Ovarian Patients Research Articles

HELPeR: Interface Design Decision and Evaluation.

Inequities in health information access contribute to disparities in health outcomes. Health recommender systems have emerged as a promising solution to help users find the right information. Despite their various applications, it remains understudied how these systems can aid cancer patients. In this paper, we introduce HELPeR, a recommender system designed to assist ovarian cancer patients with their information needs. The design addresses cold-start challenges, drawing input from health experts and ovarian cancer forum posts. We evaluated HELPeR with nurse practitioners in a cold-start scenario, highlighting its benefits and areas for future improvement.

Low testing rates and high BRCA prevalence: Poly (ADP-ribose) polymerase inhibitor use in Middle East BRCA/homologous recombination deficiency-positive cancer patients.

Poly (ADP-ribose) polymerase inhibitors (PARPis) are approved as first-line therapies for breast cancer gene (BRCA)-positive, human epidermal growth factor receptor 2-negative locally advanced or metastatic breast cancer. They are also effective for new and recurrent ovarian cancers that are BRCA- or homologous recombination deficiency (HRD)-positive. However, data on these mutations and PARPi use in the Middle East are limited. To assess BRCA/HRD prevalence and PARPi use in patients in the Middle East with breast/ovarian cancer. This was a single-center retrospective study of 57 of 472 breast cancer patients tested for BRCA mutations, and 25 of 65 ovarian cancer patients tested for HRD. These adult patients participated in at least four visits to the oncology service at our center between August 2021 and May 2023. Data were summarized using descriptive statistics and compared using counts and percentages. Response to treatment was assessed using Response Evaluation Criteria in Solid Tumors criteria. Among the 472 breast cancer patients, 12.1% underwent BRCA testing, and 38.5% of 65 ovarian cancer patients received HRD testing. Pathogenic mutations were found in 25.6% of the tested patients: 26.3% breast cancers had germline BRCA (gBRCA) mutations and 24.0% ovarian cancers showed HRD. Notably, 40.0% of gBRCA-positive breast cancers and 66.0% of HRD-positive ovarian cancers were Middle Eastern and Asian patients, respectively. PARPi treatment was used in 5 (33.3%) gBRCA-positive breast cancer patients as first-line therapy (n = 1; 7-months progression-free), for maintenance (n = 2; > 15-months progression-free), or at later stages due to compliance issues (n = 2). Four patients (66.6%) with HRD-positive ovarian cancer received PARPi and all remained progression-free. Lower testing rates but higher BRCA mutations in breast cancer were found. Ethnicity reflected United Arab Emirates demographics, with breast cancer in Middle Eastern and ovarian cancer in Asian patients.

Interpretable machine learning model based on clinical factors for predicting muscle radiodensity loss after treatment in ovarian cancer.

Muscle radiodensity loss after surgery and adjuvant chemotherapy is associated with poor outcomes in ovarian cancer. Assessing muscle radiodensity is a real-world clinical challenge owing to the requirement for computed tomography (CT) with consistent protocols and labor-intensive processes. This study aimed to use interpretable machine learning (ML) to predict muscle radiodensity loss. This study included 723 patients with ovarian cancer who underwent primary debulking surgery and platinum-based chemotherapy between 2010 and 2019 at two tertiary centers (579 in cohort 1 and 144 in cohort 2). Muscle radiodensity was assessed from pre- and post-treatment CT acquired with consistent protocols, and a decrease in radiodensity ≥ 5% was defined as loss. Six ML models were trained, and their performances were evaluated using the area under the curve (AUC) and F1-score. The SHapley Additive exPlanations (SHAP) method was applied to interpret the ML models. The CatBoost model achieved the highest AUC of 0.871 (95% confidence interval, 0.870-0.874) and F1-score of 0.688 (95% confidence interval, 0.685-0.691) among the models in the training set and outperformed in the external validation set, with an AUC of 0.839 and F1-score of 0.673. Albumin change, ascites, and residual disease were the most important features associated with a higher likelihood of muscle radiodensity loss. The SHAP force plot provided an individualized interpretation of model predictions. An interpretable ML model can assist clinicians in identifying ovarian cancer patients at risk of muscle radiodensity loss after treatment and understanding the contributors of muscle radiodensity loss.

EP2/EP4 targeting prevents tumor-derived PGE2-mediated immunosuppression in cDC2s.

Tumor-derived prostaglandin E2 (PGE2) impairs anti-tumor immunity by priming suppressive functions on various immune cell types, including dendritic cells (DCs). In this way, tumors mediate DC dysfunction and hamper their anti-tumoral activity. PGE2 is known to modulate DC function via signaling through the E-prostanoid receptor type (EP) 2 and EP4. Preclinical studies have demonstrated the therapeutic value of targeting EP2/4 receptor signaling in DCs. Ongoing phase I clinical trials with EP antagonists have shown immunomodulation in cancer patients. However, the systemic drug administration leads to off-target events and subsequent side-effects. To limit the off-target effects of EP targeting, EP2 and EP4 antagonists were encapsulated in polymeric nanoparticles (NPs). In this study we evaluated the efficacy of EP2/4 specific antagonists encapsulated in NPs to protect cDC2s from suppressive effects of tumor-derived PGE2 in different tumor models. We show that tumor-derived PGE2 signals via EP2/4 to mediate the acquisition of a suppressive phenotype of cDC2s. EP2/4 antagonists encapsulated NPs impaired the conversion of cDC2s towards a suppressive state and inhibited the occurrence of suppressive features such as IL-10 production or the ability to expand Tregs. Importantly, the NPs abolished the transition towards this suppressive state in different tumor models: Melanoma-conditioned media, ascites fluid derived from ovarian cancer patients (2D), and upon coculture with colorectal cancer patient-derived organoids (3D). We propose that targeting the PGE2-EP2/4 axis using NPs can achieve immunomodulation in the immune system of cancer patients, alleviate tumor-derived suppression, and thus facilitate the development of potent anti-tumor immunity in cancer patients.

Distinguishing Benign and Malignant Ovarian Tumors Preoperatively

Ovarian cancer ranks as the fifth leading cause of cancer-related mortality among women globally. In 2020, there were approximately 314,000 reported cases of new ovarian cancer patients, resulting in an estimated 207,000 deaths.1 The highest incidence rates were observed in non-Hispanic white women, with 12.0 cases per 100,000 individuals, followed by Hispanics (10.3 per 100,000), non-Hispanic blacks (9.4 per 100,000), and Asian/Pacific Islander women (9.2 per 100,000). Disparities in access to healthcare services and treatment modalities contribute to varied mortality patterns, with the highest rates recorded among African populations. In Indonesia, ovarian cancer ranks as the third most prevalent cancer, comprising 7.84% of total cancers diagnosed in women.1 The heightened mortality rate observed in ovarian cancer can be ascribed to several factors including asymptomatic tumor growth, delayed manifestation of symptoms, and inadequate screening methods, ultimately resulting in diagnoses at advanced stages. Consequently, ovarian cancer is frequently described as a “silent killer”.2 To address diagnostic delays and reduce mortality rates associated with ovarian cancer, early detection and differentiation between benign and malignant ovarian tumors are crucial. This requires a comprehensive assessment comprising patient history, physical examination, and supporting investigation.

Construction of a prognostic model for ovarian cancer based on a comprehensive bioinformatics analysis of cuproptosis-associated long non-coding RNA signatures

Ovarian cancer (OCa) is a common malignancy in women, and the role of cuproptosis and its related genes in OCa is unclear. Using the GSE14407 dataset, we analyzed the expression and correlation of cuproptosis-related genes (CRGs) between tumor and normal groups. From the TCGA-OV dataset, we identified 20 cuproptosis-related long non-coding RNAs (CuLncs) associated with patient survival through univariate Cox analysis. OCa patients were divided into early-stage and late-stage groups to analyze CuLncs expression. Cluster analysis classified patients into two clusters, with Cluster1 having a poorer prognosis. Significant differences in “Lymphatic Invasion” and “Cancer status” were observed between clusters. Seven CRGs showed significant expression differences, validated using the human protein atlas (HPA) databases. Immune analysis revealed a higher ImmuneScore in Cluster1. GSEA identified associated signaling pathways. LASSO regression included 11 CuLncs to construct and validate a survival prediction model, classifying patients into high-risk and low-risk groups. Correlations between riskScore, Cluster phenotype, ImmuneScore, and immune cell infiltration were explored. Cell experiments showed that knocking down AC023644.1 decreases OCa cell viability. In conclusion, we constructed an accurate prognostic model for OCa based on 11 CuLncs, providing a basis for prognosis assessment and potential immunotherapy targets.

Limitations of homologous recombination status testing and poly (ADP-ribose) polymerase inhibitor treatment in the current management of ovarian cancer.

Homologous recombination (HR) is a highly conserved DNA repair system, in which aberrations can lead to the accumulation of DNA damage and genomic scars known as homologous recombination deficiency (HRD). The identification of mutations in key genes (i.e., BRCA1, and BRCA2 (BRCA)) and the quantification of large-scale structural variants (e.g., loss of heterozygosity) are indicators of the HRD phenotype. HRD is a stable biomarker and remains unchanged during recurrence, but fails to reveal the molecular profile of tumor progression. Moreover, interpretation of the current HRD score lacks comprehensiveness, especially for the HR-proficient group. Poly (ADP-ribose) polymerase (PARP) enzymes play an important role in the repair of DNA single-strand breaks, the blockage of which using PARP inhibitors (PARPi) can generate synthetic lethality in cancer cells with HRD. Although numerous studies have demonstrated that the benefit of PARPi is substantial in ovarian cancer (OC) patients, the efficacy is limited by the development of resistance, and seems to be irrespective of HR and/or BRCA mutation status. Moreover, in addition to improving progression-free survival, long-term benefit as overall survival brought by PARPi for advanced, recurrent and refractory OC patients remains unclear. Therefore, further investigations are needed to uncover the role of HR genes beyond BRCA and their interactions with other oncogenic pathways, to determine the value of HRD in the recurrent setting, and to identify alternative strategies for the precise management of advanced, refractory OC patients.

The effect of tinzaparin on biomarkers in FIGO stages III-IV ovarian cancer patients undergoing neoadjuvant chemotherapy - the TABANETOC trial: study protocol for a randomized clinical multicenter trial.

Tinzaparin, a low-molecular weight heparin (LMWH), has shown anti-neoplastic properties in animal models and in in vitro studies of human cancer cell lines. The reduction of CA-125 levels during neoadjuvant chemotherapy (NACT) in patients with epithelial ovarian cancer (EOC) co-varies with the prognosis; the larger the decrease in CA-125, the better the prognosis. This study aims to evaluate the potential anti-neoplastic effects of tinzaparin by investigating changes in serum CA-125 levels in advanced EOC patients who receive NACT. This is an open randomized multicenter pilot trial. Forty patients with EOC selected to receive NACT will be randomized 1:1 to receive daily addition of tinzaparin or no tinzaparin. The processing and treatment of the patients will otherwise follow the recommendations in the Swedish National Guidelines for Ovarian Cancer. Before every cycle of chemotherapy, preoperatively, and 3 weeks after the last cycle of chemotherapy, a panel of biomarkers, including CA-125, will be measured. Inclusion criteria are women aged 18 years or older, World Health Organization performance status 0-1, histologically confirmed high-grade serous, endometrioid or clear cell EOC, International Federation of Gynecology and Obstetrics (FIGO) stages III-IV. In addition, a CA-125 level of ≥ 250 kIE/L at diagnosis. Exclusion criteria are contraindications to LMWH, ongoing or recent treatment with unfractionated heparin, LMWH, warfarin or non-vitamin K antagonist oral anticoagulants. This study will make an important contribution to the knowledge of the anti-neoplastic effects of tinzaparin in EOC patients and may thus guide the planning of a future study on the impact of tinzaparin on survival in EOC.

A peptide-salinomycin conjugate with a bystander effect reduces the stemness characteristics of ovarian cancer cells and enhances drug sensitivity

Salinomycin (Sal) has attracted considerable attention in the field of tumor treatment, especially for its inhibitory effect on cancer stem cells (CSCs) and drug-resistant tumor cells. However, its solubility and targeting specificity pose significant challenges to its pharmaceutical development. Sal-A6, a novel peptide-drug conjugate (PDC), was formed by linking the peptide A6 targeting the CSC marker CD44 with Sal using a specific linker. This conjugation markedly enhances the physicochemical properties of Sal and compared to Sal, Sal-A6 demonstrated a significantly increased activity against ovarian cancer. Furthermore, Sal-A6, employing a disulfide bond as a linker, exhibited bystander killing effect. Moreover, it induces substantial cytotoxic effect on both cancer stem cells and drug-resistant cells in addition to enhance chemosensitivity of resistant ovarian cancer cells. In summary, the results indicated that Sal-A6, a novel PDC derived from Sal, has potential therapeutic applications in the treatment of ovarian cancer and drug-resistant patients. Additionally, this discovery offers insights for developing PDC-type drugs using Sal as a foundation.

Diagnosis, treatment and burden in advanced ovarian cancer: a UK real-world survey of healthcare professionals and patients.

Aim: Little is known regarding uptake of epithelial ovarian cancer (EOC) treatments or patient burden in UK real-world practice.Methods: Cross-sectional surveys of patients with advanced EOC and healthcare professionals (HCPs).Results: 101 HCPs and 142 patients participated. Time from initial primary care consultation to diagnosis was ∼7weeks. 83% patients were offered hereditary genetic testing, with 89% uptake. 53% HCPs reported surgery was performed ≤1month in non-neoadjuvant setting. Surgery delay negatively impacted patient quality of life(61%), mental health (89%), and surgical outcomes (63%). 56% patients received active first-line maintenance treatment; patients on active surveillance had greater emotional/psychological distress.Conclusion: Treatment delays and low uptake of active first-line treatment should be addressed. Emotional support must be readily accessible throughout treatment.

Pixantrone as a novel MCM2 inhibitor for ovarian cancer treatment

BackgroundMini-chromosome maintenance protein 2 (MCM2) is a potential target for the development of cancer therapeutics. However, small molecule inhibitors targeting MCM2 need further investigation. MethodsMolecular dynamics simulation was performed to identify active pockets in the MCM2 protein structure (6EYC). The active pocket was used as a docking model to discover MCM2 inhibitors by using structure-based virtual screening and surface plasmon resonance (SPR) assay. Furthermore, the efficacy of pixantrone targeting MCM2 in ovarian cancer was evaluated in vitro and in vivo. ResultsPixantrone was identified as a novel inhibitor of MCM2 by virtual screening. SPR binding affinity analysis confirmed the direct binding of pixantrone to MCM2 protein. Pixantrone significantly reduced the viability of ovarian cancer cells A2780 and SKOV3 in a dose- and time-dependent manner. In addition, pixantrone inhibited DNA replication, and induced cell cycle arrest and apoptosis in ovarian cancer cells via targeting MCM2. Knockdown of MCM2 could attenuate the inhibitory activity of pixantrone in ovarian cancer cells. Furthermore, pixantrone significantly suppressed ovarian cancer growth in the A2780 cell xenograft mouse model and showed favorable safety. ConclusionThese findings suggest that pixantrone may be a promising drug for ovarian cancer patients by targeting MCM2 in the clinic.

Identification of Immune Infiltration-related Molecular Features in Ovarian Cancer Patients and Experimental Validation of Immune Response Molecular Mechanisms through Integrated WGCNA, Machine Learning, and Single-cell Sequencing Analysis.

Ovarian cancer is one of the most common gynecological malignancies globally, and immunotherapy has emerged as a promising treatment strategy in recent years. However, the effectiveness of immunotherapy is often limited by immune escape mechanisms. To unravel the immune response mechanisms in ovarian cancer, this study aimed to employ integrated Weighted Gene Co-expression Network Analysis (WGCNA), machine learning, and single-- cell sequencing analysis to systematically investigate immune infiltration-related molecular features in ovarian cancer patients and experimentally validate the molecular mechanisms of the immune response. This research may provide a new theoretical foundation and treatment strategy for immune-based therapies in ovarian cancer. Relevant ovarian cancer datasets were collected from public databases. The ConsensusCluster- Plus and ggplot2 R packages were used to perform dimensionality reduction and clustering analysis of immune infiltration-related genes. Various algorithms were employed to select the best ovarian cancer prognostic model with OC consistency. The prognostic value of angiogenesis and immune-related gene expression was evaluated through Kaplan-Meier survival analysis, and the impact of immune infiltration on immune function in ovarian cancer patients was assessed. Functional pathways were identified using the Gene Set Enrichment Analysis (GSEA) method, and the infiltration abundance of immune and stromal components was inferred using the single-sample Gene Set Enrichment Analysis (ssGSEA) method. The influence of angiogenesis on the cellular level of Ovarian Cancer (OC) was explored in single- cell sequencing data, followed by in vitro cell experiments for further validation. The effect of the angiogenesis model on OC was evaluated through the above-mentioned research and experiments, aiming to investigate the mechanism of targeted therapy strategies in ovarian cancer. Immune-related data were collected from ovarian cancer patients in this study. Through WGCNA analysis, the MEturquoise module was identified, and a total of 1018 hub genes were determined. A prediction model was constructed using machine learning, with CoxBoost+StepCox selected as the best model, leading to the identification of 10 genes associated with ovarian cancer. Patients with high AIDPS had shorter survival time, and GSEA analysis revealed enrichment in immune-related pathways. Single-sample gene set enrichment analysis demonstrated increased immune cell infiltration and malignant stromal changes in the high AIDPS group. Results from in vitro cell experiments showed that silencing RPL31 inhibited the proliferation and migration of ovarian cancer cells while enhancing immune response capability. AIDPS holds significant clinical significance in Ovarian Cancer (OC) with poor prognosis observed in patients with high AIDPS. These patients exhibit more significant genomic variations, denser immune cell infiltration, and greater tolerance toward immune therapy. Importantly, inhibiting the expression of RPL31, a key component of AIDPS, can significantly suppress the proliferation, migration, and invasive properties of ovarian cancer cells, while stimulating the cytotoxicity of effector T cells and promoting immune response, thus slowing down the progression of ovarian cancer.

Prognostic Impact of Tumor-Infiltrating Immune Cells on Efficacy of Neoadjuvant Chemotherapy in Patients with Advanced or Metastatic Ovarian Cancer: A Retrospective Study.

BACKGROUND Tumor-infiltrating immune cells (TIICs) are implicated in the survival of ovarian cancer (OVCA) patients, but their prognostic significance in advanced or metastatic OVCA patients treated with neoadjuvant chemotherapy (NCAT) has not been well documented, particularly in the Chinese population. MATERIAL AND METHODS A total of 31 advanced or metastatic OVCA patients who underwent NACT were included. The density and positive rate of tumor-infiltrating immune cells (TIICs) within cancer cell nests and in cancer stroma were explored. The correlations of pre- or post-NACT TIICs with the efficacy of NACT and the changes in TIIC subpopulation with NACT were examined. RESULTS Compared with patients with partial benefit from NACT, significantly decreased pre-NACT intratumoral CD68⁺CD163⁺ cells (P=0.0043) and increased pre-NACT intratumoral CD56⁺ cells (P=0.038) were observed in patients with benefit. The high level of pre-NACT intratumoral CD68⁺CD163⁻ M1 macrophage (P=0.075) and stromal CD3⁺PD-1⁺ cells (P=0.085) predicated improved progression-free survival, respectively. Increased post-NACT stromal CD68⁺CD163⁻ M1 macrophage (P=0.01), stromal CD8⁺ T cells (P=0.073), and stromal CD8⁺PD-1⁺ cells (P=0.072) were associated with benefit from NACT. Moreover, NACT increased intratumoral CD3⁺ (P=0.031), CD8+ (P=0.031), and CD3⁺CD8⁺ cells (P=0.031). CONCLUSIONS High intratumoral CD68⁺CD163⁻, intratumoral CD56⁺ cells, and stromal CD3⁺PD-1⁺ cells pre-NACT predicted good prognosis. Intratumoral CD3⁺, CD8⁺, and CD3⁺CD8⁺ cells were increased after NACT. Evaluation of immune profiles may help to identify patients who might benefit from NACT and allow us to further stratify advanced or metastatic OVCA patients treated with NACT for disease management.

Paired comparison of the analytical performance between the Oncomine™ Comprehensive Assay v3 and whole-exome sequencing of ovarian cancer tissue

BackgroundNext-generation sequencing (NGS) has been implemented in clinical oncology as a personalized medicine tool to identify targetable genetic alterations and to guide treatment decisions. However, the optimal NGS test strategy and target genes for clinical use are still being discussed. The aim was to compare the performance of the Oncomine™ Comprehensive Assay v3 (OCAv3) (targeted gene panel) and whole-exome sequencing (WES) to investigate somatic single and multiple nucleotide variants and small indels in ovarian cancer patients.Methods and resultsGenomic DNA was isolated from fresh frozen samples of five high-grade serous (HGSC) and three clear cell ovarian (oCCC) cancer patients. Exome sequencing libraries were prepared by using the Ion AmpliSeq Exome RDY kit, whereas libraries for OCAv3 were prepared using by Ion AmpliSeq™ Library Kit Plus. Sequencing was performed using the Ion S5XL System (Thermo Fisher Scientific). When including only variants classified as pathogenic, likely pathogenic or unknown significance based on ClinVar database verdicts and comparing overlapping regions covered both by the OCAv3 assay and WES, 23 variants were detected by both assays. However, OCAv3 detected additionally two variants: ARID1A: p.Gln563Ter and TP53: p.Ser261ValfsTer84 that have not passed WES filtering criteria due to low coverage.ConclusionsWith the present treatment possibilities, OCAv3 panel testing provided higher diagnostic yield due to better coverage. Our study emphasizes that WES, although offering the potential to identify novel findings in genes not covered by OCAv3, might overlook variants in genes relevant for OC.

Urinary-based detection of MSL, HE4 and CA125 as an additional dimension for predictive and prognostic modelling in ovarian cancer.

We have recently described a predictive/prognostic model for ovarian cancer, exploiting commonly available clinico-pathological parameters and the ovarian serum biomarkers mesothelin (MSL), human epididymis protein 4 (HE4) and cancer-antigen 125 (CA125). Considering urine as a prototype non-invasive sample, we investigated whether serum levels of these biomarkers are mirrored in urine and compared their clinical relevance in matched serum vs. urine samples. MSL, HE4 and CA125 were quantified in urinary (n=172) and matched serum samples (n=188) from ovarian cancer patients (n=192) using the Lumipulse® G chemiluminescent enzyme immunoassay (Fujirebio). While absolute concentrations of MSL or CA125 were higher in serum than in matched urine samples, HE4 concentrations were considerably higher in urine than in serum. Nonetheless, the levels of all three biomarkers strongly correlated between matched serum vs. urine samples and were unrelated to BRCA1/2 mutational status. Consequently, prediction of surgical outcome or relapse/death by MSL, HE4 or CA125 was similarly efficient among urinary- vs. serum-based detection. HE4 provided the highest capacity to predict surgical outcome or relapse/death among both body fluids (urine: AUC=0.854; serum: AUC=0.750, respectively). All clinically relevant findings regarding the investigated urinary biomarkers were equally reproducible among raw vs. creatinine-normalized datasets, suggesting that normalization may have subordinate priority for urine-based analysis of these biomarkers. We report that the capacity of MSL, HE4 and CA125 to predict surgical outcome and relapse/death is equivalent between serum vs. urine-based detection. Urinary biomarkers, in particular HE4, may provide an additional dimension for prognostic modeling in ovarian cancer.

Clinical Investigation into Survival Outcomes Among Stage IV Epithelial Ovarian Cancer Patients Receiving Curative Treatment: Indian Setting

Clinical Investigation into Survival Outcomes Among Stage IV Epithelial Ovarian Cancer Patients Receiving Curative Treatment: Indian Setting

Higher incidence of venous thromboembolism associated with increasing lines of treatment in heavily treated ovarian cancer patients

ObjectiveOvarian cancer is associated with a high rate of venous thromboembolism. Our objective is to report the incidence of venous thromboembolism in recurrent ovarian cancer, assess the impact on morbidity...

The impact of Paclitaxel-based hyperthermic intraperitoneal chemotherapy in advanced high-grade serous ovarian cancer patients - interim analysis of safety and immediate efficacy of a randomized control trial (C-HOC trial)

ObjectiveThis study evaluates the potential superiority of combining paclitaxel-based hyperthermic intraperitoneal chemotherapy (HIPEC) with sequential intravenous neoadjuvant chemotherapy over intravenous neoadjuvant chemotherapy alone in Chinese patients with Federation of Gynecology and Obstetrics (FIGO) stage IIIC, IVA and IVB high-grade serous ovarian/fallopian tube carcinoma (HGSOC). This interim analysis focuses on the safety and immediate efficacy of both regimens to determine the feasibility of the planned trial (C-HOC Trial).MethodsIn a single-center, open-label, randomized control trial, FIGO stage IIIC, IVA, and IVB HGSOC patients (FAGOTTI score ≥ 8 during laparoscopic exploration) unsuitable for optimal cytoreduction in primary debulking surgery (PDS) were randomized 2:1 during laparoscopic exploration. The Experiment Group (HIPEC Group) received one cycle of intraperitoneal neoadjuvant laparoscopic hyperthermic intraperitoneal chemotherapy (paclitaxel) followed by three cycles of intravenous chemotherapy (paclitaxel plus carboplatin), while the Control Group received only three cycles of intravenous chemotherapy. Both groups subsequently underwent interval debulking surgery (IDS). The adverse effects of chemotherapy, postoperative complications, and pathological chemotherapy response scores (CRS) after IDS were compared.ResultsAmong 65 enrolled patients, 39 HIPEC Group and 21 Control Group patients underwent IDS. Grade 3–4 chemotherapy-related adverse effects were primarily hematological with no significant differences between the two groups. The HIPEC Group exhibited a higher proportion of CRS 3 (20.5% vs. 4.8%; P = 0.000). R0 resection rates in IDS were 69.2% (HIPEC Group) and 66.7% (Control Group). R2 resection occurred in 2.6% (HIPEC Group) and 14.3% (Control Group) cases. No reoperations or postoperative deaths were reported, and complications were managed conservatively.ConclusionsCombining HIPEC with IV NACT in treating ovarian cancer demonstrated safety and feasibility, with no increased chemotherapy-related adverse effects or postoperative complications. HIPEC improved tumor response to neoadjuvant chemotherapy, potentially enhancing progression-free survival (PFS). However, the final overall survival results are pending, determining if HIPEC combined with IV NACT is superior to IV NACT alone.

Promising approach for targeting ROBO1 with CAR NK cells to combat ovarian cancer primary tumor cells and organoids.

Aim: This study aimed to explore using peripheral blood mononuclear cell (PBMC)-derived chimeric antigen receptor (CAR) NK cells targeting ROBO1 as a personalized medicine approach for ovarian cancer. Methods: A two-step strategy generated ROBO1-targeted CAR NK cells from PBMCs of ovarian cancer patients. Efficacy was evaluated using xCELLigence RTCA, CCK-8 and Live/Dead fluorescence assays. Results: ROBO1-NK cells exhibited higher efficiency in eradicating primary ovarian cancer cells and lysing ovarian tumor organoids compared with primary NK cells without ROBO1-CAR modification. Conclusion: These findings highlight the potential of developing ROBO1-targeted CAR-NK cells from patients' PBMCs as a personalized treatment option for ovarian cancer.

Comprehensive analysis of hub genes associated with cisplatin-resistance in ovarian cancer and screening of therapeutic drugs through bioinformatics and experimental validation

BackgroundTo identify key genes associated with cisplatin resistance in ovarian cancer, a comprehensive analysis was conducted on three datasets from the GEO database and through experimental validation.MethodsGene expression profiles were retrieved from the GEO database. DEGs were identified by comparing gene expression profiles between cisplatin-sensitive and resistant ovarian cancer cell lines. The identified genes were further subjected to GO, KEGG, and PPI network analysis. Potential inhibitors of key genes were identified through methods such as LibDock nuclear molecular docking. In vitro assays and RT-qPCR were performed to assess the expression levels of key genes in ovarian cancer cell lines. The sensitivity of cells to chemotherapy and proliferation of key gene knockout cells were evaluated through CCK8 and Clonogenic assays.ResultsResults showed that 12 genes influenced the chemosensitivity of the ovarian cancer cell line SKOV3, and 9 genes were associated with the prognosis and survival outcomes of ovarian cancer patients. RT-qPCR results revealed NDRG1, CYBRD1, MT2A, CNIH3, DPYSL3, and CARMIL1 were upregulated, whereas ERBB4, ANK3, B2M, LRRTM4, EYA4, and SLIT2 were downregulated in cisplatin-resistant cell lines. NDRG1, CYBRD1, and DPYSL3 knock-down significantly inhibited the proliferation of cisplatin-resistant cell line SKOV3. Finally, photofrin, a small-molecule compound targeting CYBRD1, was identified.ConclusionThis study reveals changes in the expression level of some genes associated with cisplatin-resistant ovarian cancer. In addition, a new small molecule compound was identified for the treatment of cisplatin-resistant ovarian cancer.