A radioimmunoassay using a monoclonal antibody to monitor the course of epithelial ovarian cancer

Magnetic resonance imaging-based radiomics model for preoperative assessment of risk stratification in endometrial cancer

BACKGROUND

Preoperative risk stratification is significant for the management of endometrial cancer (EC) patients. Radiomics based on magnetic resonance imaging (MRI) in combination with clinical features may be useful to predict the risk grade of EC.

AIM

To construct machine learning models to predict preoperative risk stratification of patients with EC based on radiomics features extracted from MRI.

METHODS

The study comprised 112 EC patients. The participants were randomly separated into training and validation groups with a 7:3 ratio. Logistic regression analysis was applied to uncover independent clinical predictors. These predictors were then used to create a clinical nomogram. Extracted radiomics features from the T2-weighted imaging and diffusion weighted imaging sequences of MRI images, the Mann-Whitney U test, Pearson test, and least absolute shrinkage and selection operator analysis were employed to evaluate the relevant radiomic features, which were subsequently utilized to generate a radiomic signature. Seven machine learning strategies were used to construct radiomic models that relied on the screening features. The logistic regression method was used to construct a composite nomogram that incorporated both the radiomic signature and clinical independent risk indicators.

RESULTS

Having an accuracy of 0.82 along with an area under the curve (AUC) of 0.915 [95% confidence interval (CI): 0.806-0.986], the random forest method trained on radiomics characteristics performed better than expected. The predictive accuracy of radiomics prediction models surpassed that of both the clinical nomogram (AUC: 0.75, 95%CI: 0.611-0.899) and the combined nomogram (AUC: 0.869, 95%CI: 0.702-0.986) that integrated clinical parameters and radiomic signature.

CONCLUSION

The MRI-based radiomics model may be an effective tool for preoperative risk grade prediction in EC patients.

Comparative transcriptomic study on the ovarian cancer between chicken and human

The laying hen is the spontaneous model of ovarian tumor. A comprehensive comparison based on RNA-seq from hens and women may shed light on the molecular mechanisms of ovarian cancer. We performed next-generation sequencing of microRNA and mRNA expression profiles in 9 chicken ovarian cancers and 4 normal ovaries, which has been deposited in GSE246604. Together with 6 public datasets (GSE21706, GSE40376, GSE18520, GSE27651, GSE66957, TCGA-OV), we conducted a comparative transcriptomics study between chicken and human. In the present study, miR-451, miR-2188-5p, and miR-10b-5p were differentially expressed in normal ovaries, early- and late-stage ovarian cancers. We also disclosed 499 up-regulated genes and 1,061 down-regulated genes in chicken ovarian cancer. The molecular signals from 9 cancer hallmarks, 25 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and 369 Gene Ontology (GO) pathways exhibited abnormalities in ovarian cancer compared to normal ovaries via Gene Set Enrichment Analysis (GSEA). In the comparative analysis across species, we have uncovered the conservation of 5 KEGG and 76 GO pathways between chicken and human including the mismatch repair and ECM receptor interaction pathways. Moreover, a total of 174 genes contributed to the core enrichment for these KEGG and GO pathways were identified. Among these genes, the 22 genes were found to be associated with overall survival in patients with ovarian cancer. In general, we revealed the microRNA profiles of ovarian cancers in hens and updated the mRNA profiles previously derived from microarrays. And we also disclosed the molecular pathways and core genes of ovarian cancer shared between hens and women, which informs model animal studies and gene-targeted drug development.

Immunoaffinity-free chromatographic purification of ovarian cancer biomarker CA125 (MUC16) from blood serum enables mass spectrometry characterization

The enrichment of trace proteins from human fluid samples is of great importance in diverse clinical and industrial applications. In clinical diagnostics, such enrichment may enable detection of trace proteins that serve as biomarkers of disease. Affinity-based approaches, such as immunoaffinity pulldown, are widely used to enrich trace proteins, but this strategy relies on the availability and performance of antibodies that act on all proteoforms in an unbiased manner. Our prior work to characterize MUC16 (the mucin protein that carries the ovarian cancer biomarker CA125) by mass spectrometry successfully overcame the reliance on affinity-based enrichment and was used to enrich this biomarker from ascites of individual ovarian cancer patients, however, this strategy was not demonstrated on clinically relevant volumes of serum, a biofluid that is more accessible than ascites. The present work developed a non-affinity-based chromatographic method to enrich MUC16 from serum. The enriched MUC16 sample was further processed using a Midi Top 14 abundant protein depletion column. Peptides identified using bottom-up proteomics yielded 1-8% coverage of MUC16. Additionally, MUC16 was detected in samples containing less than the clinical cut-off level of CA125 (35 U mL-1), suggesting that this strategy of enrichment and bottom-up proteomics can enable analysis of CA125 from the serum of individuals with early-stage ovarian cancer and those whose tumors express CA125 (MUC16) at low levels.

Indirect reference interval estimation using a convolutional neural network with application to cancer antigen 125

Indirect methods for reference interval (RI) estimation, which use data acquired from routine pathology testing, have the potential to accelerate the establishment of RIs to account for variables such as gender and age to improve clinical assessments. However, they require more sophisticated methods of analysis due to the potential influence of pathological patients in raw clinical datasets. In this paper we develop a novel convolutional neural network (CNN) model trained on synthetic data to identify underlying healthy distributions within pathological admixtures. We present both the methodology to generate synthetic data and the CNN model. We evaluate the CNN using two synthetic test datasets, including samples from a proposed benchmark for indirect methods (RIBench) and show significant improvements compared to the reported state-of-the-art method based on the benchmark (refineR). We also demonstrate a real-world application of the model, estimating age-specific RIs for cancer antigen 125 (CA-125), a crucial biomarker for ovarian cancer diagnostics. Our results show that CA-125 RIs are strongly age-dependent, which could have important diagnostic consequences.

Emerging role of mucins in antibody drug conjugates for ovarian cancer therapy

Ovarian cancer stands as the deadliest gynecologic malignancy, responsible for nearly 65% of all gynecologic cancer-related deaths. The challenges in early detection and diagnosis, coupled with the widespread intraperitoneal spread of cancer cells and resistance to chemotherapy, contribute significantly to the high mortality rate of this disease. Due to the absence of specific symptoms and the lack of effective screening methods, most ovarian cancer cases are diagnosed at advanced stages. While chemotherapy is a common treatment, it often leads to tumor recurrence, necessitating further interventions. In recent years, antibody-drug conjugates (ADCs) have emerged as a valuable tool in targeted cancer therapy. These complex biotherapeutics combine an antibody that specifically targets tumor specific/associated antigen(s) with a high potency anti-cancer drug through a linker, offering a promising approach for ovarian cancer treatment. The identification of molecular targets in various human tumors has paved the way for the development of targeted therapies, with ADCs being at the forefront of this innovation. By delivering cytotoxic agents directly to tumors and metastatic lesions, ADCs show potential in managing chemo-resistant ovarian cancers. Mucins such as MUC16, MUC13, and MUC1 have shown significantly higher expression in ovarian tumors as compared to normal and/or benign samples, thus have become promising targets for ADC generation. While traditional markers are limited by their elevated levels in non-cancerous conditions, mucins offer a new possibility for targeted treatment in ovarian cancer. This review comprehensively described the potential of mucins for the generation of ADC therapy, highlighting their importance in the quest to improve the outcome of ovarian cancer patients.

Glycosylation: mechanisms, biological functions and clinical implications

Protein post-translational modification (PTM) is a covalent process that occurs in proteins during or after translation through the addition or removal of one or more functional groups, and has a profound effect on protein function. Glycosylation is one of the most common PTMs, in which polysaccharides are transferred to specific amino acid residues in proteins by glycosyltransferases. A growing body of evidence suggests that glycosylation is essential for the unfolding of various functional activities in organisms, such as playing a key role in the regulation of protein function, cell adhesion and immune escape. Aberrant glycosylation is also closely associated with the development of various diseases. Abnormal glycosylation patterns are closely linked to the emergence of various health conditions, including cancer, inflammation, autoimmune disorders, and several other diseases. However, the underlying composition and structure of the glycosylated residues have not been determined. It is imperative to fully understand the internal structure and differential expression of glycosylation, and to incorporate advanced detection technologies to keep the knowledge advancing. Investigations on the clinical applications of glycosylation focused on sensitive and promising biomarkers, development of more effective small molecule targeted drugs and emerging vaccines. These studies provide a new area for novel therapeutic strategies based on glycosylation.

Detection of Carcinoma-Associated Fibroblasts Derived from Mesothelial Cells via Mesothelial-to-Mesenchymal Transition in Primary Ovarian Carcinomas

Carcinoma-associated fibroblasts (CAFs) are highly accumulated in the tumor-surrounding stroma of primary epithelial ovarian cancer (OC). CAFs exert important functions for the vascularization, growth, and progression of OC cells. However, the origin of CAFs in primary OC had not yet been studied, and they were assumed to arise from the activation of resident fibroblasts. Here, we compared CAFs in the ovary to CAFs found in peritoneal metastases from patients with advanced OC. Our findings show that CAFs from primary tumors and peritoneal metastases share the expression of mesothelial markers. Therefore, similar to peritoneal carcinomatosis, CAFs in primary ovarian carcinomas may originate from mesothelial cells via a mesothelial-to-mesenchymal transition. The detection of mesothelial-derived CAFs in tumors confined to the ovary and identification of biomarkers could be the key to the early detection of OC and peritoneal spread.

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.

Production of CA125 with Tn antigens using a glycosylphosphatidylinositol anchoring system

Cancer antigen 125 (CA125) is a serum marker associated with ovarian cancer. Despite its widespread use, CA125 levels can also be elevated in benign conditions. Recent reports suggest that detecting serum CA125 that carries the Tn antigen, a truncated O-glycan containing only N-acetylgalactosamine on serine or threonine residues, can improve the specificity of ovarian cancer diagnosis. In this study, we engineered cells to express CA125 with a Tn antigen. To achieve this, we knocked out C1GALT1 and SLC35A1, genes encoding Core1 synthase and a transporter for cytidine-5'-monophospho-sialic acid respectively, in human embryonic kidney 293 (HEK293) cells. In ClGALT1-SLC35A1-knockout (KO) cells, the expression of the Tn antigen showed a significant increase, whereas the expression of the T antigen (galactose-β1,3-N-acetylgalactosamine on serine or threonine residues) was decreased. Due to the inefficient secretion of soluble CA125, we employed a glycosylphosphatidylinositol (GPI) anchoring system. This allowed for the expression of GPI-anchored CA125 on the cell surface of ClGALT1-SLC35A1-KO cells. Cells expressing high levels of GPI-anchored CA125 were then enriched through cell sorting. By knocking out the PGAP2 gene, the GPI-anchored form of CA125 was converted to a secretory form. Through the engineering of O-glycans and the use of a GPI-anchoring system, we successfully produced CA125 with Tn antigen modification.

Sample-Treatment with the Virucidal β-Propiolactone Does Not Preclude Analysis by Large Panel Affinity Proteomics, Including the Discovery of Biomarker Candidates

Virus inactivation is a prerequisite for safe handling of high-risk infectious samples. β-Propiolactone (BPL) is an established reagent with proven virucidal efficacy. BPL primarily reacts with DNA, RNA, and amino acids. The latter may modify antigenic protein epitopes interfering with binding properties of affinity reagents such as antibodies and aptamers used in affinity proteomic screens. We investigated (i) the impact of BPL treatment on the analysis of protein levels in plasma samples using the aptamer-based affinity proteomic platform SomaScan and (ii) effects on protein detection in conditioned medium samples using the proximity extension assay-based Olink Target platform. In the former setup, BPL-treated and native plasma samples from patients with ovarian cancer (n = 12) and benign diseases (n = 12) were analyzed using the SomaScan platform. In the latter, conditioned media samples collected from cultured T cells with (n = 3) or without (n = 3) anti-CD3 antibody stimulation were analyzed using the Olink Target platform. BPL-related changes in protein detection were evaluated comparing native and BPL-treated states, simulating virus inactivation, and impact on measurable group differences was assessed. While approximately one-third of SomaScan measurements were significantly changed by the BPL treatment, a majority of antigen/aptamer interactions remained unaffected. Interaction effects of BPL treatment and disease state, potentially altering detectability of group differences, were observable for less than one percent of targets (0.6%). BPL effects on protein detection with Olink Target were also limited, affecting 3.6% of detected proteins with no observable interaction effects. Thus, effects of BPL treatment only moderately interfere with affinity proteomic detectability of differential protein expression between different experimental groups. Overall, the results prove high-throughput affinity proteomics well suited for the analysis of high-risk samples inactivated using BPL.

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.

Tumor biomarkers for diagnosis, prognosis and targeted therapy

Tumor biomarkers, the substances which are produced by tumors or the body's responses to tumors during tumorigenesis and progression, have been demonstrated to possess critical and encouraging value in screening and early diagnosis, prognosis prediction, recurrence detection, and therapeutic efficacy monitoring of cancers. Over the past decades, continuous progress has been made in exploring and discovering novel, sensitive, specific, and accurate tumor biomarkers, which has significantly promoted personalized medicine and improved the outcomes of cancer patients, especially advances in molecular biology technologies developed for the detection of tumor biomarkers. Herein, we summarize the discovery and development of tumor biomarkers, including the history of tumor biomarkers, the conventional and innovative technologies used for biomarker discovery and detection, the classification of tumor biomarkers based on tissue origins, and the application of tumor biomarkers in clinical cancer management. In particular, we highlight the recent advancements in biomarker-based anticancer-targeted therapies which are emerging as breakthroughs and promising cancer therapeutic strategies. We also discuss limitations and challenges that need to be addressed and provide insights and perspectives to turn challenges into opportunities in this field. Collectively, the discovery and application of multiple tumor biomarkers emphasized in this review may provide guidance on improved precision medicine, broaden horizons in future research directions, and expedite the clinical classification of cancer patients according to their molecular biomarkers rather than organs of origin.

Preoperative cancer antigen-125 levels as a predictor of recurrence in early-stage endometrial cancer

OBJECTIVE

Endometrial cancer is the most common gynecological cancer in developed countries, with a majority of cases being low-grade endometrioid endometrial cancer. Identifying risk factors for disease recurrence and poor prognosis is critical. This study aimed to assess the correlation between preoperative cancer antigen-125 levels and disease recurrence in early-stage endometrioid endometrial cancer patients.

METHODS

The study was a retrospective analysis of 217 patients diagnosed with endometrioid endometrial cancer who underwent surgical treatment at a university-affiliated tertiary hospital between 2016 and 2022. Patients were divided into two groups based on their preoperative cancer antigen-125 levels and compared with clinicopathological findings and disease recurrence. Disease-free survival rates were calculated, and logistic regression analysis was performed to determine independent factors affecting disease-free survival.

RESULTS

The mean age of patients was 61.59±0.75 years, and the mean follow-up time was 36.95±1.18 months. The mean cancer antigen-125 level was 27.80±37.81 IU/mL. The recurrence rate was significantly higher in the group with elevated cancer antigen-125 levels (p=0.025). Disease-free survival was lower in patients with elevated cancer antigen-125 compared with those with normal levels (p=0.005). Logistic regression analysis revealed that elevated cancer antigen-125 levels were associated with disease recurrence (OR: 3.43, 95%CI 1.13-10.37, p=0.029).

CONCLUSION

The findings of this study suggest that preoperative cancer antigen-125 levels can be used as a predictor of disease recurrence in early-stage endometrioid endometrial cancer patients. cancer antigen-125 levels may be a useful tool for risk stratification and patient management in endometrial cancer.

MUC16: clinical targets with great potential

Mucin 16 (MUC16) is a membrane-bound mucin that is abnormally expressed or mutated in a variety of diseases, especially tumors, while being expressed in normal body epithelium. MUC16 and its extracellular components are often important cancer-related biomarkers. Abnormal expression of MUC16 promotes tumor progression through mesenchymal protein, PI3K/AKT pathway, JAK2/STAT3 pathway, ERK/FBW7/c-Myc, and other mechanisms, and plays an important role in the occurrence and development of tumors. In addition, MUC16 also helps tumor immune escape by inhibiting T cells and NK cells. Many drugs and trials targeting MUC16 have been developed, and MUC16 may be a new direction for future treatments. In this paper, the mechanism of action of MUC16 in the development of cancer, especially in the immune escape of tumor, is introduced in detail, indicating the potential of MUC16 in clinical treatment.

A dual-electrode label-free immunosensor based on in situ prepared Au-MoO3-Chi/porous graphene nanoparticles for point-of-care detection of cholangiocarcinoma

A novel label-free electrochemical immunosensor was prepared for the detection of carbohydrate antigen 19-9 (CA19-9) and carcinoembryonic antigen (CEA) as biomarkers of cholangiocarcinoma (CCA). A nanocomposite of gold nanoparticles, molybdenum trioxide, and chitosan (Au-MoO3-Chi) was layer-by-layer assembled on the porous graphene (PG) modified a dual screen-printed electrode using a self-assembling technique, which increased surface area and conductivity and enhanced the adsorption of immobilized antibodies. The stepwise self-assembling procedure of the modified electrode was further characterized morphologically and functionally. The electroanalytical detection of biomarkers was based on the interaction between the antibody and antigen of each marker via linear sweep voltammetry using ferrocyanide/ferricyanide as an electrochemical redox indicator. Under optimized conditions, the fabricated immunosensor showed linear relationships between current change (ΔI) and antigen concentrations in two ranges: 0.0025-0.1 U mL-1 and 0.1-1.0 U mL-1 for CA19-9, and 0.001-0.01 ng mL-1 and 0.01-1.0 ng mL-1 for CEA. The limits of detection (LOD) were 1.0 mU mL-1 for CA19-9 and 0.5 pg mL-1 for CEA. Limits of quantitation (LOQ) were 3.3 mU mL-1 for CA19-9 and 1.6 pg mL-1 for CEA. The selectivity of the developed immunosensor was tested on mixtures of antigens and was then successfully applied to determine CA19-9 and CEA in human serum samples, producing satisfactory results consistent with the clinical method.

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.

The effectiveness of CA125 and HE4 as clinical prognostic markers in epithelial ovarian cancer patients with BRCA mutation

OBJECTIVE

To investigate the efficacy of cancer antigen 125 (CA125) and human epididymis protein 4 (HE4) in predicting survival outcomes based on breast cancer gene (BRCA) mutational status in epithelial ovarian cancer.

METHODS

Medical records of 448 patients diagnosed with epithelial ovarian cancer at a single tertiary institution in Korea were retrospectively analyzed. Area under the curve, sensitivity, specificity, and accuracy were assessed using the CA125 and HE4 values after surgery and 3 cycles of chemotherapy to predict 1-year survival based on the BRCA mutational status. Kaplan-Meier analysis was used to obtain progression-free and overall survival to evaluate CA125 and HE4 effectiveness in predicting survival outcomes.

RESULTS

A total of 423 patients were analyzed, including 180 (42.6%) who underwent interval debulking surgery (IDS) and 243 (57.4%) who underwent primary debulking surgery (PDS). BRCA mutations were observed in 37 (15.2%) and 44 (22.4%) patients in the PDS and IDS groups, respectively. CA125 and HE4 normalization demonstrated the highest specificity in patients with or without BRCA mutations, with specificities of 97.1% and 99.1% in the PDS group and 78.6% and 86.2% in the IDS group, respectively. Normalizing HE4 alone may be an effective prognostic marker, with an area under the curve of 0.774 and specificity of 75.0%, in patients with BRCA mutations.

CONCLUSION

Normalizing both biomarkers emerged as the most effective predictive marker for the 1-year recurrence rate, regardless of BRCA mutational status. A negative HE4 value can be a useful predictor for 1-year recurrence-free survival in patients with BRCA mutations.

Efficient CAR T cell targeting of the CA125 extracellular repeat domain of MUC16

BACKGROUND

Ovarian cancer (OC) is the leading cause of death from gynecologic malignancies in the Western world. Contributing factors include a high frequency of late-stage diagnosis, the development of chemoresistance, and the evasion of host immune responses. Currently, debulking surgery and platinum-based chemotherapy are the treatment cornerstones, although recurrence is common. As the clinical efficacy of immune checkpoint blockade is low, new immunotherapeutic strategies are needed. Chimeric antigen receptor (CAR) T cell therapy empowers patients' own T cells to fight and eradicate cancer, and has been tested against various targets in OC. A promising candidate is the MUC16 ectodomain. This ectodomain remains on the cell surface after cleavage of cancer antigen 125 (CA125), the domain distal from the membrane, which is currently used as a serum biomarker for OC. CA125 itself has not been tested as a possible CAR target. In this study, we examined the suitability of the CA125 as a target for CAR T cell therapy.

METHODS

We tested a series of antibodies raised against the CA125 extracellular repeat domain of MUC16 and adapted them to the CAR format. Comparisons between these candidates, and against an existing CAR targeting the MUC16 ectodomain, identified K101 as having high potency and specificity. The K101CAR was subjected to further biochemical and functional tests, including examination of the effect of soluble CA125 on its activity. Finally, we used cell lines and advanced orthotopic patient-derived xenograft (PDX) models to validate, in vivo, the efficiency of our K101CAR construct.

RESULTS

We observed a high efficacy of K101CAR T cells against cell lines and patient-derived tumors, in vitro and in vivo. We also demonstrated that K101CAR functionality was not impaired by the soluble antigen. Finally, in direct comparisons, K101CAR, which targets the CA125 extracellular repeat domains, was shown to have similar efficacy to the previously validated 4H11CAR, which targets the MUC16 ectodomain.

CONCLUSIONS

Our in vitro and in vivo results, including PDX studies, demonstrate that the CA125 domain of MUC16 represents an excellent target for treating MUC16-positive malignancies.

Comparing Four Different Risk Malignancy Indices in Differentiating Benign and Malignant Ovarian Masses

Background

Accurate prediction of ovarian masses preoperatively is crucial for optimal management of ovarian cancers.

Objective

The objective of this study was to identify the risk of malignancy index (RMI) incorporating menopausal status, serum carbohydrate antigen 125 levels, and imaging findings for presurgical differentiation of benign from malignant ovarian masses and to evaluate the diagnostic ability of four different RMIs.

Materials and Methods

Women presenting with ovarian masses from August 2018 to January 2020 were evaluated preoperatively with detailed history, examination, imaging, and tumor markers. RMI 1-4 was calculated for all patients. Evaluation of the diagnostic utility of four different RMIs for preoperative identification of malignancy was based on the increment of the area under the receiver operating characteristic curve. Histopathological diagnosis was used as the gold standard test.

Results

One hundred and twenty-one patients fulfilling the eligibility criteria were enrolled in this study. Benign tumors constituted 61 (50.4%) out of 121 cases, followed by malignant tumors and borderline tumors constituting 49 (40.49%) cases and 11 (9.09%) cases, respectively. The sensitivity of RMIs 1, 2, 3, and 4 was 77.0%, 63%, 77.0%, and 77.0%, respectively, and the specificity was 84%, 86%, 77%, and 71%, respectively. The RMI 2 had higher specificity at predicting malignancy than other RMIs while diagnostic accuracy was highest in RMI 1.

Conclusion

The RMI method is a simple and cost-effective technique in preoperative differentiation of ovarian masses.

Exploration of Preliminary Objective Triage by Menopause Score and CA 125 Result Prior to Accelerating Fast-Track Booking for Suspected Ovarian Cancer-A Role for the Pathway Navigator?

The 28-days-to-diagnosis pathway is the current expected standard of care for women with symptoms of ovarian cancer in the UK. However, the anticipated conversion rate of symptoms to cancer is only 3%, and use of the pathway is increasing. A rapid triage at the moment of receipt of the referral might allow resources to be allocated more appropriately. In secondary care, multidisciplinary teams (MDTs) use the risk of malignancy index (RMI) score, (multiply menopausal status pre = 1 or post = 3 × ultrasound score = 0 - 3 × the CA 125 level), using a score of >200, to triage urgency and management in possible ovarian cancer cases. The most powerful determinant of the RMI score variables is CA 125 level, an objective number. Could a simple modification of the RMI score retain a high sensitivity for cancer whilst improving specificity and, consequently, decrease the morbidity of false-positive classification? To test this hypothesis, a retrospective evaluation of an ovarian two-week-wait telephone clinic of one consultant gynaecological oncologist was undertaken. Enquiry re menopause status was scored as one for pre- and three for postmenopausal or uncertain. CA 125 levels of >67 u/mL for premenopausal and >23 u/mL for postmenopausal women were used to precipitate urgent cross-sectional imaging requests and MDT opinions. These CA 125 cut thresholds were calculated using an assumption that the RMI imaging score, regardless of whether the result was available, could be three. We contemplate that women who did not exceed a provisional RMI score of >200 might be informed they are extremely unlikely to have cancer, removed from the malignancy tracker and appropriate follow-up arranged. One hundred and forty consecutive cases were analysed; 43% were deemed premenopausal and 57% postmenopausal. Twenty of the women had cancer, eighteen (90%) of whom had an RMI > 200. One hundred and twenty were benign, and only twenty-three (19%) classified as urgent cases in need of accelerated referral to imaging. In contrast, CA 125 > 35 u/mL, whilst retaining the sensitivity of 90%, misclassified 36 (30%) of the benign cases. It is possible that a telephone triage via a questionnaire determining menopausal status and the CA 125 result could offer a sensitivity for cancer of 90% and urgent expert review of under 20% of benign cases. This rapid initial telephone assessment could be presented by a trained pathway navigator, physician associate or nurse specialist. Substantial savings in NHS cancer services resources, anxieties all around and reduced patient morbidity may occur as a result.

Autoantibodies, antigen-autoantibody complexes and antigens complement CA125 for early detection of ovarian cancer

BACKGROUND

Multiple antigens, autoantibodies (AAb), and antigen-autoantibody (Ag-AAb) complexes were compared for their ability to complement CA125 for early detection of ovarian cancer.

METHODS

Twenty six biomarkers were measured in a single panel of sera from women with early stage (I-II) ovarian cancers (n = 64), late stage (III-IV) ovarian cancers (186), benign pelvic masses (200) and from healthy controls (502), and then split randomly (50:50) into a training set to identify the most promising classifier and a validation set to compare its performance to CA125 alone.

RESULTS

Eight biomarkers detected ≥ 8% of early stage cases at 98% specificity. A four-biomarker panel including CA125, HE4, HE4 Ag-AAb and osteopontin detected 75% of early stage cancers in the validation set from among healthy controls compared to 62% with CA125 alone (p = 0.003) at 98% specificity. The same panel increased sensitivity for distinguishing early-stage ovarian cancers from benign pelvic masses by 25% (p = 0.0004) at 95% specificity. From 21 autoantibody candidates, 3 AAb (anti-p53, anti-CTAG1 and annt-Il-8) detected 22% of early stage ovarian cancers, potentially lengthening lead time prior to diagnosis.

CONCLUSION

A four biomarker panel achieved greater sensitivity at the same specificity for early detection of ovarian cancer than CA125 alone.

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.

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.

Preoperative and pre-chemotherapy CA-125 levels in high-risk early-stage ovarian cancer - An NRG/GOG study

OBJECTIVES

To determine clinical significance of preoperative and pre-chemotherapy CA-125 in high-risk early-stage epithelial ovarian cancer patients.

METHODS

All patients with stage IA/IB and grade 3, stage IC, clear cell, or completed resected stage II cancer were enrolled in a phase III trial and treated with chemotherapy. Kaplan-Meier method and Cox proportional hazards model were used for statistical analyses.

RESULTS

427 patients with high-risk early-stage ovarian cancer were enrolled. Of 213 patients with preoperative CA-125 data, 79% had elevated CA-125. Median preoperative CA-125 level was 103 U/mL. Patients with ≤10, 11-15, and > 15 cm tumors had median preoperative CA-125 levels of 62, 131 and 158 U/mL, respectively (p = 0.002). For the 350 patients with data for pre-chemotherapy CA-125 level, 69% had elevated pre-chemotherapy CA-125 above 35 U/mL with median value of 65 U/mL. However, age, race, stage, cell type and grade of disease were not correlated with CA-125 levels before and after surgery. On multivariate analysis, elevated pre-chemotherapy CA-125 independently predicted worse recurrence-free survival (HR = 2.13, 95% CI: 1.23-3.69; p = 0.007) and overall survival (HR = 1.99, 95% CI: 1.10-3.59; p = 0.022) after adjusting for age, stage, cell type and grade of disease. Compared to those with normal CA-125, patients with elevated pre-chemotherapy CA-125 had lower recurrence-free survival (RFS, 87% vs. 75%; p = 0.007) and overall survival (OS, 88% vs. 82%; p = 0.02). However, preoperative CA-125 was not prognostic of RFS (p = 0.699) or OS (p = 0.701).

CONCLUSIONS

Preoperative CA-125 was elevated in nearly 80% of high-risk early-stage ovarian cancer patients. Pre-chemotherapy CA-125 was associated with recurrence-free and overall survival; however, preoperative CA-125 was not prognostic.

Crimson clues: advancing endometriosis detection and management with novel blood biomarkers

Endometriosis is an inflammatory condition affecting approximately 10% of the female-born population. Despite its prevalence, the lack of noninvasive biomarkers has contributed to an established global diagnostic delay. The intricate pathophysiology of this enigmatic disease may leave signatures in the blood, which, when detected, can be used as noninvasive biomarkers. This review provides an update on how investigators are utilizing the established disease pathways and innovative methodologies, including genome-wide association studies, next-generation sequencing, and machine learning, to unravel the clues left in the blood to develop blood biomarkers. Many blood biomarkers show promise in the discovery phase, but because of a lack of standardized and robust methodologies, they rarely progress to the development stages. However, we are now seeing biomarkers being validated with high diagnostic accuracy and improvements in standardization protocols, providing promise for the future of endometriosis blood biomarkers.

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.

Prognostic Significance of Preoperative Inflammation Markers on the Long-Term Outcomes in Peritoneal Carcinomatosis from Ovarian Cancer

Ovarian cancer remains one of the most lethal gynaecological malignancies affecting women worldwide; therefore, attention has been focused on identifying new prognostic factors which might help the clinician to select cases who could benefit most from surgery versus cases in which neoadjuvant systemic therapy followed by interval debulking surgery should be performed. The aim of the current paper is to identify whether preoperative inflammation could serve as a prognostic factor for advanced-stage ovarian cancer. Material and methods: The data of 57 patients who underwent to surgery for advanced-stage ovarian cancer between 2014 and 2020 at the Cantacuzino Clinical Hospital were retrospectively reviewed. The receiver operating characteristic curve was used to determine the optimal cut-off value of different inflammatory markers for the overall survival analysis. The analysed parameters were the preoperative level of CA125, monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR) and systemic inflammation index (SII). Results: Baseline CA125 > 780 µ/mL, NLR ≥ 2.7, MLR > 0.25, PLR > 200 and a systemic immune inflammation index (SII, defined as platelet × neutrophil-lymphocyte ratio) ≥ 84,1000 were associated with significantly worse disease-free and overall survival in a univariate analysis. In a multivariate analysis, MLR and SII were significantly associated with higher values of overall survival (p < 0.0001 and p = 0.0124); meanwhile, preoperative values of CA125, PLR and MLR were not associated with the overall survival values (p = 0.5612, p = 0.6137 and p = 0.1982, respectively). In conclusion, patients presenting higher levels of MLR and SII preoperatively are expected to have a poorer outcome even if complete debulking surgery is performed and should be instead considered candidates for neoadjuvant systemic therapy followed by interval surgery.

Quantification of putative ovarian cancer serum protein biomarkers using a multiplexed targeted mass spectrometry assay

BACKGROUND

Ovarian cancer is the most lethal gynecologic malignancy in women, and high-grade serous ovarian cancer (HGSOC) is the most common subtype. Currently, no clinical test has been approved by the FDA to screen the general population for ovarian cancer. This underscores the critical need for the development of a robust methodology combined with novel technology to detect diagnostic biomarkers for HGSOC in the sera of women. Targeted mass spectrometry (MS) can be used to identify and quantify specific peptides/proteins in complex biological samples with high accuracy, sensitivity, and reproducibility. In this study, we sought to develop and conduct analytical validation of a multiplexed Tier 2 targeted MS parallel reaction monitoring (PRM) assay for the relative quantification of 23 putative ovarian cancer protein biomarkers in sera.

METHODS

To develop a PRM method for our target peptides in sera, we followed nationally recognized consensus guidelines for validating fit-for-purpose Tier 2 targeted MS assays. The endogenous target peptide concentrations were calculated using the calibration curves in serum for each target peptide. Receiver operating characteristic (ROC) curves were analyzed to evaluate the diagnostic performance of the biomarker candidates.

RESULTS

We describe an effort to develop and analytically validate a multiplexed Tier 2 targeted PRM MS assay to quantify candidate ovarian cancer protein biomarkers in sera. Among the 64 peptides corresponding to 23 proteins in our PRM assay, 24 peptides corresponding to 16 proteins passed the assay validation acceptability criteria. A total of 6 of these peptides from insulin-like growth factor-binding protein 2 (IBP2), sex hormone-binding globulin (SHBG), and TIMP metalloproteinase inhibitor 1 (TIMP1) were quantified in sera from a cohort of 69 patients with early-stage HGSOC, late-stage HGSOC, benign ovarian conditions, and healthy (non-cancer) controls. Confirming the results from previously published studies using orthogonal analytical approaches, IBP2 was identified as a diagnostic biomarker candidate based on its significantly increased abundance in the late-stage HGSOC patient sera compared to the healthy controls and patients with benign ovarian conditions.

CONCLUSIONS

A multiplexed targeted PRM MS assay was applied to detect candidate diagnostic biomarkers in HGSOC sera. To evaluate the clinical utility of the IBP2 PRM assay for HGSOC detection, further studies need to be performed using a larger patient cohort.

The High Sensitivity of the Multi-Cancer Detection Test ONCOVERYX-F Offers a Promising Platform for Ovarian Cancer Screening

We evaluated the potential relevance of our multi-cancer detection test, OncoVeryx-F, for ovarian cancer screening. For this, we compared its accuracy with that of CA125-based screening. We demonstrate here that, in contrast to CA125-based detection, OncoVeryx-F detected ovarian cancer with very high sensitivity and specificity. Importantly here, Stage I cancers too could be detected with an accuracy of >98%. Furthermore, again unlike CA 125, the detection accuracy of OncoVeryx-F remained comparable in both Caucasian and South Asian/Indian women. Thus, the robustness and accuracy of OncoVeryx-F, particularly for early-stage detection, underscores its potential utility for ovarian cancer screening.

Circulating small extracellular vesicles microRNAs plus CA-125 for treatment stratification in advanced ovarian cancer

BACKGROUND

No residual disease (R0 resection) after debulking surgery is the most critical independent prognostic factor for advanced ovarian cancer (AOC). There is an unmet clinical need for selecting primary or interval debulking surgery in AOC patients using existing prediction models.

METHODS

RNA sequencing of circulating small extracellular vesicles (sEVs) was used to discover the differential expression microRNAs (DEMs) profile between any residual disease (R0, n = 17) and no residual disease (non-R0, n = 20) in AOC patients. We further analyzed plasma samples of AOC patients collected before surgery or neoadjuvant chemotherapy via TaqMan qRT-PCR. The combined risk model of residual disease was developed by logistic regression analysis based on the discovery-validation sets.

RESULTS

Using a comprehensive plasma small extracellular vesicles (sEVs) microRNAs (miRNAs) profile in AOC, we identified and optimized a risk prediction model consisting of plasma sEVs-derived 4-miRNA and CA-125 with better performance in predicting R0 resection. Based on 360 clinical human samples, this model was constructed using least absolute shrinkage and selection operator (LASSO) and logistic regression analysis, and it has favorable calibration and discrimination ability (AUC:0.903; sensitivity:0.897; specificity:0.910; PPV:0.926; NPV:0.871). The quantitative evaluation of Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) suggested that the additional predictive power of the combined model was significantly improved contrasted with CA-125 or 4-miRNA alone (NRI = 0.471, IDI = 0.538, p < 0.001; NRI = 0.122, IDI = 0.185, p < 0.01).

CONCLUSION

Overall, we established a reliable, non-invasive, and objective detection method composed of circulating tumor-derived sEVs 4-miRNA plus CA-125 to preoperatively anticipate the high-risk AOC patients of residual disease to optimize clinical therapy.

Unlocking the power of nanomedicine: the future of nutraceuticals in oncology treatment

Cancer, an intricate and multifaceted disease, is characterized by the uncontrolled proliferation of cells that can lead to serious health complications and ultimately death. Conventional therapeutic strategies mainly target rapidly dividing cancer cells, but often indiscriminately harm healthy cells in the process. As a result, there is a growing interest in exploring novel therapies that are both effective and less toxic to normal cells. Herbs have long been used as natural remedies for various diseases and conditions. Some herbal compounds exhibit potent anti-cancer properties, making them potential candidates for nutraceutical-based treatments. However, despite their promising efficacy, there are considerable limitations in utilizing herbal preparations due to their poor solubility, low bioavailability, rapid metabolism and excretion, as well as potential interference with other medications. Nanotechnology offers a unique platform to overcome these challenges by encapsulating herbal compounds within nanoparticles. This approach not only increases solubility and stability but also enhances the cellular uptake of nutraceuticals, allowing for controlled and targeted delivery of therapeutic agents directly at tumor sites. By harnessing the power of nanotechnology-enabled therapy, this new frontier in cancer treatment presents an opportunity to minimize toxicity while maximizing efficacy. In conclusion, this manuscript provides compelling evidence for integrating nanotechnology with nutraceuticals derived from herbal sources to optimize cancer therapy outcomes. We explore the roadblocks associated with traditional herbal treatments and demonstrate how nanotechnology can help circumvent these issues, paving the way for safer and more effective cancer interventions in future oncological practice.

A Novel Predictive Multi-Marker Test for the Pre-Surgical Identification of Ovarian Cancer

Ovarian cancer remains the most lethal of gynecological malignancies, with the 5-year survival below 50%. Currently there is no simple and effective pre-surgical diagnosis or triage for patients with malignancy, particularly those with early-stage or low-volume tumors. Recently we discovered that CXCL10 can be processed to an inactive form in ovarian cancers and that its measurement has diagnostic significance. In this study we evaluated the addition of processed CXCL10 to a biomarker panel for the discrimination of benign from malignant disease. Multiple biomarkers were measured in retrospectively collected plasma samples (n = 334) from patients diagnosed with benign or malignant disease, and a classifier model was developed using CA125, HE4, Il6 and CXCL10 (active and total). The model provided 95% sensitivity/95% specificity for discrimination of benign from malignant disease. Positive predictive performance exceeded that of "gold standard" scoring systems including CA125, RMI and ROMA% and was independent of menopausal status. In addition, 80% of stage I-II cancers in the cohort were correctly identified using the multi-marker scoring system. Our data suggest the multi-marker panel and associated scoring algorithm provides a useful measurement to assist in pre-surgical diagnosis and triage of patients with suspected ovarian cancer.

A Critical Analysis of the FDA's Omics-Driven Pharmacodynamic Biomarkers to Establish Biosimilarity

Demonstrating biosimilarity entails comprehensive analytical assessment, clinical pharmacology profiling, and efficacy testing in patients for at least one medical indication, as required by the U.S. Biologics Price Competition and Innovation Act (BPCIA). The efficacy testing can be waived if the drug has known pharmacodynamic (PD) markers, leaving most therapeutic proteins out of this concession. To overcome this, the FDA suggests that biosimilar developers discover PD biomarkers using omics technologies such as proteomics, glycomics, transcriptomics, genomics, epigenomics, and metabolomics. This approach is redundant since the mode-action-action biomarkers of approved therapeutic proteins are already available, as compiled in this paper for the first time. Other potential biomarkers are receptor binding and pharmacokinetic profiling, which can be made more relevant to ensure biosimilarity without requiring biosimilar developers to conduct extensive research, for which they are rarely qualified.

Early Detection of Ovarian Cancer

The risk of death from ovarian cancer is highly associated with the clinical stage at diagnosis. Efforts to implement screening for ovarian cancer have been largely unsuccessful, due to the low prevalence of the disease in the general population and the heterogeneity of the various cancer types that fall under the ovarian cancer designation. A practical test for early detection will require both high sensitivity and high specificity to balance reducing the number of cancer deaths with minimizing surgical interventions for false positive screens. The technology must be cost-effective to deliver at scale, widely accessible, and relatively noninvasive. Most importantly, a successful early detection test must be effective not only at diagnosing ovarian cancer but also in reducing ovarian cancer deaths. Stepwise or multimodal approaches among the various areas under investigation will likely be required to make early detection a reality.

Front-line chemo-immunotherapy for treating epithelial ovarian cancer: Part I CA125 and anti-CA125

The current standard therapy of epithelial ovarian cancer (EOC) is the combination of surgery (primary cytoreductive surgery or interval cytoreductive surgery) and platinum-based chemotherapy (mainly using paclitaxel and carboplatin either by neoadjuvant chemotherapy and/or by postoperative adjuvant chemotherapy) with/without adding targeted therapy (mainly using anti-angiogenesis agent- bevacizumab). After front-line chemotherapy, the advanced-stage EOC can be successfully controlled and three-quarters of patients can achieve a complete clinical remission. Unfortunately, nearly all patients will recur and progression-free survival (PFS) of these patients is seldom more than 3 years with a dismal median PFS of 12-18 months. With each recurrence, patients finally develop resistance to standard chemotherapy regimen, contributing to fewer than half of women who survive for more than 5 years after diagnosis with a median overall survival (OS) of 40.7 months. Due to the lower PFS and OS, particularly for those advanced-stage patients, novel therapeutic options during the front-line therapy are desperately needed to decrease the occurrence of recurrence, and the majority of them are still under investigation. It is well-known that overexpression of CA125 has been associated with attenuated cellular apoptosis, platinum chemotherapy resistance, tumor proliferation and disease progression, suggesting that anti-CA125 may play a role in the management of patients with EOC. The current review is a Part I which will focus on development of anti-CA125 monoclonal antibody, hoping that alternation of the front-line therapy by chemo-immunotherapy will be beneficial for prolonged survival of patients with EOC.

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.

Serum Free Fatty Acid Changes Caused by High Expression of Stearoyl-CoA Desaturase 1 in Tumor Tissues Are Early Diagnostic Markers for Ovarian Cancer

Ovarian cancer has a poor prognosis and is difficult to detect in early stages. Therefore, developing new diagnostic markers for early-stage ovarian cancer is critical. Here, we developed a diagnostic marker for early-stage ovarian cancer on the basis of fatty acid metabolism characteristics of cancer cells. The expression of various fatty acid metabolizing enzymes such as stearoyl-CoA desaturase 1 (SCD1) was altered in early-stage ovarian cancer tissue compared with that in normal ovarian tissue. Changes in the expression of fatty acid metabolizing enzymes, particularly SCD1, in cancer tissues were found to alter concentrations of multiple free fatty acids (FFA) in serum. We were the first to show that fatty acid metabolic characteristics in tissues are related to the FFA composition of serum. Surprisingly, patients with stage I/II ovarian cancer also showed significant changes in serum levels of eight FFAs, which can be early diagnostic markers. Finally, using statistical analysis, an optimal early diagnostic model combining oleic and arachidic acid levels, fatty acids associated with SCD1, was established and confirmed to have higher diagnostic power than CA125, regardless of histology. Thus, our newly developed diagnostic model using serum FFAs may be a powerful tool for the noninvasive early detection of ovarian cancer.

SIGNIFICANCE

Measurement of serum FFA levels by changes in the expression of fatty acid metabolizing enzymes in tumor tissue would allow early detection of ovarian cancer. In particular, the SCD1-associated FFAs, oleic and arachidic acid, would be powerful new screening tools for early-stage ovarian cancer.

Tumor Markers and Their Diagnostic Significance in Ovarian Cancer

Ovarian cancer (OC) is characterized by silent progression and late-stage diagnosis. It is critical to detect and accurately diagnose the disease early to improve survival rates. Tumor markers have emerged as valuable tools in the diagnosis and management of OC, offering non-invasive and cost-effective options for screening, monitoring, and prognosis.

PURPOSE

This paper explores the diagnostic importance of various tumor markers including CA-125, CA15-3, CA 19-9, HE4,hCG, inhibin, AFP, and LDH, and their impact on disease monitoring and treatment response assessment.

METHODS

Article searches were performed on PubMed, Scopus, and Google Scholar. Keywords used for the searching process were "Ovarian cancer", "Cancer biomarkers", "Early detection", "Cancer diagnosis", "CA-125","CA 15-3","CA 19-9", "HE4","hCG", "inhibin", "AFP", "LDH", and others.

RESULTS

HE4, when combined with CA-125, shows improved sensitivity and specificity, particularly in early-stage detection. Additionally, hCG holds promise as a prognostic marker, aiding treatment response prediction and outcome assessment. Novel markers like microRNAs, DNA methylation patterns, and circulating tumor cells offer potential for enhanced diagnostic accuracy and personalized management. Integrating these markers into a comprehensive panel may improve sensitivity and specificity in ovarian cancer diagnosis. However, careful interpretation of tumor marker results is necessary, considering factors such as age, menopausal status, and comorbidities. Further research is needed to validate and refine diagnostic algorithms, optimizing the clinical significance of tumor markers in ovarian cancer management. In conclusion, tumor markers such as CA-125, CA15-3, CA 19-9, HE4, and hCG provide valuable insights into ovarian cancer diagnosis, monitoring, and prognosis, with the potential to enhance early detection.

Comparison of the diagnostic accuracy of HE4 with CA125 and validation of the ROMA index in differentiating malignant and benign epithelial ovarian tumours among patients in Lagos, Nigeria

This prospective cross-sectional study compared the diagnostic accuracy of human epididymal protein 4 (HE4) with cancer antigen 125 (CA 125) and validates the risk of malignancy algorithm (ROMA) in differentiating benign from malignant ovarian tumours. The study population included 112 women with an ultrasound diagnosis of an adnexal mass, out of whom 49 women had a diagnosis of ovarian cancer following optimal debulking surgery, and 63 women had a diagnosis of benign ovarian tumour. All diagnosis was confirmed by histopathological analysis. Serum HE4 and CA 125 were assessed preoperatively according to the manufacturer's instructions. CA 125 and HE4 cut-offs were 35 U/mL and 70 pM/L respectively. Serum CA 125 and HE4 were significantly higher in ovarian cancer patients compared to those with benign ovarian tumours (p < 0.001 and p < 0.000, respectively). HE4 had higher sensitivity (77.5% versus 69.4%), specificity (96.8% versus 82.5%), positive predictive value (PPV) (95% versus 75.6%) and negative predictive value (84.7% versus 77.6%) than CA 125. When the two markers were combined with each other in the ROMA index, Specificity and PPV reached 100% each. In the receiver operative characteristics analysis, the area under the curve for CA 125 was 0.679 (95% CI 0.566-0.791, p = 0.001), HE4 was 0.845 (95% CI 0.760-0.930, p = 0.000) and ROMA was 0.902 (95% CI 0.851-0.998, p = 0.000) and this was statistically significant (p < 0.001). Conclusively, HE4 performed better than CA 125 in differentiating benign from malignant ovarian tumours and the combination of the two biomarkers improved the detection of ovarian cancer. In addition, the cut off values corresponding to the highest accuracy for CA 125 and HE4 were 126 U/mL and 42 pM/L respectively in this study. The value for CA 125 is much higher while that of HE4 is much lower than the reference values obtained predominantly from the white population.

Comparison of Human Epididymis Protein 4, Cancer Antigen 125, and Ultrasound Prediction Model in Differentiating Benign from Malignant Adnexal Masses

Background

This study aimed to compare the diagnostic performance of carcinogenic antigen (CA) 125, (HE)-4 (Human epididymis protein 4), and ultrasound (International Ovarian Tumor Analysis [IOTA]) Simple Rules individually and to derive a composite score in the differentiating ovarian cancer from benign ovarian mass.

Subjects and Methods

Consecutive patients (n = 100) with pelvic mass admitted during February 2018-August 2019 were included prospectively. Patients with either known case of epithelial ovarian cancer (EOC) or metastatic EOC were excluded. The primary outcome was to assess the sensitivity and specificity of CA-125, HE-4, and IOTA Simple Rules in predicting benign from malignant mass independently, while secondary outcome was derivation of a new model incorporating these variables using multivariate logistic regression analysis to predict benign from malignant lesions. Receiver operator curve (ROC) was drawn to redefine the best-performing cutoff values and difference between area under the ROC (AUROC) were compared by DeLong's method.

Results

Out of 100 cases of adnexal mass selected, the sensitivity and specificity of CA-125 were 73.8% and 77.6%, HE-4 were 90.5% and 87.9%, and IOTA Simple Rules were 92.9% and 81.0%. CA-125, HE-4, and IOTA Simple Rules were independently associated with the likelihood of malignancy/borderline (P < 0.001). The area under the curve for the "composite score" (AUC = 0.93) was the highest and was significantly better than that of CA-125 (AUC = 0.786) (P = 0.004 using DeLong's test) and comparable with HE-4 (AUROC = 0.90; P = 0.128 using DeLong's Test).

Conclusion

The sensitivity and specificity of HE-4 and IOTA Simple Rules for predicting malignant ovarian tumor was better than those of CA-125. The diagnostic performance of "composite score" was comparable to those of either HE-4 or IOTA Simple Rules and significantly better than CA-125.

Capitalizing glycomic changes for improved biomarker-based cancer diagnostics

Cancer serum biomarkers are valuable or even indispensable for cancer diagnostics and/or monitoring and, currently, many cancer serum markers are routinely used in the clinic. Most of those markers are glycoproteins, carrying cancer-specific glycan structures that can provide extra-information for cancer monitoring. Nonetheless, in the majority of cases, this differential feature is not exploited and the corresponding analytical assays detect only the protein amount, disregarding the analysis of the aberrant glycoform. Two exceptions to this trend are the biomarkers α-fetoprotein (AFP) and cancer antigen 19-9 (CA19-9), which are clinically monitored for their cancer-related glycan changes, and only the AFP assay includes quantification of both the protein amount and the altered glycoform. This narrative review demonstrates, through several examples, the advantages of the combined quantification of protein cancer biomarkers and the respective glycoform analysis, which enable to yield the maximum information and overcome the weaknesses of each individual analysis. This strategy allows to achieve higher sensitivity and specificity in the detection of cancer, enhancing the diagnostic power of biomarker-based cancer detection tests.

Aptamer-based carbohydrate antigen 125 sensor with molybdenum disulfide functional hybrid materials

Epithelial ovarian cancer is a malignant tumor of the female reproductive system with insidious symptoms, aggressiveness, risk of metastasis, and high mortality. Carbohydrate antigen 125 (CA125), a standard biomarker for screening epithelial ovarian cancer, can be applied to track cancer progression and treatment response. Here, we constructed an aptamer-based electrochemical biosensor to achieve sensitive detection of CA125. Molybdenum disulfide (MoS₂) was used as the stable layered substrate, combined with the irregular branched structure of gold nanoflowers (AuNFs) to provide the sensing interface with a large specific surface area by one-step electrodeposition AuNFs@MoS₂. The simplified electrode modification step increased the stability of the electrode while ensuring excellent electrochemical performance and providing many sulfhydryl binding sites. Then, AuNFs@MoS₂/CA125 aptamer/MCH sensor was designed for CA125 detection. Based on AuNFs@MoS₂ electrode, CA125 aptamer with sulfhydryl as the sensitive layer was fixed on the electrode by gold sulfur bonds. 6-Mercapto-1-hexanol (MCH) was used to block the electrode and reduce the non-specific adsorption. Finally, DPV analysis was applied for CA125 detection with the range of 0.0001 U/mL to 500 U/mL. Our designed aptamer sensor showed reasonable specificity, reproducibility, and stability. Clinical sample testing also proved the consistency of our sensor with the gold standard in negative/positive judgment. This work demonstrated a novel strategy for integrating nanostructures and biocompatibility to build advanced cancer biomarker sensors with promising applications.

Smooth Muscle Tumors of the Uterus at MRI: Focus on Leiomyomas and FIGO Classification

Leiomyomas are smooth muscle tumors of the uterus and are the most common uterine neoplasm. Although leiomyomas are usually asymptomatic, they can manifest with symptoms such as pain or uterine bleeding. Leiomyomas are classified on the basis of their anatomic location and morphology. Localization of leiomyomas relative to the endometrium, myometrium, and uterine serosa with use of the International Federation of Gynecology and Obstetrics (FIGO) classification system is helpful for guiding management in symptomatic patients. The FIGO system is a practical and universally accepted approach for classifying leiomyomas to guide radiologists and clinicians in deciding management. The MRI appearance of conventional leiomyomas is related to their tissue contents of smooth muscle and fibrous tissue and is well established. The MRI features of some leiomyoma subtypes and forms of degeneration also have been described. Other smooth muscle tumors of the uterus recognized in the 2020 World Health Organization classification system include intravenous leiomyomatosis, smooth muscle tumors of uncertain malignant potential, and metastasizing leiomyoma. At the far end of the spectrum are leiomyosarcomas, which are frankly malignant and therefore must be managed accordingly. Although MRI features that suggest a diagnosis of leiomyosarcoma have been proposed, these features overlap with those of some leiomyoma subtypes and degeneration. ^© RSNA, 2023 See the invited commentary by Fennessy and Gargiulo in this issue. Online supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article. Quiz questions for this article are available through the Online Learning Center.

Evaluation of the Potential Diagnostic Utility of the Determination of Selected Immunological and Molecular Parameters in Patients with Ovarian Cancer

Ovarian cancer is one of the most serious challenges in modern gynaecological oncology. Due to its non-specific symptoms and the lack of an effective screening procedure to detect the disease at an early stage, ovarian cancer is still marked by a high mortality rate among women. For this reason, a great deal of research is being carried out to find new markers that can be used in the detection of ovarian cancer to improve early diagnosis and survival rates of women with ovarian cancer. Our study focuses on presenting the currently used diagnostic markers and the latest selected immunological and molecular parameters being currently investigated for their potential use in the development of new diagnostic and therapeutic strategies.

Aberrant protein glycosylation: Implications on diagnosis and Immunotherapy

Glycosylation-mediated post-translational modification is critical for regulating many fundamental processes like cell division, differentiation, immune response, and cell-to-cell interaction. Alterations in the N-linked or O-linked glycosylation pattern of regulatory proteins like transcription factors or cellular receptors lead to many diseases, including cancer. These alterations give rise to micro- and macro-heterogeneity in tumor cells. Here, we review the role of O- and N-linked glycosylation and its regulatory function in autoimmunity and aberrant glycosylation in cancer. The change in cellular glycome could result from a change in the expression of glycosidases or glycosyltransferases like N-acetyl-glucosaminyl transferase V, FUT8, ST6Gal-I, DPAGT1, etc., impact the glycosylation of target proteins leading to transformation. Moreover, the mutations in glycogenes affect glycosylation patterns on immune cells leading to other related manifestations like pro- or anti-inflammatory effects. In recent years, understanding the glycome to cancer indicates that it can be utilized for both diagnosis/prognosis as well as immunotherapy. Studies involving mass spectrometry of proteome, site- and structure-specific glycoproteomics, or transcriptomics/genomics of patient samples and cancer models revealed the importance of glycosylation homeostasis in cancer biology. The development of emerging technologies, such as the lectin microarray, has facilitated research on the structure and function of glycans and glycosylation. Newly developed devices allow for high-throughput, high-speed, and precise research on aberrant glycosylation. This paper also discusses emerging technologies and clinical applications of glycosylation.

Positive Rate of Malignant Cells in Endometrial Cytology Samples of Ovarian Cancer, Fallopian Tube Cancer, and Primary Peritoneal Cancer Patients: A Systematic Review and Meta-Analysis

To estimate the feasibility of diagnosing ovarian cancer, fallopian tube cancer, and primary peritoneal cancer through endometrial cytology, we performed a systematic review and meta-analysis to calculate the pooled positive rate of malignant cells in endometrial cytology samples. We queried PubMed, EMBASE, Medline, and Cochrane Central Register of Controlled Trails from inception to November 12, 2020 for studies estimating positive rates of malignant cells in endometrial cytology samples from patients with ovarian cancer, fallopian tube cancer, and primary peritoneal cancer. The positive rates of the included studies were calculated as pooled positive rate through meta-analyses of proportion. Subgroup analysis based on different sampling methods was conducted. Seven retrospective studies involving 975 patients were included. Pooled positive rate of malignant cells in endometrial cytology specimens of ovarian cancer, fallopian tube cancer, and primary peritoneal cancer patients was 23% (95% CI: 16% - 34%). Statistical heterogeneity between the included studies was considerable (I2 = 89%, P < 0.01). The pooled positive rates of the group of brushes and the group of aspiration smears were 13% (95% CI: 10% - 17%, I2 = 0, P = 0.45) and 33% (95% CI: 25% - 42%, I2 = 80%, P < 0.01), respectively. Although endometrial cytology is not an ideal diagnostic tool for ovarian cancer, fallopian tube cancer, and primary peritoneal cancer, it is a convenient, painless, and easy-to-implement adjunct to other tools. Sampling method is one of the factors that affect the detection rate.

CAR Based Immunotherapy of Solid Tumours-A Clinically Based Review of Target Antigens

Immunotherapy with CAR-engineered immune cells has transformed the management of selected haematological cancers. However, solid tumours have proven much more difficult to control using this emerging therapeutic modality. In this review, we survey the clinical impact of solid tumour CAR-based immunotherapy, focusing on specific targets across a range of disease indications Among the many candidates which have been the subject of non-clinical CAR T-cell research, clinical data are available for studies involving 30 of these targets. Here, we map out this clinical experience, highlighting challenges such as immunogenicity and on-target off-tumour toxicity, an issue that has been both unexpected and devastating in some cases. We also summarise how regional delivery and repeated dosing have been used in an effort to enhance impact and safety. Finally, we consider how emerging armouring systems and multi-targeted CAR approaches might be used to enhance tumour access and better enable discrimination between healthy and transformed cell types.

Beyond glyco-proteomics-Understanding the role of genetics in cancer biomarkers

The development of robust cancer biomarkers is the most effective way to improve overall survival, as early detection and treatment leads to significantly better clinical outcomes. Many of the cancer biomarkers that have been identified and are clinically utilized are glycoproteins, oftentimes a specific glycoform. Aberrant glycosylation is a common theme in cancer, with dysregulated glycosylation driving tumor initiation and metastasis, and abnormal glycosylation can be detection both on the tissue surface and in serum. However, most cancer types are heterogeneous in regard to tumor genomics, and this heterogeneity extends to cancer glycomics. This limits the sensitivity of standalone glycan-based biomarkers, which has slowed their implementation clinically. However, if targeted biomarker development can take into account genomic tumor information, the development of complementary biomarkers that target unique cancer subgroups can be accomplished. This idea suggests the need for algorithm-based cancer biomarkers, which can utilize multiple biomarkers along with relevant demographic information. This concept has already been established in the detection of hepatocellular carcinoma with the GALAD score, and an algorithm-based approach would likely be effective in improving biomarker sensitivity for additional cancer types. In order to increase cancer diagnostic biomarker sensitivity, there must be more targeted biomarker development that considers tumor genomic, proteomic, metabolomic, and clinical data while identifying tumor biomarkers.

Imaging Recommendations for Diagnosis, Staging, and Management of Ovarian and Fallopian Tube Cancers

Ovarian malignancy the third most common gynecological malignancy and is the leading cause of death in women. Non-specific clinical presentation delays the diagnosis, and they often present in the advanced stage of disease. No imaging modality is recommended for screening as there is no significant mortality reduction. Ultrasound (USG) is usually the initial modality in suspected ovarian mass. MRI is recommended for the characterization of indeterminate ovarian or adnexal mass on USG. CT abdomen and pelvis with oral and IV contrast is the recommended imaging modality in staging the disease, predicting the resectability and in selecting the patients who would benefit from neoadjuvant chemotherapy. Early ovarian cancers are staged by post-surgical histology and undergo upfront surgery. Advanced disease benefit by neoadjuvant chemotherapy and less morbidity by interval cytoreduction where image-guided biopsy is performed for histological diagnosis. Follow-up recommendations are based on tumor histology. CT/PET CT is recommended for diagnosing recurrence.

Nanotechnology-Based Nucleic Acid Vaccines for Treatment of Ovarian Cancer

Anticancer vaccines represent a promising approach for effective treatment of cancer and along with recent advantages of nucleic acid-based vaccines for other diseases form a prospective and potentially efficacious direction of the research, development and clinical applications. Despite the ongoing several clinical trials of mRNA vaccines for the treatment of various types of cancer, to-date no cancer vaccines were approved by the US Food and Drug Administration. The present review analyzes and summarizes major approaches for treating of different forms of ovarian cancer including mRNA-based vaccines as well as nanotechnology-based approaches for their delivery.