Tumor-on-Chip Platforms for Precision Oncology: Bridging Tumor Heterogeneity and Preclinical Drug Testing

Cancer drug development faces persistently low clinical success despite growing investment, mainly due to a translational gap driven by intratumoral heterogeneity, host-tumor interactions, and resistance evolution. Tumor-on-chip platforms have emerged to bridge this gap by reconstructing human-relevant microenvironments, yet current systems fall short in predictive power and scalability. Key limitations include: (i) incomplete tumor representation-reliance on single-line models, loss of stromal and immune diversity, and PDMS sorption-induced dosing errors; (ii) workflow barriers-non-standardized fabrication, low throughput, endpoint-biased analysis, and limited automation; and (iii) clinical integration issues-restricted patient tissue access, lack of interoperable cryobank resources, and weak linkage to clinical outcomes. This review critically examines these challenges and proposes strategies such as modular biomimetic designs, immune-stroma-tumor co-reconstitution using cryopreserved cells, standardized platforms with automated analytics, and cross-validation with clinical or animal data to build regulatory confidence. We further discuss the impact of the FDA Modernization Act 2.0 and emerging market incentives driving non-animal technologies. By coupling critique with pragmatic solutions, this review delineates a forward-looking roadmap for advancing tumor-on-chip platforms into decision-grade tools for precision oncology.