Goble, K. | Search bioRxiv

bioRxiv2024

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

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 cancers 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.

cancer biology↗

bioRxiv2023

CytoCellDB: A Resource Database For Classification and Analysis of Extrachromosomal DNA in Cancer

Extrachromosomal DNA (ecDNA), or double minute chromosomes, are established cytogenetic markers for malignancy and genome instability. More recently, the cancer community has gained a heightened awareness of the roles of ecDNA in cancer proliferation, drug resistance and epigenetic remodeling. A current hindrance to understanding the biological roles of ecDNA is the lack of available cell line model systems with experimental cytogenetic data that confirm ecDNA status. Although several recent landmark studies have identified common cell lines and tumor models with ecDNA, the current sample size limits our ability to detect ecDNA-driven molecular differences due to limitations in power. Increasing the number of model systems known to express ecDNA would provide new avenues for understanding the fundamental underpinnings of ecDNA biology and would unlock a wealth of potential targeting strategies for ecDNA-driven cancers. To bridge this gap, we created CytoCellDB, a resource that provides karyotype annotations and leverages publicly available global cell line data from the Cancer Dependency Map (DepMap) and the Cancer Cell Line Encyclopedia (CCLE). Here, we identify 139 cell lines that express ecDNA, which is a 200% increase from the current sample size. We expanded the total number of cancer cell lines with ecDNA annotations to 577, which is a 400% increase or 31% of cell lines in CCLE/ DepMap. We demonstrate that a strength of CytoCellDB is the ability to interrogate ecDNA, and a compendium of other chromosomal aberrations, in the context of cancer-specific vulnerabilities, drug sensitivities, and molecular data (genomics, transcriptomics, methylation, proteomics). We anticipate that CytoCellDB will advance cytogenomics research and population-scale discoveries related to ecDNA as well as provide insights into strategies and best practices for determining novel therapeutics that overcome ecDNA-driven drug resistance.

genomics↗