Accurate Identification of Primary Site in Tumors of Unknown Origin (TUO) Using DNA Methylation

Accurate Identification of Primary Site in Tumors of Unknown Origin (TUO) Using DNA Methylation | 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 Accurate Identification of Primary Site in Tumors of Unknown Origin (TUO) Using DNA Methylation LUCAS SANTANA-SANTOS, Drew Duckett, Erica Vortmittag-Nocito, Pouya Jamshidi, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3979271/v2 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Jan, 2025 Read the published version in npj Precision Oncology → Version 2 posted 11 You are reading this latest preprint version Show more versions Abstract Tumors of unknown origin (TUO) generally result in poor patient survival and are clinically difficult to address. Identification of the site of origin in TUO patients is paramount to their improved treatment and survival but is difficult to obtain with current methods. Here, we develop a random forest machine learning TUO methylation classifier using a large number of primary and metastatic tumor samples. Our classifier achieves high accuracy in primary site identification when applied to both publicly available and internal validation samples, with 97% of samples classified correctly and 85% receiving high probability scores (≥0.9). Moreover, by employing pathologist expertise and t-SNE visualization, the TUO classifier can assign samples to 46 different sites of origin/disease classes. This strategy also revealed multiple classes of yet unknown significance for future exploration. Overall, the presented TUO classifier represents a significant step forward in the diagnosis of TUO tumors. Biological sciences/Cancer/Cancer genomics Biological sciences/Computational biology and bioinformatics Health sciences/Oncology/Cancer/Cancer genomics Health sciences/Biomarkers/Diagnostic markers Health sciences/Molecular medicine Full Text Additional Declarations There is no conflict of interest Supplementary Files supplementalv21.pdf Supplemental Tables and Figures Cite Share Download PDF Status: Published Journal Publication published 10 Jan, 2025 Read the published version in npj Precision Oncology → Version 2 posted Editorial decision: revise 08 Oct, 2024 Review # 1 received at journal 25 Sep, 2024 Review # 3 received at journal 25 Sep, 2024 Review # 2 received at journal 20 Sep, 2024 Reviewer # 3 agreed at journal 13 Sep, 2024 Reviewer # 2 agreed at journal 12 Sep, 2024 Reviewer # 1 agreed at journal 11 Sep, 2024 Reviewers invited by journal 11 Sep, 2024 Editor assigned by journal 27 Aug, 2024 Submission checks completed at journal 26 Aug, 2024 First submitted to journal 22 Aug, 2024 You are reading this latest preprint version Show more versions 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-3979271","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[{"code":1,"date":"2024-02-29 05:08:33","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research 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