Patient-derived organoids for modeling therapy tolerance, persistence, and resistance in cancer

Therapy resistance remains a major obstacle to durable cancer control. Across cytotoxic chemotherapy, molecularly targeted therapy, immunotherapy, endocrine therapy, and cellular therapy, treatment failure may manifest as primary nonresponse, residual disease, reversible regrowth after treatment cessation, or stable acquired resistance. No single biological model fully accounts for the complex spectrum of treatment failure observed in clinical oncology. Clonal evolution, drug-tolerant persister states, cancer stem cell plasticity, cell-cycle heterogeneity, and tumor microenvironment-mediated protection each account for important aspects of resistance, but they are often discussed as separate mechanisms. Patient-derived organoids (PDOs) offer a tractable platform to study these processes in a clinically relevant context. They preserve key genetic, histological, and functional features of the original tumors while enabling longitudinal drug exposure, clonal tracking, single-cell analysis, perturbation experiments, and reconstruction of selected microenvironmental components. In this review, we propose a dynamic PDO-based conceptual map to understand therapy tolerance, persistence, and resistance. The value of this framework lies not in replacing existing resistance models, but in integrating four determinants—genetic background, cell-state plasticity, cell-cycle state, and microenvironmental context—along experimentally measurable dimensions of drug sensitivity, proliferative activity, and temporal reversibility. Where appropriate, evidence derived from chemotherapy is distinguished from that derived from targeted therapy, immunotherapy, and cellular therapy, thereby allowing shared principles and treatment-specific mechanisms to be interpreted separately. Collectively, co-culture systems, organ-on-chip platforms, spatial transcriptomics, multiplexed imaging, pathology-based interpretation, and standardized functional testing may help define resistance niches and support translation of PDO findings into clinical decision-making. This perspective positions PDOs not merely as drug screening tools, but as living systems for mapping the dynamic architecture of therapy resistance.