Patient-derived organoids to predict the drug response in locally advanced or metastatic lung cancer: A real-world study.

Abstract

9136 Background: Lung cancer organoids (LCOs) were expected to be the potential precision medicine approach for clinical response prediction. However, the clinical applications of both tissue and malignant serous effusions (MSE) derived LCOs were rarely reported. Our previous work demonstrated that MSE-derived LCOs maintain the genomic signature of the original tumor. In this study, we aimed to create LCOs using tissue or MSE, then validate the reliability of the model by comparing LCOs and their origin from the pathological and molecular levels. Furthermore, drug sensitivity tests of LCOs were also performed to evaluate the feasibility of LCO drug test as an approach for personalized medicine. Methods: Primary or metastatic tumor tissues were obtained from advanced lung cancer patients through core biopsy or surgically resected biopsy at the Guangdong Provincial People’s Hospital. MSEs were also collected. LCOs were generated from the obtained tissue and MSE, and the pathological features and genomic profiles were verified by analyzing the consistency with their origin. Then, the drug sensitivity scheme was formulated to follow the principles of clinical medication. In addition, proteomics analysis by 4D LC-MS/MS was also performed to analysis the molecular details of combinational therapy. Results: In our study, we generated 213 LCOs from 106 patients, mainly from MSE. The success rate to generate LCOs derived from MSE was 81.4% (131/161). The concordance rate of pathological phenotypes of LCOs samples verified by immunohistochemistry with clinical samples was 75% (63/84). In our cohort, LCO based drug sensitivity tests (LCO-DST) of targeted therapies were performed to predict the tumor response, and the AUC value of ROC analysis of osimertinib in EGFR-mutant adenocarcinoma reached 0.94(LCOs samples = 15, p= 0.0047). There were 2 patients with advanced lung adenocarcinoma, one with de novo EGFR mutation /MET amplification and the other with EGFR mutation combined with acquired RET fusion. The results of LCO based drug tests of 2 patients showed that combined targeted therapy (osimertinib plus savolitinib/cabozantinib) showed high tumor inhibition rate validated in clinical treatment and made differences. Then, 4D label-free high through-put proteomic analysis was performed in the patient with EGFR mutation and acquired RET fusion, demonstrating caspase 3 increased dramatically in combination of osimertinib and BLU-667 and the downstream proteins of EGFR and RET were down-regulated. Conclusions: LCOs derived from MSE faithfully reflected the pathological and genomic features of their original patients. The LCOs based drug test results are remarkably consistent with the tumor response. These results suggested the important prospects of LCO as an in vitro model for lung cancer precision medicine.