Patient-Derived Organoid Facilitating Personalized Medicine in Gastrointestinal Stromal Tumor With Liver Metastasis: A Case Report

Establishment of patient-derived 3D in vitro models of sarcomas: literature review and guidelines on behalf of the FORTRESS working group

Sarcomas are a large family of rare and heterogeneous mesenchymal tumors, which respond poorly to available systemic treatments. Translation of preclinical findings into clinical applications has been slow, limiting improvements in patients' outcomes and ultimately highlighting the need for a better understanding of sarcoma biology to develop more effective, subtype-specific therapies. To this end, reliable preclinical models are crucial, but the development of 3D in vitro sarcoma models has been lagging behind that of epithelial cancers. This is primarily due to the rarity and heterogeneity of sarcomas, and lack of widespread knowledge regarding the optimal growth conditions of these in vitro models. In this review, we provide an overview of currently available sarcoma tumoroid models, together with guidelines and suggestions for model development and characterization, on behalf of the FORTRESS (Forum For Translational Research in Sarcomas) international research working group on 3D sarcoma models.

Revolutionising oral organoids with artificial intelligence

The convergence of organoid technology and artificial intelligence (AI) is poised to revolutionise oral healthcare. Organoids - three-dimensional structures derived from human tissues - offer invaluable insights into the complex biology of diseases, allowing researchers to effectively study disease mechanisms and test therapeutic interventions in environments that closely mimic in vivo conditions. In this review, we first present the historical development of organoids and delve into the current types of oral organoids, focusing on their use in disease models, regeneration and microbiome intervention. We then compare single-source and multi-lineage oral organoids and assess the latest progress in bioprinted, vascularised and neural-integrated organoids. In the next part of the review, we highlight significant advancements in AI, emphasising how AI algorithms may potentially promote organoid development for early disease detection and diagnosis, personalised treatment, disease prediction and drug screening. However, our main finding is the identification of remaining challenges, such as data integration and the critical need for rigorous validation of AI algorithms to ensure their clinical reliability. Our main viewpoint is that current AI-enabled oral organoids are still limited in applications but, as we look to the future, we offer insights into the potential transformation of AI-integrated oral organoids in oral disease diagnosis, oral microbial interactions and drug discoveries. By synthesising these components, this review aims to provide a comprehensive perspective on the current state and future implications of AI-enabled oral organoids, emphasising their role in advancing oral healthcare and improving patient outcomes.

Emerging applications of machine learning in genomic medicine and healthcare

The integration of artificial intelligence technologies has propelled the progress of clinical and genomic medicine in recent years. The significant increase in computing power has facilitated the ability of artificial intelligence models to analyze and extract features from extensive medical data and images, thereby contributing to the advancement of intelligent diagnostic tools. Artificial intelligence (AI) models have been utilized in the field of personalized medicine to integrate clinical data and genomic information of patients. This integration allows for the identification of customized treatment recommendations, ultimately leading to enhanced patient outcomes. Notwithstanding the notable advancements, the application of artificial intelligence (AI) in the field of medicine is impeded by various obstacles such as the limited availability of clinical and genomic data, the diversity of datasets, ethical implications, and the inconclusive interpretation of AI models' results. In this review, a comprehensive evaluation of multiple machine learning algorithms utilized in the fields of clinical and genomic medicine is conducted. Furthermore, we present an overview of the implementation of artificial intelligence (AI) in the fields of clinical medicine, drug discovery, and genomic medicine. Finally, a number of constraints pertaining to the implementation of artificial intelligence within the healthcare industry are examined.

Characterization of a Human Gastrointestinal Stromal Tumor Cell Line Established by SV40LT-Mediated Immortalization

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumors in the digestive tract and originate from the interstitial cells of Cajal (ICC), which is the pacemaker for peristaltic movement in the gastrointestinal tract. Existing GIST cell lines are widely used as cell models for in vitro experimental studies because the mutation sites are known. However, the immortalization methods of these cell lines are unknown, and no Chinese patient-derived GIST cell lines have been documented. Here, we transfected simian virus 40 large T antigen (SV40LT) into primary GIST cells to establish an immortalized human GIST cell line (ImGIST) for the first time. The ImGIST cells had neuronal cell-like irregular radioactive growth and retained the fusion growth characteristics of GIST cells. They stably expressed signature proteins, maintained the biological and genomic characteristics of normal primary GIST cells, and responded well to imatinib, suggesting that ImGIST could be a potential in vitro model for research in GIST to explore the molecular pathogenesis, drug resistance mechanisms, and the development of new adjuvant therapeutic options.

Advances in immunology and immunotherapy for mesenchymal gastrointestinal cancers

Mesenchymal gastrointestinal cancers are represented by the gastrointestinal stromal tumors (GISTs) which occur throughout the whole gastrointestinal tract, and affect human health and economy globally. Curative surgical resections and tyrosine kinase inhibitors (TKIs) are the main managements for localized GISTs and recurrent/metastatic GISTs, respectively. Despite multi-lines of TKIs treatments prolonged the survival time of recurrent/metastatic GISTs by delaying the relapse and metastasis of the tumor, drug resistance developed quickly and inevitably, and became the huge obstacle for stopping disease progression. Immunotherapy, which is typically represented by immune checkpoint inhibitors (ICIs), has achieved great success in several solid tumors by reactivating the host immune system, and been proposed as an alternative choice for GIST treatment. Substantial efforts have been devoted to the research of immunology and immunotherapy for GIST, and great achievements have been made. Generally, the intratumoral immune cell level and the immune-related gene expressions are influenced by metastasis status, anatomical locations, driver gene mutations of the tumor, and modulated by imatinib therapy. Systemic inflammatory biomarkers are regarded as prognostic indicators of GIST and closely associated with its clinicopathological features. The efficacy of immunotherapy strategies for GIST has been widely explored in pre-clinical cell and mouse models and clinical experiments in human, and some patients did benefit from ICIs. This review comprehensively summarizes the up-to-date advancements of immunology, immunotherapy and research models for GIST, and provides new insights and perspectives for future studies.