In ovarian carcinoma, anti-Müllerian hormone (AMH) type II receptor (AMHRII) and the AMH/AMHRII signaling pathway are potential therapeutic targets. Here, AMH dose-dependent effect on signaling and proliferation was analyzed in four ovarian cancer cell lines, including sex cord stromal/granulosa cell tumors and high grade serous adenocarcinomas (COV434-AMHRII, SKOV3-AMHRII, OVCAR8 and KGN). As previously shown, incubation with exogenous AMH at concentrations above the physiological range (12.5-25 nM) decreased cell viability. Conversely, physiological concentrations of endogenous AMH improved cancer cell viability. Partial AMH depletion by siRNAs was sufficient to reduce cell viability in all four cell lines, by 20% (OVCAR8 cells) to 40% (COV434-AMHRII cells). In the presence of AMH concentrations within the physiological range (5 to 15 pM), the newly developed anti-AMH B10 antibody decreased by 25% (OVCAR8) to 50% (KGN) cell viability at concentrations ranging between 3 and 333 nM. At 70 nM, B10 reduced clonogenic survival by 57.5%, 57.1%, 64.7% and 37.5% in COV434-AMHRII, SKOV3-AMHRII, OVCAR8 and KGN cells, respectively. In the four cell lines, B10 reduced AKT phosphorylation, and increased PARP and caspase 3 cleavage. These results were confirmed in ovarian cancer cells isolated from patients' ascites, demonstrating the translational potential of these results. Furthermore, B10 reduced COV434-MISRII tumor growth in vivo and significantly enhanced the median survival time compared with vehicle (69 vs 60 days; p = 0.0173). Our data provide evidence for a novel pro-survival autocrine role of AMH in the context of ovarian cancer, which was targeted therapeutically using an anti-AMH antibody to successfully repress tumor growth.
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http://dx.doi.org/10.1038/s41598-021-81819-y | DOI Listing |
Biometrics
January 2025
Department of Biostatistics, University of Michigan at Ann Arbor, Ann Arbor, MI 48109, United States.
Graphical models are powerful tools to investigate complex dependency structures in high-throughput datasets. However, most existing graphical models make one of two canonical assumptions: (i) a homogeneous graph with a common network for all subjects or (ii) an assumption of normality, especially in the context of Gaussian graphical models. Both assumptions are restrictive and can fail to hold in certain applications such as proteomic networks in cancer.
View Article and Find Full Text PDFJ Thorac Oncol
January 2025
Department of General Internal Medicine and Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Hypothesis: To evaluate how comorbidities affect mortality benefits of lung cancer screening (LCS) with low-dose computed-tomography (LDCT).
Methods: We developed a comorbidity index (PLCO-ci) using LCS-eligible participants' data from the Prostate Lung Colorectal and Ovarian (PLCO) trial (training set) and the National Lung Screening Trial (NLST) (validation set). PLCO-ci predicts 5-year non-lung cancer (LC) mortality using a regularized Cox model; with performance evaluated by the area under the ROC curve (ROC).
Med Image Anal
January 2025
Nuffield Department of Medicine, University of Oxford, Oxford, UK; Department of Engineering Science, University of Oxford, Oxford, UK; Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Ludwig Institute for Cancer Research, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK; Oxford National Institute for Health Research (NIHR) Biomedical Research Centre, Oxford, UK. Electronic address:
Predicting disease-related molecular traits from histomorphology brings great opportunities for precision medicine. Despite the rich information present in histopathological images, extracting fine-grained molecular features from standard whole slide images (WSI) is non-trivial. The task is further complicated by the lack of annotations for subtyping and contextual histomorphological features that might span multiple scales.
View Article and Find Full Text PDFPhytomedicine
January 2025
School of Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China. Electronic address:
Background: Melittin, a major peptide component of bee venom, has demonstrated promising anti-cancer activity across various preclinical cell models, making it a potential candidate for cancer therapy. However, its molecular mechanisms, particularly in ovarian cancer, remain largely unexplored. Ovarian cancer is a life-threatening gynecological malignancy with poor clinical outcomes and limited treatment options.
View Article and Find Full Text PDFEur Radiol
January 2025
Department of Radiology, Montpellier Research Center Institute, PINKCC Laboratory, Montpellier, France.
Objective: To provide up-to-date European Society of Urogenital Radiology (ESUR) guidelines for staging and follow-up of patients with ovarian cancer (OC).
Methods: Twenty-one experts, members of the female pelvis imaging ESUR subcommittee from 19 institutions, replied to 2 rounds of questionnaires regarding imaging techniques and structured reporting used for pre-treatment evaluation of OC patients. The results of the survey were presented to the other authors during the group's annual meeting.
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