Abstract
BACKGROUND: Lung cancer is a leading cause of cancer-related mortality worldwide, with heterogeneity and acquired resistance posing major challenges to treatment. Advances in next-generation sequencing (NGS) have enabled comprehensive genomic profiling, yet there remains a need for robust patient-derived models to study tumor biology and inform precision medicine. This study aims to establish and characterize patient-derived lung cancer organoids (LCOs) using whole-genome sequencing (WGS) to explore their genomic landscape and therapeutic potential. METHODS: We established a panel of LCOs from resected tumors and malignant pleural effusions (MPEs) of 14 non-small cell lung cancer (NSCLC) patients. Organoids were authenticated and subjected to WGS to profile somatic single nucleotide variants (SNVs), insertions/deletions (InDels), copy number variations (CNVs), structural variants (SVs), and microsatellite instability (MSI). Bioinformatic analyses were performed to annotate mutations, assess tumor mutation burden (TMB), and explore mutational signatures. Furthermore, deep learning-based drug response prediction and in vitro drug sensitivity assays were conducted to evaluate therapeutic potentials in the established LCOs. RESULTS: In the established LCOs, WGS revealed recurrent mutations in TP53, TTN, MUC16, and FLG, with approximately 80% of somatic variants located in non-coding regions, highlighting the potential role of regulatory elements in lung cancer pathogenesis. Early and locally advanced-stage tumor-derived LCOs exhibited higher TMB and MSI compared to those from advanced-stage disease, suggesting greater clonal diversity prior to therapeutic intervention. Drug screening demonstrated the feasibility of using genomic data for drug prediction, but requires more advanced models to fully utilize the WGS data. CONCLUSIONS: Our comprehensive genomic characterization of patient-derived LCOs provides valuable insights into the mutational landscape and evolutionary dynamics of lung cancer. These well-annotated organoid models serve as a powerful resource for investigating tumor biology and developing genomically informed therapeutic strategies.