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Updated: Jun 18, 2025

High-Throughput Dissociation and Orthotopic Implantation of Breast Cancer Patient-Derived Xenografts
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Published on: December 20, 2024

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A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables

Brian S White1, Xing Yi Woo1,2, Soner Koc3

  • 1The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut.

Cancer Research
|July 31, 2024
PubMed
Summary
This summary is machine-generated.

This study created a large, diverse collection of over 1,000 patient-derived xenograft (PDX) images. This resource enables deep learning analysis of cancer histology for improved computational pathology and clinical predictions.

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Area of Science:

  • Computational pathology
  • Digital pathology
  • Cancer research

Background:

  • Patient-derived xenografts (PDX) replicate human tumor heterogeneity in mice.
  • Hematoxylin and eosin (H&E) staining is standard for PDX histology.
  • Deep learning can extract predictive signals from clinical H&E images.

Purpose of the Study:

  • To create a comprehensive, pan-cancer PDX H&E image repository.
  • To demonstrate the utility of this repository for computational analysis.
  • To facilitate the development of image-based predictive models.

Main Methods:

  • Curated over 1,000 PDX and paired parental tumor H&E images.
  • Integrated genomic, transcriptomic, and clinical data with pathologic assessments.
  • Applied deep learning for region classification and disease prediction.

Main Results:

  • Developed a classifier for neoplastic, stromal, and necrotic regions.
  • Created a predictor for xenograft-transplant lymphoproliferative disorder.
  • Successfully applied a microsatellite instability predictor.

Conclusions:

  • The PDX image repository is a valuable resource for digital pathology.
  • Enables controlled evaluation of computational methods.
  • Facilitates the development of PDX-based clinical prediction models.