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Organoid-based personalized medicine: from tumor outcome prediction to autologous transplantation.

Abel Soto-GamezJeremy P GunawanLara BarazzuolSarah PringleRobert P Coppes
Published in: Stem cells (Dayton, Ohio) (2024)
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 3-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 therefore represent 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.
Keyphrases
  • cell therapy
  • single cell
  • primary care
  • bone marrow
  • healthcare
  • stem cells
  • randomized controlled trial
  • high resolution
  • machine learning
  • risk assessment
  • smoking cessation
  • study protocol
  • double blind