Fragmentomic Liquid Biopsy Enables Non-invasive Detection, Molecular Subtyping and Lymph Node Assessment in Early Breast Cancer

Fragmentomic Liquid Biopsy Enables Non-invasive Detection, Molecular Subtyping and Lymph Node Assessment in Early Breast Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Fragmentomic Liquid Biopsy Enables Non-invasive Detection, Molecular Subtyping and Lymph Node Assessment in Early Breast Cancer Chao Ni, Yuxuan Zhu, Siwei Zheng, Yingkuan Shao, Jun Zhou, Xidong Gu, and 21 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6866836/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Mar, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Breast cancer (BC) remains a leading global health concern in women. While mammography is the standard screening tool, its efficacy is limited by the high breast density and accessibility challenges in China. Here, we conducted a multicenter case-control study (NCT06016790) involving 503 patients with malignant BC and 289 benign controls across seven hospitals to develop TuFEst, a machine learning model utilizing multi-feature cell-free DNA (cfDNA) fragmentomics. TuFEst showed excellent early detection performance (95.0% sensitivity and 78.3% specificity), maintaining 96.2% accuracy in an imaging-pathological inconsistency cohort (n=26). To broaden its clinical application, we extended TuFEst to molecular subtyping (TuFEst-MS) and lymph node metastasis prediction (TuFEst-LN). TuFEst-MS yielded AUCs of 0.906 (ER + /PR + HER2 − ), 0.925 (HER2 + ), and 0.891 (triple-negative) with 85.7% accuracy in the oligometastatic validation cohort (n=21). TuFEst-LN achieved a negative predictive value (NPV) of 95.2%, which improved to 97.6% in an independent cohort (n=124) with discordant axillary imaging pathology. RNA-seq of paired bulk tumor samples (n=79) demonstrated that elevated TuFEst-derived cancer scores were associated with aggressive tumor characteristics, particularly enriched immune responses and epithelial-mesenchymal transition (EMT) signatures, emphasizing the clinical importance of early detection. Our study established cfDNA fragmentomics as an integrated liquid biopsy solution for BC management, enabling concurrent detection, molecular subtyping, and lymph node evaluation with transformative clinical potential. Biological sciences/Cancer/Gynaecological cancer Biological sciences/Computational biology and bioinformatics/Genome informatics Biological sciences/Biotechnology/Sequencing Breast cancer early detection cell-free DNA whole-genome sequencing machine learning Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryTable1.xlsx Supplementary Table 1 SupplementaryTable2.xlsx Supplementary Table 2 SupplementaryTable3.docx Supplementary Table 3 Supplementarytable4.docx Supplementary Table 4 Cite Share Download PDF Status: Published Journal Publication published 06 Mar, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6866836","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":474028395,"identity":"01e9e47f-a693-4e17-aaee-63d4c219e39e","order_by":0,"name":"Chao 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While mammography is the standard screening tool, its efficacy is limited by the high breast density and accessibility challenges in China. Here, we conducted a multicenter case-control study (NCT06016790) involving 503 patients with malignant BC and 289 benign controls across seven hospitals to develop TuFEst, a machine learning model utilizing multi-feature cell-free DNA (cfDNA) fragmentomics. TuFEst showed excellent early detection performance (95.0% sensitivity and 78.3% specificity), maintaining 96.2% accuracy in an imaging-pathological inconsistency cohort (n=26). To broaden its clinical application, we extended TuFEst to molecular subtyping (TuFEst-MS) and lymph node metastasis prediction (TuFEst-LN). TuFEst-MS yielded AUCs of 0.906 (ER\u003csup\u003e+\u003c/sup\u003e/PR\u003csup\u003e+\u003c/sup\u003eHER2\u003csup\u003e−\u003c/sup\u003e), 0.925 (HER2\u003csup\u003e+\u003c/sup\u003e), and 0.891 (triple-negative) with 85.7% accuracy in the oligometastatic validation cohort (n=21). TuFEst-LN achieved a negative predictive value (NPV) of 95.2%, which improved to 97.6% in an independent cohort (n=124) with discordant axillary imaging pathology. RNA-seq of paired bulk tumor samples (n=79) demonstrated that elevated TuFEst-derived cancer scores were associated with aggressive tumor characteristics, particularly enriched immune responses and epithelial-mesenchymal transition (EMT) signatures, emphasizing the clinical importance of early detection. Our study established cfDNA fragmentomics as an integrated liquid biopsy solution for BC management, enabling concurrent detection, molecular subtyping, and lymph node evaluation with transformative clinical potential.\u003c/p\u003e","manuscriptTitle":"Fragmentomic Liquid Biopsy Enables Non-invasive Detection, Molecular Subtyping and Lymph Node Assessment in Early Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 19:20:27","doi":"10.21203/rs.3.rs-6866836/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f727711b-2723-4ee7-984c-f3b12e5dc0da","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":50342362,"name":"Biological sciences/Cancer/Gynaecological cancer"},{"id":50342363,"name":"Biological sciences/Computational biology and bioinformatics/Genome informatics"},{"id":50342364,"name":"Biological sciences/Biotechnology/Sequencing"}],"tags":[],"updatedAt":"2026-03-11T12:18:58+00:00","versionOfRecord":{"articleIdentity":"rs-6866836","link":"https://doi.org/10.1038/s41467-026-70204-w","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2026-03-06 05:00:00","publishedOnDateReadable":"March 6th, 2026"},"versionCreatedAt":"2025-07-18 19:20:27","video":"","vorDoi":"10.1038/s41467-026-70204-w","vorDoiUrl":"https://doi.org/10.1038/s41467-026-70204-w","workflowStages":[]},"version":"v1","identity":"rs-6866836","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6866836","identity":"rs-6866836","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}