Flow cytometry is a powerful method, which is widely used for high-throughput quantitative and qualitative analysis of cells. However, its straightforward applicability for extracellular vesicles (EVs) and mainly exosomes is hampered by several challenges, reflecting mostly the small size of these vesicles (exosomes: ~80–200 nm, microvesicles: ~200–1,000 nm), their polydispersity, and low refractive index. The current best and most widely used protocol for beads-free flow cytometry of exosomes uses ultracentrifugation (UC) coupled with floatation in sucrose gradient for their isolation, labeling with lipophilic dye PKH67 and antibodies, and an optimized version of commercial high-end cytometer for analysis. However, this approach requires an experienced flow cytometer operator capable of manual hardware adjustments and calibration of the cytometer. Here, we provide a novel and fast approach for quantification and characterization of both exosomes and microvesicles isolated from cell culture media as well as from more complex human samples (ascites of ovarian cancer patients) suitable for multiuser labs by using a flow cytometer especially designed for small particles, which can be used without adjustments prior to data acquisition. EVs can be fluorescently labeled with protein-(Carboxyfluoresceinsuccinimidyl ester, CFSE) and/or lipid- (FM) specific dyes, without the necessity of removing the unbound fluorescent dye by UC, which further facilitates and speeds up the characterization of microvesicles and exosomes using flow cytometry. In addition, double labeling with protein- and lipid-specific dyes enables separation of EVs from common contaminants of EV preparations, such as protein aggregates or micelles formed by unbound lipophilic styryl dyes, thus not leading to overestimation of EV numbers. Moreover, our protocol is compatible with antibody labeling using fluorescently conjugated primary antibodies. The presented methodology opens the possibility for routine quantification and characterization of EVs from various sources. Finally, it has the potential to bring a desired level of control into routine experiments and non-specialized labs, thanks to its simple bead-based standardization.

Background:Identification of aggressive endometrioid endometrial carcinomas (EECs) and non-endometrioid carcinomas (NEECs) is essential to improve outcome. L1 cell adhesion molecule (L1CAM) expression is a strong prognostic marker in stage I EECs, but less is known about L1CAM expression in advanced-stage EECs and NEECs. This study analyses L1CAM expression in a clinically representative cohort of endometrial carcinomas.Methods:The expression of L1CAM was immunohistochemically determined in 1199 endometrial carcinomas, treated at one of the European Network for Individualized Treatment of Endometrial Cancer (ENITEC) centres. Staining was considered positive when >10% of the tumour cells expressed L1CAM. The association between L1CAM expression and several clincopathological characteristics and disease outcome was calculated.Results:In all, L1CAM was expressed in 10% of the 935 stage I EECs, 18% of the 160 advanced stage EECs, and 75% of the 104 NEECs. The expression of L1CAM was associated with advanced stage, nodal involvement, high tumour grade, non-endometrioid histology, lymphovascular space invasion, and distant recurrences in all cases, and with reduced survival in the EECs, but not in the NEECs.Conclusions:The expression of L1CAM is a strong predictor of poor outcome in EECs, but not NEECs. It is strongly associated with non-endometrioid histology and distant spread, and could improve the postoperative selection of high-risk endometrial carcinomas. The value of L1CAM expression in the preoperative selection of high-risk endometrial carcinomas should be studied.

Among gynaecological cancers, epithelial ovarian cancers are the most deadly cancers while endometrial cancers are the most common diseases. Efforts to establish relevant novel diagnostic, screening and prognostic markers are aimed to help reduce the high level of mortality, chemoresistance and recurrence, particularly in ovarian cancer. MicroRNAs, the class of post-transcriptional regulators, have emerged as the promising diagnostic and prognostic markers associated with various diseased states recently. Urine has been shown as the source of microRNAs several years ago; however, there has been lack of information on urine microRNA expression in ovarian and endometrial cancers till now. In this pilot study, we examined the expression of candidate cell-free urine microRNAs in ovarian cancer and endometrial cancer patients using quantitative real-time PCR. We compared the expression between pre- and post-surgery ovarian cancer samples, and between patients with ovarian and endometrial cancers and healthy controls, within three types of experiments. These experiments evaluated three different isolation methods of urine RNA, representing two supernatant and one exosome fractions of extracellular microRNA. In ovarian cancer, we found miR-92a significantly up-regulated, and miR-106b significantly down-regulated in comparison with control samples. In endometrial cancer, only miR-106b was found down-regulated significantly compared to control samples. Using exosome RNA, no significant de-regulations in microRNAs expression could be found in either of the cancers investigated. We propose that more research should now focus on confirming the diagnostic potential of urine microRNAs in gynaecological cancers using more clinical samples and large-scale expression profiling methods.

CPH-I is a potential tumor index that is independent of menopausal status. It might be applied as a simple alternative to ROMA in settings of basic medical care.

Loss of ER and PR, and the presence of L1CAM are associated with high risk characteristics, and loss of PR is the strongest predictor of recurrent disease. Although a combination of these 3 markers is slightly superior to the traditional histological markers, a prognostic model including stage, PR expression, and LVSI is the most promising model in the identification of high risk carcinomas. In the stage I endometrioid carcinomas, PR immunohistochemistry appears to be of additional value in predicting recurrences.

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Ovarian cancer as the most fatal gynecological malignancy is often manifested by excessive fluid accumulation known as ascites or effusion. Ascites-derived microRNAs (miRNAs) may be closely associated with ovarian cancer progression. However, our knowledge of their roles, altered expression, and clinical outcomes remained limited. In this study, large-scale expression profiling of 754 human miRNAs was performed using real-time quantitative polymerase chain reaction and 384-well TaqMan array human miRNA A and B cards to identify differentially expressed miRNAs between extracellular fraction of the ascitic fluid associated with high-grade serous ovarian carcinomas and control plasma. Of the 754 miRNAs, 153 were significantly differentially expressed relative to the controls. Expression of 7 individual miRNAs (miR-200a, miR-200b, miR-200c, miR-141, miR-429, miR-1290, and miR-30a-5p) was further validated in extended sample sets, including serous, endometrioid, and mucinous subtypes. All miR-200 family members and miR-1290 were conspicuously overexpressed, while miR-30a-5p was only weakly overexpressed. The ability of miRNAs expression to discriminate the pathological samples from the controls was strong. Receiver operating characteristic curve analyses found area under the curve (AUC) values of 1.000 for miR-200a, miR-200c, miR-141, miR-429, and miR-1290 and of AUC 0.996 and 0.885 for miR-200b and miR-30a-5p, respectively. Preliminary survival analyses indicated low expression level of miR-200b as significantly related to longer overall survival (hazard ratio [HR]: 0.25, mean survival 44 months), while high expression level was related to poor overall survival (HR: 4.04, mean survival 24 months). Our findings suggested that ascites-derived miRNAs should be further explored and evaluated as potential diagnostic and prognostic biomarkers for ovarian cancer.

Background: The current model used to preoperatively stratify endometrial cancer (EC) patients into low- and high-risk groups is based on histotype, grade, and imaging method and is not optimal. Our study aims to prove whether a new model incorporating immunohistochemical markers, L1CAM, ER, PR, p53, obtained from preoperative biopsy could help refine stratification and thus the choice of adequate surgical extent and appropriate adjuvant treatment. Materials and Methods: The following data were prospectively collected from patients operated for EC from January 2016 through August 2018: age, pre- and post-operative histology, grade, lymphovascular space invasion, L1CAM, ER, PR, p53, imaging parameters obtained from ultrasound, CT chest/abdomen, final FIGO stage, and current decision model (based on histology, grade, imaging method). Results: In total, 132 patients were enrolled. The current model revealed 48% sensitivity and 89% specificity for high-risk group determination. In myometrial invasion >50%, lower levels of ER ( p = 0.024), PR (0.048), and higher levels of L1CAM ( p = 0.001) were observed; in cervical involvement a higher expression of L1CAM ( p = 0.001), lower PR ( p = 0.014); in tumors with positive LVSI, higher L1CAM ( p = 0.014); in cases with positive LN, lower expression of ER/PR ( p < 0.001), higher L1CAM ( p = 0.002) and frequent mutation of p53 ( p = 0.008). Cut-offs for determination of high-risk tumors were established: ER <78% ( p = 0.001), PR <88% ( p = 0.008), and L1CAM ≥4% ( p < 0.001). The positive predictive values (PPV) for ER, PR, and L1CAM were 87% (60.8–96.5%), 63% (52.1–72.8%), 83% (70.5–90.8%); the negative predictive values (NPV) for each marker were as follows: 59% (54.5–63.4%), 65% (55.6–74.0%), and 77% (67.3–84.2%). Mutation of p53 revealed PPV 94% (67.4–99.1%) and NPV 61% (56.1–66.3%). When immunohistochemical markers were included into the current diagnostic model, sensitivity improved (48.4 vs. 75.8%, p < 0.001). PPV was similar for both methods, while NPV (i.e., the probability of extremely low risk in negative test cases) was improved (66 vs. 78.9%, p < 0.001). Conclusion: We proved superiority of new proposed model using immunohistochemical markers over standard clinical practice and that new proposed model increases accuracy of prognosis prediction. We propose wider implementation and validation of the proposed model.