Lung-mimicking click alginate-dECM model of breast cancer lung metastasis reveals the role of ECM mechanics in tumor growth dynamics and genomic instability

Abstract Metastatic breast cancer (BC) is the main cause of cancer-related death in women. Accumulating evidence highlights the prominent changes within the lung metastatic niche, resulting in stiffening of the extracellular matrix (ECM). The prevailing concept of cancer evolution encompasses the acquisition of beneficial traits due to genomic instability, yet it remains elusive to what extend altered lung ECM mechanics feed into this. To investigate this, a tunable 3D bioengineered model, capturing the biophysical and biochemical characteristics of the BC metastatic lung niche is developed. Porcine derived lung decellularized ECM (dECM), combined with norbornene and tetrazine modified click-crosslinkable alginate, recapitulates composition and mechanics of healthy soft (3 kPa) and metastatic stiff (12 kPa) lung niches. Proof-of-concept evaluation using label-free optical microscopy revealed preferential ECM fiber orientation in human lung metastatic tissue and in stiff hydrogels. Encapsulation of MDA-MB-231, MCF7 and patient-derived triple negative BC cells demonstrated that stiffer matrices were associated with DNA damage, indicated by yH2AX and altered Lamin A/C. Pharmacological perturbation of FAK mediated mechanotransduction reversed this phenotype in stiff conditions. These findings underscore the critical role of tissue mechanics in regulating BC metastasis and demonstrate the utility of the herein developed tunable, physiologically relevant platform for patient-based models. Table of Content A bioengineered 3D model recreates mechanical and biochemical features of healthy and metastatic lung tissue. Metastatic-like stiff environments promote extracellular matrix organization, increased BC cluster growth and DNA damage, in both cell lines and patient-derived BC cells. These findings reveal how altered tissue mechanics may contribute to breast cancer progression and provide a platform for patient-derived models.