Ovarian cancer cells regulate their mitochondrial content and high mitochondrial content is associated with a poor prognosis.

Most cancer patients ultimately die from the consequences of distant metastases. As metastasis formation consumes energy mitochondria play an important role during this process as they are the most important cellular organelle to synthesise the energy rich substrate ATP, which provides the necessary energy to enable distant metastasis formation. However, mitochondria are also important for the execution of apoptosis, a process which limits metastasis formation. We therefore wanted to investigate the mitochondrial content in ovarian cancer cells and link its presence to the patient's prognosis in order to analyse which of the two opposing functions of mitochondria dominates during the malignant progression of ovarian cancer. Monoclonal antibodies directed against different mitochondrial specific proteins, namely heat shock proteins 60 (HSP60), fumarase and succinic dehydrogenase, were used in immunohistochemistry in preliminary experiments to identify the antibody most suited to detect mitochondria in ovarian cancer cells in clinical tissue samples. The clearest staining pattern, which even delineated individual mitochondria, was seen with the anti-HSP60 antibody, which was used for the subsequent clinical study staining primary ovarian cancers (n = 155), borderline tumours (n = 24) and recurrent ovarian cancers (n = 26). The staining results were semi-quantitatively scored into three groups according to their mitochondrial content: low (n = 26), intermediate (n = 50) and high (n = 84). Survival analysis showed that high mitochondrial content correlated with a statistically significant overall reduced survival rate In addition to the clinical tissue samples, mitochondrial content was analysed in ovarian cancer cells grown in vitro (cell lines: OVCAR8, SKOV3, OVCAR3 and COV644) and in vivo in severe combined immunodeficiency (SCID) mice.In in vivo grown SKOV3 and OVCAR8 cells, the number of mitochondria positive cells was markedly down-regulated compared to the in vitro grown cells indicating that mitochondrial number is subject to regulatory processes. As high mitochondrial content is associated with a poor prognosis, the provision of high energy substrates by the mitochondria seems to be more important for metastasis formation than the inhibition of apoptotic cell death, which is also mediated by mitochondria. In vivo and in vitro grown human ovarian cancer cells showed that the mitochondrial content is highly adaptable to the growth condition of the cancer cells.

Cancer Stem Cell Markers-Clinical Relevance and Prognostic Value in High-Grade Serous Ovarian Cancer (HGSOC) Based on The Cancer Genome Atlas Analysis.

Cancer stem cells (CSCs) may contribute to an increased risk of recurrence in ovarian cancer (OC). Further research is needed to identify associations between CSC markers and OC patients' clinical outcomes with greater certainty. If they prove to be correct, in the future, the CSC markers can be used to help predict survival and indicate new therapeutic targets. This study aimed to determine the CSC markers at mRNA and protein levels and their association with clinical presentation, outcome, and risk of recurrence in HGSOC (High-Grade Serous Ovarian Cancer). TCGA (The Cancer Genome Atlas) database with 558 ovarian cancer tumor samples was used for the evaluation of 13 CSC markers (ALDH1A1, CD44, EPCAM, KIT, LGR5, NES, NOTCH3, POU5F1, PROM1, PTTG1, ROR1, SOX9, and THY1). Data on mRNA and protein levels assessed by microarray and mass spectrometry were retrieved from TCGA. Models to predict chemotherapy response and survival were built using multiple variables, including epidemiological data, expression levels, and machine learning methodology. ALDH1A1 and LGR5 mRNA expressions indicated a higher platinum sensitivity (p = 3.50 × 10-3; p = 0.01, respectively). POU5F1 mRNA expression marked platinum-resistant tumors (p = 9.43 × 10-3). CD44 and EPCAM mRNA expression correlated with longer overall survival (OS) (p = 0.043; p = 0.039, respectively). THY1 mRNA and protein levels were associated with worse OS (p = 0.019; p = 0.015, respectively). Disease-free survival (DFS) was positively affected by EPCAM (p = 0.004), LGR5 (p = 0.018), and CD44 (p = 0.012). In the multivariate model based on CSC marker expression, the high-risk group had 9.1 months longer median overall survival than the low-risk group (p < 0.001). ALDH1A1, CD44, EPCAM, LGR5, POU5F1, and THY1 levels in OC may be used as prognostic factors for the primary outcome and help predict the treatment response.

History of hypertension, heart disease, and diabetes and ovarian cancer patient survival: evidence from the ovarian cancer association consortium.

PURPOSE: Survival following ovarian cancer diagnosis is generally low; understanding factors related to prognosis could be important to optimize treatment. The role of previously diagnosed comorbidities and use of medications for those conditions in relation to prognosis for ovarian cancer patients has not been studied extensively, particularly according to histological subtype. METHODS: Using pooled data from fifteen studies participating in the Ovarian Cancer Association Consortium, we examined the associations between history of hypertension, heart disease, diabetes, and medications taken for these conditions and overall survival (OS) and progression-free survival (PFS) among patients diagnosed with invasive epithelial ovarian carcinoma. We used Cox proportional hazards regression models adjusted for age and stage to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) overall and within strata of histological subtypes. RESULTS: History of diabetes was associated with increased risk of mortality (n = 7,674; HR = 1.12; 95% CI = 1.01-1.25). No significant mortality associations were observed for hypertension (n = 6,482; HR = 0.95; 95% CI = 0.88-1.02) or heart disease (n = 4,252; HR = 1.05; 95% CI = 0.87-1.27). No association of these comorbidities was found with PFS in the overall study population. However, among patients with endometrioid tumors, hypertension was associated with lower risk of progression (n = 339, HR = 0.54; 95% CI = 0.35-0.84). Comorbidity was not associated with OS or PFS for any of the other histological subtypes. Ever use of beta blockers, oral antidiabetic medications, and insulin was associated with increased mortality, HR = 1.20; 95% CI = 1.03-1.40, HR = 1.28; 95% CI = 1.05-1.55, and HR = 1.63; 95% CI = 1.20-2.20, respectively. Ever use of diuretics was inversely associated with mortality, HR = 0.71; 95% CI = 0.53-0.94. CONCLUSIONS: Histories of hypertension, diabetes, and use of diuretics, beta blockers, insulin, and oral antidiabetic medications may influence the survival of ovarian cancer patients. Understanding mechanisms for these observations could provide insight regarding treatment.

A prognostic prediction model for ovarian cancer using a cross-modal view correlation discovery network.

Ovarian cancer is a tumor with different clinicopathological and molecular features, and the vast majority of patients have local or extensive spread at the time of diagnosis. Early diagnosis and prognostic prediction of patients can contribute to the understanding of the underlying pathogenesis of ovarian cancer and the improvement of therapeutic outcomes. The occurrence of ovarian cancer is influenced by multiple complex mechanisms, including the genome, transcriptome and proteome. Different types of omics analysis help predict the survival rate of ovarian cancer patients. Multi-omics data of ovarian cancer exhibit high-dimensional heterogeneity, and existing methods for integrating multi-omics data have not taken into account the variability and inter-correlation between different omics data. In this paper, we propose a deep learning model, MDCADON, which utilizes multi-omics data and cross-modal view correlation discovery network. We introduce random forest into LASSO regression for feature selection on mRNA expression, DNA methylation, miRNA expression and copy number variation (CNV), aiming to select important features highly correlated with ovarian cancer prognosis. A multi-modal deep neural network is used to comprehensively learn feature representations of each omics data and clinical data, and cross-modal view correlation discovery network is employed to construct the multi-omics discovery tensor, exploring the inter-relationships between different omics data. The experimental results demonstrate that MDCADON is superior to the existing methods in predicting ovarian cancer prognosis, which enables survival analysis for patients and facilitates the determination of follow-up treatment plans. Finally, we perform Gene Ontology (GO) term analysis and biological pathway analysis on the genes identified by MDCADON, revealing the underlying mechanisms of ovarian cancer and providing certain support for guiding ovarian cancer treatments.

Impact of the time interval between primary or interval surgery and adjuvant chemotherapy in ovarian cancer patients.

Introduction: Primary debulking surgery (PDS), interval debulking surgery (IDS), and platinum-based chemotherapy are the current standard treatments for advanced ovarian cancer (OC). The time to initiation of adjuvant chemotherapy (TTC) could influence patient outcomes. Methods: We conducted a multicenter retrospective cohort study of advanced (International Federation of Gynecology and Obstetrics (FIGO) stage III or IV) OC treated between 2014 and 2018 to assess progression-free survival (PFS) and overall survival (OS) in relation to TTC. All patients underwent a germline multigene panel for BRCA1/2 evaluation. Results: Among the 83 patients who underwent PDS, a TTC ≥ 60 days was associated with a shorter PFS (hazard ratio (HR) 2.02, 95% confidence interval (CI) 1.04-3.93, p = 0.038), although this association lost statistical significance when adjusting for residual disease (HR 1.52, 95% CI 0.75-3.06, p = 0.244, for TTC and HR 2.73, 95% CI 1.50-4.96, p = 0.001, for residual disease). Among 52 IDS patients, we found no evidence of an association between TTC and clinical outcomes. Ascites, type of chemotherapy, or germline BRCA1/2 mutational status did not influence TTC and were not associated with clinical outcomes in PDS or IDS patients. Discussion: In conclusion, longer TTC seems to negatively affect prognosis in patients undergoing PDS, especially those with residual disease.