Optimization of a hanging-pillar plate for patient-derived organoid-based drug sensitivity testing to predict clinical outcomes of chemotherapy

Breast cancer remains the most common malignancy and a leading cause of cancer-related mortality among women worldwide. Conventional in vitro and in vivo drug-testing models, including patient-derived xenografts (PDXs), are limited by low establishment efficiency, high cost, and poor reflection of tumor heterogeneity. To address these limitations, we developed a high-throughput drug-screening platform using breast cancer patient-derived organoids (PDOs) cultured on a 384-hanging pillar plate, which prevents cell attachment and preserves three-dimensional architecture, achieving a 70% culture success rate compared with 50% using standard well plates. We further established a Cancer Organoid-based Diagnosis Reactivity Prediction (CODRP) model that integrates the area under the curve (AUC), PDO growth rate, and clinical stage to enhance drug-response prediction. Validation of the CODRP model using residual tissues from both neoadjuvant chemotherapy (NAC)-treated and non-NAC patients demonstrated improved predictive performance compared with the conventional AUC index, with higher sensitivity (88.89% vs. 55.56%) and specificity (71.43% vs. 57.14%). In a cohort of triple-negative breast cancer (TNBC) patients receiving adjuvant chemotherapy ( n = 9), the CODRP-classified sensitive group demonstrated prolonged recurrence-free survival (RFS) over two years. These findings indicate that combining the hanging pillar PDO culture system with the CODRP model improves concordance between preclinical drug-response data and clinical outcomes, supporting its potential as a precision-medicine platform for individualized breast cancer therapy optimization.