Predictive Validation of Patient-Derived 3D-Bioprinted Tumor Models for Precision Oncology: Current Evidence, Translational Gaps, and Future Standards

Abstract

Precision oncology requires functional models that preserve patient-specific tumor biology and provide treatment-response data within clinically relevant timelines. Patient-derived 3D-bioprinted tumor models combine primary tumor material with controlled spatial organization, biomimetic matrices, and, in some platforms, stromal, vascular, or immune components. This narrative review evaluates their validation as predictors of individual therapeutic response, focusing on manufacturing feasibility, biological and molecular fidelity, pharmacologic validity, turnaround time, and concordance with matched patient outcomes. Current evidence indicates that several platforms preserve clinically relevant characteristics of parental tumors and reproduce interpatient differences in therapeutic sensitivity. A limited number of studies have also reported associations between ex vivo drug responses and observed clinical outcomes. However, most evidence remains proof-of-concept, retrospective, or observational and is derived from small, single-center cohorts using heterogeneous manufacturing and response-assessment methods. Few studies have evaluated blinded prediction, model-establishment failure rates, diagnostic performance, multicenter reproducibility, or the clinical utility of model-informed treatment. Translation will require standardized manufacturing and quality-control criteria, predefined prediction rules, rapid workflows, and prospective multicenter validation. Patient-derived 3D-bioprinted tumor models therefore represent promising human-relevant preclinical platforms, but their predictive validity remains insufficient for routine treatment selection.