Patient-derived tumor organoids: a new avenue for preclinical research and precision medicine in oncology

Abstract

Over the past decade, the emergence of patient-derived tumor organoids (PDTOs) has broadened the repertoire of preclinical models and progressively revolutionized three-dimensional cell culture in oncology. PDTO can be grown from patient tumor samples with high efficiency and faithfully recapitulates the histological and molecular characteristics of the original tumor. Therefore, PDTOs can serve as invaluable tools in oncology research, and their translation to clinical practice is exciting for the future of precision medicine in oncology. In this review, we provide an overview of methods for establishing PDTOs and their various applications in cancer research, starting with basic research and ending with the identification of new targets and preclinical validation of new anticancer compounds and precision medicine. Finally, we highlight the challenges associated with the clinical implementation of PDTO, such as its representativeness, success rate, assay speed, and lack of a tumor microenvironment. Technological developments and autologous cocultures of PDTOs and stromal cells are currently ongoing to meet these challenges and optimally exploit the full potential of these models. The use of PDTOs as standard tools in clinical oncology could lead to a new era of precision oncology in the coming decade. The shift from 2D to 3D cell cultures has greatly improved cancer research, providing a more realistic model of tumors. Patient-Derived Tumor Organoids (PDTOs) have become a key tool in cancer research, allowing scientists to grow efficiently tumor cells from patient samples in a 3D environment that closely mirrors the original tumor. PDTOs are a major step forward in cancer research, bridging the gap between traditional cell cultures and clinical realities, with the potential for successful clinical applications despite some challenges that could be overcome by technological developments. Thus, they offer a promising platform for understanding cancer, testing drug responses, and developing personalized treatments, with the potential to greatly impact future patient care. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.