Bivariate deconvolution for cancer detection after surgery

Bivariate deconvolution for cancer detection after surgery | 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 Research Article Bivariate deconvolution for cancer detection after surgery Nuria Senar Villadeamigo, Stavros Makrodimitris, Michel H. Hof, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9266026/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Background: Detection of minimal residual disease (MRD) in cancer patientsafter surgery can provide an early marker for disease recurrence and guidesubsequent treatment decisions. Accurate and sensitive estimation of tumourburden after cancer surgery may be obtained through liquid biopsies, measuringcirculating tumour DNA (ctDNA) using, for example, mutation-based VariantAllele Frequency (VAF) values. However, to be applicable to all patients thiseither requires tumour-informed, patient-specific mutation panels or sensitive,tumour-agnostic genome-wide measurements. Methods: We propose a solution that accounts for patient-specific character-istics in genome-wide screens. For that, we introduce a bivariate deconvolutionmodel to estimate tumour proportion from circulating cell-free DNA (cfDNA)methylation profiles of patients before and after surgery. The observations aremodelled as a convolution of two bivariate latent variables, corresponding totumour and background signals, mixed by the tumour proportion at each mea-surement. This bivariate approach links pre- and post-surgery measurementsimproving estimation of the tumour proportion after surgery, when the tumour1signal is potentially very weak, or absent. We approximate likelihood of the con-volution through a discretisation of the bivariate density for each latent variableinto a two-dimensional grid for each pair of observations which allows for fastmaximum likelihood estimation. Conclusion: We evaluate the predictive performance of the estimated post-surgery tumour proportions based on cfDNA methylation against availablemutation-based VAF values in one-year recurrence-free survival. Deconvolution Bivariate analysis Signal recovery Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 07 May, 2026 Reviews received at journal 30 Apr, 2026 Reviews received at journal 28 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviewers invited by journal 31 Mar, 2026 Editor invited by journal 31 Mar, 2026 Editor assigned by journal 31 Mar, 2026 Submission checks completed at journal 31 Mar, 2026 First submitted to journal 30 Mar, 2026 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-9266026","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616645177,"identity":"9b3bf860-2365-43b6-8438-132b1b1d0fca","order_by":0,"name":"Nuria Senar Villadeamigo","email":"data:image/png;base64,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","orcid":"","institution":"Amsterdam University Medical Centers","correspondingAuthor":true,"prefix":"","firstName":"Nuria","middleName":"Senar","lastName":"Villadeamigo","suffix":""},{"id":616645178,"identity":"c74291c0-1513-45e2-9406-4e80324c3d1d","order_by":1,"name":"Stavros Makrodimitris","email":"","orcid":"","institution":"Erasmus MC","correspondingAuthor":false,"prefix":"","firstName":"Stavros","middleName":"","lastName":"Makrodimitris","suffix":""},{"id":616645179,"identity":"9d37ced3-8cea-4d4c-8498-f7f5500f2299","order_by":2,"name":"Michel H. 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Accurate and sensitive estimation of tumourburden after cancer surgery may be obtained through liquid biopsies, measuringcirculating tumour DNA (ctDNA) using, for example, mutation-based VariantAllele Frequency (VAF) values. However, to be applicable to all patients thiseither requires tumour-informed, patient-specific mutation panels or sensitive,tumour-agnostic genome-wide measurements.\n\nMethods: We propose a solution that accounts for patient-specific character-istics in genome-wide screens. For that, we introduce a bivariate deconvolutionmodel to estimate tumour proportion from circulating cell-free DNA (cfDNA)methylation profiles of patients before and after surgery. The observations aremodelled as a convolution of two bivariate latent variables, corresponding totumour and background signals, mixed by the tumour proportion at each mea-surement. This bivariate approach links pre- and post-surgery measurementsimproving estimation of the tumour proportion after surgery, when the tumour1signal is potentially very weak, or absent. We approximate likelihood of the con-volution through a discretisation of the bivariate density for each latent variableinto a two-dimensional grid for each pair of observations which allows for fastmaximum likelihood estimation.\n\nConclusion: We evaluate the predictive performance of the estimated post-surgery tumour proportions based on cfDNA methylation against availablemutation-based VAF values in one-year recurrence-free survival.","manuscriptTitle":"Bivariate deconvolution for cancer detection after surgery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-06 06:20:13","doi":"10.21203/rs.3.rs-9266026/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-07T16:15:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T17:11:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T12:12:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9102812218661579520030117847137930067","date":"2026-04-05T15:35:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238441437770076747336074770683744388557","date":"2026-04-02T13:34:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"307038384476842434292927875039073062322","date":"2026-04-02T11:42:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6058448574173165540209685315346459329","date":"2026-03-31T12:43:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-31T11:31:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-31T09:06:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-31T08:12:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-31T08:11:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Bioinformatics","date":"2026-03-30T11:15:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-bioinformatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"binf","sideBox":"Learn more about [BMC Bioinformatics](http://bmcbioinformatics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/binf","title":"BMC Bioinformatics","twitterHandle":"@BMC_Bioinformatics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"647a6cee-461e-4c99-af5e-214cd9711aa1","owner":[],"postedDate":"April 6th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-07T16:15:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T17:11:11+00:00","index":68,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T16:24:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-06 06:20:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9266026","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9266026","identity":"rs-9266026","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}