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 IC₅₀ from 0.40 μM (soft LungMA) to 1.23 μM (stiff LungMA), and cisplatin IC₅₀ 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.