The organoid: A research model for ovarian cancer

Exploring the black box of human reproduction: endometrial organoids and assembloids - generation, implantation modeling, and future clinical perspectives

One of the critical processes in human reproduction that is still poorly understood is implantation. The implantation of an early human embryo is considered a significant limitation of successful pregnancy. Therefore, researchers are trying to develop an ideal model of endometrium in vitro that can mimic the endometrial micro-environment in vivo as much as possible. The ultimate goal of endometrial modeling is to study the molecular interactions at the embryo-maternal interface and to use this model as an in vitro diagnostic tool for infertility. Significant progress has been made over the years in generating such models. The first experiments of endometrial modeling involved animal models, which are undoubtedly valuable, but at the same time, their dissimilarities with human tissue represent a significant obstacle to further research. This fact led researchers to develop basic monolayer coculture systems using uterine cells obtained from biopsies and, later on, complex and multilayer coculture models. With successful tissue engineering methods and various cultivation systems, it is possible to form endometrial two-dimensional (2D) models to three-dimensional (3D) organoids and novel assembloids that can recapitulate many aspects of endometrial tissue architecture and cell composition. These organoids have already helped to provide new insight into the embryo-endometrium interplay. The main aim of this paper is a comprehensive review of past and current approaches to endometrial model generation, their feasibility, and potential clinical application for infertility treatment.

Orthotopic and metastatic tumour models in preclinical cancer research

Mouse models of disease play a pivotal role at all stages of cancer drug development. Cell-line derived subcutaneous tumour models are predominant in early drug discovery, but there is growing recognition of the importance of the more complex orthotopic and metastatic tumour models for understanding both target biology in the correct tissue context, and the impact of the tumour microenvironment and the immune system in responses to treatment. The aim of this review is to highlight the value that orthotopic and metastatic models bring to the study of tumour biology and drug development while pointing out those models that are most likely to be encountered in the literature. Important developments in orthotopic models, such as the increasing use of early passage patient material (PDXs, organoids) and humanised mouse models are discussed, as these approaches have the potential to increase the predictive value of preclinical studies, and ultimately improve the success rate of anticancer drugs in clinical trials.

Organoids in ovarian cancer: a platform for disease modeling, precision medicine, and drug assessment

Ovarian cancer (OC) is a major cause of gynecological cancer mortality, necessitating enhanced research. Organoids, cellular clusters grown in 3D model, have emerged as a disruptive paradigm, transcending the limitations inherent to conventional models by faithfully recapitulating key morphological, histological, and genetic attributes. This review undertakes a comprehensive exploration of the potential in organoids derived from murine, healthy population, and patient origins, encompassing a spectrum that spans foundational principles to pioneering applications. Organoids serve as preclinical models, allowing us to predict how patients will respond to treatments and guiding the development of personalized therapies. In the context of evaluating new drugs, organoids act as versatile platforms, enabling thorough testing of innovative combinations and novel agents. Remarkably, organoids mimic the dynamic nature of OC progression, from its initial formation to the spread to other parts of the body, shedding light on intricate details that hold significant importance. By functioning at an individualized level, organoids uncover the complex mechanisms behind drug resistance, revealing strategic opportunities for effective treatments.

Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review)

Artificial intelligence (AI) has emerged as a crucial technique for extracting high‑throughput information from various sources, including medical images, pathological images, and genomics, transcriptomics, proteomics and metabolomics data. AI has been widely used in the field of diagnosis, for the differentiation of benign and malignant ovarian cancer (OC), and for prognostic assessment, with favorable results. Notably, AI‑based radiomics has proven to be a non‑invasive, convenient and economical approach, making it an essential asset in a gynecological setting. The present study reviews the application of AI in the diagnosis, differentiation and prognostic assessment of OC. It is suggested that AI‑based multi‑omics studies have the potential to improve the diagnostic and prognostic predictive ability in patients with OC, thereby facilitating the realization of precision medicine.

Patient-Derived Organoids: The Beginning of a New Era in Ovarian Cancer Disease Modeling and Drug Sensitivity Testing

Ovarian cancer (OC) is the leading cause of death from gynecological malignancies. Despite great advances in treatment strategies, therapeutic resistance and the gap between preclinical data and actual clinical efficacy justify the necessity of developing novel models for investigating OC. Organoids represent revolutionary three-dimensional cell culture models, deriving from stem cells and reflecting the primary tissue's biology and pathology. The aim of the current review is to study the current status of mouse- and patient-derived organoids, as well as their potential to model carcinogenesis and perform drug screenings for OC. Herein, we describe the role of organoids in the assessment of high-grade serous OC (HGSOC) cells-of-origin, illustrate their use as promising preclinical OC models and highlight the advantages of organoid technology in terms of disease modelling and drug sensitivity testing.