Detection Of Epithelial Ovarian Cancer Research Articles

Preoperative [18F]fluoro-PEG-folate PET/CT in advanced stage epithelial ovarian cancer: A safety and feasibility study

PurposeThe selection for either primary or interval cytoreductive surgery (CRS) in patients with epithelial ovarian cancer (EOC) is currently based on imaging techniques like computed tomography (CT), [18F]fluorodeoxyglucose-positron emission tomography ([18F]FDG-PET), diffusion-weighted magnetic resonance imaging (DW-MRI) and/or diagnostic laparoscopy, but these have limitations. Folate receptor (FR)-targeted PET/CT imaging, using [18F]fluoro-PEG-folate, could improve preoperative assessment, potentially reducing unnecessary laparotomies. This paper presents the first experience with [18F]fluoro-PEG-folate PET/CT imaging in advanced stage EOC, focusing on safety, tolerability, and feasibility for reflecting the extent of disease. MethodsTolerability and safety were monitored after administration of the [18F]fluoro-PEG-folate tracer by measurements of vital function parameters (blood pressure, heart rate, peripheral oxygen saturation, respiratory rate, and temperature). In addition, (serious) adverse events were recorded. Disease burden was quantified using the Peritoneal Cancer Index (PCI) score on preoperative [18F]fluoro-PEG-folate PET/CT and during surgery. PCI scores were compared with intraoperative findings, considering histopathologic results as the gold standard. Tissue specimens were stained for FRα and FRβ. Relative uptake of the radiotracer by EOC lesions and other tissues was quantified using body weighted standardized uptake values (SUV). ResultsThe study was terminated prematurely during the interim analysis after inclusion of eight patients of whom five had completed the study protocol. Although [18F]fluoro-PEG-folate demonstrated safety, efficacy for tumor-specific imaging was limited. Despite clear FRα overexpression, low tracer uptake was observed in EOC lesions, contrasting with high uptake in healthy tissues, posing challenges in specificity and accurately assessing tumor burden. ConclusionsOverall, while [18F]fluoro-PEG-folate was well-tolerated, its clinical utility in the preoperative assessment of the extent of disease in EOC was limited. This highlights the need for further research in developing targeted imaging agents for optimal detection of EOC metastases.Trial registration: Clinicaltrials.gov, NCT05215496. Registered 31 January 2022.

Comprehensive serum glycopeptide spectra analysis to identify early-stage epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is widely recognized as the most lethal gynecological malignancy; however, its early-stage detection remains a considerable clinical challenge. To address this, we have introduced a new method, named Comprehensive Serum Glycopeptide Spectral Analysis (CSGSA), which detects early-stage cancer by combining glycan alterations in serum glycoproteins with tumor markers. We detected 1712 glycopeptides using liquid chromatography–mass spectrometry from the sera obtained from 564 patients with EOC and 1149 controls across 13 institutions. Furthermore, we used a convolutional neural network to analyze the expression patterns of the glycopeptides and tumor markers. Using this approach, we successfully differentiated early-stage EOC (Stage I) from non-EOC, with an area under the curve (AUC) of 0.924 in receiver operating characteristic (ROC) analysis. This method markedly outperforms conventional tumor markers, including cancer antigen 125 (CA125, 0.842) and human epididymis protein 4 (HE4, 0.717). Notably, our method exhibited remarkable efficacy in differentiating early-stage ovarian clear cell carcinoma from endometrioma, achieving a ROC-AUC of 0.808, outperforming CA125 (0.538) and HE4 (0.557). Our study presents a promising breakthrough in the early detection of EOC through the innovative CSGSA method. The integration of glycan alterations with cancer-related tumor markers has demonstrated exceptional diagnostic potential.

Transformer-based AI technology improves early ovarian cancer diagnosis using cfDNA methylation markers

Epithelial ovarian cancer (EOC) is the deadliest women's cancer and has a poor prognosis. Early detection is the key for improving survival (a 5-year survival rate in stage I/II is over 70% compared to that of 25% in stage III/IV) and can be achieved through methylation markers from circulating cell-free DNA (cfDNA) using a liquid biopsy. In this study, we first identify top 500 EOC markers differentiating EOC from healthy female controls from 3.3 million methylome-wide CpG sites and validated them in 1,800 independent cfDNA samples. We then utilize a pretrained AI transformer system called MethylBERT to develop an EOC diagnostic model which achieves 80% sensitivity and 95% specificity in early-stage EOC diagnosis. We next develop a simple digital droplet PCR (ddPCR) assay which archives good performance, facilitating early EOC detection.

Validation of miRNA signatures for ovarian cancer earlier detection in the pre-diagnosis setting using machine learning approaches.

Effective strategies for early detection of epithelial ovarian cancer are lacking. We evaluated whether a panel of 14 previously established circulating microRNAs could discriminate between cases diagnosed <2 years after serum collection and those diagnosed 2-7 years after serum collection. miRNA sequencing data from subsequent ovarian cancer cases were obtained as part of the ongoing multi-cancer JanusRNA project, utilizing pre-diagnostic serum samples from the Janus Serum Bank and linked to the Cancer Registry of Norway for cancer outcomes. We included a total of 80 ovarian cancer cases contributing 80 serum samples and compared 40 serum samples from cases with samples collected <2 years prior to diagnosis with 40 serum samples from cases with sample collection ≥2 to 7 years. We employed the extreme gradient boosting (XGBoost) algorithm to train a binary classification model using 70% of the available data, while the model was tested on the remaining 30% of the dataset. The performance of the model was evaluated using repeated holdout validation. The previously established set of miRNAs achieved a median area under the receiver operating characteristic curve (AUC) of 0.771 in the test sets. Four out of 14 miRNAs (hsa-miR-200a-3p, hsa-miR-1246, hsa-miR-203a-3p, hsa-miR-23b-3p) exhibited higher expression levels closer to diagnosis, consistent with the previously reported upregulation in cancer cases, with statistical significance observed only for hsa-miR-200a-3p (beta=0.14; p=0.04). The discrimination potential of the selected models provides evidence of the robustness of the miRNA signature for ovarian cancer.

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.

Abstract LB381: Plasma microbiota as novel biomarkers of early epithelial ovarian cancer detection

Abstract Background: Ovarian cancer accounts for 2% of all female cancers in the United States yet it is the 5th leading cancer death among women. Advanced epithelial ovarian cancer (EOC) is associated with an overall survival rate of 30% but can be cured in up to 90% of cases if diagnosed early when the disease is still limited to the ovaries and fallopian tubes. While there is no effective screening method for EOC, developing novel and minimally invasive clinical tests is paramount to identify women with early disease as well as those who are at increased risk. Plasma-based microbiota signatures specific to EOC could become leading biomarkers of early disease in the field that complement existing clinical tools. Methods: We utilized an established institutional biobank to access 180 biobanked plasma samples from: (1) women with EOC; (2) women with non-EOC solid tumors; (3) women undergoing gynecologic-related procedures and non-EOC surgeries; and (4) healthy controls. Batch 1 included 96 plasma samples (n=24 per group) and Batch 2 included 84 samples (n=19 per group and 8 repeat EOC samples). Microbial profiles of plasma specimens were assessed using an amplicon-based sequencing method that targets the 16S rRNA gene in the bacterial genome to study bacterial phylogeny and taxonomy in diseased and healthy women. A stringent bioinformatic pipeline was applied to filter out suspicious bacterial contaminants at the genus level. Bacterial decontamination procedures in the pipeline included evaluating repeat plasma samples of EOC cases and sampling nucleic acid isolation kit reagents to serve as negative controls (only in Batch 2) . Differential abundances of the plasma microbiota among the groups were assessed using analysis of compositions of microbiomes (ANCOM) as implemented in R software package ANCOM-BC. Results: The 16S rRNA gene sequencing identified, classified, and quantified 564 bacterial genera in the circulation of women diagnosed with EOC, non-EOC solid tumors, benign gynecologic conditions, and healthy controls. Bioinformatic decontamination analysis removed bacteria: (1) with differing bacterial abundances between batches; (2) with higher bacteria in negative controls; (3) with differing bacterial abundances in repeat samples; and (4) known as contaminants in the literature that resulted in the removal of 225 high-risk bacterial contaminants. A total of 339 high quality bacteria remained for evaluation. Because we did not collect negative controls in Batch 1, only Batch 2 plasma samples (n=84; 27 in EOC group; 19 in each other group) were included in the final analysis. Women with EOC, which were primarily of the high-grade serous ovarian carcinoma histologic subtype had significantly distinct differential abundances of genera Brevibacterium (p &amp;lt;0.001), Chloronema (p &amp;lt;0.001), Facklamia (p &amp;lt;0.001), Zymomonas (p &amp;lt;0.001), and Sutterella (p &amp;lt;0.001) when compared to women with non-EOC solid tumors, benign gynecologic conditions, and healthy controls. Conclusions: These findings suggest that plasma EOC-associated bacteria could be exploited in the development of a microbial-based blood test for the diagnosis and early detection of EOC in routine practice settings. Citation Format: Diane E. Mahoney, Prabhakar Chalise, Dong Pei, Rachel Griffard, Trisha Home, Harsh Pathak, Shahid Umar, Andrew K. Godwin. Plasma microbiota as novel biomarkers of early epithelial ovarian cancer detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB381.

Recent Advances in Surface Plasmon Resonance (SPR) Technology for Detecting Ovarian Cancer Biomarkers

Epithelial Ovarian Cancer (EOC) is a leading cause of cancer-related deaths among women, mainly due to a lack of early detection and screening methods. Advanced immunoassay techniques, such as Luminex and proximity extension assay (PEA) technology, show promise in improving EOC detection by utilizing highly sensitive and specific multiplex panels to detect multiple combinations of biomarkers. However, these advanced immunoassay techniques have certain limitations, especially in validating the performance characteristics such as specificity, sensitivity, limit of detection (LOD), and dynamic range for each EOC biomarker within the panel. Implementing multiplexing in point-of-care (POC) biosensors can enhance EOC biomarker detection, with Surface Plasmon Resonance (SPR) being a versatile option among optical biosensors. There is no study on multiplex SPR biosensors specifically tailored for diagnosing EOC. Recent studies have shown promising results in the single detection of EOC biomarkers using SPR, with LOD for cancer antigen 125 (CA125) at 0.01 U/mL−1 and human epididymis protein 4 (HE4) at 1pM. This study proposes a potential roadmap for scientists and engineers in academia and industry to develop a cost effective yet highly efficient SPR biosensor platform for detecting EOC.

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

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

Mir-21 and Mir-125b as theranostic biomarkers for epithelial ovarian cancer in Tunisian women.

Ovarian cancer (OC) is the third most common cancer in women and the leading cause of death associated with gynecologic tumors. Because this disease is asymptomatic in the early stages, most patients are not diagnosed until the late stages. This highlights the need for the development of diagnostic biomarkers. MicroRNAs (miRNAs), small non-coding RNAs, are currently being explored as potential biomarkers for the early detection of various malignancies in humans. However, their expression and diagnostic value in OC have not been well studied. the plasma levels of miR-21, miR-200a, miR-200b, miR-200c, miR-205 and miR-125b were determined in epithelial ovarian cancer (EOC) patients and healthy controls by Reverse Transcription Quantitative Realtime Polymerase Chain Reaction (RT-qPCR). The expression levels of the deregulated microRNAs were analysed according to clinical characteristics. It was found that miR-21 and miR-125b were upregulated in EOC compared with healthy controls. Moreover, decreased miR-125b was associated with resistance to platinum-based chemotherapy. Our data suggest that miR-21 and miR-125b in plasma may serve as potential circulating biomarkers for the early detection of EOC. MiR-125b may also be useful for predicting chemosensitivity in EOC patients.

The application of a DNA nanobiosensor based on MXene/MWCNTs/PPY nanocomposite as a sensitive detection method of CEACAM5 for diagnosis of epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is responsible for the high mortality rate related to gynecologic cancers. Due to the late-stage diagnosis, a 5-year survival rate is expected in only about 30% of patients. In this study, an electrochemical DNA biosensor based on MXene/MWCNTs/PPY nanocomposite was designed and introduced as an alternative method for Real-time PCR based detection methods of tumor markers. The biosensor specifically detected CEACAM5 as an ovarian cancer-related tumor marker in 26 real clinical samples. The statistical analysis results represented that the biosensor is able to differentiate between the case and control groups with 93.3 % sensitivity and high specificity. Moreover, the biosensor revealed a wide linear range of 5 × 10-11 − 5 × 10-7 M and good stability for up to 20 days. In conclusion, the fabricated biosensor provides a fast, low-cost, simple, and novel method for EOC detection.

Sensitive Detection of the Human Epididymis Protein-4 (HE4) Ovarian Cancer Biomarker through a Sandwich-Type Immunoassay Method with Laser-Induced Breakdown Spectroscopy.

Detection of cancer before the appearance of any symptoms is crucial for successful treatment. Early detection is, however, very challenging, particularly for the types of cancer with few or no symptoms at early stages, such as epithelial ovarian cancer (EOC). Developing a user-friendly method that can detect biomarkers with sufficient selectivity, sensitivity, and reproducibility is a promising approach for overcoming the challenges of early detection of EOC. In this study, we report a sandwich-type microparticle immunoassay for sensitive detection of the HE4 biomarker with laser-induced breakdown spectroscopy. Here, we cross-linked elemental particles to a specific functional group of the targeted biomolecules based on a covalent and non-covalent linking chemistry to improve the sensitivity and selectivity of biomarker detection, in which Fe3O4 and SiO2 microparticles were used to conjugate and purify the antibody-antigen in complex media. Simultaneous detection of Fe and Si from a magnetically purified assay significantly improves the HE4 biomarker's detectability, in which HE4 was detected with a limit of detection of 0.0022 pM. We also determined the coupling ratio between HE4 and silica particles using a silicon calibration curve.

Identification of two theranostic biomarker panels for epithelial ovarian cancer.

Epithelial Ovarian cancer (EOC) is the leading cause of death associated with gynecologic tumors. Because the disease is asymptomatic in early-stage, the majority of patients are not diagnosed until late stages, highlighting the need for the development of novel diagnostic biomarkers. Mediators of tumoral microenvironment may affect EOC progression and resistance to treatment. Analysis of serum proteins to identify a panel of theranostic biomarkers for EOC. Serum levels of 65 analytes were determined in EOC patients, and healthy controls with the ProcartaPlex Human Immune Monitoring 65-Plex Panel. Twenty-one analytes: 7 cytokines (IFN-γ, IL-12p70, IL-13, IL-18 and TSLP), 7 chemokines (Eotaxin, eotaxin-2, IP-10, BLC, I-TAC, SDF-1α, and fractalkine), 2 growth factors (MMP-1, VEGF-α), and 5 soluble receptors (APRIL, CD40L, TWEAK, CD30 and TNFRII; were significantly differentially expressed between the two groups. ROC curves showed that only seven of them (IL-9, TNF-α, Eotaxin, IP-10, BLC, Fractalkine, and Tweak) had AUC values greater than 0.70 and thus had potential clinical utility. Moreover, five cytokines: IFN-γ, IL-1 β, IL-8, MIP-1β, and TNF-α are positively associated with patients who developed resistance to taxol-platinum-based chemotherapy (CT). This study has revealed a first panel of 7 analytes (IL-9, TNF-α, Eotaxin, IP-10, BLC, Fractalkine and Tweak) that can be used for early detection of EOC and a second panel of five cytokines (IFN-γ, IL-1β, IL-8, MIP-1β, TNF-α) that can help clinicians to identify EOC patients who are at higher risk to develop resistance to CT of EOC.

Rapid and sensitive detection of ovarian cancer biomarker using a portable single peak Raman detection method

Raman spectroscopy (RS) is a widely used non-destructive technique for biosensing applications because of its ability to detect unique ‘fingerprint’ spectra of biomolecules from the vibrational bands. To detect these weak fingerprint spectra, a complex detection system consisting of expensive detectors and optical components are needed. As a result, surface enhanced Raman spectroscopy (SERS) method were used to increase the Raman signal multifold beyond 1012 times. However, complexity of the entire Raman detection system can be greatly reduced if a short wavelength region/unique single spectral band can distinctly identify the investigating analyte, thereby reducing the need of multiple optical components to capture the entire frequency range of Raman spectra. Here we propose the development of a rapid, single peak Raman technique for the detection of epithelial ovarian cancers (EOC)s through haptoglobin (Hp), a prognostic biomarker. Hp concentration in ovarian cyst fluid (OCF) can be detected and quantified using Raman spectroscopy-based in vitro diagnostic assay. The uniqueness of the Raman assay is that, only in the presence of the analyte Hp, the assay reagent undergoes a biochemical reaction that results in product formation. The unique Raman signature of the assay output falls within the wavenumber region 1500–1700 cm−1 and can be detected using our single peak Raman system. The diagnostic performance of our Raman system had 100.0% sensitivity, 85.0% specificity, 100.0% negative predictive value and 84.2% positive predictive value when compared to gold standard paraffin histology in a proof-of-concept study on 36 clinical OCF samples. When compared to blood-based serum cancer antigen 125 (CA125) levels, the Raman system-based assay had higher diagnostic accuracy when compared to CA125, especially in early-stage EOCs.

Abstract 5147: The prion-like protein doppel as a seurm biomarker for epithelial ovarian cancer

Abstract Ovarian cancer has a dire prognosis with an overall 5-year survival rate of ~40%, whereas stage III and IV has a survival rate of only 3-19%. Failure to detect early contributes highly to the poor survival rate of ovarian cancer. Thus, early detection of EOCs is likely to translate into mortality benefit. Two tests used clinically to detect ovarian cancer are serum cancer antigen-125 (CA-125) and transvaginal ultrasound. Both have limitations of specificity and sensitivity. CA-125 along with other biomarkers that are in clinical trials express both in the ovaries and serum of healthy females. Thus, the specific detection among cancer-free controls versus ovarian cancer cases remains highly variable. We hypothesize to assess the Doppel (PRND gene) as a serum biomarker in the detection of epithelial ovarian cancers (EOC) at the early stages. Doppel, a prion-like protein first identified in 1996, is known to express in testis and appears to regulate male fertility. Previously, we and others reported that Doppel is a highly specific tumor endothelium marker. In this study, to validate the potential of Doppel as an EOC serum biomarker, using ELISA, we detected Doppel concentration in the sera of 76 sera samples from patients with stage and histopathology information, and in 18 normal human sera (NHS). Doppel level was significantly higher in sera of EOC patients than NHS (0.720 vs 6.653 ng/ml, p&amp;lt;0.0001); 72 out of 76 patients were positive for Doppel expression (cut off value was calculated as Mean+3×SEM = 1.296 ng/mL) while only 3 out of 18 cases were positive for NHS. Receiver operating characteristic (ROC) analysis showed that Doppel can differentiate EOC patients from normal individuals with AUC of 0.957, sensitivity of 90.8%, and specificity of 90%. Doppel was markedly higher in all stages of EOC than NHS (Stage I, 6.261 ng/mL; Stage II, 8.045 ng/ml; Stage III, 7.040 ng/mL; and Stage IV, 5.726 ng/ml vs NHS 0.720 ng/ml; p&amp;lt;0.001). Interestingly, serum Doppel level was higher among EOC of serous adenocarcinoma (7.020 ng/mL; n = 39), mucinous (10.780 ng/mL; n = 8), and endometroid (7.295 ng/mL; n = 7) than clear cell (3.114 ng/mL; n = 7) or other types (4.855 ng/mL; n = 15). Together, our study showed the potential of Doppel as a serological biomarker for EOC detection. Citation Format: Xiaojun Zhang, Bowon Kang, Mazharul Karim, Jianying Zhang, Jae Yun Song, In-San Kim, Taslim A. Al-Hilal. The prion-like protein doppel as a seurm biomarker for epithelial ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5147.

A Translational Model to Improve Early Detection of Epithelial Ovarian Cancers.

Neural network analyses of circulating miRNAs have shown potential as non-invasive screening tests for ovarian cancer. A clinically useful test would detect occult disease when complete cytoreduction is most feasible. Here we used murine xenografts to sensitize a neural network model to detect low volume disease and applied the model to sera from 75 early-stage ovarian cancer cases age-matched to 200 benign adnexal masses or healthy controls. The 14-miRNA model efficiently discriminated tumor bearing animals from controls with 100% sensitivity down to tumor inoculums of 50,000 cells. Among early-stage patient samples, the model performed well with 73% sensitivity at 91% specificity. Applied to a population with 1% disease prevalence, we hypothesize the model would detect most early-stage ovarian cancers while maintaining a negative predictive value of 99.97% (95% CI 99.95%-99.98%). Overall, this supports the concept that miRNAs may be useful as screening markers for early-stage disease.

Hyperspectral Imaging for Tissue Classification after Advanced Stage Ovarian Cancer Surgery-A Pilot Study.

Simple SummarySurvival of ovarian cancer patients largely relies on the surgical removal of all cancer cells. To achieve this, good vision is crucial. In this study, we evaluate the ability of hyperspectral imaging to detect ovarian cancer. Images of surgically removed tissue samples of 11 patients were taken and compared to histopathology in order to train machine learning software. For training purposes, only healthy tissues and tissues with high tumor cell content (>50%) were included. In total, 26 tissue samples and 26,446 data points of 10 patients were included. Tissue classification as either tumorous or healthy was evaluated by leave-one-out cross-validation. This resulted in a power of 0.83, a sensitivity of 0.81, a specificity of 0.70 and a Matthew’s correlation coefficient of 0.41. To conclude, this study shows that hyperspectral imaging can be used to recognize ovarian cancer. In the future, the technique may enable real-time image guidance during surgery.The most important prognostic factor for the survival of advanced-stage epithelial ovarian cancer (EOC) is the completeness of cytoreductive surgery (CRS). Therefore, an intraoperative technique to detect microscopic tumors would be of great value. The aim of this pilot study is to assess the feasibility of near-infrared hyperspectral imaging (HSI) for EOC detection in ex vivo tissue samples. Images were collected during CRS in 11 patients in the wavelength range of 665–975 nm, and processed by calibration, normalization, and noise filtering. A linear support vector machine (SVM) was employed to classify healthy and tumorous tissue (defined as >50% tumor cells). Classifier performance was evaluated using leave-one-out cross-validation. Images of 26 tissue samples from 10 patients were included, containing 26,446 data points that were matched to histopathology. Tumorous tissue could be classified with an area under the curve of 0.83, a sensitivity of 0.81, a specificity of 0.70, and Matthew’s correlation coefficient of 0.41. This study paves the way to in vivo and intraoperative use of HSI during CRS. Hyperspectral imaging can scan a whole tissue surface in a fast and non-contact way. Our pilot study demonstrates that HSI and SVM learning can be used to discriminate EOC from surrounding tissue.

NGF/TRKA Promotes ADAM17-Dependent Cleavage of P75 in Ovarian Cells: Elucidating a Pro-Tumoral Mechanism.

Nerve growth factor (NGF) and its high-affinity receptor TRKA are overexpressed in epithelial ovarian cancer (EOC) displaying a crucial role in the disease progression. Otherwise, NGF interacts with its low-affinity receptor P75, activating pro-apoptotic pathways. In neurons, P75 could be cleaved by metalloproteinases (α and γ-secretases), leading to a decrease in P75 signaling. Therefore, this study aimed to evaluate whether the shedding of P75 occurs in EOC cells and whether NGF/TRKA could promote the cleavage of the P75 receptor. The immunodetection of the α-secretase, ADAM17, TRKA, P75, and P75 fragments was assessed by immunohisto/cytochemistry and Western blot in biopsies and ovarian cell lines. The TRKA and secretases’ inhibition was performed using specific inhibitors. The results show that P75 immunodetection decreased during EOC progression and was negatively correlated with the presence of TRKA in EOC biopsies. NGF/TRKA increases ADAM17 levels and the fragments of P75 in ovarian cells. This effect is abolished when cells are previously treated with ADAM17, γ-secretase, and TRKA inhibitors. These results indicate that NGF/TRKA promotes the shedding of P75, involving the activation of secretases such as ADAM17. Since ADAM17 has been proposed as a screening marker for early detection of EOC, our results contribute to understanding better the role of ADAM17 and NGF/TRKA in EOC pathogenesis, which includes the NGF/TRKA-mediated cleavage of P75.

Predicting Diagnostic Potential of Cathepsin in Epithelial Ovarian Cancer: A Design Validated by Computational, Biophysical and Electrochemical Data.

Background: Epithelial ovarian cancer remains one of the leading variants of gynecological cancer with a high mortality rate. Feasibility and technical competence for screening and detection of epithelial ovarian cancer remain a major obstacle and the development of point of care diagnostics (POCD) may offer a simple solution for monitoring its progression. Cathepsins have been implicated as biomarkers for cancer progression and metastasis; being a protease, it has an inherent tendency to interact with Cystatin C, a cysteine protease inhibitor. This interaction was assessed for designing a POCD module. Methods: A combinatorial approach encompassing computational, biophysical and electron-transfer kinetics has been used to assess this protease-inhibitor interaction. Results: Calculations predicted two cathepsin candidates, Cathepsin K and Cathepsin L based on their binding energies and structural alignment and both predictions were confirmed experimentally. Differential pulse voltammetry was used to verify the potency of Cathepsin K and Cathepsin L interaction with Cystatin C and assess the selectivity and sensitivity of their electrochemical interactions. Electrochemical measurements indicated selectivity for both the ligands, but with increasing concentrations, there was a marked difference in the sensitivity of the detection. Conclusions: This work validated the utility of dry-lab integration in the wet-lab technique to generate leads for the design of electrochemical diagnostics for epithelial ovarian cancer.

Evaluation of the Diagnostic Potential of Candidate Hypermethylated Genes in Epithelial Ovarian Cancer in North Indian Population.

Most ovarian cancers, despite improvement in management of cancer, are still diagnosed at an advanced stage. Early detection plays an essential role in reducing ovarian cancer mortality and, therefore, is critically needed. Liquid biopsies-based approaches hold significant promise for cancer detection. The present study investigates a panel of epigenetic biomarkers for the detection of epithelial ovarian cancer. A qPCR assay has been developed based on the assessment of DNA methylation markers in circulating cell-free DNA as a minimally invasive tool. Herein, the promoter methylation of seven ovarian cancer-specific genes (RASSF1A, DAPK1, SOX1, HOXA9, HIC1, SPARC, and SFRP1) was analyzed quantitatively in 120 tissue samples by MethyLight assay. The best-performing genes were further evaluated for their methylation status in 70 matched serum cell-free DNA of cancerous and non-cancerous samples. Additionally, DNA methylation patterns of these best-performing genes were validated by clonal bisulfite sequencing. The ROC (Receiver-operator characteristic) curves were constructed to evaluate the diagnostic performances of both individual and combined gene panels. The seven candidate genes displayed a methylation frequency of 61.0–88.0% in tissue samples. The promoter methylation frequencies for all the seven candidate genes were significantly higher in cancer samples than in normal matched controls. In tissue samples, the multiplex MethyLight assay for HOXA9, HIC1, and SOX1 were the best performing gene panels in terms of sensitivity and specificity. The three best-performing genes exhibited individual frequencies of 53.0–71.0% in serum CFDNA, and the multiplex assay for these genes were identified to discriminate serum from cancer patients and healthy individuals (area under the curve: HOXA9+HIC1 = 0.95, HIC1+SOX1 = 0.93 and HOXA9+SOX1 = 0.85). The results of MethyLight showed high concordance with clonal bisulfite sequencing results. Individual genes and combined panel exhibited better discriminatory efficiencies to identify ovarian cancer at various stages of disease when analyzed in tissue and serum cell-free DNA. We report a qPCR-based non-invasive epigenetic biomarker assay with high sensitivity and specificity for OC screening. Our findings also reveal the potential utility of methylation-based detection of circulating cell-free tumor DNA in the clinical management of ovarian cancer.

Biomarker-based early detection of epithelial ovarian cancer based on a five-protein signature in patient\u2019s plasma \u2013 a prospective trial

BackgroundTrial on five plasma biomarkers (CA125, HE4, OPN, leptin, prolactin) and their possible role in differentiating benign from malignant ovarian tumors.MethodsIn this unicentric prospective trial preoperative blood samples of 43 women with ovarian masses determined for ovarian surgery were analyzed. 25 patients had pathologically confirmed benign, 18 malignant ovarian tumors. Blood plasma was analyzed for CA125, HE4, OPN, leptin, prolactin and MIF by multiplex immunoassay analysis. Each single protein and a logistical regression model including all the listed proteins were tested as preoperative predictive marker for suspect ovarian masses.ResultsPlasma CA125 was confirmed as a highly accurate tumor marker in ovarian cancer. HE4, OPN, leptin and prolactin plasma levels differed significantly between benign and malignant ovarian masses. With a logistical regression model a formula including CA125, HE4, OPN, leptin and prolactin was developed to predict malignant ovarian tumors. With a discriminatory AUC of 0.96 it showed to be a highly sensitive and specific diagnostic test for a malignant ovarian tumor.ConclusionsThe calculated formula with the combination of CA125, HE4, OPN, leptin and prolactin plasma levels surpasses each single marker in its diagnostic value to discriminate between benign and malignant ovarian tumors. The formula, applied to our patient population was highly accurate but should be validated in a larger cohort.Trial registrationClinical Trials.gov under NCT01763125, registered Jan. 8, 2013.