The metastatic capacity of high-grade serous ovarian cancer cells changes along disease progression: inhibition by mifepristone

Beyond 2D cell cultures: how 3D models are changing the in vitro study of ovarian cancer and how to make the most of them

3D cell cultures are a fundamental tool in ovarian cancer research that can enable more effective study of the main features of this lethal disease, including the high rates of recurrence and chemoresistance. A clearer, more comprehensive understanding of the biological underpinnings of these phenomena could aid the development of more effective treatments thus improving patient outcomes. Selecting the most appropriate model to investigate the different aspects of cell biology that are relevant to cancer is challenging, especially since the assays available for the study of 3D cultures are not fully established yet. To maximise the usefulness of 3D cell cultures of ovarian cancer, we undertook an in-depth review of the currently available models, taking into consideration the strengths and limitations of each approach and of the assay techniques used to evaluate the results. This integrated analysis provides insight into which model-assay pair is best suited to study different parameters of ovarian cancer biology such as cell proliferation, gene expression or treatment response. We also describe how the combined use of multiple models is likely to be the most effective strategy for the in vitro characterisation of complex behaviours.

Accurate Identification of Cancer Cells in Complex Pre-Clinical Models Using a Deep-Learning Neural Network: A Transfection-Free Approach

3D co-cultures are key tools for in vitro biomedical research as they recapitulate more closely the in vivo environment while allowing a tighter control on the culture's composition and experimental conditions. The limited technologies available for the analysis of these models, however, hamper their widespread application. The separation of the contribution of the different cell types, in particular, is a fundamental challenge. In this work, ORACLE (OvaRiAn Cancer ceLl rEcognition) is presented, a deep neural network trained to distinguish between ovarian cancer and healthy cells based on the shape of their nucleus. The extensive validation that are conducted includes multiple cell lines and patient-derived cultures to characterize the effect of all the major potential confounding factors. High accuracy and reliability are maintained throughout the analysis (F1score> 0.9 and Area under the ROC curve -ROC-AUC- score = 0.99) demonstrating ORACLE's effectiveness with this detection and classification task. ORACLE is freely available (https://github.com/MarilisaCortesi/ORACLE/tree/main) and can be used to recognize both ovarian cancer cell lines and primary patient-derived cells. This feature is unique to ORACLE and thus enables for the first time the analysis of in vitro co-cultures comprised solely of patient-derived cells.

The high-grade serous ovarian cancer metastasis and chemoresistance in 3D models

High-grade serous ovarian cancer (HGSOC) is the most frequent and aggressive type of epithelial ovarian cancer, with high recurrence rate and chemoresistance being the main issues in its clinical management. HGSOC is specifically challenging due to the metastatic dissemination via spheroids in the ascitic fluid. The HGSOC spheroids represent the invasive and chemoresistant cellular fraction, which is impossible to investigate in conventional two-dimensional (2D) monolayer cell cultures lacking critical cell-to-cell and cell-extracellular matrix interactions. Three-dimensional (3D) HGSOC cultures, where cells aggregate and exhibit relevant interactions, offer a promising in vitro model of peritoneal metastasis and multicellular drug resistance. This review summarizes recent studies of HGSOC in 3D culture conditions and highlights the role of multicellular HGSOC spheroids and ascitic environment in HGSOC metastasis and chemoresistance.

Glucocorticoid Receptor and Ovarian Cancer: From Biology to Therapeutic Intervention

Ovarian cancer (OC) is the leading cause of death from gynecological malignancies worldwide. Fortunately, recent advances in OC biology and the discovery of novel therapeutic targets have led to the development of novel therapeutic agents that may improve the outcome of OC patients. The glucocorticoid receptor (GR) is a ligand-dependent transcriptional factor known for its role in body stress reactions, energy homeostasis and immune regulation. Notably, evidence suggests that GR may play a relevant role in tumor progression and may affect treatment response. In cell culture models, administration of low levels of glucocorticoids (GCs) suppresses OC growth and metastasis. Conversely, high GR expression has been associated with poor prognostic features and long-term outcomes in patients with OC. Moreover, both preclinical and clinical data have shown that GR activation impairs the effectiveness of chemotherapy by inducing the apoptotic pathways and cell differentiation. In this narrative review, we summarize data related to the function and role of GR in OC. To this aim, we reorganized the controversial and fragmented data regarding GR activity in OC and herein describe its potential use as a prognostic and predictive biomarker. Moreover, we explored the interplay between GR and BRCA expression and reviewed the latest therapeutic strategies such as non-selective GR antagonists and selective GR modulators to enhance chemotherapy sensitivity, and to finally provide new treatment options in OC patients.

Survival Mechanisms of Metastatic Melanoma Cells: The Link between Glucocorticoids and the Nrf2-Dependent Antioxidant Defense System

Circulating glucocorticoids increase during stress. Chronic stress, characterized by a sustained increase in serum levels of cortisol, has been associated in different cases with an increased risk of cancer and a worse prognosis. Glucocorticoids can promote gluconeogenesis, mobilization of amino acids, fat breakdown, and impair the body's immune response. Therefore, conditions that may favor cancer growth and the acquisition of radio- and chemo-resistance. We found that glucocorticoid receptor knockdown diminishes the antioxidant protection of murine B16-F10 (highly metastatic) melanoma cells, thus leading to a drastic decrease in their survival during interaction with the vascular endothelium. The BRAF^V600E mutation is the most commonly observed in melanoma patients. Recent studies revealed that VMF/PLX40-32 (vemurafenib, a selective inhibitor of mutant BRAF^V600E) increases mitochondrial respiration and reactive oxygen species (ROS) production in BRAF^V600E human melanoma cell lines. Early-stage cancer cells lacking Nrf2 generate high ROS levels and exhibit a senescence-like growth arrest. Thus, it is likely that a glucocorticoid receptor antagonist (RU486) could increase the efficacy of BRAF-related therapy in BRAF^V600E-mutated melanoma. In fact, during early progression of skin melanoma metastases, RU486 and VMF induced metastases regression. However, treatment at an advanced stage of growth found resistance to RU486 and VMF. This resistance was mechanistically linked to overexpression of proteins of the Bcl-2 family (Bcl-xL and Mcl-1 in different human models). Moreover, melanoma resistance was decreased if AKT and NF-κB signaling pathways were blocked. These findings highlight mechanisms by which metastatic melanoma cells adapt to survive and could help in the development of most effective therapeutic strategies.