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Updated: Mar 11, 2026

Creating Matched In vivo/In vitro Patient-Derived Model Pairs of PDX and PDX-Derived Organoids for Cancer Pharmacology Research
Published on: May 5, 2021
Patient-derived organoids predict chemotherapy response of locally advanced gastric cancer
Miao Huang1,2, Jiahui Chu3, Wenbin Yu4
1Department of Medical Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Abstract:
The efficacy of standard adjuvant chemotherapy for locally advanced gastric cancer (GC) remains suboptimal, particularly in patients with signet-ring cell carcinoma (SRCC). Urgent demand exists for reliable preclinical models to predict therapeutic responses, and in vitro drug sensitivity testing using patient-derived organoids (PDOs) has emerged as a promising platform. In this study, PDOs were established from patients with locally advanced GC and analyzed via next-generation sequencing (NGS) and pharmacotyping. Seventeen GC PDOs were successfully generated, achieving a success rate of 63%. These PDOs closely recapitulated the histopathological and genetic features of their parental tumors. Drug sensitivity tests revealed subtype-specific response patterns: PDOs derived from SRCC were sensitive to epirubicin and paclitaxel but resistant to 5-fluorouracil (5-FU) and oxaliplatin. In contrast, non-SRCC PDOs demonstrated robust sensitivity to paclitaxel, epirubicin, and oxaliplatin. Among all tested drugs, paclitaxel showed the highest tumor-inhibitory efficacy in both subtypes. Furthermore, non-SRCC PDOs were significantly more sensitive to 5-FU and oxaliplatin than SRCC PDOs. Ex vivo pharmacotyping of PDOs accurately predicted clinical therapeutic responses in GC patients, with a sensitivity of 85.7%, specificity of 100%, and accuracy of 90.9%. Notably, patients whose PDOs were drug-sensitive in vitro had significantly longer disease-free survival than those whose PDOs were drug-resistant (P = 0.044). These findings highlight the potential of GC PDOs as reliable preclinical models that faithfully recapitulate tumor biology and therapeutic responses, thereby providing a valuable tool for predicting individualized treatment outcomes and advancing precision oncology for GC.

