# Mechano-Organ-on-Chip for Cancer Research

## TL;DR

This review discusses Mechano-Organ-on-Chip platforms that model cancer by incorporating mechanical cues, aiming to improve preclinical cancer research and therapy.

## Contribution

The paper introduces a unified framework for integrating mechanical cues and advanced analytics in tumor modeling.

## Key findings

- Mechano-OoC platforms integrate mechanical cues with microfluidics and dynamic loading to model cancer progression.
- Sensor-integrated platforms and AI-assisted data analysis are emerging for real-time tumor modeling readouts.
- Challenges include device complexity, limited throughput, and insufficient validation against clinical data.

## Abstract

Mechano-Organ-on-Chip (Mechano-OoC) platforms are emerging as powerful microphysiological systems that place mechanical cues at the center of tumor modeling, providing a scalable and human-relevant approach to recapitulate the biophysical complexity of the tumor microenvironment. Mechanical factors such as matrix stiffness, viscoelasticity, solid stress, interstitial flow, confinement, and shear critically regulate cancer progression, metastasis, immune interactions, and treatment response, yet remain poorly captured by conventional in vitro models and are often studied separately in tumor-on-chip and mechanobiology research. In this review, we summarize recent advances in mechano-OoC technologies for cancer research, highlighting strategies that integrate engineered mechanical cues with microfluidics, tunable extracellular matrices, vascular and stromal interfaces, and dynamic loading to model tumor invasion, vascular transport, immune trafficking, and drug delivery. We also discuss emerging approaches for real-time, multimodal readouts, including sensor-integrated platforms and artificial intelligence-assisted data analysis, and outline key challenges that limit translation, such as device complexity, limited throughput, insufficient standardization, and inadequate validation against in vivo and clinical data. By organizing progress across platform engineering, sensing and readout, data standardization, and AI-driven analytics, this review provides a unified framework for advancing mechanobiology-aware tumor models and guiding the development of predictive preclinical platforms for precision cancer therapy.