AUTHOR=Goble Kohen , Mehta Aarav , Guilbaud Damien , Fessler Jacob , Chen Jingting , Nenad William , Ford Christina G. , Cope Oliver , Cheng Darby , Dennis William , Gurumurthy Nithya , Wang Yue , Shukla Kriti , Brunk Elizabeth 

TITLE=Leveraging AI to automate detection and quantification of extrachromosomal DNA to decode drug responses

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 15 - 2024

YEAR=2025

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2024.1516621

DOI=10.3389/fphar.2024.1516621

ISSN=1663-9812

ABSTRACT=IntroductionTraditional 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.MethodsIn 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.ResultsOur approach effectively quantified ecDNA changes, providing a robust framework for analyzing the adaptive responses of cancer cells under therapeutic pressure.DiscussionThe 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.