ArticleFrontiers in immunology2025
Renal capsule patient-derived xenograft model for gastric cancer: establishment and MRI characterization.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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