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

TL;DR

This study used patient-derived xenograft models to identify harmful mutations in gastric cancer, preserving tumor characteristics and revealing potential targets for treatment.

Contribution

The study introduces a method using PDX models and WES to identify conserved, potentially harmful mutations in gastric cancer.

Findings

Nine PDX models were established, with 7 reaching the third generation, showing a 45% engraftment success rate.

28 somatic mutations were conserved across generations, with 10 predicted to be deleterious.

Four mutations (PTPRK, PIK3CB, LRP1B, IGF2R) were identified as significantly affecting protein stability.

Abstract

This study aimed to identify novel mutations associated with the progression of gastric cancer by establishing patient-derived xenograft (PDX) models and performing comprehensive genomic characterization of these PDX models and their corresponding primary tumors.

Fresh gastric cancer tissue samples were collected from 20 patients who underwent surgical resection at Shanxi Cancer Hospital and were subsequently implanted into NOD-SCID mice to establish PDX models. Histopathological features were evaluated using hematoxylin and eosin (H&E) staining. Whole-exome sequencing (WES) was performed on both primary tumors and their corresponding F1-PDX and F3-PDX tumors, focusing on mutations within 559 cancer-related genes. Predictive tools, including SIFT, Polyphen2_HVAR, Polyphen2_HDIV, and Mutation Taster, were utilized to identify potentially deleterious mutations, while I-Mutant and MUpro were employed to assess protein stability.

Nine gastric cancer PDX models were successfully established, with seven models propagated to the third generation (F3-PDX), achieving an initial engraftment success rate of 45%. The latency of tumor establishment significantly decreased with each successive generation. The histological characteristics of the primary tumors were well preserved in the PDX models. WES of the three selected models revealed key mutated genes in primary tumors (F0), including IRS2, BLM, PDE4DIP, NUMA1, MYH9, TP53, PIK3CD, ERCC5, and ASXL1. A total of 28 somatic mutations were conserved across all three generations (F0, F1-PDX, and F3-PDX) in these models, representing a conservation rate of 43.75% (28/64). Among these conserved mutations, 10 were identified as potentially deleterious by multiple bioinformatics algorithms. Mutations in PTPRK (p.L988S), PIK3CB (p.F934L), LRP1B (p.A1912T), and IGF2R (p.G2052R) were predicted to significantly decrease protein stability.

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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