Patient-derived organoids potentiate precision medicine in advanced clear cell renal cell carcinoma

Yizheng Xue1, Bingran Wang2, Yiying Tao2

  • 1Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.

Precision Clinical Medicine
|December 22, 2022
PubMed

Insights

Patient-derived organoids (PDO) accurately model clear cell renal cell carcinoma (ccRCC) tumors. This preclinical model effectively predicts immunotherapy response, aiding precision medicine for advanced ccRCC.

Area of Science:

  • Oncology
  • Translational Medicine
  • Biotechnology

Background:

  • Advanced clear cell renal cell carcinoma (ccRCC) presents challenges for effective treatment.
  • Precision medicine approaches are crucial for improving patient outcomes in ccRCC.
  • Predictive preclinical models are needed to guide personalized therapy selection.

Purpose of the Study:

  • To evaluate the utility of patient-derived organoids (PDO) as a model for precision medicine in advanced ccRCC.
  • To assess the ability of PDO models to predict immunotherapy response.
  • To investigate the potential of PDOs for personalized medicine recommendations in ccRCC.

Main Methods:

  • Retrospective analysis of seven advanced ccRCC cases.
  • Establishment of PDO models using an air-liquid interface system from surgical tumor resections.
  • Drug screening and analysis of PDOs using Hematoxylin and eosin (H&E) staining, immunohistochemistry, immunofluorescence, and fluorescence-activated cell sorting (FACS).

Main Results:

  • PDO models successfully recapitulated the histological features of the parent ccRCC tumors.
  • Key cellular components including T cells, cancer-associated fibroblasts, and endothelial cells were preserved in PDOs.
  • Treatment with toripalimab in PDO models increased the CD8+/CD4+ T cell ratio and tumor cell apoptosis, reversing immune exhaustion.

Conclusions:

  • Patient-derived organoids serve as a faithful preclinical model for advanced ccRCC.
  • PDOs demonstrate promise in predicting patient response to immunotherapy.
  • This model supports the development of personalized medicine strategies for ccRCC patients.

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