MicroRNA-625-3p improved proliferation and involved chemotherapy resistance via targeting PTEN in high grade ovarian serous carcinoma

Objective High-grade serous ovarian cancer (HGSOC) is an aggressive gynaecological malignancy and associated with poor prognosis. Here we examined the effects of miR-625-3p on proliferation, treatment, migration and invasion in HGSOC. Methods The proliferation of HGSOC cells was evaluated by MTT assay. Transwell assay was performed to examine migration and matrigel assay were used to assess invasion. The effect of miR-625-3p on cisplatin-induced apoptosis was investigated by Caspase-Glo3/7 assay. The dual-luciferase reporter assay was carried out to confirm the potential binding site. Results Overexpression of miR-625-3p promoted proliferation, and increased migration and invasion in HGSOC cells. MiR-625-3p significantly inhibited cisplatin sensitivity in HGSOC cells. Meanwhile, miR-625-3p decreased cisplatin-induced apoptosis by regulation of BAX and Bcl-2 expression. Furthermore, aberrant expression of miR-625-3p changed PTEN expression by directly binding to 3’UTR of PTEN. Further study showed miR-625-3p expression was higher in human HGSOC tissue than normal ovarian tissues and associated with higher clinical stage. Conclusions miR-625-3p promotes HGSOC growth, involves chemotherapy resistance and might serve as a potential biomarker to predict chemotherapy response and prognosis in HGSOC.

The Evolution of Ovarian Carcinoma Subclassification

The phenotypically informed histotype classification remains the mainstay of ovarian carcinoma subclassification. Histotypes of ovarian epithelial neoplasms have evolved with each edition of the WHO Classification of Female Genital Tumours. The current fifth edition (2020) lists five principal histotypes: high-grade serous carcinoma (HGSC), low-grade serous carcinoma (LGSC), mucinous carcinoma (MC), endometrioid carcinoma (EC) and clear cell carcinoma (CCC). Since histotypes arise from different cells of origin, cell lineage-specific diagnostic immunohistochemical markers and histotype-specific oncogenic alterations can confirm the morphological diagnosis. A four-marker immunohistochemical panel (WT1/p53/napsin A/PR) can distinguish the five principal histotypes with high accuracy, and additional immunohistochemical markers can be used depending on the diagnostic considerations. Histotypes are further stratified into molecular subtypes and assessed with predictive biomarker tests. HGSCs have recently been subclassified based on mechanisms of chromosomal instability, mRNA expression profiles or individual candidate biomarkers. ECs are composed of the same molecular subtypes (POLE-mutated/mismatch repair-deficient/no specific molecular profile/p53-abnormal) with the same prognostic stratification as their endometrial counterparts. Although methylation analyses and gene expression and sequencing showed at least two clusters, the molecular subtypes of CCCs remain largely elusive to date. Mutational and immunohistochemical data on LGSC have suggested five molecular subtypes with prognostic differences. While our understanding of the molecular composition of ovarian carcinomas has significantly advanced and continues to evolve, the need for treatment options suitable for these alterations is becoming more obvious. Further preclinical studies using histotype-defined and molecular subtype-characterized model systems are needed to expand the therapeutic spectrum for women diagnosed with ovarian carcinomas.

Clinical significance of metabolism-related genes and FAK activity in ovarian high-grade serous carcinoma

Background Administration of poly (ADP-ribose) polymerase (PARP) inhibitors after achieving a response to platinum-containing drugs significantly prolonged relapse-free survival compared to placebo administration. PARP inhibitors have been used in clinical practice. However, patients with platinum-resistant relapsed ovarian cancer still have a poor prognosis and there is an unmet need. The purpose of this study was to examine the clinical significance of metabolic genes and focal adhesion kinase (FAK) activity in advanced ovarian high-grade serous carcinoma (HGSC). Methods The RNA sequencing (RNA-seq) data and clinical data of HGSC patients were obtained from the Genomic Data Commons (GDC) Data Portal and analysed (https://portal.gdc.cancer.gov/). In addition, tumour tissue was sampled by laparotomy or screening laparoscopy prior to treatment initiation from patients diagnosed with stage IIIC ovarian cancer (International Federation of Gynecology and Obstetrics (FIGO) classification, 2014) at the Saitama Medical University International Medical Center, and among the patients diagnosed with HGSC, 16 cases of available cryopreserved specimens were included in this study. The present study was reviewed and approved by the Institutional Review Board of Saitama Medical University International Medical Center (Saitama, Japan). Among the 6307 variable genes detected in both The Cancer Genome Atlas-Ovarian (TCGA-OV) data and clinical specimen data, 35 genes related to metabolism and FAK activity were applied. RNA-seq data were analysed using the Subio Platform (Subio Inc, Japan). JMP 15 (SAS, USA) was used for statistical analysis and various types of machine learning. The Kaplan-Meier method was used for survival analysis, and the Wilcoxon test was used to analyse significant differences. P < 0.05 was considered significant. Results In the TCGA-OV data, patients with stage IIIC with a residual tumour diameter of 1-10 mm were selected for K means clustering and classified into groups with significant prognostic correlations (p = 0.0444). These groups were significantly associated with platinum sensitivity/resistance in clinical cases (χ2 test, p = 0.0408) and showed significant relationships with progression-free survival (p = 0.0307). Conclusion In the TCGA-OV data, 2 groups classified by clustering focusing on metabolism-related genes and FAK activity were shown to be associated with platinum resistance and a poor prognosis.

Association between effector-type regulatory T cells and immune checkpoint expression on CD8+ T cells in malignant ascites from epithelial ovarian cancer

Background Regulatory T cells (Tregs) play an important role in the antitumor immune response in epithelial ovarian cancer (EOC). To understand the immune-inhibitory networks of EOC, we addressed the association between Tregs and immune checkpoint expression on T cells in the tumor microenvironment of EOC Methods A total of 41 patients with stage IIIC and IV EOC were included in the analysis. We harvested cells from malignant ascites and investigated them using multi-color flow cytometry. We categorized the Tregs into 3 groups: effector-type Tregs, naïve Tregs and non-Tregs, based on the expression patterns of CD45RA and Foxp3 in CD4+ T cells. Furthermore, the relationships between the expression of various immune checkpoint molecules, such as PD-1, on CD8+T cells and each of the Treg subtypes was also evaluated. Results The median frequency of naïve Tregs, effector-type Tregs and non-Tregs were 0.2% (0-0.8), 2.0% (0-11.4) and 1.5% (0.1-6.3) in CD4+ T cells of malignant ascites from EOC patients, respectively. A high frequency of effector-type Tregs was associated with high-grade serous carcinoma compared with the other histotypes. Patients with higher proportions of effector-type Tregs showed a trend towards increased progression-free survival. We also demonstrated a correlation between a higher proportion of effector-type Tregs and increased PD-1 expression on CD8+ T cells. In addition, C-C chemokine receptor 4 expression was also observed in effector-type Tregs. Conclusion These data suggest that multiple immune-inhibitory networks exist in malignant ascites from EOC patients, suggesting an approach towards combinational immunotherapies for advanced EOC patients.

Identification of a Novel Oncogenic Fusion Gene SPON1-TRIM29 in Clinical Ovarian Cancer That Promotes Cell and Tumor Growth and Enhances Chemoresistance in A2780 Cells

Gene structure alterations, such as chromosomal rearrangements that develop fusion genes, often contribute to tumorigenesis. It has been shown that the fusion genes identified in public RNA-sequencing datasets are mainly derived from intrachromosomal rearrangements. In this study, we explored fusion transcripts in clinical ovarian cancer specimens based on our RNA-sequencing data. We successfully identified an in-frame fusion transcript SPON1-TRIM29 in chromosome 11 from a recurrent tumor specimen of high-grade serous carcinoma (HGSC), which was not detected in the corresponding primary carcinoma, and validated the expression of the identical fusion transcript in another tumor from a distinct HGSC patient. Ovarian cancer A2780 cells stably expressing SPON1-TRIM29 exhibited an increase in cell growth, whereas a decrease in apoptosis was observed, even in the presence of anticancer drugs. The siRNA-mediated silencing of SPON1-TRIM29 fusion transcript substantially impaired the enhanced growth of A2780 cells expressing the chimeric gene treated with anticancer drugs. Moreover, a subcutaneous xenograft model using athymic mice indicated that SPON1-TRIM29-expressing A2780 cells rapidly generated tumors in vivo compared to control cells, whose growth was significantly repressed by the fusion-specific siRNA administration. Overall, the SPON1-TRIM29 fusion gene could be involved in carcinogenesis and chemotherapy resistance in ovarian cancer, and offers potential use as a diagnostic and therapeutic target for the disease with the fusion transcript.

A deep hybrid learning pipeline for accurate diagnosis of ovarian cancer based on nuclear morphology

Nuclear morphological features are potent determining factors for clinical diagnostic approaches adopted by pathologists to analyze the malignant potential of cancer cells. Considering the structural alteration of the nucleus in cancer cells, various groups have developed machine learning techniques based on variation in nuclear morphometric information like nuclear shape, size, nucleus-cytoplasm ratio and various non-parametric methods like deep learning have also been tested for analyzing immunohistochemistry images of tissue samples for diagnosing various cancers. We aim to correlate the morphometric features of the nucleus along with the distribution of nuclear lamin proteins with classical machine learning to differentiate between normal and ovarian cancer tissues. It has already been elucidated that in ovarian cancer, the extent of alteration in nuclear shape and morphology can modulate genetic changes and thus can be utilized to predict the outcome of low to a high form of serous carcinoma. In this work, we have performed exhaustive imaging of ovarian cancer versus normal tissue and developed a dual pipeline architecture that combines the matrices of morphometric parameters with deep learning techniques of auto feature extraction from pre-processed images. This novel Deep Hybrid Learning model, though derived from classical machine learning algorithms and standard CNN, showed a training and validation AUC score of 0.99 whereas the test AUC score turned out to be 1.00. The improved feature engineering enabled us to differentiate between cancerous and non-cancerous samples successfully from this pilot study.

Endometrial Metaplastic/Reactive Changes Coexistent with Endometrial Hyperplasia and Carcinoma: A Morphological and Immunohistochemical Study

The aim of this study was to assess the relationship between endometrial metaplastic/reactive changes (EMRCs) and endometrial neoplastic lesions. Twenty cases of “simple” (without architecture complexity) EMRCs coexistent with endometrial malignant/premalignant lesions, twenty cases of neoplasia-unassociated EMRCs, and eight cases of complex metaplastic lesions were assessed by immunohistochemistry. EMRCs coexisted with endometrioid carcinoma (n = 12), atypical endometrial hyperplasia (n = 3), serous carcinoma (n = 2), and clear cell carcinoma (n = 3). Neoplasia-associated EMRCs showed a mean Ki67 labeling index of 12.6% (range 0–30%); with nuclear atypia in 16/20 (80%) cases; diffuse p16 expression in 15/20 (75%) cases; and heterogeneous ER, PR, and vimentin expression. Compared to the associated neoplasia, EMRCs showed a lower Ki67 expression (p < 0.001) and higher p16 expression (p < 0.001). No EMRC case showed mitotic activity, PTEN loss, MMR deficiency, nuclear β-catenin, p53-mutant pattern, Napsin A, or AMACR expression. No significant differences were found between neoplasia-associated and neoplasia-unassociated EMRCs. Complex metaplastic lesions showed a lower Ki67 expression than EMRCs (p = 0.044) and PTEN loss in 5/8 cases, even in the absence of nuclear atypia. In conclusion, neoplasia-associated simple EMRCs may show evident atypia and a worrisome immunophenotype, but no data support their involvement in endometrial carcinogenesis. Architectural complexity appears as a crucial factor to identify precancerous lesions.