM) in epithelial ovarian cancers (EOCs) using radiomics features derived from computed tomography (CT) and clinical-radiological predictors.

MATERIALS AND METHODS

A total of 224 patients with EOCs were randomly divided into training dataset (N = 156) and test dataset (N = 86). Five clinical factors and seven radiological features were collected. The radiomics features were extracted from CT images of each patient. Multivariate logistic regression was employed to construct clinical and radiological models. The correlation analysis and least absolute shrinkage and selection operator algorithm were used to select radiomics features and build radiomics model. The important clinical, radiological factors, and radiomics features were integrated into a combined model by multivariate logistic regression. Receiver operating characteristics curve with area under the curve (AUC) were used to evaluate and compare predictive performance.

RESULTS

Carbohydrate antigen 125 (CA-125) and human epididymal protein 4 (HE-4) were independent clinical predictors. Laterality, thickened septa and margin were independent radiological predictors. In the training dataset, the AUCs for the clinical, radiological and radiomics models in evaluating OPM were 0.759, 0.819, and 0.830, respectively. In the test dataset, the AUCs for these models were 0.846, 0.835, and 0.779, respectively. The combined model outperformed other models in both the training and the test datasets with AUCs of 0.901 and 0.912, respectively. Decision curve analysis indicated that the combined model yielded a higher net benefit compared to the other models.

CONCLUSION

The combined model, integrating radiomics features with clinical and radiological predictors exhibited improved accuracy in predicting OPM in EOCs.

A novel clinical nomogram for predicting cancer-specific survival in patients with non-serous epithelial ovarian cancer: A real-world analysis based on the Surveillance, Epidemiology, and End Results database and external validation in a tertiary center

BACKGROUND

Currently, there is a lack of prognostic evaluation methods for non-serous epithelial ovarian cancer (EOC).

METHOD

We collected patients with non-serous EOC diagnosed between 2010 and 2017 from the Surveillance, Epidemiology, and End Results (SEER) database into a training cohort (n = 2078) and an internal validation cohort (n = 891). Meanwhile, patients meeting the criteria were screened from the Fujian Provincial Maternal and Child Health Hospital from 2013 to 2022 as an external validation cohort (n = 56). Univariate and multivariable logistic regression were used to determine the independent prognostic factors of cancer-specific survival (CSS) to construct the nomogram. The nomogram was validated by the concordance index (C-index), receiver operating characteristics (ROC) curve and calibration curves.

RESULT

Age, laterality, preoperative CA125 status, histologic type, tumor grade, AJCC stage, surgery lesion, number of lymph nodes examined, residual lesion size, and bone metastasis were identified as independent prognostic factors to construct the nomogram. The nomogram showed better predictive ability than FIGO stage through internal and external cohorts validation. The C-index of the nomogram in the training cohort, validation cohort, and external validation cohort were 0.831, 0.835 and 0.944 higher than those of the Federation International of Gynecology and Obstetric (FIGO) stage, P<0.05. The Area Under Curve (AUC) values results indicated great clinical usefulness of the nomogram. The calibration curve indicated good agreement between the nomogram prediction and actual survival.

CONCLUSION

We developed a nomogram with high predictive accuracy to predict survival in patients with non-serous EOC.

Toward ovarian cancer screening with protein biomarkers using dried, self-sampled cervico-vaginal fluid

Early detection is key for increased survival in ovarian cancer, but no general screening program exists today due to lack of biomarkers and overall cost versus benefit over traditional clinical methods. Here, we used dried cervico-vaginal fluid (CVF) as sampling matrix coupled with mass spectrometry for detection of protein biomarkers. We find that self-collected CVF on paper cards yields robust results and is suitable for high-throughput proteomics. Artificial intelligence-based methods were used to identify an 11-protein panel that separates cases from controls. In validation data, the panel achieved a sensitivity of 0.97 (95% CI 0.91-1.00) at a specificity of 0.67 (0.40-0.87). Analyses of samples collected prior to development of symptoms indicate that the panel is informative also of future risk of disease. Dried CVF is used in cervical cancer screening, and our results opens the possibility for a screening program also for ovarian cancer, based on self-collected CVF samples.

HE4 and CA-125 kinetics to predict outcome in patients with recurrent epithelial ovarian carcinoma: the META4 clinical trial

HE4 and CA-125 are used for epithelial ovarian cancer (EOC) screening, diagnosis, and follow-up. Our objective was to study HE4 and CA-125 kinetics in patients treated for recurrent EOC. Serum samples were prospectively collected before the first chemotherapy cycle and every 3 months until disease progression. Data from 89/101 patients could be analyzed. At baseline, the median CA-125 and HE4 concentrations were 210 IU/L (7-10,310) and 184 pM (31-4,836). Among the 12 patients (13%) with normal CA-125 (<35 IU/L) concentration, eight had HE4 concentration ≥75 pM, and among the 16 patients with normal HE4 concentration (18%), 12 had increased CA-125 concentration. The median nadir concentrations were 31 IU/L (3-8,744) for CA-125 and 75 pM (20-4,836) for HE4. The median times to nadir were 14 (0-130) weeks for CA-125 and 12 (0-52) weeks for HE4. In multivariate analysis, CA-125 and HE4 nadir concentrations (<35 IU/L, HR 0.35, 95% CI: 0.17-0.72 and<75 pM, HR 0.40, 95% CI: 0.20-0.79) and time to CA-125 and HE4 nadir (>14 weeks, HR 0.37, 95% CI: 0.20-0.70 and >12 weeks, HR 0.43, 95% CI: 0.23-0.83) were prognostic factors of progression-free survival. More investigations on HE4 kinetics could help to better monitor patients with CA-125 concentration within normal values.

Lung cancer biomarkers: Raising the clinical value of the classical and the new ones

Blood-based diagnostics for lung cancer support the diagnosis, estimation of prognosis, prediction, and monitoring of therapy response in lung cancer patients. The clinical utility of serum tumor markers has considerably increased due to developments in serum protein tumor markers analytics and clinical biomarker studies, the exploration of preanalytical and influencing conditions, the interpretation of biomarker combinations and individual biomarker kinetics, as well as the implementation of biostatistical models. In addition, circulating tumor DNA (ctDNA) and other liquid biopsy markers are playing an increasingly prominent role in the molecular tumor characterization and the monitoring of tumor evolution over time. Thus, modern lung cancer biomarkers may considerably contribute to an individualized companion diagnostics and provide a sensitive guidance for patients throughout the course of their disease. In this special edition on Tumor Markers in Lung Cancer, experts summarize recent developments in clinical laboratory diagnostics of lung cancer and give an outlook on future challenges and opportunities.

Evaluating the Effectiveness of 2D and 3D CT Image Features for Predicting Tumor Response to Chemotherapy

Background and Objective: 2D and 3D tumor features are widely used in a variety of medical image analysis tasks. However, for chemotherapy response prediction, the effectiveness between different kinds of 2D and 3D features are not comprehensively assessed, especially in ovarian-cancer-related applications. This investigation aims to accomplish such a comprehensive evaluation. Methods: For this purpose, CT images were collected retrospectively from 188 advanced-stage ovarian cancer patients. All the metastatic tumors that occurred in each patient were segmented and then processed by a set of six filters. Next, three categories of features, namely geometric, density, and texture features, were calculated from both the filtered results and the original segmented tumors, generating a total of 1403 and 1595 features for the 2D and 3D tumors, respectively. In addition to the conventional single-slice 2D and full-volume 3D tumor features, we also computed the incomplete-3D tumor features, which were achieved by sequentially adding one individual CT slice and calculating the corresponding features. Support vector machine (SVM)-based prediction models were developed and optimized for each feature set. Five-fold cross-validation was used to assess the performance of each individual model. Results: The results show that the 2D feature-based model achieved an AUC (area under the ROC curve (receiver operating characteristic)) of 0.84 ± 0.02. When adding more slices, the AUC first increased to reach the maximum and then gradually decreased to 0.86 ± 0.02. The maximum AUC was yielded when adding two adjacent slices, with a value of 0.91 ± 0.01. Conclusions: This initial result provides meaningful information for optimizing machine learning-based decision-making support tools in the future.

Identification of serum miR-1246 and miR-150-5p as novel diagnostic biomarkers for high-grade serous ovarian cancer

Epithelial ovarian cancer (EOC) is one of the leading cancers in women, with high-grade serous ovarian cancer (HGSOC) being the most common and lethal subtype of this disease. A vast majority of HGSOC are diagnosed at the late stage of the disease when the treatment and total recovery chances are low. Thus, there is an urgent need for novel, more sensitive and specific methods for early and routine HGSOC clinical diagnosis. In this study, we performed miRNA expression profiling using the NanoString miRNA assay in 34 serum samples from patients with HGSOC and 36 healthy women. We identified 13 miRNAs that were differentially expressed (DE). For additional exploration of expression patterns correlated with HGSOC, we performed weighted gene co-expression network analysis (WGCNA). As a result, we showed that the module most correlated with tumour size, nodule and metastasis contained 8 DE miRNAs. The panel including miR-1246 and miR-150-5p was identified as a signature that could discriminate HGSOC patients with AUCs of 0.98 and 1 for the training and test sets, respectively. Furthermore, the above two-miRNA panel had an AUC = 0.946 in the verification cohorts of RT-qPCR data and an AUC = 0.895 using external data from the GEO public database. Thus, the model we developed has the potential to markedly improve the diagnosis of ovarian cancer.

Clinical Applicability of Tissue Polypeptide Antigen and CA-125 in Gynecological Malignancies

BACKGROUND

Nowadays there still is no sufficient screening tool for ovarian and uterine cancer.

OBJECTIVE

The current study aimed to investigate whether cancer antigen 125 (CA-125), tissue polypeptide antigen (TPA) or the combination of both markers are able to act as screening tools for ovarian or uterine cancer.

METHODS

A total of 275 blood samples from different cohorts (ovarian cancer, uterine cancer, benign control group) were prospectively drawn and analyzed.

RESULTS

Established biomarkers TPA and CA-125 showed elevated serum concentrations in patients with malignant tumors as compared to healthy women and women with benign diseases. In ROC curve analyses, both biomarkers were well able to discriminate between malignant and healthy, benign or overall non-malignant cases in the whole sample, with AUCs of 0.842 and above. While TPA was the best diagnostic marker in patients with uterine cancer, CA 125 was the best in patients with ovarian cancer.

CONCLUSIONS

TPA and CA-125 both showed promising results for the detection of gynecologic malignancies. The combination of CA-125 and TPA did not improve sensitivity in comparison to single markers.

Comparison of glutaminase and cancer antigen 125 for distinguishing benign and malignant ovarian tumors

Increasing demand for glutaminase (GLS) due to high rates of glutamine metabolism is considered one of the hallmarks of malignancy. In parallel, cancer antigen 125 (CA-125) is a commonly used ovarian tumor marker. This study aimed to compare the roles of GLS and CA-125 in distinguishing between benign and malignant ovarian tumors. The research was conducted as a comparative study, enrolling 156 patients with ovarian tumors. Preoperative serum CA-125 and GLS levels were analyzed to evaluate their effectiveness in distinguishing between benign and malignant ovarian tumors. The results revealed that the mean levels of CA-125 and GLS were significantly higher in malignant ovarian tumors compared with benign ones (389.54 ± 494.320 vs. 193.15 ± 529.932 (U/mL) and 17.37 ± 12.156 vs. 7.48 ± 4.095 (μg/mL), respectively). The CA-125 and GLS cutoff points of 108.2 U/mL and 18.32 μg/mL, respectively, were associated with malignant ovarian tumors. Multivariate analyses showed that GLS had higher predictive capabilities compared with CA-125 (odds ratio 9.4 vs. 2.1). The accuracy of using GLS combined with CA-125 was higher than using CA-125 alone (73.1% vs. 68.8%). In conclusion, higher levels of CA-125 and GLS are associated with malignant ovarian tumors. GLS outperforms CA-125 in distinguishing between benign and malignant ovarian tumors. The combination of GLS and CA-125 demonstrated improved accuracy for distinguishing benign and malignant ovarian tumors when compared with using CA-125 alone.

Immunogenic Biomarkers HMGB1 and sRAGE Are Potential Diagnostic Tools for Ovarian Malignancies

BACKGROUND

High mobility group box 1 (HMGB1), soluble receptor of advanced glycation end products (sRAGE) and programmed cell death markers PD-1 and PD-L1 are immunogenic serum biomarkers that may serve as novel diagnostic tools for cancer diagnosis.

METHODS

We investigated the four markers in sera of 231 women, among them 76 with ovarian cancer, 87 with benign diseases and 68 healthy controls, using enzyme immunoassays. Discrimination between groups was calculated using receiver operating characteristic (ROC) curves and sensitivities at fixed 90% and 95% specificities.

RESULTS

HMGB1 levels were significantly elevated and sRAGE levels were decreased in cancer patients as compared to benign and healthy controls. In consequence, the ratio of HMGB1 and sRAGE discriminated best between diagnostic groups. The areas under the curve (AUCs) of the ROC curves for differentiation of cancer vs. healthy were 0.77 for HMGB1, 0.65 for sRAGE and 0.78 for the HMGB1/sRAGE ratio, and slightly lower for the differentiation of cancer vs. benigns with 0.72 for HMGB1, 0.61 for sRAGE and 0.74 for the ratio of both. The highest sensitivities for cancer detection at 90% specificity versus benign diseases were achieved using HMGB1 with 41.3% and the HMGB1/sRAGE ratio with 39.2%, followed by sRAGE with 18.9%. PD-1 showed only minor and PD-L1 no power for discrimination between ovarian cancer and benign diseases.

CONCLUSION

HMGB1 and sRAGE have differential diagnostic potential for ovarian cancer detection and warrant inclusion in further validation studies.

The Role of Interleukin 6 (IL6), Cancer Antigen-125 (CA-125), and Human Epididymis Protein 4 (HE4) to predict tumor resectability in the advanced epithelial ovarian cancer patients

INTRODUCTION

A study of tumor resectability in pre-operative patients with advanced epithelial ovarian cancer is required to predict primary surgical benefits accurately. This study aims to investigate IL6, CA-125 and HE4 to predict tumor resectability in the pre-operative patients with advanced epithelial ovarian cancer.

METHODS

This cross-sectional study was conducted in the polyclinic, oncology and gynecology inpatient room of Dr. M. Jamil Padang Hospital from June until December 2022. Advanced epithelial ovarian cancer stage based on histology result from FIGO stages IIIB-IVA. IL6, CA-125, and HE4 were measured using ECLIA (electrochemiluminescence immunoassay). Categorical data were assessed using Chi-square and Mann-Whitney tests. Numerical variable correlations were analyzed using Pearson Correlation tests. While the correlation between numerical and nominal variables was analyzed using the Eta correlation test. A p-value of <0,05 was considered a significant correlation. The cut-off value of serum IL6, CA-125, and HE4 was determined with a ROC curve. The sensitivity and specificity of each clinical parameter were calculated.

RESULTS

There was a significant difference in IL-6 (1328 vs 752 pg/ml; p<0,001), CA-125 (1260,5 vs 819,5 U/ml; p<0,001), and HE4 levels (1320 vs 760 pmol/L; p<0,001) between patients with tumor resectability of > 1 cm (suboptimal) vs < 1 cm (optimal). There was a correlation between IL6 (r = 0,832), CA-125 (r = 0,716), and HE4 (r = 0,716) with tumor resectability.

CONCLUSION

Measuring IL6, CA-125, and HE4 levels is useful for clinicians to predict tumor resectability in pre-operative patients with advanced epithelial ovarian cancer.

Extracellular urinary microRNAs as non-invasive biomarkers of endometrial and ovarian cancer

INTRODUCTION

Gynecological cancers account for a large number of cancer-related deaths in women. Endometrial cancer is the most prevalent, while ovarian cancer is the deadliest gynecological cancer worldwide. To overcome the clinical need for easy and rapid testing, there is a growing interest in cancer detection in non-invasive modalities. With a growing field of liquid biopsy, urine became interesting source of cancer biomarkers.

OBJECTIVES

The aim of this manuscript is to provide an overview on the origin, analysis and the clinical significance of urine microRNAs in gynecological cancers, with a focus on ovarian and endometrial cancer. MicroRNAs, a class of small non-coding nucleic acids, are emerging as a non-invasive biomarkers due to the feasibility and the extreme stability in body fluids. Specific miRNA expression signatures have been previously identified in ovarian and endometrial cancer.

RESULTS

The aim of this manuscript is to provide an overview on the origin, analysis and the clinical significance of urine microRNAs in gynecological cancers, with the focus on ovarian and endometrial cancer.  CONCLUSION: The advantages and limitations of urine microRNA utility and technologies are discussed. Previously detected microRNA from urine of the patients are summarized to evaluate their potential as non-invasive clinical biomarkers in gynecological oncology.

Brenner tumors of the ovary: A case series in a teaching institute center

Brenner tumors of the ovary are very rare, and mostly benign. Borderline Brenner tumors are rare and malignant Brenner tumors are even much rarer with a worse prognosis. This study was carried out in the Department of Pathology, VSSIMSAR, Burla, Odisha over a period of 3 years. A histopathology study was done in all the cases, while immunostaining was done in selected cases only. A total of seven cases were studied, out of which four (57.14%) cases were diagnosed as benign Brenner tumors, two (28.57%) cases as borderline Brenner tumors, and one (14.28%) case as malignant. Histopathology study is the gold standard for diagnosis of Brenner tumor, with the aid of immunostain whenever necessary.

Advances in application of circulating tumor DNA in ovarian cancer

Ovarian cancer is the third most common gynecologic cancer worldwide and has the highest mortality rate among gynecologic cancers. Identifying timely and effective biomarkers at different stages of the disease is the key to improve the prognosis of ovarian cancer patients. Circulating tumor DNA (ctDNA) is a fragment of free DNA produced by tumor cells in the blood. Current techniques for detecting ctDNA mainly include quantitative polymerase chain reaction (PCR), targeted next-generation sequencing (NGS), and non-targeted NGS (such as whole exon or whole genome sequencing). As a non-invasive liquid biopsy technique, ctDNA has a good application prospect in the ovarian cancer diagnosis, monitoring of treatment response and efficacy evaluation, detection of reverse mutation and related medication guidance, and prognosis evaluation. This article reviews the advances in application of ctDNA in ovarian cancer.

Update value and clinical application of MUC16 (cancer antigen 125)

INTRODUCTION

The largest transmembrane mucin, mucin 16 (MUC16), contains abundant glycosylation sites on the molecular surface, allowing it to participate in various molecular pathways. When cells lose polarity and become cancerous, MUC16 is overexpressed, and more of the extracellular region (cancer antigen [CA]125) is released into serum and possibly, promote the development of diseases. Thus, MUC16 plays an indispensable role in clinical research and application.

AREAS COVERED

This review summarizes the update proposed role of MUC16 in carcinogenesis and metastasis. Most importantly, we prospect its potential value in targeted therapy after screening 1226 articles published within the last 10 years from PubMed. Two reviewers screened each record and each report retrieved independently. We have summarized the progress of MUC16/CA125 in basic research and clinical application, and predicted its possible future development directions.

EXPERT OPINION

As an important noninvasive co-factor in the diagnosis of gynecological diseases, MUC16 has been used for a long time, especially in the diagnosis and treatment of ovarian cancer. The overexpression of MUC16 plays a very obvious role in regulating inflammatory response, supporting immune suppression, and promoting the proliferation, division, and metastasis of cancer cells. In the next 20 years, there will be a luxuriant clinical application of MUC16 as a target for immune monitoring and immunotherapy.

SPON1 is an independent prognostic biomarker for ovarian cancer

BACKGROUND

Ovarian cancer has the worst outcome among gynecological malignancies; therefore, biomarkers that could contribute to the early diagnosis and/or prognosis prediction are urgently required. In the present study, we focused on the secreted protein spondin-1 (SPON1) and clarified the prognostic relevance in ovarian cancer.

METHODS

We developed a monoclonal antibody (mAb) that selectively recognizes SPON1. Using this specific mAb, we determined the expression of SPON1 protein in the normal ovary, serous tubal intraepithelial carcinoma (STIC), and ovarian cancer tissues, as well as in various normal adult tissues by immunohistochemistry, and verified its clinicopathological significance in ovarian cancer.

RESULTS

The normal ovarian tissue was barely positive for SPON1, and no immunoreactive signals were detected in other healthy tissues examined, which was in good agreement with data obtained from gene expression databases. By contrast, upon semi-quantification, 22 of 242 ovarian cancer cases (9.1%) exhibited high SPON1 expression, whereas 64 (26.4%), 87 (36.0%), and 69 (28.5%) cases, which were designated as SPON1-low, possessed the moderate, weak, and negative SPON1 expression, respectively. The STIC tissues also possessed SPON1-positive signals. The 5-year recurrence-free survival (RFS) rate in the SPON1-high group (13.6%) was significantly lower than that in the SPON1-low group (51.2%). In addition, high SPON1 expression was significantly associated with several clinicopathological variables. Multivariable analysis revealed that high SPON1 was an independent prognostic factor for RFS of ovarian cancer.

CONCLUSIONS

SPON1 represents a prognostic biomarker for ovarian cancer, and the anti-SPON1 mAb could be valuable as an outcome predictor.

Individual recombinant repeats of MUC16 display variable binding to CA125 antibodies

BACKGROUND

Despite its importance in the clinical management of ovarian cancer, the CA125 biomarker - located on the mucin protein MUC16 - is still not completely understood. Questions remain about MUC16's function and structure, specifically the identity and location of the CA125 epitopes.

OBJECTIVE

The goal of this study was to characterize the interaction of individual recombinant repeats from the tandem repeat domain of MUC16 with antibodies used in the clinical CA125 II test.

METHODS

Using E. coli expression, we isolated nine repeats from the putative antigenic domain of CA125. Amino acid composition of recombinant repeats was confirmed by high-resolution mass spectrometry. We characterized the binding of four antibodies - OC125, M11, "OC125-like," and "M11-like" - to nine recombinant repeats using Western blotting, indirect enzyme-linked immunosorbent assay (ELISA), and localized surface plasmon resonance (SPR) spectroscopy.

RESULTS

Each recombinant repeat was recognized by a different combination of CA125 antibodies. OC125 and "OC125-like" antibodies did not bind the same set of recombinant repeats, nor did M11 and "M11-like" antibodies.

CONCLUSIONS

Characterization of the interactions between MUC16 recombinant repeats and CA125 antibodies will contribute to ongoing efforts to identify the CA125 epitopes and improve our understanding of this important biomarker.

Circulating cf-miRNA as a more appropriate surrogate liquid biopsy marker than cfDNA for ovarian cancer

Ovarian cancer (OC) is an aggressive disease, primarily diagnosed in late stages with only 20% of patients surviving more than 5 years. Liquid biopsy markers have great potential to improve current diagnostic and prognostic methods. Here, we compared miRNAs and DNA methylation in matched plasma, whole blood and tissues as a surrogate marker for OC. We found that while both cfDNA and cf-miRNAs levels were upregulated in OC compared to patients with benign lesions or healthy controls, only cf-miRNA levels were an independent prognosticator of survival. Following on our previous work, we found members of the miR-200 family, miR-200c and miR-141 to be upregulated in both plasma and matched tissues of OC patients which correlated with adverse clinical features. We could also show that the upregulation of miR-200c and -141 correlated with promoter DNA hypomethylation in tissues, but not in plasma or matched whole blood samples. As cf-miRNAs are more easily obtained and very stable in blood, we conclude that they might serve as a more appropriate surrogate liquid biopsy marker than cfDNA for OC.

Individual recombinant repeats of MUC16 display variable binding to CA125 antibodies

BACKGROUND

Despite its importance in the clinical management of ovarian cancer, the CA125 biomarker-located on the mucin protein MUC16-is still not completely understood. Questions remain about MUC16's function and structure, specifically the identity and location of the CA125 epitopes.

OBJECTIVE

The goal of this study was to characterize the interaction of individual recombinant repeats from the tandem repeat domain of MUC16 with antibodies used in the clinical CA125 II test.

METHODS

Using E. coli expression, we isolated nine repeats from the putative antigenic domain of CA125. Amino acid composition of recombinant repeats was confirmed by high-resolution mass spectrometry. We characterized the binding of four antibodies-OC125, M11, "OC125-like," and "M11-like"-to nine recombinant repeats using Western blotting, indirect enzyme-linked immunosorbent assay (ELISA), and localized surface plasmon resonance (SPR) spectroscopy.

RESULTS

Each recombinant repeat was recognized by a different combination of CA125 antibodies. OC125 and "OC125-like" antibodies did not bind the same set of recombinant repeats, nor did M11 and "M11-like" antibodies.

CONCLUSIONS

Characterization of the interactions between MUC16 recombinant repeats and CA125 antibodies will contribute to ongoing efforts to identify the CA125 epitopes and improve our understanding of this important biomarker.

Identification of TP53 mutations in circulating tumour DNA in high grade serous ovarian carcinoma using next generation sequencing technologies

Plasma circulating tumour DNA (ctDNA) has been suggested to be a viable biomarker of response to treatment in patients with high grade serous ovarian carcinoma (HGSOC). TP53 mutations are present in more than 90% of HGSOCs but somatic variants are distributed across all exonic regions of the gene, requiring next generation sequencing (NGS) technologies for mutational analysis. In this study, we compared the suitability of the Accel (Swift) and Oncomine (ThermoFisher) panels for identification of TP53 mutations in ctDNA of HGSOC patients (N = 10). Only 6 patients (60%) were found to have TP53 mutations using the ACCEL panel but the addition of molecular tags in the Oncomine panel improved ctDNA detection with at least one mutation detected in all cases (100%). Orthogonal validation of the 14 somatic variants found by Oncomine, using droplet digital PCR, confirmed 79% (11/14) of the identified mutations. Overall, the Oncomine panel with unique molecular identifiers (UMI) appears more useful for ctDNA analysis in HGSOC.

The Discrepancy between Preoperative Tumor Markers and Imaging Outcomes in Predicting Ovarian Malignancy

Preoperative tumor markers and imaging often differ in predicting whether an ovarian tumor is malignant. Therefore, we evaluated the correlation between the predictive values of imaging and tumor markers for diagnosing ovarian tumors, especially when there were discrepancies between the two. We enrolled 1047 patients with ovarian tumors. The predictive values and concordance rates between the preoperative risk of ovarian malignancy algorithm (ROMA) and imaging, including CT and MRI, were evaluated. Diagnoses of 561 CT (77.9%) and 322 MRI group (69.2%) participants were consistent with the ROMA. Among them, 96.4% of the CT (541/561) and 92.5% of the MRI (298/322) group predicted an accurate diagnosis. In contrast, 67.3% (101/150) of CT and 75.2% (100/133) of MRI cases accurately predicted the diagnosis in cases with discrepancies between ROMA and CT or MRI; a total of 32% (48/150) of the CT and 25.5% (34/133) of the MRI group showed an accurate ROMA diagnosis in cases with discrepancies between ROMA and imaging. In the event of a discrepancy between ROMA and imaging when ovarian tumor malignancy prediction, the question is which method should take precedence. This study demonstrates that MRI has the greatest diagnostic accuracy, followed by CT and ROMA. It is also important to understand underlying diseases and benign conditions and rare histopathologies of malignant tumors.

Endometriosis-Associated Ovarian Cancer: What Are the Implications for Women with Intact Endometrioma Planning for a Future Pregnancy? A Reproductive Clinical Outlook

Endometriosis is a chronic, universal, and prevalent disease estimated to affect up to 1:10 women of reproductive age. Endometriosis-associated ovarian cancer (EAOC) developing at reproductive age is challenging and of concern for women and practitioners alike. This outlook review focuses on the occurrence of EAOC, especially in infertile women or those planning for a future pregnancy, from the perspective of a reproductive endocrinologist, based on recent evidence. Contemporary pathogenesis, genetic profiles, evidence of causality, clinical diagnosis, prognosis, and up-to-date management are discussed. EAOC seems to be merely associated with endometrioma and includes clear-cell and endometrioid ovarian carcinoma. Although endometrioma is frequently found in women of reproductive age (up to 1:18 of women), EAOC appears to be a rare occurrence. These women are of more advanced reproductive age, nulliparous, and hyperestrogenic, with a large-sized unilateral endometrioma (>9 cm) containing solid components and papillary projections. Each case suspected to have EAOC has specific characteristics, and a multidisciplinary discussion and appropriate patient counseling should be conducted to reach an optimal therapeutic plan. Since most of these cases are diagnosed at an early stage with a favorable prognosis, fertility-sparing surgery may be feasible. The pros and cons of fertility preservation techniques should be discussed.

Precision gynecologic oncology: circulating cell free DNA epigenomic analysis, artificial intelligence and the accurate detection of ovarian cancer

Ovarian cancer (OC) is the most lethal gynecologic cancer due primarily to its asymptomatic nature and late stage at diagnosis. The development of non-invasive markers is an urgent priority. We report the accurate detection of epithelial OC using Artificial Intelligence (AI) and genome-wide epigenetic analysis of circulating cell free tumor DNA (cfTDNA). In a prospective study, we performed genome-wide DNA methylation profiling of cytosine (CpG) markers. Both conventional logistic regression and six AI platforms were used for OC detection. Further, we performed Gene Set Enrichment Analysis (GSEA) analysis to elucidate the molecular pathogenesis of OC. A total of 179,238 CpGs were significantly differentially methylated (FDR p-value < 0.05) genome-wide in OC. High OC diagnostic accuracies were achieved. Conventional logistic regression achieved an area under the ROC curve (AUC) [95% CI] 0.99 [± 0.1] with 95% sensitivity and 100% specificity. Multiple AI platforms each achieved high diagnostic accuracies (AUC = 0.99-1.00). For example, for Deep Learning (DL)/AI AUC = 1.00, sensitivity = 100% and 88% specificity. In terms of OC pathogenesis: GSEA analysis identified 'Adipogenesis' and 'retinoblastoma gene in cancer' as the top perturbed molecular pathway in OC. This finding of epigenomic dysregulation of molecular pathways that have been previously linked to cancer adds biological plausibility to our results.

Data-driven analysis of a validated risk score for ovarian cancer identifies clinically distinct patterns during follow-up and treatment

Background

Ovarian cancer is the eighth most common cancer among women and due to late detection prognosis is poor with an overall 5-year survival of 30-50%. Novel biomarkers are needed to reduce diagnostic surgery and enable detection of early-stage cancer by population screening. We have previously developed a risk score based on an 11-biomarker plasma protein assay to distinguish benign tumors (cysts) from malignant ovarian cancer in women with adnexal ovarian mass.

Methods

Protein concentrations of 11 proteins were characterized in plasma from 1120 clinical samples with a custom version of the proximity extension assay. The performance of the assay was evaluated in terms of prediction accuracy based on receiver operating characteristics (ROC) and multiple hypothesis adjusted Fisher's Exact tests on achieved sensitivity and specificity.

Results

The assay's performance is validated in two independent clinical cohorts with a sensitivity of 0.83/0.91 and specificity of 0.88/0.92. We also show that the risk score follows the clinical development and is reduced upon treatment, and increased with relapse and cancer progression. Data-driven modeling of the risk score patterns during a 2-year follow-up after diagnosis identifies four separate risk score trajectories linked to clinical development and survival. A Cox proportional hazard regression analysis of 5-year survival shows that at time of diagnosis the risk score is the second-strongest predictive variable for survival after tumor stage, whereas MUCIN-16 (CA-125) alone is not significantly predictive.

Conclusion

The robust performance of the biomarker assay across clinical cohorts and the correlation with clinical development indicates its usefulness both in the diagnostic work-up of women with adnexal ovarian mass and for predicting their clinical course.

Performance of the IOTA ADNEX model combined with HE4 for identifying early-stage ovarian cancer

Objective

This work was designed to investigate the performance of the International Ovarian Tumor Analysis (IOTA) ADNEX (Assessment of Different NEoplasias in the adneXa) model combined with human epithelial protein 4 (HE4) for early ovarian cancer (OC) detection.

Methods

A total of 376 women who were hospitalized and operated on in Women and Children’s Hospital of Chongqing Medical University were selected. Ultrasonographic images, cancer antigen-125 (CA 125) levels, and HE4 levels were obtained. All cases were analyzed and the histopathological diagnosis serves as the reference standard. Based on the IOTA ADNEX model post-processing software, the risk prediction value was calculated. We analyzed receiver operating characteristic curves to determine whether the IOTA ADNEX model alone or combined with HE4 provided better diagnostic accuracy.

Results

The area under the curve (AUC) of the ADNEX model alone or combined with HE4 in predicting benign and malignant ovarian tumors was 0.914 (95% CI, 0.881–0.941) and 0.916 (95% CI, 0.883–0.942), respectively. With the cutoff risk of 10%, the ADNEX model had a sensitivity of 0.93 (95% CI, 0.87–0.97) and a specificity of 0.73 (95% CI, 0.67–0.78), while combined with HE4, it had a sensitivity of 0.90 (95% CI, 0.84–0.95) and a specificity of 0.81 (95% CI, 0.76–0.86). The IOTA ADNEX model combined with HE4 was better at improving the accuracy of the differential diagnosis between different OCs than the IOTA ADNEX model alone. A significant difference was found in separating borderline masses from Stage II–IV OC (p = 0.0257).

Conclusions

A combination of the IOTA ADNEX model and HE4 can improve the specificity of diagnosis of ovarian benign and malignant tumors and increase the sensitivity and effectiveness of the differential diagnosis of Stage II–IV OC and borderline tumors.

CT imaging phenotypes linked to CA125 and HE4 biomarkers are highly predictive in discriminating between hereditary and sporadic ovarian cancer patients

BACKGROUND: Hereditary ovarian cancers (HOC) represent about 23% of ovarian cancer (OC) cases: they are most frequently related to germline mutations in the BRCA genes. OBJECTIVE: We aimed to compare CA125/HE4 serum levels and Computed Tomography (CT) features at time of ovarian cancer (OC) diagnosis in two populations: BRCA mutant and BRCA wild-type (WT) OC, and to investigate the relationship between this laboratory and radiological biomarker and BRCA mutation status. METHODS: This retrospective study included 60 newly diagnosed OC patients with FIGO stage IIIC-IV disease, tested for BRCA1/2 germline mutation status of which preoperative CT scan and serum tumor marker assay were available. RESULTS: The median level of CA125 (708 U/mL) was significantly higher (p < 0.002) in BRCA1/2 mutated patients than in WT patients (176 U/mL), whereas the median level of HE4 (492 pmol/L) was significantly higher (p < 0.002) in WT than in BRCA-mutated patients (252 pmol/L). BRCA mutation carriers showed a higher incidence of bilateral ovarian masses (p = 0.0303) characterized by solid structures (p < 0.00001), higher peritoneal tumor load, macronodular implants >2 cm (p = 0.000099), increased frequency of lymphadenopathies (p = 0.019), and metastasis (p = 0.052) compared to patients with BRCA WT. CONCLUSIONS: Tumor markers and CT patterns may help in identifying BRCA mutation status in OC directing patients towards a personalized treatment.

Biomarkers in Gynecologic Tumors

Gynecologic malignancies are one of the most frequent cancers amongst women. Biomarkers are crucial for the differential diagnosis of adnexal masses; however, their potential for diagnosis is limited. In the era of difficulty in ovarian cancer screening, novel biomarkers are defined, but CA125 still remains the most valuable one. Circulating tumor DNAs, DNA hypermethylation, metabolites, microRNAs, and kallikreins have recently turned out as ovarian cancer biomarkers and are being applied to clinical practice. For uterine cancer, genomic classification has now been described, it will be used as a prognostic tool. In this chapter, we describe ovarian, endometrial, and cervical cancer biomarkers in detail.

Evaluation of miRNA-21 and CA-125 as a promising diagnostic biomarker in patients with ovarian cancer

Introduction/objective

Ovarian cancer is the 6th leading cause of mortality in women, killing more women than any other reproductive system cancer. We studied the expression of serum micro-ribonucleic acid-21 (miRNA-21) in ovarian cancer patients and explored associations with diagnosis, clinicopathological parameters, and prognosis.

Methods

Real-time fluorescence-quantitative polymerase chain reaction was used to examine the relative expression of miRNA-21 in serum. Cancer antigen 125 (CA-125) levels were measured using an enzyme immunoassay test kit (ELISA).

Results

Serum miR-21 expression was significantly elevated in ovarian cancer patients compared to controls (p < 0.001). The same was true for CA-125 serum levels, which were also significantly in cancer patients (p < 0.001). The sensitivity and specificity of miR-21 detection in the diagnosis of ovarian cancer were 96%, 88% versus 74%, and 80% for CA-125.

Conclusions

miR-21 is highly expressed in the serum of ovarian cancer patients and may be important in the development and progression of ovarian cancer, with more sensitivity and specificity than CA-125. Our results suggest that circulating serum miRNA-21 is a promising tumor marker for use in the diagnosis and prognosis of ovarian cancer.

A novel circulating miRNA panel for non-invasive ovarian cancer diagnosis and prognosis

Background

Ovarian cancer (OC) is an aggressive disease, primarily diagnosed in late stages with only 20% of patients surviving more than 5 years after diagnosis. There is a pending need to improve current diagnostics and prognostics.

Methods

In this study, we investigated total circulating cell-free microRNA (cf-miRNA) levels as well as a panel of cf-miRNAs in the plasma of OC patients (n = 100), patients with benign lesions (n = 45) and healthy controls (n = 99).

Results

High levels of cf-miRNAs correlated with unfavourable clinical features and were an independent prognosticator of patient survival. By mining NGS data, we identified a signature panel of seven individual cf-miRNAs which could distinguish controls from benign cases with an AUC of 0.77 and controls from cancer cases with an AUC of 0.87. Importantly, in combination with the current gold-standard marker, CA-125, the panel could predict early OC with an AUC of 0.93.

Conclusion

Our findings highlight the potential of cf-miRNA levels as well as individual cf-miRNAs for OC diagnosis and prognosis that warrants further clinical evaluation.

Targeted Mutational Analysis of Circulating Tumor DNA to Decipher Temporal Heterogeneity of High-Grade Serous Ovarian Cancer

Simple Summary

The issue of spatial and temporal heterogeneity of high-grade serous ovarian cancer (HGS-EOC) has hampered the possibility to shape the molecular portrait of relapsed disease, which ultimately impacts our ability to develop a more rational second-line treatment. Liquid biopsy offers the unique opportunity to track tumor evolution over time and infer the dynamic changes of tumor clonal architecture. Differently from other tumors, no actionable driving lesions characterize HGS-EOC, thus genome-scale analysis like whole-exome sequencing is not compatible with the clinical turnaround time. In the present work, we provided a novel framework based on the analysis of both qualitative and quantitative features of circulating tumor DNA (ctDNA) in order to identify, at the time of molecular relapse, the early genetic vulnerabilities that will characterize the clinical recurrence and thus be amenable of a more rational second-line treatment.

Abstract

We have previously demonstrated that longitudinal untargeted analysis of plasma samples withdrawn from patients with high-grade serous ovarian cancer (HGS-EOC) can intercept the presence of molecular recurrence (TRm) earlier than the diagnosis of clinical recurrence (TRc). This finding opens a clinical important temporal window to acquire through plasma sample analysis a real-time picture of those emerging molecular lesions that will drive and sustain the growth of relapsed disease and ultimately will confer resistance. In this proof of principle study, the same genomic libraries obtained at the diagnosis (T0), TRm and TRc were further analyzed by targeted resequencing approach to sequence the coding region of a panel of 65 genes to provide longitudinal analysis of clonal evolution as a novel strategy to support clinical decisions for the second-line treatment. Experiments were performed on plasma and tumor tissues withdrawn on a selection of previously analyzed cohorts of cases (i.e., 33 matched primary and synchronous lesions and 43 plasma samples from 18 patients). At T0, the median concordance of mutations shared by each tumor tissue biopsy and its matched plasma sample was 2.27%. This finding confirms the limit of a single tumor biopsy to be representative of the entire disease, while plasma analysis can recapitulate most of the main molecular lesions of the disease. A comparable scenario was observed during longitudinal analysis, where, with the exception of the TP53 gene and germline mutations in BRCA1/2 genes, no other gene shared the same locus specific gene mutation across T0, TRm and TRc time points. This high level of temporal heterogeneity has important implications for planning second-line treatment. For example, in three out of 13 cases, plasma ctDNA analysis at TRm or TRc reported acquired novel variants in the TP53BP1 gene not present at T0. In particular, patient 21564, potentially eligible for PARP-inhibitor (PARPi) treatment at the time of diagnosis (BRCA1 c.5182delA mutation), would unlikely respond to these drugs in second-line therapy due to the presence of eight distinct TP53BP1 variants in plasma samples collected TRc. This study demonstrates that liquid biopsy provides a real-time molecular picture to intercept those actionable genetic vulnerabilities or drug resistance mechanisms that could be used to plan a more rational second-line treatment.

Management of the Adnexal Mass: Considerations for the Family Medicine Physician

Ovarian cancer is the most deadly gynecological cancer, so proper assessment of a pelvic mass is necessary in order to determine which are at high risk for malignancy and should be referred to a gynecologic oncologist. However, in a family medicine setting, evaluation and treatment of these masses can be challenging due to a lack of resources. A number of risk assessment tools are available to family medicine physicians, including imaging techniques, imaging systems, and blood-based biomarker assays each with their respective pros and cons, and varying ability to detect malignancy in pelvic masses. Effective utilization of these assessment tools can inform the care pathway for patients which present with an adnexal mass, such as expectant management for those with a low risk of malignancy, or referral to a gynecologic oncologist for surgery and staging, for those at high risk of malignancy. Triaging patients to the appropriate care pathway improves patient outcomes and satisfaction, and family medicine physicians can play a key role in this decision-making process.

MiR-320b and miR-320d as Biomarkers to Predict and Participate in the Formation of Platinum Resistance in Ovarian Cancer Patients

Patients with ovarian cancer who receive platinum-based chemotherapy typically develop platinum resistance, which leads to tumor recurrence and mortality. Therefore, finding the underlying mechanisms and biomarkers is critical. A total of 51 platinum-resistant and 70 platinum-sensitive ovarian cancer patients were enrolled in this study. We examined the GSE131978 dataset in the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus database for differentially expressed long non-coding RNAs and messenger RNAs (mRNAs) between platinum-resistant and platinum-sensitive patients and completed a microRNA chip analysis. After filtering by Pearson correlation analysis, the competitive endogenous RNA (ceRNA) networks were subsequently constructed. Then, the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology enrichment analyses about mRNAs in ceRNA networks were accomplished. More crucially, we demonstrated the differentially expressed microRNAs using quantitative real-time PCR and fluorescence in situ hybridization. The feasibility of microRNAs as biomarkers to predict platinum resistance and tumor recurrence was assessed using the receiver operating characteristic curve and survival analysis. MiR-320b and miR-320d exhibited high area under the curve values of 0.757 and 0.702, respectively. In our study, ceRNA networks including miR-320b and miR-320d probably provided novel insights for platinum resistance in ovarian cancer patients.

BRAK and APRIL as novel biomarkers for ovarian tumors

Aims: To evaluate BRAK and APRIL in serum samples from healthy patients and an ovarian tumor group and analyze their effective value as biomarkers. Materials & methods: BRAK and APRIL were measured in 197 serum samples including 34 healthy controls, 48 patients with benign ovarian cysts and 115 patients with ovarian cancer, and the best statistical cutoff values were calculated. Then, the sensitivity, specificity, accuracy, positive predictive value and negative predictive value for selected cutoff points were assessed. Results: The healthy control group had statistically significant higher BRAK and lower APRIL than the ovarian tumor group. BRAK was excellent for differentiating healthy patients from patients with ovarian tumors, showing area under the receiver operating characteristic curve 0.983, 98.16% sensitivity and 100% specificity. When BRAK was combined with APRIL and CA-125, it also played a role in distinguishing benign cysts from malignancies with area under the curve 0.864, 81.74% sensitivity and 79.17% specificity. Conclusions: BRAK and APRIL are good candidates for ovarian tumor biomarkers.

Assessment of the diagnostic and prognostic relevance of ACAT1 and CE levels in plasma, peritoneal fluid and tumor tissue of epithelial ovarian cancer patients - a pilot study

Background

Abnormal accumulation of acyl-CoA cholesterol acyltransferase-1 (ACAT1) and ACAT1-mediated cholesterol esterified with fatty acids (CE) contribute to cancer progression in various cancers. Our findings of increased CE and ACAT1 levels in epithelial ovarian cancer (EOC) cell lines prompted us to investigate whether such an increase occurs in primary clinical samples obtained from human subjects diagnosed with EOC. We evaluated the diagnostic/prognostic potential of ACAT1 and CE in EOC by: 1) assessing ACAT1 and CE levels in plasma, peritoneal fluid, and ovarian/tumor tissues; 2) assessing diagnostic performance by Receiver Operating Characteristic (ROC) analysis; and 3) comparing expression of ACAT1 and CE with that of tumor proliferation marker, Ki67.

Methods

ACAT1 protein levels in plasma, peritoneal fluid and tissue were measured via enzyme-linked immunosorbent assay. Tissue expression of ACAT1 and Ki67 proteins were confirmed by immunohistochemistry and mRNA transcript levels were evaluated using quantitative real-time polymerase chain reaction (qRT-PCR). CE levels were assessed in plasma, peritoneal fluid (colorimetric assay) and in tissue (thin layer chromatography).

Results

Preoperative levels of ACAT1 and CE on the day of surgery were significantly higher in tissue and peritoneal fluid from EOC patients vs. the non-malignant group, which included subjects with benign tumors and normal ovaries; however, no significant differences were observed in plasma. In tissue and peritoneal fluid, positive correlations were observed between CE and ACAT1 levels, as well as between ACAT1/CE and Ki67.

Conclusions

ACAT1 and CE accumulation may be linked to the aggressive potential of EOC; therefore, these mediators may be useful biomarkers for EOC prognosis and target-specific treatments.

Next Generation Plasma Proteomics Identifies High-Precision Biomarker Candidates for Ovarian Cancer

Simple Summary

Ovarian cancer is the eighth most common cancer among women and has a 5-year survival of only 30–50%. The survival is close to 90% for patients in stage I but only 20% for patients in stage IV. The presently available biomarkers have insufficient sensitivity and specificity for early detection and there is an urgent need to identify novel biomarkers. The aim of our study was to broadly measure protein biomarkers to find tests for the early detection of ovarian cancer. We found that combinations of 4–7 protein biomarkers can provide highly accurate detection of early- and late-stage ovarian cancer compared to benign conditions. The performance of the tests was then validated in a second independent cohort.

Abstract

Background: Ovarian cancer is the eighth most common cancer among women and has a 5-year survival of only 30–50%. The survival is close to 90% for patients in stage I but only 20% for patients in stage IV. The presently available biomarkers have insufficient sensitivity and specificity for early detection and there is an urgent need to identify novel biomarkers. Methods: We employed the Explore PEA technology for high-precision analysis of 1463 plasma proteins and conducted a discovery and replication study using two clinical cohorts of previously untreated patients with benign or malignant ovarian tumours (N = 111 and N = 37). Results: The discovery analysis identified 32 proteins that had significantly higher levels in malignant cases as compared to benign diagnoses, and for 28 of these, the association was replicated in the second cohort. Multivariate modelling identified three highly accurate models based on 4 to 7 proteins each for separating benign tumours from early-stage and/or late-stage ovarian cancers, all with AUCs above 0.96 in the replication cohort. We also developed a model for separating the early-stage from the late-stage achieving an AUC of 0.81 in the replication cohort. These models were based on eleven proteins in total (ALPP, CXCL8, DPY30, IL6, IL12, KRT19, PAEP, TSPAN1, SIGLEC5, VTCN1, and WFDC2), notably without MUCIN-16. The majority of the associated proteins have been connected to ovarian cancer but not identified as potential biomarkers. Conclusions: The results show the ability of using high-precision proteomics for the identification of novel plasma protein biomarker candidates for the early detection of ovarian cancer.

Applications of artificial intelligence (AI) in ovarian cancer, pancreatic cancer, and image biomarker discovery

BACKGROUND

Artificial intelligence (AI), including machine learning (ML) and deep learning, has the potential to revolutionize biomedical research. Defined as the ability to "mimic" human intelligence by machines executing trained algorithms, AI methods are deployed for biomarker discovery.

OBJECTIVE

We detail the advancements and challenges in the use of AI for biomarker discovery in ovarian and pancreatic cancer. We also provide an overview of associated regulatory and ethical considerations.

METHODS

We conducted a literature review using PubMed and Google Scholar to survey the published findings on the use of AI in ovarian cancer, pancreatic cancer, and cancer biomarkers.

RESULTS

Most AI models associated with ovarian and pancreatic cancer have yet to be applied in clinical settings, and imaging data in many studies are not publicly available. Low disease prevalence and asymptomatic disease limits data availability required for AI models. The FDA has yet to qualify imaging biomarkers as effective diagnostic tools for these cancers.

CONCLUSIONS

Challenges associated with data availability, quality, bias, as well as AI transparency and explainability, will likely persist. Explainable and trustworthy AI efforts will need to continue so that the research community can better understand and construct effective models for biomarker discovery in rare cancers.

RNA biomarkers from proximal liquid biopsy for diagnosis of ovarian cancer

BACKGROUND

Most ovarian cancer patients are diagnosed at an advanced stage and have a high mortality rate. Current screening strategies fail to improve prognosis because markers that are sensitive for early stage disease are lacking. This medical need justifies the search for novel approaches using utero-tubal lavage as a proximal liquid biopsy.

METHODS

In this study, we explore the extracellular transcriptome of utero-tubal lavage fluid obtained from 26 ovarian cancer patients and 48 controls using messenger RNA (mRNA) capture and small RNA sequencing.

RESULTS

We observed an enrichment of ovarian and fallopian tube specific messenger RNAs in utero-tubal lavage fluid compared to other human biofluids. Over 300 mRNAs and 41 miRNAs were upregulated in ovarian cancer samples compared with controls. Upregulated genes were enriched for genes involved in cell cycle activation and proliferation, hinting at a tumor-derived signal.

CONCLUSION

This is a proof-of-principle that mRNA capture sequencing of utero-tubal lavage fluid is technically feasible, and that the extracellular transcriptome of utero-tubal lavage should be further explored in larger cohorts to assess the diagnostic value of the biomarkers identified in this study.

IMPACT

Proximal liquid biopsy from the gynecologic tract is a promising source for mRNA and miRNA biomarkers for diagnosis of early-stage ovarian cancer.

Circulating Exosomal miR-1290 for Diagnosis of Epithelial Ovarian Cancer

The aim of the study was to develop a new diagnostic biomarker for identifying serum exosomal miRNAs specific to epithelial ovarian cancer (EOC) and to find out target gene of the miRNA for exploring the molecular mechanisms in EOC. A total of 84 cases of ovarian masses and sera were enrolled, comprising EOC (n = 71), benign ovarian neoplasms (n = 13). We detected expression of candidate miRNAs in the serum and tissue of both benign ovarian neoplasm group and EOC group using real-time polymerase chain reaction. Immunohistochemistry were constructed using formalin fixed paraffin embedded (FFPE) tissue to detect expression level of suppressor of cytokine signaling 4 (SOCS4). In the EOC group, miRNA-1290 was significantly overexpressed in serum exosomes and tissues as compared to benign ovarian neoplasm group (fold change ≥ 2, p < 0.05). We observed area under the receiver operating characteristic curve (AUC) for miR-1290, using a cut-off of 0.73, the exosomal miR-1290 from serum had AUC, sensitivity, and specificity values of 0.794, 69.2 and 87.3, respectively. In immunohistochemical study, expression of SOCS4 in EOC was lower than that in benign ovarian neoplasm. Serum exosomal miR-1290 could be considered as a biomarker for differential diagnosis of EOC from benign ovarian neoplasm and SOCS4 might be potential target gene of miR-1290 in EOC.

[Clinical use and evolution of circulating biomarkers in the era of personalized oncology: From protein markers to bioclinical scores]

In oncology, the identification of targets that correlate with a type of cancer has led to a profound change in the notion of "tumor markers". Technological advances, in particular the development of high-throughput sequencing, have led to the emergence of a new generation of molecular biomarkers for tumors. Despite their limited utility for screening and diagnosis, conventional tumor markers remain interesting for evaluation of prognoses, the choice and optimization of treatments, as well as for monitoring the effectiveness of those treatments. In this article, we revisit the conventional serum markers that are enjoying a 'come back' thanks to the development of high-performance scores based on biological, cytological, clinical, or radiological criteria.

Circulating tumor DNA: a noninvasive biomarker for tracking ovarian cancer

Ovarian cancer is the fifth leading cause of cancer-related mortality in women worldwide. Despite the development of technologies over decades to improve the diagnosis and treatment of patients with ovarian cancer, the survival rate remains dismal, mainly because most patients are diagnosed at a late stage. Traditional treatment methods and biomarkers such as cancer antigen-125 as a cancer screening tool lack specificity and cannot offer personalized combinatorial therapy schemes. Circulating tumor DNA (ctDNA) is a promising biomarker for ovarian cancer and can be detected using a noninvasive liquid biopsy. A wide variety of ctDNA applications are being elucidated in multiple studies for tracking ovarian carcinoma during diagnostic and prognostic evaluations of patients and are being integrated into clinical trials to evaluate the disease. Furthermore, ctDNA analysis may be used in combination with multiple "omic" techniques to analyze proteins, epigenetics, RNA, nucleosomes, exosomes, and associated immune markers to promote early detection. However, several technical and biological hurdles impede the application of ctDNA analysis. Certain intrinsic features of ctDNA that may enhance its utility as a biomarker are problematic for its detection, including ctDNA lengths, copy number variations, and methylation. Before the development of ctDNA assays for integration in the clinic, such issues are required to be resolved since these assays have substantial potential as a test for cancer screening. This review focuses on studies concerning the potential clinical applications of ctDNA in ovarian cancer diagnosis and discusses our perspective on the clinical research aimed to treat this daunting form of cancer.