Leveraging AI to automate detection and quantification of extrachromosomal DNA to decode drug responses
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 sel...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2024.1516621/full |
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| author | Kohen Goble Aarav Mehta Damien Guilbaud Jacob Fessler Jingting Chen William Nenad William Nenad Christina G. Ford Oliver Cope Darby Cheng William Dennis Nithya Gurumurthy Yue Wang Kriti Shukla Elizabeth Brunk Elizabeth Brunk Elizabeth Brunk Elizabeth Brunk Elizabeth Brunk |
| author_facet | Kohen Goble Aarav Mehta Damien Guilbaud Jacob Fessler Jingting Chen William Nenad William Nenad Christina G. Ford Oliver Cope Darby Cheng William Dennis Nithya Gurumurthy Yue Wang Kriti Shukla Elizabeth Brunk Elizabeth Brunk Elizabeth Brunk Elizabeth Brunk Elizabeth Brunk |
| author_sort | Kohen Goble |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ee4805d9c9944c03a0c2e446a15eacd7 |
| institution | Kabale University |
| issn | 1663-9812 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Pharmacology |
| spelling | doaj-art-ee4805d9c9944c03a0c2e446a15eacd72025-02-03T06:33:33ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122025-02-011510.3389/fphar.2024.15166211516621Leveraging AI to automate detection and quantification of extrachromosomal DNA to decode drug responsesKohen Goble0Aarav Mehta1Damien Guilbaud2Jacob Fessler3Jingting Chen4William Nenad5William Nenad6Christina G. Ford7Oliver Cope8Darby Cheng9William Dennis10Nithya Gurumurthy11Yue Wang12Kriti Shukla13Elizabeth Brunk14Elizabeth Brunk15Elizabeth Brunk16Elizabeth Brunk17Elizabeth Brunk18Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesDepartment of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesIntegrative Program for Biological and Genome Sciences (IBGS), University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesDepartment of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesDepartment of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesComputational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesCurriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesIntegrative Program for Biological and Genome Sciences (IBGS), University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesIntegrative Program for Biological and Genome Sciences (IBGS), University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesDepartment of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesIntegrative Program for Biological and Genome Sciences (IBGS), University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesIntegrative Program for Biological and Genome Sciences (IBGS), University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesDepartment of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesDepartment of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesDepartment of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesIntegrative Program for Biological and Genome Sciences (IBGS), University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesDepartment of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesComputational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesLineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesIntroductionTraditional 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.https://www.frontiersin.org/articles/10.3389/fphar.2024.1516621/fullcytogeneticsextrachromosomal DNAecDNAdouble minute chromosomesmachine learningcomputer vision |
| spellingShingle | Kohen Goble Aarav Mehta Damien Guilbaud Jacob Fessler Jingting Chen William Nenad William Nenad Christina G. Ford Oliver Cope Darby Cheng William Dennis Nithya Gurumurthy Yue Wang Kriti Shukla Elizabeth Brunk Elizabeth Brunk Elizabeth Brunk Elizabeth Brunk Elizabeth Brunk Leveraging AI to automate detection and quantification of extrachromosomal DNA to decode drug responses Frontiers in Pharmacology cytogenetics extrachromosomal DNA ecDNA double minute chromosomes machine learning computer vision |
| title | Leveraging AI to automate detection and quantification of extrachromosomal DNA to decode drug responses |
| title_full | Leveraging AI to automate detection and quantification of extrachromosomal DNA to decode drug responses |
| title_fullStr | Leveraging AI to automate detection and quantification of extrachromosomal DNA to decode drug responses |
| title_full_unstemmed | Leveraging AI to automate detection and quantification of extrachromosomal DNA to decode drug responses |
| title_short | Leveraging AI to automate detection and quantification of extrachromosomal DNA to decode drug responses |
| title_sort | leveraging ai to automate detection and quantification of extrachromosomal dna to decode drug responses |
| topic | cytogenetics extrachromosomal DNA ecDNA double minute chromosomes machine learning computer vision |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2024.1516621/full |
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