Mandatory Validation Gates for Molecular Patient Stratification: A Disease-Agnostic Pathway Subtyping Framework

Mandatory Validation Gates for Molecular Patient Stratification: A Disease-Agnostic Pathway Subtyping Framework | 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 Mandatory Validation Gates for Molecular Patient Stratification: A Disease-Agnostic Pathway Subtyping Framework Rohit Chauhan, Mohit Chauhan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9284565/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Unsupervised molecular clustering is fundamental to precision medicine, yet most published subtyping studies lack standardized validation criteria, making it impossible to distinguish biological signal from statistical artifact. We present the Pathway Subtyping Framework, which enforces four mandatory validation gates—label shuffle, random gene set, bootstrap stability with adaptive thresholds, and ancestry independence—before any clustering result is reported. On TCGA-COAD (n=452), the framework independently recovered CMS4 (Mesenchymal) with 75.9% CMS4 enrichment (Fisher OR=16.71, p=1.4×10⁻²⁵), outperforming PCA+k-means (67%), gene-level k-means (50%), and NMF (32%) on the same data. Applied to autism (n=56) and schizophrenia (n=281) as pilot demonstrations, the framework produced replicable subtypes across platforms (ARI=0.870) and achieved the strongest gate performance across all datasets (bootstrap ARI=0.923). Cross-disease analysis reveals that molecular subtypes are independent of DSM diagnosis (Cramér's V=0.036) but strongly dependent on brain region (V=0.528); within-region concordance is 84% (ARI=0.862), decomposing the 87% global concordance into disease-agnostic and anatomical components. The adaptive bootstrap threshold model, calibrated on 47 independent benchmarks (R²=0.889, LOOCV RMSE=0.051), replaces arbitrary fixed thresholds with data-driven, silhouette-calibrated gates. The framework is open-source (PyPI, Docker, Codeberg; 1,054 tests; DOI:10.5281/zenodo.18867165). Biological sciences/Computational biology and bioinformatics/Software Biological sciences/Computational biology and bioinformatics/Machine learning Biological sciences/Computational biology and bioinformatics/Statistical methods Biological sciences/Computational biology and bioinformatics/Genome informatics Biological sciences/Cancer/Cancer genomics patient stratification validation methodology disease-agnostic framework pathway analysis computational reproducibility Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9284565","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":616069626,"identity":"938e7dfe-78ed-4de2-ac92-a1e464f570ee","order_by":0,"name":"Rohit Chauhan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYFACHoYDQDIBxGBgqACJMDYQ1nIAruWMAUTLAQJaGOBaGNsMIIL4tJi39x48/IHBLo9/Ru7Bz4Xz/siZix1u/gwUkdPF4T6ZM+cSgA5LLpa4kZcsPXObgbHl7MQ2CaCIsRkOqyQkcgyAWg4kNtzIMZDm3WaQuOF2YhsDSGQbIS3zb+QY/+adA9bS/IEoLRtu5JhJ8zaAtTRI4NXCc8bgwBmD5MSNZ96YWfMcM4b4BSiC2y/sPcYfKirsEucdzzG+zVMjJ2cunf4YJCKHSwsEGGCwDbArJKx9FIyCUTAKRgEIAADsLmNXSiSEhgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0003-9895-4629","institution":"Topmist LLC","correspondingAuthor":true,"prefix":"","firstName":"Rohit","middleName":"","lastName":"Chauhan","suffix":""},{"id":616069627,"identity":"69c913b6-9eb6-4832-a049-a691736a47b6","order_by":1,"name":"Mohit Chauhan","email":"","orcid":"https://orcid.org/0000-0001-8848-4385","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Mohit","middleName":"","lastName":"Chauhan","suffix":""}],"badges":[],"createdAt":"2026-03-31 22:15:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9284565/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9284565/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108007089,"identity":"aa6f3e91-e6f1-4aa0-810e-b46ee0cc21a4","added_by":"auto","created_at":"2026-04-28 12:58:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4343694,"visible":true,"origin":"","legend":"Article File","description":"","filename":"ChauhanMandatoryValidationGatesManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9284565/v1_covered_1c46bcc4-e916-4372-865d-142d3c0d042c.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Mandatory Validation Gates for Molecular Patient Stratification: A Disease-Agnostic Pathway Subtyping Framework","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"patient stratification, validation methodology, disease-agnostic framework, pathway analysis, computational reproducibility","lastPublishedDoi":"10.21203/rs.3.rs-9284565/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9284565/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Unsupervised molecular clustering is fundamental to precision medicine, yet most published subtyping studies lack standardized validation criteria, making it impossible to distinguish biological signal from statistical artifact. 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