Identification of potentially deleterious mutations in gastric cancer using patient-derived xenograft models

This study demonstrated that PDX models effectively preserve the biological and genetic characteristics of primary gastric tumors, underscoring their utility in studying tumor heterogeneity. The integrated analysis of longitudinal WES data from primary tumors and matched PDXs enabled the identification of a core set of conserved, potentially deleterious mutations. The four prioritized mutations (PTPRK, PIK3CB, LRP1B, and IGF2R) provide new insights into the genetic landscape of gastric cancer and represent promising candidates for the development of targeted therapeutic strategies.

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Publication Details

Journal
Frontiers in Genetics
Published
2026-01-29
DOI
https://doi.org/10.3389/fgene.2025.1571535
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
0.00

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Identification of potentially deleterious mutations in gastric cancer using patient-derived xenograft models

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Identification of potentially deleterious mutations in gastric cancer using patient-derived xenograft models

Junqi Zheng, Luke Kong, Jiahui Kou, Yi-Xiang Wang, Ruifang Hope Sun, X. Yang, Yujie Yao, Feng Li, Fuhua Wang, Sutang Guo
article en

Abstract

This study demonstrated that PDX models effectively preserve the biological and genetic characteristics of primary gastric tumors, underscoring their utility in studying tumor heterogeneity. The integrated analysis of longitudinal WES data from primary tumors and matched PDXs enabled the identification of a core set of conserved, potentially deleterious mutations. The four prioritized mutations (PTPRK, PIK3CB, LRP1B, and IGF2R) provide new insights into the genetic landscape of gastric cancer and represent promising candidates for the development of targeted therapeutic strategies.

Frontiers in GeneticsVol. 16
Shanxi Medical University (CN), Shanxi Provincial Cancer Hospital (CN), Zoucheng People's Hospital (CN), Cancer Hospital of Chinese Academy of Medical Sciences (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 74%
Cancer Genomics and Diagnostics
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