CA125 and Ovarian Cancer: A Comprehensive Review

Biomarkers in Alzheimer's disease

Alzheimer's disease (AD) is among the most common neurodegenerative disorders. AD is characterized by deposition of neurofibrillary tangles and amyloid plaques, leading to associated secondary pathologies, progressive neurodegeneration, and eventually death. Currently used diagnostics are largely image-based, lack accuracy and do not detect early disease, ie, prior to onset of symptoms, thus limiting treatment options and outcomes. Although biomarkers such as amyloid-β and tau protein in cerebrospinal fluid have gained much attention, these are generally limited to disease progression. Unfortunately, identification of biomarkers for early and accurate diagnosis remains a challenge. As such, body fluids such as sweat, serum, saliva, mucosa, tears, and urine are under investigation as alternative sources for biomarkers that can aid in early disease detection. This review focuses on biomarkers identified through proteomics in various biofluids and their potential for early and accurate diagnosis of AD.

Single-detector multiplex imaging flow cytometry for cancer cell classification with deep learning

Imaging flow cytometry, which combines the advantages of flow cytometry and microscopy, has emerged as a powerful tool for cell analysis in various biomedical fields such as cancer detection. In this study, we develop multiplex imaging flow cytometry (mIFC) by employing a spatial wavelength division multiplexing technique. Our mIFC can simultaneously obtain brightfield and multi-color fluorescence images of individual cells in flow, which are excited by a metal halide lamp and measured by a single detector. Statistical analysis results of multiplex imaging experiments with resolution test lens, magnification test lens, and fluorescent microspheres validate the operation of the mIFC with good imaging channel consistency and micron-scale differentiation capabilities. A deep learning method is designed for multiplex image processing that consists of three deep learning networks (U-net, very deep super resolution, and visual geometry group 19). It is demonstrated that the cluster of differentiation 24 (CD24) imaging channel is more sensitive than the brightfield, nucleus, or cancer antigen 125 (CA125) imaging channel in classifying the three types of ovarian cell lines (IOSE80 normal cell, A2780, and OVCAR3 cancer cells). An average accuracy rate of 97.1% is achieved for the classification of these three types of cells by deep learning analysis when all four imaging channels are considered. Our single-detector mIFC is promising for the development of future imaging flow cytometers and for the automatic single-cell analysis with deep learning in various biomedical fields.

Gynecologic Cancer InterGroup CA125 response has a high negative predictive value for CHK1 inhibitor RECIST response in recurrent ovarian cancer

We investigated the association of CA125 response with prognosis and RECIST response/progressive disease (PD) criteria in recurrent high grade serous ovarian cancer (HGSOC) patients treated with a cell cycle checkpoint kinase 1 inhibitor (CHK1i), prexasertib. 81 patients had measurable disease per RECISTv1.1, of which 72 and 70 were measurable by Gynecologic Cancer InterGroup (GCIG) CA125 response and PD criteria, respectively. Univariate and multivariate analyses showed that GCIG CA125 response (n = 32) is associated with improved progression-free survival (PFS) and overall survival (OS) compared to no GCIG CA125 response (n = 40) (median PFS 8.0 vs. 3.5 months [HR: 0.30, 95% CI: 0.18-0.51, p < 0.0001]; median OS 19.8 vs. 10.0 months [HR: 0.38, 95% CI: 0.23-0.64, p < 0.001]) independent of BRCA mutation status, platinum-sensitivity, previous PARP inhibitor therapy, ECOG performance status, and FIGO stage. Notably, GCIG CA125 response had a high negative predictive value (NPV: 93%, 95% CI: 80-98), but poor positive predictive value (PPV: 53%, 95% CI: 35-71) in predicting RECIST response. CA125 PD criteria also showed poor concordance with RECIST PD (PPV 56%, 95% CI: 40-71; NPV 33%, 95% CI: 17-54). Therefore, serum CA125 may be useful as a highly accessible prognostic and predictive biomarker to CHK1i therapy in recurrent HGSOC.

Development and validation of a nomogram for preoperatively predicting permanent stoma after rectal cancer surgery with ileostomy: a retrospective cohort study

BACKGROUND

For patients with rectal cancer, the utilization of temporary ileostomy (TI) has proven effective in minimizing the occurrence of severe complications post-surgery, such as anastomotic leaks; however, some patients are unable to reverse in time or even develop a permanent stoma (PS). We aimed to determine the preoperative predictors associated with TS failure and develop and validate appropriate predictive models to improve patients' quality of life.

METHODS

This research included 403 patients with rectal cancer who underwent temporary ileostomies between January 2017 and December 2021. All patients were randomly divided into either the developmental (70%) or validation (30%) group. The independent risk factors for PS were determined using univariate and multivariate logistic regression analyses. Subsequently, a nomogram was constructed, and the prediction probability was estimated by calculating the area under the curve (AUC) using receiver operating characteristic (ROC) analysis. A calibration plot was used to evaluate the nomogram calibration.

RESULTS

Of the 403 enrolled patients, 282 were randomized into the developmental group, 121 into the validation group, and 58 (14.39%) had a PS. The development group consisted of 282 patients, of whom 39 (13.81%) had a PS. The validation group consisted of 121 patients, of whom, 19 (15.70%) had a PS; 37 related factors were analyzed in the study. Multivariate logistic regression analysis demonstrated significant associations between the occurrence of PS and various factors in this patient cohort, including tumor location (OR = 6.631, P = 0.005), tumor markers (OR = 2.309, P = 0.035), American Society of Anesthesiologists (ASA) score (OR = 4.784, P = 0.004), T4 stage (OR = 2.880, P = 0.036), lymph node metastasis (OR = 4.566, P = 0.001), and distant metastasis (OR = 4.478, P = 0.036). Furthermore, a preoperative nomogram was constructed based on these data and subsequently validated in an independent validation group.

CONCLUSION

We identified six independent preoperative risk factors associated with PS following rectal cancer resection and developed a validated nomogram with an area under the ROC curve of 0.7758, which can assist surgeons in formulating better surgical options, such as colostomy, for patients at high risk of PS.

CdS/Bi2S3/NiS ternary heterostructure-based photoelectrochemical immunosensor for the sensitive detection of carbohydrate antigen 125

The sensitive, accurate and rapid detection of carbohydrate antigen 125 (CA125) is essential for the early diagnosis and clinical management of ovarian cancer, but there is still challenge. Herein, a photoelectrochemical (PEC) immunosensor based on CdS/Bi2S3/NiS ternary sulfide heterostructured photocatalyst was presented for the detection of CA125. The CdS/Bi2S3/NiS was synthesized by a one-step hydrothermal approach. The heterojunction comprising of CdS and Bi2S3 could separate photogenerated carriers, the introduced narrow bandgap NiS could act as electron-conducting bridge to facilitate the transfer of interfacial photogenerated electrons, thereby improving the photoelectric conversion efficiency. Due to their synergistic effect, the photocurrent response produced by the composite was up to 14.6 times of pure CdS. On the basis, a PEC immunosensor was constructed by introducing the CA125 antibody through thioglycolic acid linkage. It was found that the resulting immunosensor showed good performance. Under the optimized conditions, its linear detection range was as wide as 1 pg mL-1-50 ng mL-1, and the detection limit was low to 0.85 pg mL-1. Furthermore, we experimentally tested its anti-interference, stability and reproducibility, and satisfactory results were achieved. The practicable feasibility of the sensor was confirmed by testing serum sample. Thus this work provided a simple, fast and enough sensitive approach for CA125 monitoring.

Biotin-functionalized nanoparticles: an overview of recent trends in cancer detection

Electrochemical bio-sensing is a potent and efficient method for converting various biological recognition events into voltage, current, and impedance electrical signals. Biochemical sensors are now a common part of medical applications, such as detecting blood glucose levels, detecting food pathogens, and detecting specific cancers. As an exciting feature, bio-affinity couples, such as proteins with aptamers, ligands, paired nucleotides, and antibodies with antigens, are commonly used as bio-sensitive elements in electrochemical biosensors. Biotin-avidin interactions have been utilized for various purposes in recent years, such as targeting drugs, diagnosing clinically, labeling immunologically, biotechnology, biomedical engineering, and separating or purifying biomolecular compounds. The interaction between biotin and avidin is widely regarded as one of the most robust and reliable noncovalent interactions due to its high bi-affinity and ability to remain selective and accurate under various reaction conditions and bio-molecular attachments. More recently, there have been numerous attempts to develop electrochemical sensors to sense circulating cancer cells and the measurement of intracellular levels of protein thiols, formaldehyde, vitamin-targeted polymers, huwentoxin-I, anti-human antibodies, and a variety of tumor markers (including alpha-fetoprotein, epidermal growth factor receptor, prostate-specific Ag, carcinoembryonic Ag, cancer antigen 125, cancer antigen 15-3, etc.). Still, the non-specific binding of biotin to endogenous biotin-binding proteins present in biological samples can result in false-positive signals and hinder the accurate detection of cancer biomarkers. This review summarizes various categories of biotin-functional nanoparticles designed to detect such biomarkers and highlights some challenges in using them as diagnostic tools.

MUC16 as a serum-based prognostic indicator of prometastatic gastric cancer

Metastatic gastric cancer (GC) presents significant clinical challenges due to its poor prognosis and limited treatment options. To address this, we conducted a targeted protein biomarker discovery study to identify markers predictive of metastasis in advanced GC (AGC). Serum samples from 176 AGC patients (T stage 3 or higher) were analyzed using the Olink Proteomics Target panels. Patients were retrospectively categorized into nonmetastatic, metastatic, and recurrence groups, and differential protein expression was assessed. Machine learning and gene set enrichment analysis (GSEA) methods were applied to discover biomarkers and predict prognosis. Four proteins (MUC16, CAIX, 5'-NT, and CD8A) were significantly elevated in metastatic GC patients compared to the control group. Additionally, GSEA indicated that the response to interleukin-4 and hypoxia-related pathways were enriched in metastatic patients. Random forest classification and decision-tree modeling showed that MUC16 could be a predictive marker for metastasis in GC patients. Additionally, ELISA validation confirmed elevated MUC16 levels in metastatic patients. Notably, high MUC16 levels were independently associated with metastatic progression in T3 or higher GC. These findings suggest the potential of MUC16 as a clinically relevant biomarker for identifying GC patients at high risk of metastasis.

Prediction of ovarian cancer using artificial intelligence tools

Purpose

Ovarian cancer is a common type of cancer and a leading cause of death in women. Therefore, accurate and fast prediction of ovarian tumors is crucial. One of the appropriate and precise methods for predicting and diagnosing this cancer is to build a model based on artificial intelligence methods. These methods provide a tool for predicting ovarian cancer according to the characteristics and conditions of each person.

Method

In this study, a data set included records related to 171 cases of benign ovarian tumors, and 178 records related to cases of ovarian cancer were analyzed. The data set contains the records of blood test results and tumor markers of the patients. After data preprocessing, including removing outliers and replacing missing values, the weight of the effective factors was determined using information gain indices and the Gini index. In the next step, predictive models were created using random forest (RF), support vector machine (SVM), decision trees (DT), and artificial neural network (ANN) models. The performance of these models was evaluated using the 10-fold cross-validation method using the indicators of specificity, sensitivity, accuracy, and the area under the receiver operating characteristic curve. Finally, by comparing the performance of the models, the best predictive model of ovarian cancer was selected.

Results

The most important predictive factors were HE4, CA125, and NEU. The RF model was identified as the best predictive model, with an accuracy of more than 86%. The predictive accuracy of DT, SVM, and ANN models was estimated as 82.91%, 85.25%, and 79.35%, respectively. Various artificial intelligence (AI) tools can be used with high accuracy and sensitivity in predicting ovarian cancer.

Conclusion

Therefore, the use of these tools can help specialists and patients with early, easier, and less expensive diagnosis of ovarian cancer. Future studies can leverage AI to integrate image data with serum biomarkers, thereby facilitating the creation of novel models and advancing the diagnosis and treatment of ovarian cancer.

Ovarian cancer: A review for primary care providers

ABSTRACT

Ovarian cancer is the second most common gynecologic cancer in the United States and the deadliest gynecologic cancer worldwide, with a 5-year survival rate of less than 50%. Because of its vague symptoms, more than half of patients present with advanced disease and metastasis. This article reviews the epidemiology, pathogenesis, risk factors, screening, presentation, and diagnosis of ovarian cancer, in addition to providing an overview of the standard approach to treatment and novel targeted biologic therapies.

An increase of serum CA-125 to two times of nadir level strongly predicts the image-identified relapse of serous ovarian cancer

Using 70 U/ml or 35 U/ml as CA125 routine abnormal threshold may result in omissions in the relapse detection of Ovarian cancer (OvCa). This study aimed to clarify the association between a biochemical relapse (only the elevation of CA125) and an image-identified relapse to predict the relapsed lesions better. 162 patients who achieved complete clinical response were enrolled from women diagnosed with stage I-IV serous ovarian, tubal, and peritoneal cancers from January 2013 to June 2019 at our center. The CA125 level of 2 × nadir was defined as the indicator of image-identified relapse (P < 0.001). Compared to CA125 level exceeding 35 U/ml, the 2 × nadir of CA125 improve the sensitivity of image-identified relapse (84.9% vs 67.4%, P < 0.001); the 2 × nadir value can act as an earlier warning relapse signal with a longer median time to image-identified relapse (2.7 vs. 0 months, P < 0.001). Of the relapsed population, there was no difference of CA125 changing trend between the neoadjuvant chemotherapy (NACT) and primary debulking surgery (PDS) group after initial treatment. Compared with 35 U/ml, CA125 reaching 2 × nadir during the follow-up process might be a more sensitive and early relapse signal in patients with serous OvCa. This criterion may help guide patients to be recommended for imaging examination to detect potential relapse in time.

A signature of circulating miRNAs predicts the prognosis and therapeutic outcome of taxane/platinum regimen in advanced ovarian carcinoma patients

PURPOSE

Ovarian carcinoma (OC) is ranked as the eighth most lethal gynecological cancer due to late diagnosis and high recurrence. Existing biomarkers are lacking to predict the recurrence and stratify patients who are likely to benefit from chemotherapy. MicroRNAs (miRNAs/miRs) are persistently present in humans and are capable of predicting treatment outcomes. Thus, the purpose of the study was to assess the potential of circulatory miRNAs to predict the efficacy of OC.

METHODS

Newly diagnosed n = 208 OC patients were administrated neoadjuvant/adjuvant chemotherapy (taxane + platinum) after surgery. Their demographic, gynecologic, clinical parameters, response, and survival were recorded. MiR-27a, miR-182, miR-199a, miR-214, and miR-591 were taken and the expression were analyzed using real-time PCR at different treatment intervals. Further, its prognostic value (Kaplan-Meier, and Cox regression analysis) and diagnostic importance (receiver operating characteristic curve) were validated.

RESULT

The mean age of patients with poorly differentiated (45.2%) serous OC was 48.69 ± 10.38. The majority experienced menarche at ≥ 12 (62.2%) with poor menstrual hygiene (81.8%) and were post-menopausal (69.4%), some were associated with high risk of survival (HR =  > 1). MiRNA signature showed three over-expression and two under-expression (miR-27a, miR-182, and miR-214; miR-199a and miR-591) in advanced OC compared to the control (P=  < 0.05). Also, a significant difference was detected at each time interval of treatment with the response (P =  ≤ 0.001) associated with resistance and overall survival (P =  ≤ 0.001) with risk (HR =  > 1). ROC analysis showed enhanced the diagnostics accuracy (< 0.001).

CONCLUSION

Our findings indicate that circulating miRNAs might be a potential minimally invasive diagnostic marker for treatment outcome and recurrence in ovarian carcinoma.

Demons-Meigs syndrome caused by a giant ovarian fibroma: A case report

Demons-Meigs syndrome is a very rare entity. It combines a benign ovarian "fibroma-like" tumor with ascites and hydrothorax. The notion of benignancy is the key point. CA-125 levels are most of the time normal, but high levels can be observed in rare cases which makes it difficult to have a diagnostic. We present here the case of a 43-year-old female patient who presented with abdominopelvic pain. Imaging discovered a 30 cm large intraabdominal mass with ascites and bilateral pleural effusion. Surgical resection of the tumor was performed, and pathology identified an ovarian fibroma. No postintervention complications were observed, with resorption of the ascites and hydrothorax.

Investigating the efficacy of tissue factor pathway inhibitor‑2 as a promising prognostic marker for ovarian cancer

Tissue factor pathway inhibitor-2 (TFPI2) is a tumor marker for diagnosing ovarian cancer and ovarian clear cell carcinoma (OCCC); however, its effectiveness as a prognostic marker remains unclear. The present study aimed to investigate the utility of TFPI2 as a prognostic marker for ovarian cancer. A total of 256 cases of ovarian cancer was collected at Nara Medical University (Kashihara, Japan) from January 2008 to January 2022. The majority of cases were serous carcinoma (109, 42.6%), followed by OCCC (66, 25.8%), mucinous carcinoma (40, 15.6%), endometrial carcinoma (15, 5.9%), and other (26, 10.2%). The median preoperative serum TFPI2 for ovarian cancer was 219.0 (82.5-5,824.2) pg/ml. Overall survival (OS) of patients with non-OCCC and OCCC was calculated using the cut-off value determined obtained through receiver operating characteristic curve analysis. Cut-off values of TFPI2 for OS were 201 for non-OCCC and 255 pg/ml for OCCC. In univariate analysis, OS was significantly elevated in patients with non-OCCC and OCCC who had TFPI2 levels ≥201 pg/ml (P<0.001) and ≥255 pg/ml (P=0.036), respectively. Progression-free survival (PFS) was significantly elevated in patients with non-OCCC and OCCC who had TFPI2 levels ≥201 and ≥255 pg/ml (both P<0.001), respectively. Multivariate analysis revealed that OS was significantly higher in patients with non-OCCC who had TFPI2 levels ≥201 pg/ml (P=0.021), while PFS was significantly higher in patients with OCCC who had TFPI2 levels ≥255 pg/ml (P=0.020). These findings suggest that TFPI2 is a potential prognostic marker for ovarian carcinoma.

An electrochemical immunosensor based on MXene-GQD/AuNPs for the detection of trace amounts of CA-125 as specific tracer of ovarian cancer

An electrochemical immunoassay system was developed to detect CA-125 using a glassy carbon electrode (GCE) modified with MXene, graphene quantum dots (GQDs), and gold nanoparticles (AuNPs). The combined MXene-GQD/AuNPs modification displayed advantageous electrochemical properties due to the synergistic effects of MXene, GQDs, and AuNPs. The MXene-GQD composite in the modified layer provided strong mechanical properties and a large specific surface area. Furthermore, the presence of AuNPs significantly improved conductivity and facilitated the binding of anti-CA-125 on the modified GCE, thereby enhancing sensitivity. Various analytical techniques such as FE-SEM and EDS were utilized to investigate the structural and morphological characteristics as well as the elemental composition. The performance of the developed immunosensor was assessed using electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), square wave voltammetry (SWV), and differential pulse voltammetry (DPV). Under optimized conditions in a working potential range of -0.2 to 0.6 V (vs. Ag/AgCl), the sensitivity, linear range (LR), limit of detection (LOD), and correlation coefficient (R2) were determined to be 315.250 µA pU.mL-1/cm2, 0.1 to 1 nU/mL, 0.075 nU/mL, and 0.9855, respectively. The detection of CA-125 in real samples was investigated using the developed immunoassay platform, demonstrating satisfactory results including excellent selectivity and reproducibility.

Longitudinal evaluation of external quality assessment results for CA 15-3, CA 19-9, and CA 125

Background

Tumor markers are established laboratory tools that help to diagnose, estimate prognosis, and monitor the course of cancer. For meaningful decision-making in patient care, it is essential that methods and analytical platforms demonstrate high sensitivity, specificity, precision, and comparability. Regular participation at external quality assessment (EQA) schemes is mandatory for laboratories. Here, a longitudinal evaluation of EQA data was performed to assess the performance of tumor marker assays over time.

Methods

Longitudinal data of the cancer antigens (CA) 15-3 (n = 5,492), CA 19-9 (n = 6,802), and CA 125 (n = 5,362) from 14 INSTAND EQAs conducted between 2019 and 2023 were evaluated. A median of 197, 244 and 191 laboratories participated at the EQAs for CA 15-3, CA 19-9 and CA 125, respectively. Data evaluation encompasses intra- and inter-manufacturer specific variations over time, assay precision, and adherence to the EQA limits of ±24% for CA 15-3, ±27% for CA 19-9 and ±36% for CA 125.

Results

The study showed median manufacturer-dependent differences of up to 107% for CA 15-3, 99% for CA 125, and even 549% for CA 19-9 between the highest and the lowest methods over the studied period. Regarding the normalized median of all methods, the values of the most deviant methods were 0.42 for CA 15-3, 7.61 for CA 19-9, and 1.82 for CA 125. Intra-manufacturer variability was generally low, with median coefficients of variation (CV) below 10%. As the methods were evaluated according to method-specific consensus values, most participants passed the EQAs within the acceptance criteria. When the criteria were consistently set at 24%, the central 90% of participants passed the EQAs in 78.6%-100% for CA 15-3 (with exception of AX), 89.3%-100% for CA 125, and 64.3%-100% for CA 19-9.

Conclusion

While intra-method precision of most analytical platforms is acceptable for all three tumor markers, considerable inter-method variability was observed over the whole studied period demonstrating the necessity for better standardization and harmonization of the methods, development of international reference materials, and comprehensive commutability studies with patient samples.

Combined aqupla, paclitaxel liposome, and docetaxel treatment: survival and biomarker outcomes in recurrent ovarian cancer patients

As one lethal malignancy in women's reproductive systems, ovarian cancer (OC) is frequently detected at an advanced phase during diagnosis. when the disease has spread widely. The absence of obvious symptoms and powerful screening tools in the early stages makes treatment difficult and the prognosis poor. Despite the clinical remission that can be achieved in some patients after initial treatment, the recurrence rate is conspicuous, posing a considerable challenge in treating recurrent OC (ROC). In the retrospective analysis, we compared the effects of two treatment regimens, aqupla combined with paclitaxel liposome (NP group) versus aqupla combined with docetaxel (ND group), on survival and biomarkers in patients with ROC. The study included 121 OC patients, and clinical data were collected through an electronic medical record system, outpatient review records, and a follow-up record system. The results revealed a notably higher overall remission rate in the ND group than the NP group, but revealed no notable inter-group discrepancy in toxicities, implying that the aqupla combined with docetaxel regimen may be more effective in platinum-sensitive ROC patients. Additionally, post-treatment CA125 levels were lower in patients in the ND group, suggesting that the regimen may be more effective in reducing tumour load. Survival analysis further revealed that treatment regimen, FIGO stage, number of recurrent lesions, and pretreatment CA125 level were independent prognostic factors affecting patients' 5-year OS and PFS. Overall for ROC patients, especially platinum-sensitive patients, the aqupla in combination with docetaxel regimen provided an improved survival benefit with a comparable safety profile, highlighting the importance of individualised treatment strategies.

Polymer-Based Terbium Complex as a Fluorescent Probe for Cancer Antigen 125 Detection: A Promising Tool for Early Diagnosis of Ovarian Cancer

A novel photoprobe, Tb-acetylacetone (Tb-ACAC) doped within a modified epoxy cellulose polymer immobilized with CA-125 monoclonal antibody, offers an accurate and highly selective method for early ovarian cancer (OC) diagnosis by detecting cancer antigen 125 (CA-125) in serum samples. This approach leverages quenching of the Tb-ACAC luminescence upon binding to CA-125. Characterization of the photoprobe film through UV-vis and fluorescence measurements confirmed the presence of Tb-ACAC within the polymer matrix. In aqueous solution (pH 6.8, λex = 365 nm), the characteristic emission band of Tb-ACAC at λem = 546.2 nm exhibited significant quenching upon CA-125 binding. This quenching effect enabled the sensitive and specific detection of CA-125 in diverse serum samples from OC patients, demonstrating the applicability, simplicity, and effectiveness of this novel approach.

High-throughput viable circulating tumor cell isolation using tapered-slit membrane filter-based chipsets in the differential diagnosis of ovarian tumors

OBJECTIVE

To evaluate the diagnostic performance of circulating tumor cells (CTCs) using tapered-slit membrane filter (TSF)-based chipsets for the differential diagnosis of adnexal tumors.

METHODS

A total of 230 women with indeterminate adnexal tumors were prospectively enrolled. The sensitivity, specificity, and accuracy of the CTC-detecting chipsets were analyzed according to postoperative pathological results and compared with those of cancer antigen (CA)-125 and imaging tests.

RESULTS

Eighty-one (40.3%) benign tumors, 31 (15.4%) borderline tumors, and 89 (44.3%) ovarian cancers were pathologically confirmed. The sensitivity, specificity, and accuracy of CTC-detecting chipsets (75.3%, 58.0%, and 67.1%) for differentiating ovarian cancer from benign tumors were similar to CA-125 (78.7%, 53.1%, and 66.5%), but lower than CT/MRI (94.2%, 77.9%, and 86.5%). "CTC or CA125" showed increased sensitivity (91.0%) and "CTC and CA-125" revealed increased specificity (77.8%), comparable to CT/MRI. CTC detection rates in stage I/II and stage III/IV ovarian cancers were 69.6% and 81.4%, respectively. The sensitivity to detect high-grade serous (HGS) cancer from benign tumors (84.6%) was higher than that to detect non-HGS cancers (68.0%).

CONCLUSION

Although the diagnostic performance of the TSF platform to differentiate between ovarian cancer and benign tumors did not yield significant results, the combination of CTC and CA-125 showed promising potential in the diagnostic accuracy of ovarian cancer.

Circulating tumor DNA as a biomarker for predicting progression-free survival and overall survival in patients with epithelial ovarian cancer: a systematic review and meta-analysis

OBJECTIVES

Circulating tumor DNA (ctDNA) is emerging as a potential prognostic biomarker in multiple tumor types. However, despite the many studies available on small series of patients with ovarian cancer, a recent systematic review and meta-analysis is lacking. The objective of this study was to determine the association of ctDNA with progression-free-survival and overall survival in patients with epithelial ovarian cancer.

METHODS

An electronic search was conducted using PubMed (MEDLINE), Embase, CENTRAL (Cochrane Library), and CINAHL-Complete from January 2000 to September 15, 2023. To be included in the analysis the studies had to meet the following pre-specified inclusion criteria: (1) evaluable ctDNA; (2) progression-free-survival and overall survival reported as hazard ratio (HR); and (3) the patient population had epithelial ovarian cancer at the time of ctDNA detection. We evaluated the association of ctDNA with progression-free survival and overall survival. Secondary outcomes focused on sub-group analysis of genomic alterations and international Federation of Gynecology and Obstetrics (FIGO) stage.

RESULTS

A total of 26 studies reporting on 1696 patients with epithelial ovarian cancer were included. The overall concordance rate between plasma-based and tissue-based analyses was approximately 62%. We found that a high level of ctDNA in epithelial ovarian cancer was associated with worse progression-free survival (HR 5.31, 95% CI 2.14 to 13.17, p<0.001) and overall survival (HR 2.98, 95% CI 1.86 to 4.76, p<0.0001). The sub-group analysis showed a greater than threefold increase in the risk of relapse in patients with positive HOXA9 meth-ctDNA (HR 3.84, 95% CI 1.57 to 9.41, p=0.003).

CONCLUSIONS

ctDNA was significantly associated with worse progression-free survival and overall survival in patients with epithelial ovarian cancer. Further prospective studies are needed.

PROSPERO REGISTRATION NUMBER

CRD42023469390.

Diagnosing and staging epithelial ovarian cancer by serum glycoproteomic profiling

BACKGROUND

There is a need for diagnostic tests for screening, triaging and staging of epithelial ovarian cancer (EOC). Glycoproteomics of blood samples has shown promise for biomarker discovery.

METHODS

We applied glycoproteomics to serum of people with EOC or benign pelvic masses and healthy controls. A total of 653 analytes were quantified and assessed in multivariable models, which were tested in an independent cohort. Additionally, we analyzed glycosylation patterns in serum markers and in tissues.

RESULTS

We identified a biomarker panel that distinguished benign lesions from EOC with sensitivity and specificity of 83.5% and 90.1% in the training set, and of 86.7 and 86.7% in the test set, respectively. ROC analysis demonstrated strong performance across a range of cutoffs. Fucosylated multi-antennary glycopeptide markers were higher in late-stage than in early-stage EOC. A comparable pattern was found in late-stage EOC tissues.

CONCLUSIONS

Blood glycopeptide biomarkers have the potential to distinguish benign from malignant pelvic masses, and early- from late-stage EOC. Glycosylation of circulating and tumor tissue proteins may be related. This study supports the hypothesis that blood glycoproteomic profiling can be used for EOC diagnosis and staging and it warrants further clinical evaluation.

Sensitive circulating tumor DNA-based residual disease detection in epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is one of the leading causes of cancer-related death in women worldwide, and is characterized by a high rate of recurrence after surgery and chemotherapy. We sought to implement a circulating tumor DNA (ctDNA)-based blood test for more accurate post-operative surveillance of this disease. We analyzed 264 plasma samples collected between June 2016 and September 2021 from 63 EOC patients using tumor-guided plasma cell-free DNA analysis to detect residual disease after treatment. Assay specificity was verified using cross-patient analysis of 1,195 control samples. ctDNA was detected in 51 of 55 (93%) samples at diagnosis, and 18 of 18 (100%) samples at progression. Positive ctDNA in the last on-treatment sample was associated with rapid progression (median 1.02 versus 3.38 yr, HR = 5.63, P < 0.001) and reduced overall survival (median 2.31 versus NR yr, HR = 8.22, P < 0.001) in patients with high-grade serous cancer. In the case of 12 patients, ctDNA assays detected progression earlier than standard surveillance, with a median lead time of 5.9 mo. To approach the physical limits of ctDNA detection, five patients were analyzed using ultra-sensitive assays interrogating 479-1,856 tumor mutations, capable of tracking ctDNA fractions down to 0.0004%. Our results demonstrate that ctDNA assays achieve high sensitivity and specificity in detecting post-operative residual disease in EOC.

Surgical treatment for pulmonary metastasis from ovarian cancer: a retrospective case series

BACKGROUND

Distant metastases of ovarian cancer are rarely detected alone. The effectiveness of surgical intervention for pulmonary metastases from ovarian cancer remains uncertain. This study aimed to investigate the clinicopathologic characteristics and outcomes of patients undergoing resection for pulmonary metastasis from ovarian cancer.

CASE PRESENTATION

The clinicopathologic characteristics and outcomes of radical surgery for pulmonary metastasis from ovarian cancer were investigated. Out of 537 patients who underwent pulmonary metastasis resection at two affiliated hospitals between 2010 and 2021, four (0.74%) patients who underwent radical surgery for pulmonary metastasis from ovarian cancer were included. The patients were aged 67, 47, 21, and 59 years; the intervals from primary surgery to detection of pulmonary metastasis from ovarian cancer were 94, 21, 36, and 50 months; and the overall survival times after pulmonary metastasectomy were 53, 50, 94, and 34 months, respectively. Three of the four patients experienced recurrence after pulmonary metastasectomy. Further, preoperative carbohydrate antigen (CA) 125 levels were normal in two surviving patients and elevated in the two deceased patients.

CONCLUSION

In this study, three of the four patients experienced recurrence after pulmonary metastasectomy, but all patients survived for > 30 months after surgery. Patients with ovarian cancer and elevated CA125 levels may not be optimal candidates for pulmonary metastasectomy. To establish appropriate criteria for pulmonary metastasectomy in patients with ovarian cancer, further research on a larger patient cohort is warranted.

Exosomes in diagnostic and therapeutic applications of ovarian cancer

Ovarian cancer accounts for more deaths than any other female reproductive tract cancer. The major reasons for the high mortality rates include delayed diagnoses and drug resistance. Hence, improved diagnostic and therapeutic options for ovarian cancer are a pressing need. Extracellular vesicles (EVs), that include exosomes provide hope in both diagnostic and therapeutic aspects. They are natural lipid nanovesicles secreted by all cell types and carry molecules that reflect the status of the parent cell. This facilitates their potential use as biomarkers for an early diagnosis. Additionally, EVs can be loaded with exogenous cargo, and have features such as high stability and favorable pharmacokinetic properties. This makes them ideal for tumor-targeted delivery of biological moieties. The International Society of Extracellular Vesicles (ISEV) based on the Minimal Information for Studies on Extracellular Vesicles (MISEV) recommends the usage of the term "small extracellular vesicles (sEVs)" that includes exosomes for particles that are 30-200 nm in size. However, majority of the studies reported in the literature and relevant to this review have used the term "exosomes". Therefore, this review will use the term "exosomes" interchangeably with sEVs for consistency with the literature and avoid confusion to the readers. This review, initially summarizes the different isolation and detection techniques developed to study ovarian cancer-derived exosomes and the potential use of these exosomes as biomarkers for the early diagnosis of this devastating disease. It addresses the role of exosome contents in the pathogenesis of ovarian cancer, discusses strategies to limit exosome-mediated ovarian cancer progression, and provides options to use exosomes for tumor-targeted therapy in ovarian cancer. Finally, it states future research directions and recommends essential research needed to successfully transition exosomes from the laboratory to the gynecologic-oncology clinic.

Oncogenic Pathways and Targeted Therapies in Ovarian Cancer

Epithelial ovarian cancer (EOC) is one of the most aggressive forms of gynaecological malignancies. Survival rates for women diagnosed with OC remain poor as most patients are diagnosed with advanced disease. Debulking surgery and platinum-based therapies are the current mainstay for OC treatment. However, and despite achieving initial remission, a significant portion of patients will relapse because of innate and acquired resistance, at which point the disease is considered incurable. In view of this, novel detection strategies and therapeutic approaches are needed to improve outcomes and survival of OC patients. In this review, we summarize our current knowledge of the genetic landscape and molecular pathways underpinning OC and its many subtypes. By examining therapeutic strategies explored in preclinical and clinical settings, we highlight the importance of decoding how single and convergent genetic alterations co-exist and drive OC progression and resistance to current treatments. We also propose that core signalling pathways such as the PI3K and MAPK pathways play critical roles in the origin of diverse OC subtypes and can become new targets in combination with known DNA damage repair pathways for the development of tailored and more effective anti-cancer treatments.

Ovarian Cancer surgical consideration is markedly improved by the neural network powered-MIA3G multivariate index assay

Background

Surgery remains the main treatment option for an adnexal mass suspicious of ovarian cancer. The malignancy rate is, however, only 10-15% in women undergoing surgery. This results in a high number of unnecessary surgeries. A surveillance-based approach is recommended to form the basis for surgical referrals. We have previously reported the clinical performance of MIA3G, a deep neural network-based algorithm, for assessing ovarian cancer risk. In this study, we show that MIA3G markedly improves the surgical selection for women presenting with adnexal masses.

Methods

MIA3G employs seven serum biomarkers, patient age, and menopausal status. Serum samples were collected from 785 women (IQR: 39-55 years) across 12 centers that presented with adnexal masses. MIA3G risk scores were calculated for all subjects in this cohort. Physicians had no access to the MIA3G risk score when deciding upon a surgical referral. The performance of MIA3G for surgery referral was compared to clinical and surgical outcomes. MIA3G was also tested in an independent cohort comprising 29 women across 14 study sites, in which the physicians had access to and utilized MIA3G prior to surgical consideration.

Results

When compared to the actual number of surgeries (n = 207), referrals based on the MIA3G score would have reduced surgeries by 62% (n = 79). The reduction was higher in premenopausal patients (77%) and in patients ≤55 years old (70%). In addition, a 431% improvement in malignancy prediction would have been observed if physicians had utilized MIA3G scores for surgery selection. The accuracy of MIA3G referral was 90.00% (CI 87.89-92.11), while only 9.18% accuracy was observed when the MIA3G score was not used. These results were corroborated in an independent multi-site study of 29 patients in which the physicians utilized MIA3G in surgical consideration. The surgery reduction was 87% in this cohort. Moreover, the accuracy and concordance of MIA3G in this independent cohort were each 96.55%.

Conclusion

These findings demonstrate that MIA3G markedly augments the physician's decisions for surgical intervention and improves malignancy prediction in women presenting with adnexal masses. MIA3G utilization as a clinical diagnostic tool might help reduce unnecessary surgeries.

Mathematical modeling of the evolution of resistance and aggressiveness of high-grade serous ovarian cancer from patient CA-125 time series

A time-series analysis of serum Cancer Antigen 125 (CA-125) levels was performed in 791 patients with high-grade serous ovarian cancer (HGSOC) from the Australian Ovarian Cancer Study to evaluate the development of chemoresistance and response to therapy. To investigate chemoresistance and better predict the treatment effectiveness, we examined two traits: resistance (defined as the rate of CA-125 change when patients were treated with therapy) and aggressiveness (defined as the rate of CA-125 change when patients were not treated). We found that as the number of treatment lines increases, the data-based resistance increases (a decreased rate of CA-125 decay). We use mathematical models of two distinct cancer cell types, treatment-sensitive cells and treatment-resistant cells, to estimate the values and evolution of the two traits in individual patients. By fitting to individual patient HGSOC data, our models successfully capture the dynamics of the CA-125 level. The parameters estimated from the mathematical models show that patients with inferred low growth rates of treatment-sensitive cells and treatment-resistant cells (low model-estimated aggressiveness) and a high death rate of treatment-resistant cells (low model-estimated resistance) have longer survival time after completing their second-line of therapy. These findings show that mathematical models can characterize the degree of resistance and aggressiveness in individual patients, which improves our understanding of chemoresistance development and could predict treatment effectiveness in HGSOC patients.

Successful Management of a Giant Mucinous Cystadenocarcinoma of the Ovary Through Laparotomy: A Case Report

Giant mucinous cystadenocarcinoma of the ovary is rarely described. Huge ovarian masses are mostly benign, but malignancy should be ruled out by investigations and clinical assessment. Here, we present a case of a large mucinous cystadenocarcinoma of the ovary in a 48-year-old postmenopausal woman. Imaging examinations revealed a large cystic tumor that filled the whole abdominal cavity. Despite the difficulties presented by the size of the tumor and its malignant potential, laparotomy was carried out, which included bilateral salpingo-oophorectomy, total abdominal hysterectomy, exploration of other intra-abdominal organs, and pelvic lymphadenectomy. Histopathology indicated the presence of mucinous cystadenocarcinomas. Adjuvant chemotherapy was given post-operatively, and the patient maintained remission during follow-up. This case emphasizes the need for early detection by simple imaging modalities such as ultrasonography in cases of ovarian masses. Most adnexal masses, if detected early, are amenable to surgical management with a good prognosis. Large masses underline the need for a multidisciplinary approach to improve patient outcomes.

Emerging biomarkers and molecular targets for precision medicine in cervical cancer

Cervical cancer remains a significant global health burden, necessitating innovative approaches for improved diagnostics and personalized treatment strategies. Precision medicine has emerged as a promising paradigm, leveraging biomarkers and molecular targets to tailor therapy to individual patients. This review explores the landscape of emerging biomarkers and molecular targets in cervical cancer, highlighting their potential implications for precision medicine. By integrating these biomarkers into comprehensive diagnostic algorithms, clinicians can identify high-risk patients at an earlier stage, enabling timely intervention and improved patient outcomes. Furthermore, the identification of specific molecular targets has paved the way for the development of targeted therapies aimed at disrupting key pathways implicated in cervical carcinogenesis. In conclusion, the evolving landscape of biomarkers and molecular targets presents exciting opportunities for advancing precision medicine in cervical cancer. By harnessing these insights, clinicians can optimize treatment selection, enhance patient outcomes, and ultimately transform the management of this devastating disease.

Sensitivity and Specificity of Selected Biomarkers and Their Combinations in the Diagnosis of Ovarian Cancer

One of the greatest challenges in modern gynecological oncology is ovarian cancer. Despite the numerous studies currently being conducted, it is still sometimes detected at late clinical stages, where the prognosis is unfavorable. One significant contributing factor is the absence of sensitive and specific parameters that could aid in early diagnosis. An ideal screening test, in view of the low incidence of ovarian cancer, should have a sensitivity of greater than 75% and a specificity of at least 99.6%. To enhance sensitivity and specificity, diagnostic panels are being created by combining individual markers. The drive to develop better screening tests for ovarian cancer focuses on modern diagnostic methods based on molecular testing, which in turn aims to find increasingly effective biomarkers. Currently, researchers' efforts are focused on the search for a complementary parameter to those most commonly used that would satisfactorily enhance the sensitivity and specificity of assays. Several biomarkers, including microRNA molecules, autoantibodies, cDNA, adipocytokines, and galectins, are currently being investigated by researchers. This article reviews recent studies comparing the sensitivity and specificity of selected parameters used alone and in combination to increase detection of ovarian cancer at an early stage.

Stretchable Photonic Crystal-Assisted Glycoprotein Identification for Ovarian Cancer Diagnosis

Photonic crystals with specific wavelengths can realize surface-enhanced excitation and emission intensities of fluorophores and enhance the fluorescence signals of fluorescent molecules. Herein, stretchable photonic crystals with good mechanochromic properties provide continuously adjustable forbidden wavelengths by stretching to change the lattice spacing, with reflectance peaks blue-shifted up to 110 nm to match indicators of different wavelengths and produce differentiated optical enhancement effects. Glycoproteins are significantly identified as clinical markers. However, the wide participation of glycoproteins in various life processes poses enormous complexity and critical challenges for rapid, facile, high-throughput, and accurate clinical analysis or health assessment. In this work, we proposed a stretchable photonic crystal-assisted glycoprotein identification approach for early ovarian cancer diagnosis. Stretchable photonic crystals can provide rich optical information to efficiently identify glycoproteins in complex matrices. A double-indicator fluorescence sensor was designed to respond to the protein trunk and oligosaccharide segment of glycoproteins separately for improved recognition accuracy. Seven typical glycoproteins could be discriminated from proteins, saccharides, or mixture interferents. Clinical ovarian cancer samples for early, intermediate, and advanced ovarian cancer and healthy subjects were verified with 100% accuracy. This strategy of stretchable photonic crystal-assisted glycoprotein identification provides an effective method for accurate, rapid ovarian cancer diagnosis and timely clinical treatment.

Unravelling driver genes as potential therapeutic targets in ovarian cancer via integrated bioinformatics approach

Target-driven cancer therapy is a notable advancement in precision oncology that has been accompanied by substantial medical accomplishments. Ovarian cancer is a highly frequent neoplasm in women and exhibits significant genomic and clinical heterogeneity. In a previous publication, we presented an extensive bioinformatics study aimed at identifying specific biomarkers associated with ovarian cancer. The findings of the network analysis indicate the presence of a cluster of nine dysregulated hub genes that exhibited significance in the underlying biological processes and contributed to the initiation of ovarian cancer. Here in this research article, we are proceeding our previous research by taking all hub genes into consideration for further analysis. GEPIA2 was used to identify patterns in the expression of critical genes. The KM plotter analysis indicated that the out of all genes 5 genes are statistically significant. The cBioPortal platform was further used to investigate the frequency of genetic mutations across the board and how they affected the survival of the patients. Maximum mutation was reported by ELAVL2. In order to discover viable therapeutic candidates after competitive inhibition of ELAVL2 with small molecular drug complex, high throughput screening and docking studies were used. Five compounds were identified. Overall, our results suggest that the ELAV-like protein 2-ZINC03830554 complex was relatively stable during the molecular dynamic simulation. The five compounds that have been found can also be further examined as potential therapeutic possibilities. The combined findings suggest that ELAVL2, together with their genetic changes, can be investigated in therapeutic interventions for precision oncology, leveraging early diagnostics and target-driven therapy.

Current strategies for early epithelial ovarian cancer detection using miRNA as a potential tool

Ovarian cancer is one of the most aggressive and significant malignant tumor forms in the female reproductive system. It is the leading cause of death among gynecological cancers owing to its metastasis. Since its preliminary disease symptoms are lacking, it is imperative to develop early diagnostic biomarkers to aid in treatment optimization and personalization. In this vein, microRNAs, which are short sequence non-coding molecules, displayed great potential as highly specific and sensitive biomarker. miRNAs have been extensively advocated and proven to serve an instrumental part in the clinical management of cancer, especially ovarian cancer, by promoting the cancer cell progression, invasion, delayed apoptosis, epithelial-mesenchymal transition, metastasis of cancer cells, chemosensitivity and resistance and disease therapy. Here, we cover our present comprehension of the most up-to-date microRNA-based approaches to detect ovarian cancer, as well as current diagnostic and treatment strategies, the role of microRNAs as oncogenes or tumor suppressor genes, and their significance in ovarian cancer progression, prognosis, and therapy.

Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study

BACKGROUND

Accurate preoperative identification of ovarian tumour subtypes is imperative for patients as it enables physicians to custom-tailor precise and individualized management strategies. So, we have developed an ultrasound (US)-based multiclass prediction algorithm for differentiating between benign, borderline, and malignant ovarian tumours.

METHODS

We randomised data from 849 patients with ovarian tumours into training and testing sets in a ratio of 8:2. The regions of interest on the US images were segmented and handcrafted radiomics features were extracted and screened. We applied the one-versus-rest method in multiclass classification. We inputted the best features into machine learning (ML) models and constructed a radiomic signature (Rad_Sig). US images of the maximum trimmed ovarian tumour sections were inputted into a pre-trained convolutional neural network (CNN) model. After internal enhancement and complex algorithms, each sample's predicted probability, known as the deep transfer learning signature (DTL_Sig), was generated. Clinical baseline data were analysed. Statistically significant clinical parameters and US semantic features in the training set were used to construct clinical signatures (Clinic_Sig). The prediction results of Rad_Sig, DTL_Sig, and Clinic_Sig for each sample were fused as new feature sets, to build the combined model, namely, the deep learning radiomic signature (DLR_Sig). We used the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) to estimate the performance of the multiclass classification model.

RESULTS

The training set included 440 benign, 44 borderline, and 196 malignant ovarian tumours. The testing set included 109 benign, 11 borderline, and 49 malignant ovarian tumours. DLR_Sig three-class prediction model had the best overall and class-specific classification performance, with micro- and macro-average AUC of 0.90 and 0.84, respectively, on the testing set. Categories of identification AUC were 0.84, 0.85, and 0.83 for benign, borderline, and malignant ovarian tumours, respectively. In the confusion matrix, the classifier models of Clinic_Sig and Rad_Sig could not recognise borderline ovarian tumours. However, the proportions of borderline and malignant ovarian tumours identified by DLR_Sig were the highest at 54.55% and 63.27%, respectively.

CONCLUSIONS

The three-class prediction model of US-based DLR_Sig can discriminate between benign, borderline, and malignant ovarian tumours. Therefore, it may guide clinicians in determining the differential management of patients with ovarian tumours.

Poor Concordance Between Cancer Antigen-125 and RECIST Assessment for Progression in Patients With Platinum-Sensitive Relapsed Ovarian Cancer on Maintenance Therapy With a Poly(ADP-ribose) Polymerase Inhibitor

PURPOSE

Cancer antigen-125 (CA-125) is recommended by treatment guidelines and widely used to diagnose ovarian cancer recurrence. The value of CA-125 as a surrogate for disease progression (PD) and its concordance with radiologic progression are unclear, particularly for women with platinum-sensitive relapsed ovarian cancer (PSROC) who have responded to chemotherapy and treated with maintenance poly(ADP-ribose) polymerase inhibitor (PARPi).

METHODS

In this pooled analysis of four randomized trials of maintenance PARPi or placebo (Study 19, SOLO2, ARIEL3, and NOVA), we extracted data on CA-125 PD as defined by Gynecologic Cancer InterGroup criteria and RECIST v1.1. We evaluated the concordance between CA-125 and RECIST PD and reported on the negative predictive value (NPV) and positive predictive value (PPV).

RESULTS

Of 1,262 participants (n = 818 PARPi, n = 444 placebo), 403 (32%) had CA-125 PD, and of these, 366 had concordant RECIST PD (PPV, 91% [95% CI, 88 to 93]). However, of 859 (68%) without CA-125 PD, 382 also did not have RECIST PD (NPV, 44% [95% CI, 41 to 48]). Within the treatment arms, PPV remained high (PARPi, 91% [95% CI, 86 to 94]; placebo, 91% [95% CI, 86 to 95]) but NPV was lower on placebo (PARPi, 53% [95% CI, 49 to 57]; placebo, 25% [95% CI, 20 to 31]). Of 477 with RECIST-only PD, most (95%) had a normal CA-125 at the start of maintenance therapy and the majority (n = 304, 64%) had CA-125 that remained within normal range. Solid organ recurrence without peritoneal disease was more common in those with RECIST-only PD than in those with CA-125 and RECIST PD (36% v 24%; P < .001).

CONCLUSION

In patients with PSROC treated with maintenance PARPi, almost half with RECIST PD did not have CA-125 PD, challenging current guidelines. Periodic computed tomography imaging should be considered as part of surveillance, particularly in those with a normal CA-125 at the start of maintenance therapy and on treatment.

Development and validation of an ultrasound-based deep learning radiomics nomogram for predicting the malignant risk of ovarian tumours

BACKGROUND

The timely identification and management of ovarian cancer are critical determinants of patient prognosis. In this study, we developed and validated a deep learning radiomics nomogram (DLR_Nomogram) based on ultrasound (US) imaging to accurately predict the malignant risk of ovarian tumours and compared the diagnostic performance of the DLR_Nomogram to that of the ovarian-adnexal reporting and data system (O-RADS).

METHODS

This study encompasses two research tasks. Patients were randomly divided into training and testing sets in an 8:2 ratio for both tasks. In task 1, we assessed the malignancy risk of 849 patients with ovarian tumours. In task 2, we evaluated the malignancy risk of 391 patients with O-RADS 4 and O-RADS 5 ovarian neoplasms. Three models were developed and validated to predict the risk of malignancy in ovarian tumours. The predicted outcomes of the models for each sample were merged to form a new feature set that was utilised as an input for the logistic regression (LR) model for constructing a combined model, visualised as the DLR_Nomogram. Then, the diagnostic performance of these models was evaluated by the receiver operating characteristic curve (ROC).

RESULTS

The DLR_Nomogram demonstrated superior predictive performance in predicting the malignant risk of ovarian tumours, as evidenced by area under the ROC curve (AUC) values of 0.985 and 0.928 for the training and testing sets of task 1, respectively. The AUC value of its testing set was lower than that of the O-RADS; however, the difference was not statistically significant. The DLR_Nomogram exhibited the highest AUC values of 0.955 and 0.869 in the training and testing sets of task 2, respectively. The DLR_Nomogram showed satisfactory fitting performance for both tasks in Hosmer-Lemeshow testing. Decision curve analysis demonstrated that the DLR_Nomogram yielded greater net clinical benefits for predicting malignant ovarian tumours within a specific range of threshold values.

CONCLUSIONS

The US-based DLR_Nomogram has shown the capability to accurately predict the malignant risk of ovarian tumours, exhibiting a predictive efficacy comparable to that of O-RADS.

Integrating the analysis of human biopsies using post-translational modifications proteomics

Proteome diversities and their biological functions are significantly amplified by post-translational modifications (PTMs) of proteins. Shotgun proteomics, which does not typically survey PTMs, provides an incomplete picture of the complexity of human biopsies in health and disease. Recent advances in mass spectrometry-based proteomic techniques that enrich and study PTMs are helping to uncover molecular detail from the cellular level to system-wide functions, including how the microbiome impacts human diseases. Protein heterogeneity and disease complexity are challenging factors that make it difficult to characterize and treat disease. The search for clinical biomarkers to characterize disease mechanisms and complexity related to patient diagnoses and treatment has proven challenging. Knowledge of PTMs is fundamentally lacking. Characterization of complex human samples that clarify the role of PTMs and the microbiome in human diseases will result in new discoveries. This review highlights the key role of proteomic techniques used to characterize unknown biological functions of PTMs derived from complex human biopsies. Through the integration of diverse methods used to profile PTMs, this review explores the genetic regulation of proteoforms, cells of origin expressing specific proteins, and several bioactive PTMs and their subsequent analyses by liquid chromatography and tandem mass spectrometry.

Bayesian and deep-learning models applied to the early detection of ovarian cancer using multiple longitudinal biomarkers

BACKGROUND

Ovarian cancer is the most lethal of all gynecological cancers. Cancer Antigen 125 (CA125) is the best-performing ovarian cancer biomarker which however is still not effective as a screening test in the general population. Recent literature reports additional biomarkers with the potential to improve on CA125 for early detection when using longitudinal multimarker models.

METHODS

Our data comprised 180 controls and 44 cases with serum samples sourced from the multimodal arm of UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). Our models were based on Bayesian change-point detection and recurrent neural networks.

RESULTS

We obtained a significantly higher performance for CA125-HE4 model using both methodologies (AUC 0.971, sensitivity 96.7% and AUC 0.987, sensitivity 96.7%) with respect to CA125 (AUC 0.949, sensitivity 90.8% and AUC 0.953, sensitivity 92.1%) for Bayesian change-point model (BCP) and recurrent neural networks (RNN) approaches, respectively. One year before diagnosis, the CA125-HE4 model also ranked as the best, whereas at 2 years before diagnosis no multimarker model outperformed CA125.

CONCLUSIONS

Our study identified and tested different combination of biomarkers using longitudinal multivariable models that outperformed CA125 alone. We showed the potential of multivariable models and candidate biomarkers to increase the detection rate of ovarian cancer.

The Influence of Tumor-Specific Markers in Breast Cancer on Other Blood Parameters

BACKGROUND

Breast cancer is the most frequently diagnosed cancer among women, responsible for the highest number of cancer-related deaths worldwide. There is limited data available related to serum tumor markers in breast cancer and other blood parameters or other glandular laboratory parameters. This study aims to evaluate the correlation of tumor-specific markers for breast cancer with other blood parameters and how these correlations could impact clinical management.

MATERIAL AND METHOD

This retrospective study represents a data analysis from 1 January 2020 to 31 May 2023, in the County Hospital of Timisoara, Romania. We reviewed all the cases where, in the laboratory analyses, the serum tumor specific biomarkers for breast cancer were analyzed.

RESULTS

A statistical analysis was performed in order to identify a possible relationship between CA 15-3 and the various biomarkers and blood parameters included in the present study. Values were classified according to reference ranges. The tests revealed no statistically significant associations between CA 15-3 values and the levels of CA125 (χ2(1) = 1.852, p = 0.174), CEA (χ2(1) = 1.139, p = 0.286), AFP (Fisher's exact test, p = 0.341), fT4 (Fisher's exact test, p = 0.310), TSH (Fisher's exact test, p = 0.177), or PTH (Fisher's exact test, p = 0.650).

CONCLUSION

The findings indicate a lack of strong correlation between CA 15-3 and CA125, CEA, AFP, thyroid function markers, or PTH within this cohort.

Decoding the glycoproteome: a new frontier for biomarker discovery in cancer

Cancer early detection and treatment response prediction continue to pose significant challenges. Cancer liquid biopsies focusing on detecting circulating tumor cells (CTCs) and DNA (ctDNA) have shown enormous potential due to their non-invasive nature and the implications in precision cancer management. Recently, liquid biopsy has been further expanded to profile glycoproteins, which are the products of post-translational modifications of proteins and play key roles in both normal and pathological processes, including cancers. The advancements in chemical and mass spectrometry-based technologies and artificial intelligence-based platforms have enabled extensive studies of cancer and organ-specific changes in glycans and glycoproteins through glycomics and glycoproteomics. Glycoproteomic analysis has emerged as a promising tool for biomarker discovery and development in early detection of cancers and prediction of treatment efficacy including response to immunotherapies. These biomarkers could play a crucial role in aiding in early intervention and personalized therapy decisions. In this review, we summarize the significant advance in cancer glycoproteomic biomarker studies and the promise and challenges in integration into clinical practice to improve cancer patient care.

Postbiotic butyrate: role and its effects for being a potential drug and biomarker to pancreatic cancer

Postbiotics are produced by microbes and have recently gained importance in the field of oncology due to their beneficial effects to the host, effectiveness against cancer cells, and their ability to suppress inflammation. In particular, butyrate dominates over all other postbiotics both in quantity and anticancer properties. Pancreatic cancer (PC), being one of the most malignant and lethal cancers, reported a decreased 5-year survival rate in less than 10% of the patients. PC causes an increased mortality rate due to its inability to be detected at an early stage but still a promising strategy for its diagnosis has not been achieved yet. It is necessary to diagnose Pancreatic cancer before the metastatic progression stage. The available blood biomarkers lack accurate and proficient diagnostic results. Postbiotic butyrate is produced by gut microbiota such as Rhuminococcus and Faecalibacterium it is involved in cell signalling pathways, autophagy, and cell cycle regulation, and reduction in butyrate concentration is associated with the occurrence of pancreatic cancer. The postbiotic butyrate is a potential biomarker that could detect PC at an early stage, before the metastatic progression stage. Thus, this review focused on the gut microbiota butyrate's role in pancreatic cancer and the immuno-suppressive environment, its effects on histone deacetylase and other immune cells, microbes in major butyrate synthesis pathways, current biomarkers in use for Pancreatic Cancer.

Barriers and Unequal Access to Timely Molecular Testing Results: Addressing the Inequities in Cancer Care Delays across Canada

Genomic medicine is a powerful tool to improve diagnosis and outcomes for cancer patients by facilitating the delivery of the right drug at the right dose at the right time for the right patient. In 2023, a Canadian conference brought together leaders with expertise in different tumor types. The objective was to identify challenges and opportunities for change in terms of equitable and timely access to biomarker testing and reporting at the education, delivery, laboratory, patient, and health-system levels in Canada. Challenges identified included: limited patient and clinician awareness of genomic medicine options with need for formal education strategies; failure by clinicians to discuss genomic medicine with patients; delays in or no access to hereditary testing; lack of timely reporting of results; intra- and inter-provincial disparities in access; lack of funding for patients to access testing and for laboratories to provide testing; lack of standardized testing; and impact of social determinants of health. Canada must standardize its approach to biomarker testing across the country, with a view to addressing current inequities, and prioritize access to advanced molecular testing to ensure systems are in place to quickly bring innovation and evidence-based treatments to Canadian cancer patients, regardless of their place of residence or socioeconomic status.

Peritoneal tuberculosis masquerading as an ovarian malignancy in a young female: A case report

The clinical manifestations of peritoneal tuberculosis are quite variable, nonspecific and mimic many diseases and pathological conditions such as lymphoma, and ovarian malignancy. Due to this clinical overlap and limited accuracy of diagnostic tests, more awareness of this disease is required to enable early diagnosis and prompt treatment. This is a case of a 25-year-old female with no known chronic illness who presented with worsening generalized abdominal pains and distension of 2 months duration. There was an associated significant weight loss of 17 kg. She was initially diagnosed with ovarian malignancy based of ultrasound findings and elevated CA-125 levels. However, further evaluation later was consistent with peritoneal tuberculosis for which she was treated. Her symptoms resolved completely after 6 months of anti-tuberculosis treatment. Diagnosis of abdominal TB remains challenging as it is non-specific. Its features and clinical manifestation overlap with other conditions such as ovarian malignancy. A high index of suspicions and judicious application of the available diagnostic test is need for prompt diagnosis. No single test can effectively diagnose peritoneal TB, but a combination of history, and radiological, immunologic, molecular, and cytologic tests are important.

Are preoperative serum cancer antigen 125 levels a prognostic factor for outcome in epithelial ovarian cancer? A systematic review

Background

Most patients with epithelial ovarian cancers (EOC) present with advanced-stage disease because of non-specific symptoms and lack of reliable strategies for early diagnosis. Cancer antigen 125 (CA-125) is suggested as a useful prognostic biomarker, its serum level is raised in over 80.0% of patients with EOC. Primary debulking surgery (PDS) followed by chemotherapy is the conventional treatment, but neoadjuvant chemotherapy followed by interval debulking surgery (NACT-IDS) is offered to patients with unresectable disease. There are inconsistencies regarding the role of preoperative CA-125 serum levels to adopt in stratifying patients for treatment choice that offers the most benefit. This review aimed to determine the role of preoperative CA-125 levels in predicting optimal cytoreduction and the association between optimal cytoreduction and survival outcome in patients with EOC.

Methodology

Three electronic databases CINAHL, Cochrane library and PubMed were searched for potentially relevant articles from 2016 to 2021 on the role of preoperative CA-125 levels in predicting optimal cytoreduction and survival in patients with epithelial ovarian carcinomas.

Conclusion

In patients who underwent NACT-IDS, a lower preoperative CA-125 value is a predictor of optimal cytoreduction and an increase in preoperative CA-125 value is consistently associated with a decrease in optimal cytoreduction. There was insufficient data to assess overall survival. However, a raised preoperative CA-125 level is poor predictor of chance of achieving optimal cytoreduction and the rate of optimal cytoreduction was a weak predictor of overall survival in women who had primary debulking surgery.

Tuberculous peritonitis diagnosed following laparoscopic examination for suspected advanced ovarian cancer

Laparoscopy for intra-abdominal exploration and tissue sampling is useful in advanced ovarian cancers, in which it is presumed to be difficult to achieve complete tumour reduction in the initial surgery. This is a report of a case of suspected advanced ovarian cancer in a patient, who underwent laparoscopic screening and was later pathologically diagnosed with tuberculous peritonitis. A woman in her 50s visited her local doctor with constipation. Since imaging showed massive ascites she was referred for further evaluation. We initially suspected advanced ovarian cancer due to the presence of massive ascites and multiple peritoneal nodules. However, histopathological examination indicated that the nodules were tubercles, and the patient was subsequently diagnosed with tuberculous peritonitis. It is important to be aware that tuberculosis peritonitis can be misdiagnosed or mistaken for advanced ovarian cancer. Preoperative diagnosis of tuberculous peritonitis is often difficult. Tuberculous peritonitis should be considered if intraoperative findings show diffuse nodular disseminated lesions.

Biomarker-specific biosensors revolutionise breast cancer diagnosis

Breast cancer is the most common cancer among women across the globe. In order to treat breast cancer successfully, it is crucial to conduct a comprehensive assessment of the condition during its initial stages. Although mammogram screening has long been a common method of breast cancer screening, high rates of type I error and type II error results as well as radiation exposure have always been of concern. The outgrowth cancer mortality rate is primarily due to delayed diagnosis, which occurs most frequently in a metastatic III or IV stage, resulting in a poor prognosis after therapy. Traditional detection techniques require identifying carcinogenic properties of cells, such as DNA or RNA alterations, conformational changes and overexpression of certain proteins, and cell shape, which are referred to as biomarkers or analytes. These procedures are complex, long-drawn-out, and expensive. Biosensors have recently acquired appeal as low-cost, simple, and super sensitive detection methods for analysis. The biosensor approach requires the existence of biomarkers in the sample. Thus, the development of novel molecular markers for diverse forms of cancer is a rising complementary affair. These biosensor devices offer two major advantages: (1) a tiny amount of blood collected from the patient is sufficient for analysis, and (2) it could help clinicians swiftly select and decide on the best therapy routine for the individual. This review will include updates on prospective cancer markers and biosensors in cancer diagnosis, as well as the associated detection limitations, with a focus on biosensor development for marker detection.

The IGF-PAPP-A-Stanniocalcin Axis in Serum and Ascites Associates with Prognosis in Patients with Ovarian Cancer

Pregnancy-associated plasma protein-A (PAPP-A) and PAPP-A2 modulate insulin-like growth factor (IGF) action and are inhibited by the stanniocalcins (STC1 and STC2). We previously demonstrated increased PAPP-A and IGF activity in ascites from women with ovarian carcinomas. In this prospective, longitudinal study of 107 women with ovarian cancer and ascites accumulation, we determined corresponding serum and ascites levels of IGF-1, IGF-2, PAPP-A, PAPP-A2, STC1, and STC2 and assessed their relationship with mortality. As compared to serum, we found highly increased ascites levels of PAPP-A (51-fold) and PAPP-A2 (4-fold). Elevated levels were also observed for IGF-1 (12%), STC1 (90%) and STC2 (68%). In contrast, IGF-2 was reduced by 29% in ascites. Patients were followed for a median of 38.4 months (range: 45 days to 8.9 years), during which 73 patients (68.2%) died. Overall survival was longer for patients with high serum IGF-1 (hazard ratio (HR) per doubling in protein concentration: 0.60, 95% CI: 0.40-0.90). However, patients with high ascites levels of IGF-1 showed a poorer prognosis (HR: 2.00 (1.26-3.27)). High serum and ascites IGF-2 levels were associated with increased risk of mortality (HR: 2.01 (1.22-3.30) and HR: 1.78 (1.24-2.54), respectively). Similarly, serum PAPP-A2 was associated with mortality (HR: 1.26 (1.08-1.48)). Our findings demonstrate the presence and activity of the IGF system in the local tumor ecosystem, which is likely a characteristic feature of malignant disease and plays a role in its peritoneal dissemination. The potential clinical implications are supported by our finding that serum levels of the proteins are associated with patient prognosis.

Targeted Mass Spectrometry of Longitudinal Patient Sera Reveals LTBP1 as a Potential Surveillance Biomarker for High-Grade Serous Ovarian Carcinoma

High-grade serous ovarian carcinoma (HGSC) is the most prevalent subtype of epithelial ovarian cancer. The combination of a high rate of recurrence and novel therapies in HGSC necessitates an accurate assessment of the disease. Currently, HGSC response to treatment and recurrence are monitored via immunoassay of serum levels of the glycoprotein CA125. CA125 levels predictably rise at HGSC recurrence; however, it is likely that the disease is progressing even before it is detectable through CA125. This may explain why treating solely based on CA125 increase has not been associated with improved outcomes. Thus, additional biomarkers that monitor HGSC progression and cancer recurrence are needed. For this purpose, we developed a scheduled parallel reaction monitoring mass spectrometry (PRM-MS) assay for the quantification of four previously identified HGSC-derived glycopeptides (from proteins FGL2, LGALS3BP, LTBP1, and TIMP1). We applied the assay to quantify their longitudinal expression profiles in 212 serum samples taken from 34 HGSC patients during disease progression. Analyses revealed that LTBP1 best-mirrored tumor load, dropping as a result of cancer treatment in 31 out of 34 patients and rising at HGSC recurrence in 28 patients. Additionally, LTBP1 rose earlier during remission than CA125 in 11 out of 25 platinum-sensitive patients with an average lead time of 116.4 days, making LTBP1 a promising candidate for monitoring of HGSC recurrence.

CASC4/GOLM2 drives high grade serous carcinoma anoikis resistance through the recycling of EGFR

Ovarian cancer is the deadliest gynecological malignancy, and accounts for over 150,000 deaths per year worldwide. The high grade serous ovarian carcinoma (HGSC) subtype accounts for almost 70% of ovarian cancers and is the deadliest. HGSC originates in the fimbria of the fallopian tube and disseminates through the peritoneal cavity. HGSC survival in peritoneal fluid requires cells to resist anoikis (anchorage-independent apoptosis). Most anoikis resistant mechanisms are dependent on microenvironment interactions with cell surface-associated proteins, such as integrins and receptor tyrosine kinases (RTKs). We previously identified the gene CASC4 as a driver of anoikis resistance. CASC4 is predicted to be a Golgi-associated protein that may regulate protein trafficking to the plasma membrane, but CASC4 is largely uncharacterized in literature; thus, we sought to determine how CASC4 confers anoikis resistance to HGSC cells. Mining of publicly available ovarian cancer datasets (TCGA) showed that CASC4 is associated with worse overall survival and increased resistance to platinum-based chemotherapies. For experiments, we cultured three human HGSC cell lines (PEO1, CaOV3, OVCAR3), and a murine HGSC cell line, (ID8) with shRNA-mediated CASC4 knockdowns (CASC4 KD) in suspension, to recapitulate the peritoneal fluid environment in vitro. CASC4 KD significantly inhibited cell proliferation and colony formation ability, and increased apoptosis. A Reverse Phase Protein Assay (RPPA) showed that CASC4 KD resulted in a broad re-programming of membrane-associated proteins. Specifically, CASC4 KD led to decreased protein levels of the RTK Epidermal Growth Factor Receptor (EGFR), an initiator of several oncogenic signaling pathways, leading us to hypothesize that CASC4 drives HGSC survival through mediating recycling and trafficking of EGFR. Indeed, loss of CASC4 led to a decrease in both EGFR membrane localization, reduced turnover of EGFR, and increased EGFR ubiquitination. Moreover, a syngeneic ID8 murine model of ovarian cancer showed that knocking down CASC4 leads to decreased tumor burden and dissemination.

A Revised Molecular Model of Ovarian Cancer Biomarker CA125 (MUC16) Enabled by Long-read Sequencing

The biomarker CA125, a peptide epitope located in several tandem repeats of the mucin MUC16, is the gold standard for monitoring regression and recurrence of high-grade serous ovarian cancer in response to therapy. However, the CA125 epitope along with several structural features of the MUC16 molecule are ill defined. One central aspect still unresolved is the number of tandem repeats in MUC16 and how many of these repeats contain the CA125 epitope. Studies from the early 2000s assembled short DNA reads to estimate that MUC16 contained 63 repeats.Here, we conduct Nanopore long-read sequencing of MUC16 transcripts from three primary ovarian tumors and established cell lines (OVCAR3, OVCAR5, and Kuramochi) for a more exhaustive and accurate estimation and sequencing of the MUC16 tandem repeats.The consensus sequence derived from these six sources was confirmed by proteomics validation and agrees with recent additions to the NCBI database. We propose a model of MUC16 containing 19-not 63-tandem repeats. In addition, we predict the structure of the tandem repeat domain using the deep learning algorithm, AlphaFold.The predicted structure displays an SEA domain and unstructured linker region rich in proline, serine, and threonine residues in all 19 tandem repeats. These studies now pave the way for a detailed characterization of the CA125 epitope. Sequencing and modeling of the MUC16 tandem repeats along with their glycoproteomic characterization, currently underway in our laboratories, will help identify novel epitopes in the MUC16 molecule that improve on the sensitivity and clinical utility of the current CA125 assay.

SIGNIFICANCE

Despite its crucial role in clinical management of ovarian cancer, the exact molecular sequence and structure of the biomarker, CA125, are not defined. Here, we combine long-read sequencing, mass spectrometry, and in silico modeling to provide the foundational dataset for a more complete characterization of the CA125 epitope.

Current Applications and Challenges of Next-Generation Sequencing in Plasma Circulating Tumour DNA of Ovarian Cancer

Circulating tumour DNA (ctDNA) facilitates longitudinal study of the tumour genome, which, unlike tumour tissue biopsies, globally reflects intratumor and intermetastatis heterogeneity. Despite its costs, next-generation sequencing (NGS) has revolutionised the study of ctDNA, ensuring a more comprehensive and multimodal approach, increasing data collection, and introducing new variables that can be correlated with clinical outcomes. Current NGS strategies can comprise a tumour-informed set of genes or the entire genome and detect a tumour fraction as low as 10-5. Despite some conflicting studies, there is evidence that ctDNA levels can predict the worse outcomes of ovarian cancer (OC) in both early and advanced disease. Changes in those levels can also be informative regarding treatment efficacy and tumour recurrence, capable of outperforming CA-125, currently the only universally utilised plasma biomarker in high-grade serous OC (HGSOC). Qualitative evaluation of sequencing shows that increasing copy number alterations and gene variants during treatment may correlate with a worse prognosis in HGSOC. However, following tumour clonality and emerging variants during treatment poses a more unique opportunity to define treatment response, select patients based on their emerging resistance mechanisms, like BRCA secondary mutations, and discover potential targetable variants. Sequencing of tumour biopsies and ctDNA is not always concordant, likely as a result of clonal heterogeneity, which is better captured in the plasma samples than it is in a large number of biopsies. These incoherences may reflect tumour clonality and reveal the acquired alterations that cause treatment resistance. Cell-free DNA methylation profiles can be used to distinguish OC from healthy individuals, and NGS methylation panels have been shown to have excellent diagnostic capabilities. Also, methylation signatures showed promise in explaining treatment responses, including BRCA dysfunction. ctDNA is evolving as a promising new biomarker to track tumour evolution and clonality through the treatment of early and advanced ovarian cancer, with potential applicability in prognostic prediction and treatment selection. While its role in HGSOC paves the way to clinical applicability, its potential interest in other histological subtypes of OC remains unknown.