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ORIGINAL RESEARCH article

Front. Pharmacol.
Sec. Pharmacogenetics and Pharmacogenomics
Volume 15 - 2024 | doi: 10.3389/fphar.2024.1516621
This article is part of the Research Topic State-of-the-art hypothesis-driven systems pharmacology and artificial intelligence approaches to decipher disease complexity View all 3 articles

Leveraging AI to Automate Detection and Quantification of Extrachromosomal DNA (ecDNA) to Decode Drug Responses

Provisionally accepted
Kohen Goble Kohen Goble Aarav Mehta Aarav Mehta Damien Guilbaud Damien Guilbaud Jacob Fessler Jacob Fessler Jingting Chen Jingting Chen William Nenad William Nenad Oliver Cope Oliver Cope Darby Cheng Darby Cheng William Dennis William Dennis Nithya Gurumurthy Nithya Gurumurthy Yue Wang Yue Wang Kriti Shukla Kriti Shukla Elizabeth Brunk Elizabeth Brunk *
  • University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States

The final, formatted version of the article will be published soon.

    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 metaphase Fluorescence in situ Hybridization (FISH) images using the Microscopy Image Analyzer (MIA) tool. Our approach is particularly designed to monitor ecDNA changes 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 metaphase FISH images but also sheds light on the adaptive mechanisms of ecDNA in response to epigenetic remodeling agents like JQ1.

    Keywords: Cytogenetics, extrachromosomal DNA, ecDNA, double minute chromosomes, machine learning, Computer Vision, fluorescence in situ hybridization, deep neural networks

    Received: 24 Oct 2024; Accepted: 12 Dec 2024.

    Copyright: © 2024 Goble, Mehta, Guilbaud, Fessler, Chen, Nenad, Cope, Cheng, Dennis, Gurumurthy, Wang, Shukla and Brunk. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Elizabeth Brunk, University of North Carolina at Chapel Hill, Chapel Hill, 27599, North Carolina, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.