Biomedicine & Pharmacotherapy (Jul 2023)

Patient-derived organoid culture of gastric cancer for disease modeling and drug sensitivity testing

  • Ming Zu,
  • Xinyu Hao,
  • Jing Ning,
  • Xin Zhou,
  • Yueqing Gong,
  • Yanfei Lang,
  • Weichao Xu,
  • Jing Zhang,
  • Shigang Ding

Journal volume & issue
Vol. 163
p. 114751

Abstract

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Background: Gastric cancer treatment is complicated by the molecular heterogeneity of human tumor cells, which limits the efficacy of standard therapy and necessitates the need for personalized treatment development. Patient-derived organoids (PDOs) are promising preclinical cancer models, exhibiting high clinical efficacy in predicting drug sensitivity, thus providing a new means for personalized precision medicine. Methods: PDOs were established from surgically resected gastric cancer tumor tissues. Molecular characterization of the tumor tissues and PDOs was performed using whole-exome sequencing analysis. Drug sensitivity tests were performed by treating the PDO cultures with 21 standard-of-care drugs corresponding to patient treatment. We evaluated whether the PDO drug phenotype reflects the corresponding patient's treatment response by comparing the drug sensitivity test results with clinical data. Results: Twelve PDOs that satisfied the drug sensitivity test criteria were successfully constructed. PDOs closely recapitulated the pathophysiology and genetic changes in the corresponding tumors, and exhibited different sensitivities to the tested drugs. In one clinical case study, the PDO accurately predicted the patient's sensitivity to capecitabine and oxaliplatin, and in a second case study the PDO successfully predicted the patient's insensitivity to S-1 chemotherapy. In summary, six of the eight cases exhibited consistency between PDO drug susceptibility test results and the clinical response of the matched patient. Conclusions: PDO drug sensitivity tests can predict the clinical response of patients with gastric cancer to drugs, and PDOs can therefore be used as a preclinical platform to guide the development of personalized cancer treatment.

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