Soto-Gamez A, Gunawan JP, Barazzuol L, Pringle S, Coppes RP. Organoid-based personalized medicine: from tumor outcome prediction to autologous transplantation.
Stem Cells 2024:sxae023. [PMID:
38525972 DOI:
10.1093/stmcls/sxae023]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Indexed: 03/26/2024]
Abstract
Inter-individual variation largely influences disease susceptibility, as well as response to therapy. In a clinical context, the optimal treatment of a disease should consider inter-individual variation and formulate tailored decisions at an individual level. In recent years, emerging organoid technologies promise to capture part of an individual's phenotypic variability and prove helpful in providing clinically relevant molecular insights. Organoids are stem cell-derived three-dimensional models that contain multiple cell types that can self-organize and give rise to complex structures mimicking the organization and functionality of the tissue of origin. Organoids represent thus a more faithful recapitulation of the dynamics of the tissues of interest, compared to conventional monolayer cultures, thus supporting their use in evaluating disease prognosis, or as a tool to predict treatment outcomes. Additionally, the individualized nature of patient-derived organoids enables the use of autologous organoids as a source of transplantable material not limited by histocompatibility. An increasing amount of preclinical evidence has paved the way for clinical trials exploring the applications of organoid-based technologies, some of which are in phase I/II. This review focuses on the recent progress concerning the use of patient-derived organoids in personalized medicine, including (1) diagnostics and disease prognosis, (2) treatment outcome prediction to guide therapeutic advice and (3) organoid transplantation or cell-based therapies. We discuss examples of these potential applications and the challenges associated with their future implementation.
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