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

Zhenyu Yin,Qian Liu,Ewetse Paul Maswikiti,Zhuanfang Wang,Yuping Bai,Lin Xiang,Yuhan Wang,Bin Ma,Lei Gao,Jian-Wei Shi,Hao Chen

Published 2025 in Frontiers in Immunology

ABSTRACT

Objective Identify factors associated with the engraftment of gastric cancer patient-derived xenograft (GC PDX) in the renal capsule and explore optimal MRI sequence parameters for observing renal capsule PDX. Methods Tumor tissues from 33 gastric cancer patients were cut into fragments of 1×1×1 mm, 2×2×2 mm, and 3×3×3 mm, then transplanted beneath the renal capsule of NOD/SCID mice within 2, 8 and 24 hours. Depending on tissue availability, tumor samples from each patient were implanted into 1–4 mice, totaling 73 mice. Clinical data were collected. Tumor growth was monitored weekly via MRI. T1WI, contrast-enhanced T1WI, T2WI was used to measure tumor length. After euthanasia (10g/L sodium pentobarbital, 180mg/kg, intraperitoneal), tumors were excised, and caliper-measured were compared with MRI results. The xenografts were serially passage into new mice for three generations. Histopathological (H&E), Ki67 immunohistochemistry were performed to assess similarity with primary tumors. Results Tumors from 20 out of 33 patients successfully engrafted in 28 out of 73 mice. The 2×2×2 mm grafts and transplantation in 2 hours had a higher success rate. Patient serum albumin was associated with successful engraftment. PDX exhibited isointense and hyperintense on T2WI and marked enhancement on T1WI post-contrast. No significant difference was observed between MRI and caliper-measured. H&E staining, Ki67 expression confirmed that PDX tumors retained histological features of primary tumors. Conclusion Optimal conditions for establishing GC PDX models involve transplanting 2×2×2 mm tumor fragments within 2 hours. MRI enables sensitive tumor detection and accurate size quantification. T2WI was the most effective and efficient imaging technique. Providing an efficient preclinical model for personalized therapy of gastric cancer.

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