On-Demand Generation of Patient-Derived Organoids for Precision Oncology: Progress and Prospects.
TL;DR
This review examines on-demand PDO generation through the selection of sample-processing methods, matrices, and platform architectures matched to specific treatment-response questions and compares conventional and engineered systems in terms of biological complexity, sample requirements, reported timelines, assay readouts, and clinical evidence.
Abstract
Precision oncology has become a focus of both clinical practice and basic research. With the rapid evolution of organoid culture techniques and the integration of biomedical engineering approaches, patient-derived organoid (PDO) systems are playing an increasingly pivotal role in precision cancer therapy. By recapitulating patient-specific tumor characteristics, these platforms demonstrate considerable potential for clinical translation. Despite remarkable progress, the impact of PDO models on real-world therapeutic decision-making remains limited. Most existing studies have focused on preclinical concept validations or small-scale trials, leaving a substantial gap before these models can be implemented to guide individualized cancer therapy. This review examines on-demand PDO generation through the selection of sample-processing methods, matrices, and platform architectures matched to specific treatment-response questions. We compare conventional and engineered systems in terms of biological complexity, sample requirements, reported timelines, assay readouts, and clinical evidence. Particular attention is given to engineering trade-offs and the distinction between technical feasibility, clinical response correlation, and demonstrated utility in treatment selection.