Comparison among different preclinical models derived from the same patient with a non-functional pancreatic

Yan Wang1,2,3,4,5, Zeng Ye1,2,3,4,5, Xin Lou1,2,3,4,5

  • 1Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.

Human Cell
|July 30, 2024
PubMed

Insights

Developing new preclinical models for pancreatic neuroendocrine tumors (PNETs) is crucial. Patient-derived xenografts closely mimic human PNETs, offering a valuable platform for research.

Area of Science:

  • Oncology
  • Translational Research
  • Biomedical Engineering

Background:

  • Pancreatic neuroendocrine tumors (PNETs) are the second most common pancreatic neoplasms.
  • Half of PNET patients develop liver metastases, and current treatments have limited efficacy.
  • Scarcity of preclinical models hinders research into PNET molecular mechanisms due to their inert biology.

Purpose of the Study:

  • To establish and characterize novel preclinical models for pancreatic neuroendocrine tumors.
  • To identify the model that best recapitulates the original human tumor characteristics.
  • To provide advanced platforms for studying PNET biology and developing new therapies.

Main Methods:

  • Construction of patient-derived organoids and patient-derived xenografts from a single PNET patient.
  • Characterization using immunohistochemistry, whole-exome sequencing, and single-cell transcriptome sequencing.
  • Tumor formation experiments in immunodeficient mice to evaluate model fidelity.

Main Results:

  • Successful establishment of patient-derived organoid and patient-derived xenograft models.
  • Comprehensive molecular and histological characterization of the models.
  • The patient-derived xenograft model demonstrated the highest resemblance to the parental human tumor tissue.

Conclusions:

  • Patient-derived xenografts represent a highly accurate preclinical model for pancreatic neuroendocrine tumors.
  • These models offer valuable platforms for advancing PNET research and therapeutic development.
  • Further investigation using these models can elucidate PNET molecular mechanisms and improve patient outcomes.

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