Organ-on-Chip: An in vitro model to study the efficacy and prediction of adjuvant therapy in lung cancer patient-derived cells

Lung cancer is the leading cause of cancer-related deaths worldwide, accounting for 11.6% of cancer diagnoses and 18.4% of cancer mortality. Non-small cell lung cancer (NSCLC) represents 85% of cases and is often driven by genetic alterations, including EGFR mutations in 16% of patients. Despite targeted therapies, overall survival remains poor, emphasizing the need for more effective, personalized treatments. In early-stage lung cancer, adjuvant chemotherapy is given after surgery to reduce recurrence, but its variable efficacy limits its predictive value. Systems that better mimic the tumor microenvironment ex vivo may improve identification of patient-specific therapeutic strategies. To address this, we used Organoid-on-Chip (OoC) technology, an advanced in vitro platform that recreates key features of the tumor microenvironment and has emerged as a promising tool for evaluating drug responses. The aim of this study is to determine whether an organ-on-chip system can predict responses to adjuvant chemotherapy in NSCLC patient-derived organoids (PDOs). In our study, we generated a cohort of 30 patient-derived organoids (PDOs) from NSCLC sample. These PDOs were validated via immunohistochemistry, whole exome sequencing (WES), and RNA sequencing to confirm their similarity to the original tumors. From this cohort, we will select PDOs that derived from patients who received the same adjuvant chemotherapy regimen (Cisplatin + Vinorelbine). PDOs were cultured alone or co-cultured with cancer-associated fibroblasts (CAFs) in the chip and treated for 72 hours. Viability was measured using live/dead assays. Our preliminary results, on a specific patient used as proof-of-concept methodology, shows that CAFs contribute to therapy resistance compared to monoculture conditions showing an increase of survival of 50%. We will correlate the findings on the selected cohort of PDOs with the clinical outcome of the respective patient to evaluate the predictive power of the system. In conclusion, the OoC platform shows strong potential for modeling and predicting patient-specific therapy responses, supporting more personalized and effective treatment strategies in lung cancer.