Chan-Yuan Zhang

Predicting the clinical response to chemotherapeutic or targeted treatment in patients with locally advanced or metastatic lung cancer requires an accurate and affordable tool. Tumor organoids are a potential approach in precision medicine for predicting the clinical response to treatment. However, their clinical application in lung cancer has rarely been reported because of the difficulty in generating pure tumor organoids. In this study, we have generated 214 cancer organoids from 107 patients, of which 212 are lung cancer organoids (LCOs), primarily derived from malignant serous effusions. LCO-based drug sensitivity tests (LCO-DSTs) for chemotherapy and targeted therapy have been performed in a real-world study to predict the clinical response to the respective treatment. LCO-DSTs accurately predict the clinical response to treatment in this cohort of patients with advanced lung cancer. In conclusion, LCO-DST is a promising precision medicine tool in treating of advanced lung cancer.

BACKGROUND: Small-cell lung cancer (SCLC) transformation is one of the major mechanisms of resistance to Epidermal Growth Factor Receptor tyrosine kinase inhibitors (EGFR-TKIs). Chemotherapy is typically the recommended treatment for transformed SCLC (T-SCLC), similar to primary SCLC. However, the benefits of chemotherapy alone are minimal. Prior research highlights differences between the biological traits of T-SCLC and primary SCLC or EGFR-mutated lung adenocarcinoma (LUAD). This study aims to elucidate the molecular characteristics of T-SCLC and identify potential treatment modalities. METHODS: We retrospectively collected tissue samples from LUAD, T-SCLC post-EGFR-TKI resistance, and primary SCLC. Genomics, transcriptomics, and proteomics were performed to clarify the differences between T-SCLC, LUAD, and primary SCLC. Hierarchical clustering analysis was then used to categorize the molecular subtype of T-SCLC, followed by a survival analysis based on these subtypes. RESULTS: A study involving 61 patients investigated differences between LUAD, SCLC, and primary SCLC. RNA sequencing revealed distinct gene expression variations, particularly up-regulation in PPM1E, INSM1, and KCNC1 genes in T-SCLC. Pathway analysis linked T-SCLC to the cell cycle and neural differentiation. By conducting Hierarchical clustering analysis on RNA-seq data, the entire population can be categorized into two distinct groups. While certain T-SCLC showed similarities and differences compared to SCLC, with subtypes: LUAD-like and Non-LUAD-like. Notably, the LUAD-like subtype had significantly higher NKX2-1 expression (mean 371.8 vs. 41.8, P < 0.0001). T-SCLC treatment approaches were categorized into matched and unmatched groups, delineated by the alignment of specific therapies with corresponding pathologies. The matched group (13 cases) exhibited a significantly prolonged median progression-free survival compared to the unmatched group (10 cases) (5.4 months vs. 3.6 months, P = 0.02). CONCLUSIONS: T-SCLC exhibits marked molecular distinctiveness, setting it apart not only from LUAD but also from classical SCLC. This distinction extends to its classification into two discernible molecular subtypes: LUAD-like and Non-LUAD-like. Customizing therapeutic protocols to align with these specific subtypes have the potential to identify the most appropriate treatment for T-SCLC.