The Tumor Microenvironment: Translating Intravital Insights into Microphysiological Systems

Abstract Purpose Over the past four decades, Dr. Rakesh K. Jain has fundamentally reshaped the conceptual framework of solid tumor biology by establishing tumor pathophysiology as a transport- and mechanics-governed system. Through pioneering intravital microscopy studies, his work revealed how abnormal vasculature, elevated interstitial fluid pressure, solid stress, and microenvironmental heterogeneity collectively regulate drug delivery, immune infiltration, and therapeutic response. These mechanistic insights transformed cancer from a purely cellular disease into a complex biophysical and multicellular ecosystem. In parallel with these conceptual advances, bioengineering technologies have evolved to reconstruct and interrogate tumor pathophysiology in controlled in vitro settings. Cancer-on-chip (CoC) platforms now integrate three-dimensional (3D) architecture, dynamic perfusion, tunable extracellular matrices, vascular interfaces, and immune components to recapitulate key determinants of tumor progression and treatment resistance. By enabling precise perturbation of mechanical forces, transport barriers, and multicellular interactions, these microphysiological systems provide experimentally accessible “biological twins” of patient tumors. Methods In this Review, we examine how next-generation CoC models translate foundational principles of tumor pathophysiology into engineered platforms for mechanistic investigation and functional precision oncology. We discuss advances in vascularized and immune-competent systems, microenvironment-mediated drug resistance modeling, and the integration of real-time biosensing and spatial omics. Finally, we outline how data generated from these biological twins can inform emerging digital twin frameworks, bridging experimental tumor bioengineering with predictive computational oncology. Results State-of-the-art CoC systems enable controlled interrogation of microenvironment-driven tumor behaviors, including drug-delivery constraints, immune exclusion, and adaptive resistance, which remain difficult to dissect mechanistically in conventional experimental systems. However, clinical translation remains limited, with existing studies limited to small cohorts and employing heterogeneous methodologies. The lack of standardized endpoints and correlation frameworks remains a major barrier. In this sense, CoC technologies do not depart from tumor physiology, rather, they represent its engineered continuation, extending the mechanistic legacy of intravital tumor biology into human-based, perturbable, and quantitatively interpretable systems. Conclusions CoC models can capture key features of the native TME and be interrogated for mechanistic studies and drug response evaluation. Through real-time monitoring and comprehensive endpoint analysis, their outputs can be correlated with human clinical outcomes. However, the effective use of these platforms in pre-clinical and clinical research workflows still requires further validation. Increased standardization, together with tighter integration of CoC systems with computational modelling and digital twin approaches, will be essential to fully realize their potential in precision oncology.