Renal capsule patient-derived xenograft model for gastric cancer: establishment and MRI characterization

Zhenyu Yin1, Qian Liu1, Ewetse Paul Maswikiti1

  • 1The Second Hospital and Clinical Medical School, Lanzhou University, Lanzhou, China.

Frontiers in Immunology
|November 24, 2025
PubMed
Abstract

Insights

Establishing gastric cancer patient-derived xenografts (GC PDX) is optimized by using 2×2×2 mm fragments transplanted within 2 hours. Magnetic resonance imaging (MRI) effectively monitors GC PDX growth and characteristics.

Area of Science:

  • Oncology
  • Preclinical Research
  • Medical Imaging

Background:

  • Gastric cancer patient-derived xenografts (GC PDX) are crucial for preclinical research.
  • Optimizing the engraftment process and imaging techniques for GC PDX models is essential for advancing personalized therapy.

Purpose of the Study:

  • To identify factors influencing the engraftment of gastric cancer patient-derived xenografts (GC PDX) in the renal capsule.
  • To determine optimal MRI sequence parameters for monitoring GC PDX within the renal capsule.

Main Methods:

  • Tumor fragments (1-3 mm) from 33 gastric cancer patients were transplanted into NOD/SCID mice within 2, 8, or 24 hours.
  • Tumor growth was monitored weekly using MRI (T1WI, contrast-enhanced T1WI, T2WI), followed by ex vivo caliper measurements.
  • Histopathological analysis (H&E, Ki67) was performed on xenografts and compared to primary tumors.

Main Results:

  • Successful engraftment was achieved in 20 out of 33 patients (28/73 mice).
  • Optimal engraftment conditions included 2×2×2 mm fragments transplanted within 2 hours; patient serum albumin correlated with success.
  • MRI accurately quantified tumor size, with T2WI proving most effective; PDX retained primary tumor histology.

Conclusions:

  • The study identified optimal conditions (2×2×2 mm fragments, <2-hour transplantation) for establishing GC PDX models.
  • MRI, particularly T2WI, is a sensitive and accurate method for monitoring GC PDX.
  • These findings facilitate the development of efficient preclinical models for personalized gastric cancer therapy.

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