Patient-derived malignant effusion organoids guide tailored therapy and dissect third-generation EGFR-tyrosine kinase inhibitor resistance mechanisms in lung cancer.

Lung cancer remains the leading cause of cancer-related mortality and drug resistance represents a major challenge in treatment. However, the scarcity of models that faithfully recapitulate the features of lung cancer has hindered advances in understanding drug resistance mechanisms and developing precision therapies. To address these challenges, we devised an optimized protocol that coupled Ficoll density-gradient separation with low-adhesion culture conditions, constructing 48 organoids from 58 malignant effusion (ME) samples, with a success rate of 82.8%. Further drug sensitivity experiments demonstrated that the patient-derived organoid (PDO) model could effectively predict responses to clinical treatment, with a specificity of 91.7%, sensitivity of 71.4%, and overall accuracy of 87.1%. Transcriptomic analysis of PDOs identified that the key signal pathways, including extracellular matrix remodeling, cell cycle dysregulation and angiogenesis, were involved in the resistance mechanisms of third-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). Connectivity Map (CMap) screening revealed that the candidate ibuprofen could enhance the anti-tumor activity of osimertinib and overcome the resistance, validated in PDO and TKI-resistant lung cancer cell models. These findings not only provide a new biological perspective for the study of EGFR-TKI resistance mechanisms but also offer a novel tool and strategy for precision treatment and personalized drug screening in advanced lung cancer patients, with significant clinical application potential.