Human epidermal growth factor receptor-2 expression and subsequent dynamic changes in patients with ovarian cancer.

Human epidermal growth factor receptor-2 (HER2)-targeting drugs are increasingly being incorporated into therapeutic paradigms for non-breast cancers, yet studies on HER2 expression in ovarian cancer (OC) are inadequate. Here, we studied the HER2 status and dynamic changes in OC by reviewing the records of patients who underwent HER2 testing at a single institution. Clinical parameters, including histology, BRCA status, and immunohistochemistry (IHC), were evaluated alongside HER2 expression, timing, and anatomical location. Among 200 patients, 28% and 6% exhibited expression scores of 2+ and 3+, respectively. HER2 3+ scores were observed in 23%, 11%, 9%, and 5% of mucinous, endometrioid, clear cell, and high-grade serous tumors, respectively, and were exclusively identified in BRCA-wildtype, mismatch repair-proficient, or PD-L1-low-expressing tumors. The TP53 mutation rate was low, whereas ARID1A, KRAS, and PIK3CA mutations were relatively more prevalent with HER2 scores of 2+ or 3+ than with 0 or 1+. Four of the five tumors with an HER2 3+ score exhibited ERBB2 amplification. Among 19 patients who underwent multiple time-lagged biopsies, 11 showed increased HER2 expression in subsequent biopsies. Patients with HER2-overexpressing OC exhibited distinct histological, IHC, and genomic profiles. HER2-targeting agents are potential options for BRCA-wildtype patients, particularly as later lines of treatment.

Stromal cartilage oligomeric matrix protein as a tumorigenic driver in ovarian cancer via Notch3 signaling and epithelial-to-mesenchymal transition.

BACKGROUND: Cartilage oligomeric matrix protein (COMP), an extracellular matrix glycoprotein, is vital in preserving cartilage integrity. Further, its overexpression is associated with the aggressiveness of several types of solid cancers. This study investigated COMP's role in ovarian cancer, exploring clinicopathological links and mechanistic insights. METHODS: To study the association of COMP expression in cancer cells and stroma with clinicopathological features of ovarian tumor patients, we analyzed an epithelial ovarian tumor cohort by immunohistochemical analysis. Subsequently, to study the functional mechanisms played by COMP, an in vivo xenograft mouse model and several molecular biology techniques such as transwell migration and invasion assay, tumorsphere formation assay, proximity ligation assay, and RT-qPCR array were performed. RESULTS: Based on immunohistochemical analysis of epithelial ovarian tumor tissues, COMP expression in the stroma, but not in cancer cells, was linked to worse overall survival (OS) of ovarian cancer patients. A xenograft mouse model showed that carcinoma-associated fibroblasts (CAFs) expressing COMP stimulate the growth and metastasis of ovarian tumors through the secretion of COMP. The expression of COMP was upregulated in CAFs stimulated with TGF-ß. Functionally, secreted COMP by CAFs enhanced the migratory capacity of ovarian cancer cells. Mechanistically, COMP activated the Notch3 receptor by enhancing the Notch3-Jagged1 interaction. The dependency of the COMP effect on Notch was confirmed when the migration and tumorsphere formation of COMP-treated ovarian cancer cells were inhibited upon incubation with Notch inhibitors. Moreover, COMP treatment induced epithelial-to-mesenchymal transition and upregulation of active ß-catenin in ovarian cancer cells. CONCLUSION: This study suggests that COMP secretion by CAFs drives ovarian cancer progression through the induction of the Notch pathway and epithelial-to-mesenchymal transition.

Cytotoxic activity and cell specificity of a novel LHRH peptide drug conjugate, D-Cys6-LHRH vedotin, against ovarian cancer cell lines.

Ovarian cancer is the most deadly female gynaecological malignancy in developed countries and new treatments are urgently needed. The luteinising hormone releasing hormone (LHRH) peptide drug conjugate Zoptarelin doxorubicin is one such potential new drug modality that entered clinical trials for treating LHRH receptor-positive gynaecological cancers. However, development stopped after disappointing Phase 3 results in 2017. We believe the lack of efficacy was due to linker instability and payload potency. In this work, we replaced its linker-toxin with vedotin (MC-VC-PABC-MMAE), yielding the novel peptide drug conjugate D-Cys6-LHRH vedotin. A GI50 and cell specificity comparison against cancerous and non-cancerous ovarian cell lines showed significantly superior bioactivity and selectivity over Zoptarelin doxorubicin (GI50 4 vs. 453 nM) and other chemotherapeutic drugs used for treating ovarian cancers. Our results suggest D-Cys6-LHRH vedotin can potentially be used as a treatment for ovarian cancer.

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

BACKGROUND: Accurate preoperative identification of ovarian tumour subtypes is imperative for patients as it enables physicians to custom-tailor precise and individualized management strategies. So, we have developed an ultrasound (US)-based multiclass prediction algorithm for differentiating between benign, borderline, and malignant ovarian tumours. METHODS: We randomised data from 849 patients with ovarian tumours into training and testing sets in a ratio of 8:2. The regions of interest on the US images were segmented and handcrafted radiomics features were extracted and screened. We applied the one-versus-rest method in multiclass classification. We inputted the best features into machine learning (ML) models and constructed a radiomic signature (Rad_Sig). US images of the maximum trimmed ovarian tumour sections were inputted into a pre-trained convolutional neural network (CNN) model. After internal enhancement and complex algorithms, each sample's predicted probability, known as the deep transfer learning signature (DTL_Sig), was generated. Clinical baseline data were analysed. Statistically significant clinical parameters and US semantic features in the training set were used to construct clinical signatures (Clinic_Sig). The prediction results of Rad_Sig, DTL_Sig, and Clinic_Sig for each sample were fused as new feature sets, to build the combined model, namely, the deep learning radiomic signature (DLR_Sig). We used the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) to estimate the performance of the multiclass classification model. RESULTS: The training set included 440 benign, 44 borderline, and 196 malignant ovarian tumours. The testing set included 109 benign, 11 borderline, and 49 malignant ovarian tumours. DLR_Sig three-class prediction model had the best overall and class-specific classification performance, with micro- and macro-average AUC of 0.90 and 0.84, respectively, on the testing set. Categories of identification AUC were 0.84, 0.85, and 0.83 for benign, borderline, and malignant ovarian tumours, respectively. In the confusion matrix, the classifier models of Clinic_Sig and Rad_Sig could not recognise borderline ovarian tumours. However, the proportions of borderline and malignant ovarian tumours identified by DLR_Sig were the highest at 54.55% and 63.27%, respectively. CONCLUSIONS: The three-class prediction model of US-based DLR_Sig can discriminate between benign, borderline, and malignant ovarian tumours. Therefore, it may guide clinicians in determining the differential management of patients with ovarian tumours.

Genetically identification of endometriosis and cancers risk in women through a two-sample Mendelian randomization study.

Endometriosis is a prevalent and chronic inflammatory gynecologic disorder affecting approximately 6-10% of women globally, and has been associated with an increased risk of cancer. Nevertheless, previous studies have been hindered by methodological limitations that compromise the validity and robustness of their findings. In this study we conducted a comprehensive two-sample Mendelian randomization analysis to explore the genetically driven causal relationship between endometriosis and the risk of cancer. We conducted the analysis via the inverse variance weighted method, MR Egger method, and weighted median method utilizing publicly available genome-wide association study summary statistics. Furthermore, we implemented additional sensitivity analyses to assess the robustness and validity of the causal associations identified. We found strong evidence of a significant causal effect of endometriosis on a higher risk of ovarian cancer via inverse-variance weighted method (OR = 1.19, 95% CI 1.11-1.29, p < 0.0001), MR-Egger regression, and weighted median methodologies. Remarkably, our findings revealed a significant association between endometriosis and an increased risk of clear cell ovarian cancer (OR = 2.04, 95% CI 1.66-2.51, p < 0.0001) and endometrioid ovarian cancer (OR = 1.45, 95% CI 1.27-1.65, p < 0.0001). No association between endometriosis and other types of cancer was observed. We uncovered a causal relationship between endometriosis and an elevated risk of ovarian cancer, particularly clear cell ovarian cancer and endometrioid ovarian cancer. No significant associations between endometriosis and other types of cancer could be identified.

MicroRNA Expression Profiles in Human Samples and Cell Lines Revealed Nine miRNAs Associated with Cisplatin Resistance in High-Grade Serous Ovarian Cancer.

Metastasis and drug resistance are major contributors to cancer-related fatalities worldwide. In ovarian cancer (OC), a staggering 70% develop resistance to the front-line therapy, cisplatin. Despite proposed mechanisms, the molecular events driving cisplatin resistance remain unclear. Dysregulated microRNAs (miRNAs) play a role in OC initiation, progression, and chemoresistance, yet few studies have compared miRNA expression in OC samples and cell lines. This study aimed to identify key miRNAs involved in the cisplatin resistance of high-grade-serous-ovarian-cancer (HGSOC), the most common gynecological malignancy. MiRNA expression profiles were conducted on RNA isolated from formalin-fixed-paraffin-embedded human ovarian tumor samples and HGSOC cell lines. Nine miRNAs were identified in both sample types. Targeting these with oligonucleotide miRNA inhibitors (OMIs) reduced proliferation by more than 50% for miR-203a, miR-96-5p, miR-10a-5p, miR-141-3p, miR-200c-3p, miR-182-5p, miR-183-5p, and miR-1206. OMIs significantly reduced migration for miR-183-5p, miR-203a, miR-296-5p, and miR-1206. Molecular pathway analysis revealed that the nine miRNAs regulate pathways associated with proliferation, invasion, and chemoresistance through PTEN, ZEB1, FOXO1, and SNAI2. High expression of miR-1206, miR-10a-5p, miR-141-3p, and miR-96-5p correlated with poor prognosis in OC patients according to the KM plotter database. These nine miRNAs could be used as targets for therapy and as markers of cisplatin response.

A Stem-like Patient-Derived Ovarian Cancer Model of Platinum Resistance Reveals Dissociation of Stemness and Resistance.

To understand chemoresistance in the context of cancer stem cells (CSC), a cisplatin resistance model was developed using a high-grade serous ovarian cancer patient-derived, cisplatin-sensitive sample, PDX4. As a molecular subtype-specific stem-like cell line, PDX4 was selected for its representative features, including its histopathological and BRCA2 mutation status, and exposed to cisplatin in vitro. In the cisplatin-resistant cells, transcriptomics were carried out, and cell morphology, protein expression, and functional status were characterized. Additionally, potential signaling pathways involved in cisplatin resistance were explored. Our findings reveal the presence of distinct molecular signatures and phenotypic changes in cisplatin-resistant PDX4 compared to their sensitive counterparts. Surprisingly, we observed that chemoresistance was not inherently linked with increased stemness. In fact, although resistant cells expressed a combination of EMT and stemness markers, functional assays revealed that they were less proliferative, migratory, and clonogenic-features indicative of an underlying complex mechanism for cell survival. Furthermore, DNA damage tolerance and cellular stress management pathways were enriched. This novel, syngeneic model provides a valuable platform for investigating the underlying mechanisms of cisplatin resistance in a clinically relevant context, contributing to the development of targeted therapies tailored to combat resistance in stem-like ovarian cancer.

The Clinicopathological Significance of the Cyclin D1/E1-Cyclin-Dependent Kinase (CDK2/4/6)-Retinoblastoma (RB1/pRB1) Pathway in Epithelial Ovarian Cancers.

Cyclin-dependent kinases (CDK2, CDK4, CDK6), cyclin D1, cyclin E1 and phosphorylated retinoblastoma (pRB1) are key regulators of the G1/S cell cycle checkpoint and may influence platinum response in ovarian cancers. CDK2/4/6 inhibitors are emerging targets in ovarian cancer therapeutics. In the current study, we evaluated the prognostic and predictive significance of the CDK2/4/6-cyclin D1/E1-pRB1 axis in clinical ovarian cancers (OC). The CDK2/4/6, cyclin D1/E1 and RB1/pRB1 protein expression were investigated in 300 ovarian cancers and correlated with clinicopathological parameters and patient outcomes. CDK2/4/6, cyclin D1/E1 and RB1 mRNA expression were evaluated in the publicly available ovarian TCGA dataset. We observed nuclear and cytoplasmic staining for CDK2/4/6, cyclins D1/E1 and RB1/pRB1 in OCs with varying percentages. Increased nuclear CDK2 and nuclear cyclin E1 expression was linked with poor progression-free survival (PFS) and a shorter overall survival (OS). Nuclear CDK6 was associated with poor OS. The cytoplasmic expression of CDK4, cyclin D1 and cyclin E1 also has predictive and/or prognostic significance in OCs. In the multivariate analysis, nuclear cyclin E1 was an independent predictor of poor PFS. Tumours with high nuclear cyclin E1/high nuclear CDK2 have a worse PFS and OS. Detailed bioinformatics in the TCGA cohort showed a positive correlation between cyclin E1 and CDK2. We also showed that cyclin-E1-overexpressing tumours are enriched for genes involved in insulin signalling and release. Our data not only identified the prognostic/predictive significance of these key cell cycle regulators but also demonstrate the importance of sub-cellular localisation. CDK2 targeting in cyclin-E1-amplified OCs could be a rational approach.

Application of interpretable machine learning algorithms to predict distant metastasis in ovarian clear cell carcinoma.

BACKGROUND: Ovarian clear cell carcinoma (OCCC) represents a subtype of ovarian epithelial carcinoma (OEC) known for its limited responsiveness to chemotherapy, and the onset of distant metastasis significantly impacts patient prognoses. This study aimed to identify potential risk factors contributing to the occurrence of distant metastasis in OCCC. METHODS: Utilizing the Surveillance, Epidemiology, and End Results (SEER) database, we identified patients diagnosed with OCCC between 2004 and 2015. The most influential factors were selected through the application of Gaussian Naive Bayes (GNB) and Adaboost machine learning algorithms, employing a Venn test for further refinement. Subsequently, six machine learning (ML) techniques, namely XGBoost, LightGBM, Random Forest (RF), Adaptive Boosting (Adaboost), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were employed to construct predictive models for distant metastasis. Shapley Additive Interpretation (SHAP) analysis facilitated a visual interpretation for individual patient. Model validity was assessed using accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and the area under the receiver operating characteristic curve (AUC). RESULTS: In the realm of predicting distant metastasis, the Random Forest (RF) model outperformed the other five machine learning algorithms. The RF model demonstrated accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and AUC (95% CI) values of 0.792 (0.762-0.823), 0.904 (0.835-0.973), 0.759 (0.731-0.787), 0.221 (0.186-0.256), 0.974 (0.967-0.982), 0.353 (0.306-0.399), and 0.834 (0.696-0.967), respectively, surpassing the performance of other models. Additionally, the calibration curve's Brier Score (95%) for the RF model reached the minimum value of 0.06256 (0.05753-0.06759). SHAP analysis provided independent explanations, reaffirming the critical clinical factors associated with the risk of metastasis in OCCC patients. CONCLUSIONS: This study successfully established a precise predictive model for OCCC patient metastasis using machine learning techniques, offering valuable support to clinicians in making informed clinical decisions.

Comprehensive analysis of the interaction of antigen presentation during anti-tumour immunity and establishment of AIDPS systems in ovarian cancer.

There are hundreds of prognostic models for ovarian cancer. These genes are based on different gene classes, and there are many ways to construct the models. Therefore, this paper aims to build the most stable prognostic evaluation system known to date through 101 machine learning strategies. We combined 101 algorithm combinations with 10 machine learning algorithms to create antigen presentation-associated genetic markers (AIDPS) with outstanding precision and steady performance. The inclusive set of algorithms comprises the elastic network (Enet), Ridge, stepwise Cox, Lasso, generalized enhanced regression model (GBM), random survival forest (RSF), supervised principal component (SuperPC), Cox partial least squares regression (plsRcox), survival support vector machine (Survival-SVM). Then, in the train cohort, the prediction model was fitted using a leave-one cross-validation (LOOCV) technique, which involved 101 different possible combinations of prognostic genes. Seven validation data sets (GSE26193, GSE26712, GSE30161, GSE63885, GSE9891, GSE140082 and ICGC_OV_AU) were compared and analysed, and the C-index was calculated. Finally, we collected 32 published ovarian cancer prognostic models (including mRNA and lncRNA). All data sets and prognostic models were subjected to a univariate Cox regression analysis, and the C-index was calculated to demonstrate that the antigen presentation process should be the core criterion for evaluating ovarian cancer prognosis. In a univariate Cox regression analysis, 22 prognostic genes were identified based on the expression profiles of 283 genes involved in antigen presentation and the intersection of genes (p < 0.05). AIDPS were developed by our machine learning-based integration method, which was applied to these 22 genes. One hundred and one prediction models are fitted using the LOOCV framework, and the C-index is calculated for each model across all validation sets. Interestingly, RSF + Lasso was the best model overall since it had the greatest average C-index and the highest C-index of any combination of models tested on the validated data sets. In comparing external cohorts, we found that the C-index correlated AIDPS method using the RSF + Lasso method in 101 prediction models was in contrast to other features. Notably, AIDPS outperformed the vast majority of models across all data sets. Antigen-presenting anti-tumour immune pathways can be used as a representative gene set of ovarian cancer to track the prognosis of patients with cancer. The antigen-presenting model obtained by the RSF + Lasso method has the best C-INDEX, which plays a key role in developing antigen-presenting targeted drugs in ovarian cancer and improving the treatment outcome of patients.

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.

Outcomes after fertility-sparing surgery of early-stage ovarian cancer: A nationwide population-based study.

BACKGROUND: Fertility-sparing surgery (FSS) is an alternative choice of young patients who have not completed their family planning and still have fertility needs. The aims of this study were to compare the outcomes of early-stage epithelial ovarian cancer (EOC) patients undergoing FSS and radical comprehensive staging surgery (RCS), and the suitability of FSS. METHODS: A total of 1297 patients aged between 20 and 44 years with newly diagnosed early-stage EOC were recruited from the Taiwan Cancer Registry database between 2009 and 2017. Site-specific surgery codes were used to distinguish patients in FSS group or RCS group. Cancer-specific survival (CSS) was evaluated using Kaplan-Meier method with log-rank test and Cox regression model. RESULTS: There were 401 and 896 patients in FSS and RCS group. Patients in FSS group were with younger age and mostly had Stage I disease. In contrast, patients in RCS group were older. There were more Stage II, high-grade (Grade 3) disease, and adjuvant chemotherapy in RCS group. Stage and tumor grade were two independent factors correlating with CSS and the type of surgery showed no effect on CSS (HR: 1.09, 95% CI: 0.66-1.77, p = 0.73) in multivariable analysis. In multivariable analysis, the clear cell carcinoma group who underwent FSS demonstrated better CSS compared to those in the RCS group (HR: 0.28, 95% CI: 0.06-0.82, p = 0.04). A total of 17 women who underwent FSS developed second malignancies of the uterine corpus or contralateral ovary. CONCLUSION: FSS can be a safe alternative procedure in selected young patients of Stage I EOC who have fertility desire. Endometrial biopsy before or during FSS and regular surveillance to detect recurrence are mandatory for ovarian cancer patients undergoing FSS.

Spotlight on New Hallmarks of Drug-Resistance towards Personalized Care for Epithelial Ovarian Cancer.

Epithelial ovarian cancer (EOC) is the deadliest gynecological malignancy worldwide. Despite the latest advances, a major clinical issue in EOC is the disappointing prognosis related to chemoresistance in almost one-third of cases. Drug resistance relies on heterogeneous cancer stem cells (CSCs), endowed with tumor-initiating potential, leading to relapse. No biomarkers of chemoresistance have been validated yet. Recently, major signaling pathways, micro ribonucleic acids (miRNAs), and circulating tumor cells (CTCs) have been advocated as putative biomarkers and potential therapeutic targets for drug resistance. However, further investigation is mandatory before their routine implementation. In accordance with the increasing rate of therapeutic efforts in EOC, the need for biomarker-driven personalized therapies is growing. This review aims to discuss the emerging hallmarks of drug resistance with an in-depth insight into the underlying molecular mechanisms lacking so far. Finally, a glimpse of novel therapeutic avenues and future challenges will be provided.

Common misconceptions and myths about ovarian cancer causation: a national cross-sectional study from palestine.

BACKGROUND: Women's inability to recognize ovarian cancer (OC) causation myths to be incorrect may lead to behavioral changes that could distract them from actual risk factors and impact their treatment decision making. This study examined Palestinian women's recognition of OC mythical causes, and explored factors associated with good recognition. METHODS: A national cross-sectional study was conducted. Adult Palestinian women were recruited from hospitals, primary healthcare facilities, and public areas in 11 governorates. The Cancer Awareness Measure-Mythical Causes Scale was modified and utilized for data collection. Awareness level was determined based on the number of myths around OC causation recognized to be incorrect: poor (0-4), fair (5-9), and good (10-13). RESULTS: A total of 5618 participants agreed and completed the questionnaire out of 6095 approached (response rate = 92.1%), and 5411 questionnaires were included in the final analysis. The most recognized food-related myth was 'drinking from plastic bottles' (n = 1370, 25.3%) followed by 'eating burnt food' (n = 1298, 24.0%). The least recognized food-related myth was 'eating food containing additives' (n = 611, 11.3%). The most recognized food-unrelated myth was 'having a physical trauma' (n = 2899, 53.6%), whereas the least recognized was 'using mobile phones' (n = 1347, 24.9%). Only 273 participants (5.1%) had good awareness of OC causation myths as incorrect. Earning higher monthly incomes as well as visiting governmental healthcare facilities were associated with a decrease in the likelihood of exhibiting good awareness. CONCLUSION: The overall recognition of OC causation myths was low. Addressing mythical beliefs should be included in OC prevention strategies and public health interventions to improve women's understanding of OC risk factors versus mythical causes.

Functional analysis and validation of oncodrive gene AP3S1 in ovarian cancer through filtering of mutation data from whole-exome sequencing.

BACKGROUND: High-grade serous ovarian carcinoma (HGSOC) is the most aggressive and prevalent subtype of ovarian cancer and accounts for a significant portion of ovarian cancer-related deaths worldwide. Despite advancements in cancer treatment, the overall survival rate for HGSOC patients remains low, thus highlighting the urgent need for a deeper understanding of the molecular mechanisms driving tumorigenesis and for identifying potential therapeutic targets. Whole-exome sequencing (WES) has emerged as a powerful tool for identifying somatic mutations and alterations across the entire exome, thus providing valuable insights into the genetic drivers and molecular pathways underlying cancer development and progression. METHODS: Via the analysis of whole-exome sequencing results of tumor samples from 90 ovarian cancer patients, we compared the mutational landscape of ovarian cancer patients with that of TCGA patients to identify similarities and differences. The sequencing data were subjected to bioinformatics analysis to explore tumor driver genes and their functional roles. Furthermore, we conducted basic medical experiments to validate the results obtained from the bioinformatics analysis. RESULTS: Whole-exome sequencing revealed the mutational profile of HGSOC, including BRCA1, BRCA2 and TP53 mutations. AP3S1 emerged as the most weighted tumor driver gene. Further analysis of AP3S1 mutations and expression demonstrated their associations with patient survival and the tumor immune response. AP3S1 knockdown experiments in ovarian cancer cells demonstrated its regulatory role in tumor cell migration and invasion through the TGF-ß/SMAD pathway. CONCLUSION: This comprehensive analysis of somatic mutations in HGSOC provides insight into potential therapeutic targets and molecular pathways for targeted interventions. AP3S1 was identified as being a key player in tumor immunity and prognosis, thus providing new perspectives for personalized treatment strategies. The findings of this study contribute to the understanding of HGSOC pathogenesis and provide a foundation for improved outcomes in patients with this aggressive disease.

Reciprocal crosstalk between Th17 and mesothelial cells promotes metastasis-associated adhesion of ovarian cancer cells.

BACKGROUND: IL-17A and TNF synergistically promote inflammation and tumorigenesis. Their interplay and impact on ovarian carcinoma (OC) progression are, however, poorly understood. We addressed this question focusing on mesothelial cells, whose interaction with tumor cells is known to play a pivotal role in transcoelomic metastasis formation. METHODS: Flow-cytometry and immunohistochemistry experiments were employed to identify cellular sources of IL-17A and TNF. Changes in transcriptomes and secretomes were determined by bulk and single cell RNA sequencing as well as affinity proteomics. Functional consequences were investigated by microscopic analyses and tumor cell adhesion assays. Potential clinical implications were assessed by immunohistochemistry and survival analyses. RESULTS: We identified Th17 cells as the main population of IL-17A- and TNF producers in ascites and detected their accumulation in early omental metastases. Both IL-17A and its receptor subunit IL-17RC were associated with short survival of OC patients, pointing to a role in clinical progression. IL-17A and TNF synergistically induced the reprogramming of mesothelial cells towards a pro-inflammatory mesenchymal phenotype, concomitantly with a loss of tight junctions and an impairment of mesothelial monolayer integrity, thereby promoting cancer cell adhesion. IL-17A and TNF synergistically induced the Th17-promoting cytokines IL-6 and IL-1ß as well as the Th17-attracting chemokine CCL20 in mesothelial cells, indicating a reciprocal crosstalk that potentiates the tumor-promoting role of Th17 cells in OC. CONCLUSIONS: Our findings reveal a novel function for Th17 cells in the OC microenvironment, which entails the IL-17A/TNF-mediated induction of mesothelial-mesenchymal transition, disruption of mesothelial layer integrity and consequently promotion of OC cell adhesion. These effects are potentiated by a positive feedback loop between mesothelial and Th17 cells. Together with the observed clinical associations and accumulation of Th17 cells in omental micrometastases, our observations point to a potential role in early metastases formation and thus to new therapeutic options.

Targeting PARG induces tumor cell growth inhibition and antitumor immune response by reducing phosphorylated STAT3 in ovarian cancer.

BACKGROUND: Ovarian cancer is the most lethal gynecological malignancy, with limited treatment options after failure of standard therapies. Despite the potential of poly(ADP-ribose) polymerase inhibitors in treating DNA damage response (DDR)-deficient ovarian cancer, the development of resistance and immunosuppression limit their efficacy, necessitating alternative therapeutic strategies. Inhibitors of poly(ADP-ribose) glycohydrolase (PARG) represent a novel class of inhibitors that are currently being assessed in preclinical and clinical studies for cancer treatment. METHODS: By using a PARG small-molecule inhibitor, COH34, and a cell-penetrating antibody targeting the PARG's catalytic domain, we investigated the effects of PARG inhibition on signal transducer and activator of transcription 3 (STAT3) in OVCAR8, PEO1, and Brca1-null ID8 ovarian cancer cell lines, as well as in immune cells. We examined PARG inhibition-induced effects on STAT3 phosphorylation, nuclear localization, target gene expression, and antitumor immune responses in vitro, in patient-derived tumor organoids, and in an immunocompetent Brca1-null ID8 ovarian mouse tumor model that mirrors DDR-deficient human high-grade serous ovarian cancer. We also tested the effects of overexpressing a constitutively activated STAT3 mutant on COH34-induced tumor cell growth inhibition. RESULTS: Our findings show that PARG inhibition downregulates STAT3 activity through dephosphorylation in ovarian cancer cells. Importantly, overexpression of a constitutively activated STAT3 mutant in tumor cells attenuates PARG inhibitor-induced growth inhibition. Additionally, PARG inhibition reduces STAT3 phosphorylation in immune cells, leading to the activation of antitumor immune responses, shown in immune cells cocultured with ovarian cancer patient tumor-derived organoids and in immune-competent mice-bearing mouse ovarian tumors. CONCLUSIONS: We have identified a novel antitumor mechanism underlying PARG inhibition beyond its primary antitumor effects through blocking DDR in ovarian cancer. Furthermore, targeting PARG activates antitumor immune responses, thereby potentially increasing response rates to immunotherapy in patients with ovarian cancer.

Targeting branched N-glycans and fucosylation sensitizes ovarian tumors to immune checkpoint blockade.

Aberrant glycosylation is a crucial strategy employed by cancer cells to evade cellular immunity. However, it's unclear whether homologous recombination (HR) status-dependent glycosylation can be therapeutically explored. Here, we show that the inhibition of branched N-glycans sensitizes HR-proficient, but not HR-deficient, epithelial ovarian cancers (EOCs) to immune checkpoint blockade (ICB). In contrast to fucosylation whose inhibition sensitizes EOCs to anti-PD-L1 immunotherapy regardless of HR-status, we observe an enrichment of branched N-glycans on HR-proficient compared to HR-deficient EOCs. Mechanistically, BRCA1/2 transcriptionally promotes the expression of MGAT5, the enzyme responsible for catalyzing branched N-glycans. The branched N-glycans on HR-proficient tumors augment their resistance to anti-PD-L1 by enhancing its binding with PD-1 on CD8+ T cells. In orthotopic, syngeneic EOC models in female mice, inhibiting branched N-glycans using 2-Deoxy-D-glucose sensitizes HR-proficient, but not HR-deficient EOCs, to anti-PD-L1. These findings indicate branched N-glycans as promising therapeutic targets whose inhibition sensitizes HR-proficient EOCs to ICB by overcoming immune evasion.

Stearoyl-CoA desaturase 1 inhibition induces ER stress-mediated apoptosis in ovarian cancer cells.

Ovarian cancer is a leading cause of death among gynecologic tumors, often detected at advanced stages. Metabolic reprogramming and increased lipid biosynthesis are key factors driving cancer cell growth. Stearoyl-CoA desaturase 1 (SCD1) is a crucial enzyme involved in de novo lipid synthesis, producing mono-unsaturated fatty acids (MUFAs). Here, we aimed to investigate the expression and significance of SCD1 in epithelial ovarian cancer (EOC). Comparative analysis of normal ovarian surface epithelial (NOSE) tissues and cell lines revealed elevated SCD1 expression in EOC tissues and cells. Inhibition of SCD1 significantly reduced the proliferation of EOC cells and patient-derived organoids and induced apoptotic cell death. Interestingly, SCD1 inhibition did not affect the viability of non-cancer cells, indicating selective cytotoxicity against EOC cells. SCD1 inhibition on EOC cells induced endoplasmic reticulum (ER) stress by activating the unfolded protein response (UPR) sensors and resulted in apoptosis. The addition of exogenous oleic acid, a product of SCD1, rescued EOC cells from ER stress-mediated apoptosis induced by SCD1 inhibition, underscoring the importance of lipid desaturation for cancer cell survival. Taken together, our findings suggest that the inhibition of SCD1 is a promising biomarker as well as a novel therapeutic target for ovarian cancer by regulating ER stress and inducing cancer cell apoptosis.

MATN2 overexpression suppresses tumor growth in ovarian cancer via PTEN/PI3K/AKT pathway.

The incidence rate of developing ovarian cancer decreases over the years; however, mortality ranks top among malignancies of women, mainly metastasis through local invasion. Matrilin-2 (MATN2) is a member of the matrilin family that plays an important role in many cancers. However, its relationship with ovarian cancer remains unknown. Our study aimed to explore the function and possible mechanism of MATN2 in ovarian cancer. Human ovarian cancer tissue microarrays were used to detect the MATN2 expression in different types of ovarian cancer using immunohistochemistry (IHC). CCK-8, wound scratch healing assay, transwell assay, and flow cytometry were used to detect cell mobility. Gene and protein expression were detected using quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting. MATN2 interacts with phosphatase, and the tensin homolog (PTEN) deleted on chromosome 10 was analyzed using TCGA database and co-immunoprecipitation (Co-IP). In vivo experiments were conducted using BALB/c nude mice, and tumor volume and weight were recorded. Tumor growth was determined using hematoxylin and eosin (H&E) and IHC staining. MATN2 was significantly downregulated in ovarian cancer cells. The SKOV3 and A2780 cell mobility was significantly inhibited by MATN2 overexpression, while the cell apoptosis rate was significantly increased. MATN2 overexpression decreased transplanted tumor size in vivo. These results were reversed by inhibiting MATN2. Furthermore, we found that PTEN closely interacted with MATN2 using bioinformatics and Co-IP. MATN2 overexpression significantly inhibited the PI3K/AKT pathway, however, PTEN suppression reversed this effect of MATN2 overexpression. These results indicated that MATN2 may play a critical role in ovarian cancer development by inhibiting cells proliferation and migration. The mechanism was related to interacting with PTEN, thus inhibiting downstream effectors in the PI3K/AKT pathway, which may be a novel target for treating ovarian cancer.