Abstract 389P
Background
Lung cancer organoid-based drug sensitivity tests predict clinical responses. However, traditional methods, such as AUC or IC50 values derived from dose-response curves, have limitations in accuracy. To address this, we developed Cancer Organoid-Based Diagnostic Response Prediction (CODRP), a multi-parameter analysis method considering factors reflecting lung cancer severity, including AUC value, patient-derived organoid (PDO) growth rates, and cancer stage. Progression-free survival (PFS) analysis showed CODRP’s superior prognostic accuracy compared to conventional AUC-based methods.
Methods
Patient-derived cells from lung cancer tissue were used to generate PDOs mimicking characteristics of lung cancer. Limited patient cells were optimized using a disposable nozzle-type cell spotter for high-throughput screening, enabling precise distribution of minimal cell quantities. The key characteristics of PDOs were validated through pathological comparisons with tissue slides. Anticancer drug sensitivity tests were performed using PDOs, and PFS was analyzed based on the drugs prescribed to the patients.
Results
Drug sensitivity tests were conducted on PDOs from 12 lung cancer patients. Conventional AUC analysis could not clearly distinguish between sensitivity and resistance groups. In contrast, CODRP-based tests aligned with clinical drug responses. Moreover, while AUC analysis showed minor differences in PFS between responders and non-responders, CODRP identified significant distinctions, demonstrating superior predictive power.
Conclusions
CODRP-based drug sensitivity tests using PDOs offer a valuable tool for predicting and analyzing anticancer drug efficacy in lung cancer patients. This method has the potential to be integrated into precision medicine platforms to support tailored therapeutic strategies.
Funding
Has not received any funding
Disclosure
The author has declared no conflicts of interest.