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Leveraging AI to automate detection and quantification of extrachromosomal DNA to decode drug responses
Kohen Goble1, Aarav Mehta2, Damien Guilbaud3
1Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
Introduction:
Traditional drug discovery efforts primarily target rapid, reversible protein-mediated adaptations to counteract cancer cell resistance. However, cancer cells also utilize DNA-based strategies, often perceived as slow, irreversible changes like point mutations or drug-resistant clone selection. Extrachromosomal DNA (ecDNA), in contrast, represents a rapid, reversible, and predictable DNA alteration critical for cancer's adaptive response.
Methods:
In this study, we developed a novel post-processing pipeline for automated detection and quantification of ecDNA in metaphase Fluorescence in situ Hybridization (FISH) images, leveraging the Microscopy Image Analyzer (MIA) tool. This pipeline is tailored to monitor ecDNA dynamics during drug treatment.
Results:
Our approach effectively quantified ecDNA changes, providing a robust framework for analyzing the adaptive responses of cancer cells under therapeutic pressure.
Discussion:
The pipeline not only serves as a valuable resource for automating ecDNA detection in metaphase FISH images but also highlights the role of ecDNA in facilitating swift and reversible adaptation to epigenetic remodeling agents such as JQ1.
Insights
This study introduces a new pipeline to automatically detect and quantify extrachromosomal DNA (ecDNA) in cancer cells. This tool aids in understanding how ecDNA drives rapid, reversible cancer adaptation to drugs.
Area of Science:
- Cancer Biology
- Genetics
- Molecular Oncology
Background:
- Cancer cells adapt to therapies via protein-mediated changes and DNA alterations.
- Extrachromosomal DNA (ecDNA) represents a key mechanism for rapid, reversible cancer adaptation.
- Understanding ecDNA dynamics is crucial for developing effective cancer treatments.
Purpose of the Study:
- To develop and validate a novel computational pipeline for automated detection and quantification of ecDNA.
- To analyze ecDNA dynamics in cancer cells during drug treatment.
- To investigate the role of ecDNA in cancer cell adaptation to epigenetic remodeling agents.
Main Methods:
- Development of a post-processing pipeline for automated ecDNA detection.
- Utilized metaphase Fluorescence in situ Hybridization (FISH) images.
- Leveraged the Microscopy Image Analyzer (MIA) tool for image analysis.
Main Results:
- Successfully quantified ecDNA changes in cancer cells.
- Demonstrated the pipeline's effectiveness in analyzing ecDNA dynamics under therapeutic pressure.
- Provided a robust framework for studying cancer cell adaptive responses.
Conclusions:
- The developed pipeline automates ecDNA detection in metaphase FISH images.
- The study highlights ecDNA's role in facilitating swift, reversible adaptation to epigenetic agents like JQ1.
- This tool advances the study of ecDNA-mediated cancer plasticity.
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