Photocrosslinkable lung dECM hydrogels promote stiffness-dependent lung cancer growth and chemoresistance.

Decellularized extracellular matrices (dECMs) are promising biomaterials for generating tissue-specific in vitro models due to their organotypic extracellular matrix (ECM) protein profiles compared to natural and synthetic alternatives. However, most dECM-based hydrogels rely on collagen fibrillogenesis, resulting in limited mechanical tuneability and cell instructivity. Here, we developed LungMA, a photocrosslinkable, methacrylated lung dECM hydrogel engineered for precise stiffness modulation and tissue-specific lung cancer modelling. The decellularization process removed >99 % of native DNA, ensuring minimal cellular remnants while preserving key ECM components including laminin-332, collagen VI, and heparan sulfate proteoglycan 2 (HSPG2). Methacrylation and photoinitiation enabled formation of stable LungMA hydrogels with tunable stiffnesses ranging from 1 kPa (healthy lung) to 4 kPa (fibrotic lung). Using A549 non-small-cell lung cancer (NSCLC) cells, we demonstrated that matrix composition and stiffness influenced cell morphology, proliferation, and drug response. Soft LungMA (1 kPa) promoted motile, sheet-like cellular organization, whereas stiff LungMA (>4 kPa) induced compact spheroids associated with chemoresistance. Increasing matrix stiffness resulted in an increase in doxorubicin IC(50) from 0.40 μM (soft LungMA) to 1.23 μM (stiff LungMA), and cisplatin IC(50) from 0.03 μM to 8.34 μM, reflecting clinical observations where fibrosis correlates with poor prognosis. In contrast, gelatin methacryloyl (GelMA) and basement membrane extract (BME)-based hydrogels failed to induce these stiffness-dependent effects during cisplatin treatment underscoring the instructive role of lung-specific ECM components and matrix stiffness on chemotherapeutic outcomes. LungMA provides a physiologically relevant, mechanically tunable, lung-specific platform that replicates in vivo-like cancer phenotypes and drug responses. This work supports the application of LungMA for oncology research, disease modelling, and high-throughput drug screening as a clinically relevant, non-animal alternative for lung cancer studies.