Material-aware preclinical digital twins for breast cancer: engineered matrices as causal and auditable interfaces

Imaging-calibrated breast cancer digital twins have retrospectively forecast treatment response in selected longitudinal imaging cohorts. Patient-derived (patient-sourced) organoids, tunable hydrogels, bioprinted tissues, and tumor-on-chip platforms now reproduce selected features of the tumor microenvironment. We propose integrating these components into a closed-loop architecture that has not been prospectively validated as a complete system in breast cancer. The missing interface is a material layer in which extracellular-matrix chemistry, mechanics, architecture, transport, remodeling, and spatial heterogeneity are measured and perturbed as model variables. The synchronized physical referent is a versioned ensemble of engineered tumor constructs. Patient -conditioned computational states are initialized or constrained by individual-patient observations, while construct measurements govern the experimental state. Uncertainty-aware algorithms select feasible perturbations to distinguish mechanisms or reduce predictive uncertainty; batch metrology and biological phenotypes update or falsify the computational counterpart. Biomaterials thereby serve as experimental actuators, measurement interfaces, and reproducible benchmark conditions. We specify twin identity and synchronization, a five-level validation ladder from material validity to health-system readiness, and a staged translational pathway. Biological incompleteness, coupled material properties, limited tissue, and uncertain transfer from constructs to patients constrain near-term use. The opportunity is a reproducible preclinical learning system with explicit validation gates before patient-level claims. Its predictions remain useful only when a physical experiment can refute them.