Mechanotransduction in breast cancer and implications for preclinical modeling

Breast cancer remains a leading cause of cancer morbidity and mortality, representing the most frequently diagnosed new cancer of 2024. Despite overall improvements in survival, women of African ancestry experience disproportionately higher rates of aggressive subtypes and adverse outcomes, signifying persistent biological and clinical gaps. Mechanotransduction, the process by which cells sense and respond to mechanical cues, regulates mammary homeostasis, tumor progression, metastatic dissemination, and therapeutic response. However, its context-dependent and bidirectional roles remain incompletely resolved, and reproducing biomechanical environments in preclinical models remains a substantial challenge. This review synthesizes current understandings of canonical and cancer-associated mechanotransduction in breast tissue, including cell–extracellular matrix (ECM) and cell–cell adhesions, mechanosensitive ion channels, glycocalyx remodeling, cytoskeletal dynamics, nuclear force transmission, and mechanosensitive transcription. It further examines how ECM stiffness, viscoelasticity, ligand availability, collagen architecture, tensile and compressive stresses, and stromal and immune cell activity influence invasion, metastasis, and treatment resistance. Ancestry-associated stromal, molecular, and structural variations are considered as a critical motivation, but remain incompletely characterized as sources of ancestry-dependent heterogeneity. Thus, this review explores mechanotransduction as a framework for assessing proliferation and metastasis mechanisms through two-dimensional culture, three-dimensional matrices, spheroids, organoids, co-cultures, bioreactors, organ-on-a-chip systems, bioprinting, animal models, and computational approaches for their capacity to reproduce these processes. We ultimately propose an iterative design–build–validate framework that links empirically derived mechanical and molecular measurements to model selection, construction, and multilevel validation. Integrating mechanobiological heterogeneity into rigorously benchmarked models could improve understanding of metastatic progression, reveal context-dependent therapeutic resistance, and support more representative and patient-informed breast cancer research.