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Updated: Mar 19, 2026

Microsatellite DNA Genotyping and Flow Cytometry Ploidy Analyses of Formalin-fixed Paraffin-embedded Hydatidiform Molar Tissues

Microsatellite DNA Genotyping and Flow Cytometry Ploidy Analyses of Formalin-fixed Paraffin-embedded Hydatidiform Molar Tissues

Published on: October 20, 2019

Detecting Extrachromosomal DNA from Routine Histopathology.

Muhammad Anwaar Khalid^1,2, Michael Gratius¹, Christopher Brown^3,4

¹Peter L. Reichertz Institute for Medical Informatics (PLRI), Hannover Medical School, Hannover, Germany.

Biorxiv : the Preprint Server for Biology

|March 18, 2026

Summary

Extrachromosomal DNA (ecDNA) can be detected using standard pathology images, not just specialized tests. This deep learning method identifies ecDNA in tumors, aiding cancer diagnosis and prognosis.

Keywords:

Computational Pathology Deep Learning Extrachromosomal DNA (ecDNA)Multiple Instance Learning (MIL)Whole-Slide Imaging

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Published on: July 12, 2024

Area of Science:

Oncology
Genomics
Computational Pathology

Background:

Extrachromosomal DNA (ecDNA) drives oncogene amplification, tumor heterogeneity, and poor outcomes.
Current ecDNA detection methods require specialized genomic assays, limiting routine diagnostic use.

Purpose of the Study:

To develop a method for inferring ecDNA status directly from standard histopathology images.
To enable scalable screening of tumors for ecDNA amplification using routine diagnostics.

Main Methods:

Development of an end-to-end, weakly supervised deep learning framework.
Aggregation of thousands of high-magnification image patches per slide with augmentation and attention.
Validation across twelve cancer types from The Cancer Genome Atlas.

Main Results:

The framework successfully identifies tumors with genomic amplifications from standard pathology slides.
It distinguishes ecDNA-amplified tumors from chromosomally amplified or non-amplified ones, particularly in glioblastoma.
Attention maps highlight regions with altered nuclear chromatin intensity and texture, correlating with ecDNA status and survival.

Conclusions:

Histomorphologic features indicative of ecDNA amplification are detectable in routine pathology images.
This approach allows for scalable screening to prioritize tumors for molecular testing.
Deep learning on histopathology images offers a promising avenue for ecDNA detection in clinical settings.