An organotypic model of high-grade serous ovarian cancer to test the anti-metastatic potential of ROR2 targeted Polyion complex nanoparticles

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.

A comparative analysis of 2D and 3D experimental data for the identification of the parameters of computational models

Computational models are becoming an increasingly valuable tool in biomedical research. Their accuracy and effectiveness, however, rely on the identification of suitable parameters and on appropriate validation of the in-silico framework. Both these steps are highly dependent on the experimental model used as a reference to acquire the data. Selecting the most appropriate experimental framework thus becomes key, together with the analysis of the effect of combining results from different experimental models, a common practice often necessary due to limited data availability. In this work, the same in-silico model of ovarian cancer cell growth and metastasis, was calibrated with datasets acquired from traditional 2D monolayers, 3D cell culture models or a combination of the two. The comparison between the parameters sets obtained in the different conditions, together with the corresponding simulated behaviours, is presented. It provides a framework for the study of the effect of the different experimental models on the development of computational systems. This work also provides a set of general guidelines for the comparative testing and selection of experimental models and protocols to be used for parameter optimization in computational models.

Design and fabrication of intracellular therapeutic cargo delivery systems based on nanomaterials: current status and future perspectives

Intracellular cargo delivery, the introduction of small molecules, proteins, and nucleic acids into a specific targeted site in a biological system, is an important strategy for deciphering cell function, directing cell fate, and reprogramming cell behavior. With the advancement of nanotechnology, many researchers use nanoparticles (NPs) to break through biological barriers to achieving efficient targeted delivery in biological systems, bringing a new way to realize efficient targeted drug delivery in biological systems. With a similar size to many biomolecules, NPs possess excellent physical and chemical properties and a certain targeting ability after functional modification on the surface of NPs. Currently, intracellular cargo delivery based on NPs has emerged as an important strategy for genome editing regimens and cell therapy. Although researchers can successfully deliver NPs into biological systems, many of them are delivered very inefficiently and are not specifically targeted. Hence, the development of efficient, target-capable, and safe nanoscale drug delivery systems to deliver therapeutic substances to cells or organs is a major challenge today. In this review, on the basis of describing the research overview and classification of NPs, we focused on the current research status of intracellular cargo delivery based on NPs in biological systems, and discuss the current problems and challenges in the delivery process of NPs in biological systems.

Heterotypic tumor spheroids: a platform for nanomedicine evaluation

Nanomedicine has emerged as a promising therapeutic approach, but its translation to the clinic has been hindered by the lack of cellular models to anticipate how tumor cells will respond to therapy. Three-dimensional (3D) cell culture models are thought to more accurately recapitulate key features of primary tumors than two-dimensional (2D) cultures. Heterotypic 3D tumor spheroids, composed of multiple cell types, have become more popular than homotypic spheroids, which consist of a single cell type, as a superior model for mimicking in vivo tumor heterogeneity and physiology. The stromal interactions demonstrated in heterotypic 3D tumor spheroids can affect various aspects, including response to therapy, cancer progression, nanomedicine penetration, and drug resistance. Accordingly, to design more effective anticancer nanomedicinal therapeutics, not only tumor cells but also stromal cells (e.g., fibroblasts and immune cells) should be considered to create a more physiologically relevant in vivo microenvironment. This review aims to demonstrate current knowledge of heterotypic 3D tumor spheroids in cancer research, to illustrate current advances in utilizing these tumor models as a novel and versatile platform for in vitro evaluation of nanomedicine-based therapeutics in cancer research, and to discuss challenges, guidelines, and future directions in this field.

Advantages and Potential Benefits of Using Organoids in Nanotoxicology

Organoids are microtissues that recapitulate the complex structural organization and functions of tissues and organs. Nanoparticles have several specific properties that must be considered when replacing animal models with in vitro studies, such as the formation of a protein corona, accumulation, ability to overcome tissue barriers, and different severities of toxic effects in different cell types. An increase in the number of articles on toxicology research using organoid models is related to an increase in publications on organoids in general but is not related to toxicology-based publications. We demonstrate how the quantitative assessment of toxic changes in the structure of organoids and the state of their cell collections provide more valuable results for toxicological research and provide examples of research methods. The impact of the tested materials on organoids and their differences are also discussed. In conclusion, we highlight the main challenges, the solution of which will allow researchers to approach the replacement of in vivo research with in vitro research: biobanking and standardization of the structural characterization of organoids, and the development of effective screening imaging techniques for 3D organoid cell organization.

The Anti-ROR1 Monoclonal Antibody Zilovertamab Inhibits the Proliferation of Ovarian and Endometrial Cancer Cells

The non-canonical Wnt signalling receptor ROR1 is aberrantly expressed in numerous cancers, including ovarian and endometrial cancer. We previously reported that silencing ROR1 could inhibit the proliferation and metastatic potential of ovarian and endometrial cancer cells in vitro. Zilovertamab is an ROR1-targeting humanised monoclonal antibody, with demonstrated safety and efficacy in clinical trials of several ROR1-related malignancies. The aim of this study was to investigate the potential of zilovertamab alone, or in combination with commonly utilised gynaecological cancer therapies (cisplatin, paclitaxel and the PARP inhibitor—Olaparib) on high-grade serous ovarian cancer (HGSOC), including models of platinum resistance and homologous recombination deficiency (CaOV3, CaOV3CisR, PEO1 and PEO4) and endometrial cancer (EC) cell lines (Ishikawa and KLE). The effect of zilovertamab (at 25 µg/mL or 50 µg/mL) +/− agents was investigated using the IncuCyte S3 Live Cell imaging system. Zilovertamab alone inhibited the proliferation of HGSOC and EC cells in vitro, including in models of platinum resistance and homologous recombination deficiency. In general, the addition of commonly used chemotherapies to a fixed dose of zilovertamab did not enhance the observed anti-proliferative activity. This study supports the potential of zilovertamab, or other ROR1-targeting therapies, for treating women with HGSOC and EC.

Three-Dimensional Modelling of Ovarian Cancer: From Cell Lines to Organoids for Discovery and Personalized Medicine

Ovarian cancer has the highest mortality of all of the gynecological malignancies. There are several distinct histotypes of this malignancy characterized by specific molecular events and clinical behavior. These histotypes have differing responses to platinum-based drugs that have been the mainstay of therapy for ovarian cancer for decades. For histotypes that initially respond to a chemotherapeutic regime of carboplatin and paclitaxel such as high-grade serous ovarian cancer, the development of chemoresistance is common and underpins incurable disease. Recent discoveries have led to the clinical use of PARP (poly ADP ribose polymerase) inhibitors for ovarian cancers defective in homologous recombination repair, as well as the anti-angiogenic bevacizumab. While predictive molecular testing involving identification of a genomic scar and/or the presence of germline or somatic BRCA1 or BRCA2 mutation are in clinical use to inform the likely success of a PARP inhibitor, no similar tests are available to identify women likely to respond to bevacizumab. Functional tests to predict patient response to any drug are, in fact, essentially absent from clinical care. New drugs are needed to treat ovarian cancer. In this review, we discuss applications to address the currently unmet need of developing physiologically relevant in vitro and ex vivo models of ovarian cancer for fundamental discovery science, and personalized medicine approaches. Traditional two-dimensional (2D) in vitro cell culture of ovarian cancer lacks critical cell-to-cell interactions afforded by culture in three-dimensions. Additionally, modelling interactions with the tumor microenvironment, including the surface of organs in the peritoneal cavity that support metastatic growth of ovarian cancer, will improve the power of these models. Being able to reliably grow primary tumoroid cultures of ovarian cancer will improve the ability to recapitulate tumor heterogeneity. Three-dimensional (3D) modelling systems, from cell lines to organoid or tumoroid cultures, represent enhanced starting points from which improved translational outcomes for women with ovarian cancer will emerge.