Lung cancer organoid-based diagnostic response prediction (CODRP) for predicting anticancer drug response and progression-free survival

Background: Anticancer drug sensitivity analysis using patient-derived lung cancer organoids is being actively investigated as a means to predict individual responses to anticancer therapies. Conventional drug sensitivity analysis methods typically rely on the area under the dose-response curve (AUC) or the half-maximal inhibitory concentration (IC₅₀) to distinguish drug-sensitive and drug-resistant phenotypes, yet these approaches have notable accuracy limitations. To address this issue, we developed Cancer Organoid-based Diagnostic Response Prediction (CODRP), a multiparametric analytical method that integrates AUC values, patient-derived organoid (PDO) growth rates, cancer stage. In progression-free survival (PFS) analyses, CODRP demonstrated superior prognostic accuracy compared with conventional AUC-based methods. Methods: Lung cancer cells obtained from patient-derived tissue were used to generate PDOs that mimic the characteristics of the original lung cancer tissue. Because patient-derived lung cancer cells could only be obtained in limited amounts, a disposable nozzle-type cell spotter, which enables the precise distribution of minimal cell numbers, was employed for high-throughput screening. The key characteristics of the PDOs were validated through pathological comparison with the corresponding patient tissue slides. Anticancer drug sensitivity testing was performed using the PDOs, and PFS was analyzed according to the therapeutic agents prescribed to the patients. Results: Anticancer drug sensitivity analysis was performed using lung cancer PDOs derived from 12 patients. The conventional AUC-based approach failed to clearly differentiate drug-sensitive from drug-resistant cases. In contrast, the CODRP index showed strong concordance with actual clinical treatment outcomes. Moreover, whereas AUC analysis revealed only modest differences in PFS between responders and non-responders, CODRP identified a distinct separation, demonstrating its superior predictive capability. Conclusions: CODRP-based anticancer drug response analysis using lung cancer PDOs provides an effective approach for forecasting and evaluating treatment outcomes in patients with lung cancer. This method has the potential to be incorporated into precision medicine workflows, supporting more individualized treatment planning and enhancing the clinical relevance of therapeutic decision-making.