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

Abstract

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.