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Leveraging AI to Automate Detection and Quantification of Extrachromosomal DNA (ecDNA) to Decode Drug Responses
Abstract:
Traditional drug discovery efforts have largely focused on targeting rapid, reversible protein-mediated adaptations to undermine cancer cells' resistance to therapy. However, cancer cells also exploit DNA-based strategies, typically viewed as slow, irreversible, and unpredictable changes like point mutations or the selection of drug-resistant clones. Contrary to this perception, extrachromosomal DNA (ecDNA) represents a form of DNA alteration that is rapid, reversible, and predictable, playing a crucial role in cancer's adaptive response. In this study, we present a novel post-processing pipeline for the automated detection and quantification of ecDNA in Fluorescence in situ Hybridization (FISH) images using the Microscopy Image Analyzer (MIA) tool. Our approach is particularly designed to monitor ecDNA dynamics during drug treatment, providing a quantitative framework to understand how ecDNA enables cancer cells to swiftly and reversibly adapt to therapeutic pressure. This pipeline not only offers a valuable resource for researchers aiming to automate ecDNA detection in FISH images but also sheds light on the adaptive mechanisms of ecDNA in response to epigenetic remodeling agents like JQ1.
Insights
Cancer cells adapt to drugs using extrachromosomal DNA (ecDNA), which is fast and reversible. We developed an automated pipeline using Microscopy Image Analyzer (MIA) to quantify ecDNA in FISH images during treatment.
Area of Science:
- Cancer Biology
- Genetics
- Molecular Biology
Background:
- Cancer cells develop drug resistance through various adaptive strategies.
- Extrachromosomal DNA (ecDNA) is increasingly recognized as a key player in rapid, reversible cancer cell adaptation.
- Traditional focus has been on protein-mediated resistance, overlooking dynamic DNA alterations.
Purpose of the Study:
- To present a novel automated pipeline for ecDNA detection and quantification in Fluorescence in situ Hybridization (FISH) images.
- To enable the monitoring of ecDNA dynamics during drug treatment.
- To provide a quantitative framework for understanding ecDNA's role in cancer therapy resistance.
Main Methods:
- Development of a post-processing pipeline for automated ecDNA detection.
- Utilizing the Microscopy Image Analyzer (MIA) tool for image analysis.
- Quantification of ecDNA in FISH images under drug treatment conditions.
Main Results:
- Successful automated detection and quantification of ecDNA in FISH images.
- Demonstration of ecDNA dynamics during therapeutic pressure.
- Quantitative insights into ecDNA's adaptive role in response to epigenetic modifiers like JQ1.
Conclusions:
- The developed pipeline facilitates automated ecDNA analysis in FISH images.
- ecDNA plays a crucial, dynamic role in cancer cell adaptation and therapy resistance.
- This work provides a tool to study ecDNA-mediated resistance mechanisms.
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