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Machine-learning integration yields highly accurate diagnostic models, including an EXCEL model with 85.1% sensitivity at 95% specificity for cross-tumor 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hypothesis does the study test about tumor-derived cfDNA?","Question",{"text":63,"@type":64},"It hypothesizes that in patients with cancer, tumor-derived cfDNA can be enriched by selecting molecules whose ends are located within nucleosomes.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How do the researchers enrich tumor-derived cfDNA?",{"text":68,"@type":64},"They select cfDNA molecules with end locations discordant with hematopoietic nucleosomes, which produces relatively shorter fragments with altered end motif patterns.",{"name":70,"@type":61,"acceptedAnswer":71},"What diagnostic performance do the machine-learning models achieve?",{"text":72,"@type":64},"The work reports high-performance models with an overall AUC of 0.95 and 85.1% sensitivity at 95% specificity for the cross-tumor dataset using the EXCEL diagnosis 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Zhao (赵新), Yunyun An (安昀昀), ..., Fei Fan (樊非), Xuetong Shen (申雪桐), Kun Sun (孙坤)  \nCorrespondence  \n[sunkun@szbl.ac.cn](sunkun@szbl.ac.cn)  \nIn brief  \nJu et al. explore the end characteristics incfDNA for cancer liquid biopsy. Inpatients with cancer, the selection of cfDNA with ends discordant to hematopoietic nucleosomes could enrich the tumor-derived ones and uncover biomarkers; further incorporation with machine learning enables the development of highly accurate and sensitive diagnostic models.  \nd Selecting cfDNA with ends discordant to nucleosomes enriches tumor-derived signals  \nd We develop 3 diagnostic biomarkers based on cfDNA endselection  \nd We validate using multiple datasets of various cancer types  \nd We develop the EXCEL diagnosis model with 85.1% sensitivity for cross-tumor dataset  \nJu et al., 2024, Cell Reports Methods 4, 100877  \nOctober 21, 2024 ª 2024 The Author(s) . Published by Elsevier Inc.  \n[https://doi.org/10.1016/j.crmeth.2024.100877](https://doi.org/10.1016/j.crmeth.2024.100877)  \nll  \nll  \nOPEN ACCESS  \nArticle  \nCell-free DNA end characteristics  \nenable accurate and sensitive cancer diagnosis  \nJia Ju (鞠佳),1,5 Xin Zhao (赵新),2,5 Yunyun An (安昀昀),1,5 Mengqi Yang (杨梦琦),1,5 Ziteng Zhang (张子腾),2,5 Xiaoyi Liu (刘晓懿),1 Dingxue Hu (胡丁雪),1 Wanqiu Wang (王婉秋),1 Yuqi Pan (潘裕琦),1 Zhaohua Xia (夏照华),3 Fei Fan (樊非),4 Xuetong Shen (申雪桐),1 and Kun Sun (孙坤)1,6,*  \n1Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen 518132, China  \n2Hepato-Biliary Surgery Division, Shenzhen Third People’s Hospital, The Second Affiliated Hospital, Southern University of Science and Technology, Shenzhen 518100, China  \n3Thoracic Surgical Department, Shenzhen Third People’s Hospital, The Second Affiliated Hospital, Southern University of Science and Technology, Shenzhen 518100, China  \n4Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China  \n5These authors contributed equally  \n6Lead contact  \n*Correspondence: [sunkun@szbl.ac.cn](sunkun@szbl.ac.cn)  \n[https://doi.org/10.1016/j.crmeth.2024.100877](https://doi.org/10.1016/j.crmeth.2024.100877)  \nMOTIVATION Fragmentation patterns of plasma cell-free DNA (cfDNA) are emerging directions in cancer diagnostic biomarkers. In a previous study, we found that, in tumors, the nucleases tend to cut within nucleosomes during apoptotic DNA fragmentation to generate short-sized cfDNA molecules. We hypothesized that the tumor-derived cfDNA in patients with cancer could be enriched by picking up the molecules with ends located within the nucleosomes. We further analyzed alterations in the cfDNA fragmentomic patterns to assess their potential as a cancer biomarker.  \nSUMMARY  \nThe fragmentation patterns of cell-free DNA (cfDNA) in plasma can potentially be utilized as diagnostic biomarkers in liquid biopsy. However, our knowledge of this biological process and the information encoded in fragmentation patterns remains preliminary. Here, we investigated the cfDNA fragmentomic characteristics against nucleosome positioning patterns in hematopoietic cells. cfDNA molecules with ends located within nucleosomes were relatively shorter with altered end motif patterns, demonstrating the feasibility of enriching tumor-derived cfDNA in patients with cancer through the selection of molecules possessing such ends. We then developed three cfDNA fragmentomic metrics after end selection, which showed significant alterations in patients with cancer and enabled cancer diagnosis. By incorporating machine learning, we further built high-performance diagnostic models, which achieved an overall area under the curve of 0.95 and 85.1% sensitivity at 95% specificity. Hence, our investigations explored the end characteristics of cfDNA fragmentomics and their merits in building accurate and sensitive cancer diagnostic models","cbCail0jkCE0RjX2","https://ap.wps.com/l/cbCail0jkCE0RjX2","pdf",4435925,13,"English","# Motivation\n# Summary\n# Introduction","[{\"question\":\"What hypothesis does the study test about tumor-derived cfDNA?\",\"answer\":\"It hypothesizes that in patients with cancer, tumor-derived cfDNA can be enriched by selecting molecules whose ends are located within nucleosomes.\"},{\"question\":\"How do the researchers enrich tumor-derived cfDNA?\",\"answer\":\"They select cfDNA molecules with end locations discordant with hematopoietic nucleosomes, which produces relatively shorter fragments with altered end motif patterns.\"},{\"question\":\"What diagnostic performance do the machine-learning models achieve?\",\"answer\":\"The work reports high-performance models with an overall AUC of 0.95 and 85.1% sensitivity at 95% specificity for the cross-tumor dataset using the EXCEL diagnosis model.\"}]","Cell-free DNA end characteristics enable accurate and sensitive cancer diagnosis | PDF",1789993781,33]