Patient-Derived Organoids in Functional Precision Oncology: Tumor Modeling, Drug-Response Prediction, and Clinical Translation.

Authors

Abdullah Jabri, Mohamed Alsharif, Abdulaziz Mhannayeh, Bader Taftafa, Dania Sibai, Abdulrahman Elsalti, Raghad Alsharif, Tanveer Ahmad Mir, Mohammed Imran Khan, Jahan Salma, Ahmed Yaqinuddin

Published in

Cell biochemistry and function. Volume 44. Issue 10. Pages e70321.

Abstract

Patient-derived organoids (PDOs) have emerged as an effective experimental platform for bridging the long-standing gap between traditional in vitro cancer models and the biological complexity of actual tumors. These three-dimensional cultures (3D), derived directly from patient tissues, preserve essential architectural, genomic, and phenotypic characteristics of their tumors of origin, enabling more accurate modeling of tumor heterogeneity and disease evolution. Over the last decade, rapid methodological advances in organoid culture systems, extracellular matrices (ECMs), and growth factor optimization have facilitated the establishment of organoid models for a wide range of malignancies and the development of large living biobanks representing diverse patient populations. These models are increasingly being utilized to study cancer biology, elucidate mechanisms of treatment resistance, and evaluate patient-specific therapeutic responses, highlighting their potential as tools for precision oncology. However, important challenges remain, including incomplete representation of the tumor microenvironment (TME), variability in culture conditions, and limited scalability, and the need for greater standardization and validation. Consequently, although PDOs show considerable promise for translational research and personalized medicine, their routine clinical implementation remains largely investigational, and their clinical utility has not yet been fully established. This review examines the technological foundations of PDO generation, assesses their role as experimental models of tumor biology, and discusses their expanding applications in translational cancer research and precision oncology while highlighting key challenges that must be addressed before their widespread clinical adoption.

PMID:
42895119
Bibliographic data and abstract were imported from PubMed on 19 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.