Defining and Targeting Esophagogastric Cancer Genomic Subsets With Patient-Derived Xenografts

Ryan H Moy1,2,3, Henry S Walch4, Marissa Mattar5

  • 1Department of Medicine, Gastrointestinal Oncology Service, Memorial Sloan Kettering Cancer Center, New York, NY.

JCO Precision Oncology
|February 9, 2022
PubMed
Abstract

Insights

Researchers created patient-derived xenografts (PDXs) to study esophagogastric cancer. These models help test new therapies for specific molecular subtypes, especially in treatment-resistant cases.

Area of Science:

  • Oncology
  • Genetics
  • Translational Research

Background:

  • Comprehensive genomic profiling has identified oncogenic drivers and molecular subtypes in esophagogastric cancer.
  • The number of clinically actionable alterations remains limited, necessitating better preclinical models.

Purpose of the Study:

  • To generate and characterize a large collection of esophagogastric cancer patient-derived xenografts (PDXs).
  • To establish preclinical models for testing genomically driven therapeutic strategies.

Main Methods:

  • Established a biobank of 98 esophagogastric cancer PDX models from primary tumors and metastases.
  • Annotated clinicopathologic features and performed targeted sequencing on PDX and parent tumor pairs.
  • Conducted xenograft trials in genomically defined models with identified oncogenic drivers.

Main Results:

  • Successfully established 98 PDX models (35.5% engraftment rate) from 276 patient tumors.
  • Collection is enriched for human epidermal growth factor receptor 2-positive (HER2+) models (63%), many refractory to trastuzumab.
  • Identified predominant oncogenic drivers including TP53 mutations, ERBB2/EGFR alterations, RAS/PI3K pathway genes, CDKN2A/CCNE1, and CDH1.
  • Observed antitumor activity with combination strategies in models from treatment-refractory disease.

Conclusions:

  • The PDX collection recapitulates esophagogastric cancer heterogeneity.
  • This resource aids in investigating tumor progression mechanisms and identifying predictive biomarkers.
  • Enables development of therapeutic strategies for molecularly defined esophagogastric cancer subsets.

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