Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer

Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal 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 Research Article Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer Hossein Sadeghi, Fatemeh Seif This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7028381/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Apr, 2026 Read the published version in Biochemical Genetics → Version 1 posted 11 You are reading this latest preprint version Abstract Chemotherapy response in colorectal cancer (CRC) exhibits significant heterogeneity, with current clinical predictors failing to capture complex genomic determinants of resistance. We developed a hybrid deep learning framework integrating convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) networks to analyze whole-genome somatic mutations, evolutionary conservation, chromatin accessibility, and 3D genome architecture in 2,546 TCGA patients. An attention mechanism identified predictive genomic regions. The model achieved an AUC of 0.92 (95% CI: 0.89–0.94) in cross-validation and 0.88 (95% CI: 0.85–0.91) in independent validation, outperforming clinical models (ΔAUC = +0.18, p <0.001). Key predictors included non-coding variants in TP53, KRAS, and PIK3CA regulatory regions. Triple-positive patients (mutations in all 3 regions) had significantly worse progression free survival (HR = 4.7, p < 0.001). Our framework enables accurate chemotherapy response prediction and reveals novel non-coding resistance mechanisms, advancing precision oncology in CRC. Deep learning Chemotherapy response prediction Colorectal cancer genomics Whole- genome sequencing Precision oncology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Apr, 2026 Read the published version in Biochemical Genetics → Version 1 posted Editorial decision: Revision requested 23 Sep, 2025 Reviews received at journal 22 Sep, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviews received at journal 21 Aug, 2025 Reviewers agreed at journal 11 Aug, 2025 Reviewers agreed at journal 29 Jul, 2025 Reviewers invited by journal 28 Jul, 2025 Editor assigned by journal 03 Jul, 2025 Submission checks completed at journal 03 Jul, 2025 First submitted to journal 02 Jul, 2025 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-7028381","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492519640,"identity":"5d14ee7b-1847-4afe-9f22-5be01e46f68f","order_by":0,"name":"Hossein Sadeghi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYBACAxDBw8CQwMbeAGQdgIgeIE4LzwFStTBIJBBSCwXmDOwPP7ypqM3jk3z87MOPM4cZ+NsPMB6uwKPFsoHHWHLOmePFbNJpxjN7bhxmkDiTwHDwDD6H3X/DIM3bdiyxTTrBmIHnw2EGhhsMDAcb8Gk5wP74N+8/oBbJ458Z/wC1yBPWwmAmzdtQk9gmwWPMzAN0mAFhLTxmlnOOHUhs48kpZpY5k85jeCaxgaDDbrypqUuc3358M+ObY9ZycscPH/6ITwsUHIazgHHESIQGBoY6YhSNglEwCkbBSAUAMjRTiykjMkwAAAAASUVORK5CYII=","orcid":"","institution":"Arak University","correspondingAuthor":true,"prefix":"","firstName":"Hossein","middleName":"","lastName":"Sadeghi","suffix":""},{"id":492519641,"identity":"8951df76-380c-471b-9a72-e8684d747f57","order_by":1,"name":"Fatemeh Seif","email":"","orcid":"","institution":"Arak University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Fatemeh","middleName":"","lastName":"Seif","suffix":""}],"badges":[],"createdAt":"2025-07-02 10:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7028381/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7028381/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10528-026-11368-4","type":"published","date":"2026-04-11T15:57:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":106808885,"identity":"b7c4dc96-7665-4498-9fa6-436331d4f7c1","added_by":"auto","created_at":"2026-04-13 16:04:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1719093,"visible":true,"origin":"","legend":"","description":"","filename":"deeplecorogd.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7028381/v1_covered_5a9bab2b-403d-488a-8053-05281b992e5e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biochemical-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bigi","sideBox":"Learn more about [Biochemical Genetics](http://link.springer.com/journal/10528)","snPcode":"10528","submissionUrl":"https://submission.nature.com/new-submission/10528/3","title":"Biochemical Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Deep learning, Chemotherapy response prediction, Colorectal cancer genomics, Whole- genome sequencing, Precision oncology","lastPublishedDoi":"10.21203/rs.3.rs-7028381/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7028381/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Chemotherapy response in colorectal cancer (CRC) exhibits significant heterogeneity, with current clinical predictors failing to capture complex genomic determinants of resistance. We developed a hybrid deep learning framework integrating convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) networks to analyze whole-genome somatic mutations, evolutionary conservation, chromatin accessibility, and 3D genome architecture in 2,546 TCGA patients. An attention mechanism identified predictive genomic regions. The model achieved an AUC of 0.92 (95% CI: 0.89–0.94) in cross-validation and 0.88 (95% CI: 0.85–0.91) in independent validation, outperforming clinical models (ΔAUC = +0.18, p \u003c0.001). Key predictors included non-coding variants in TP53, KRAS, and PIK3CA regulatory regions. Triple-positive patients (mutations in all 3 regions) had significantly worse progression free survival (HR = 4.7, p \u003c 0.001). Our framework enables accurate chemotherapy response prediction and reveals novel non-coding resistance mechanisms, advancing precision oncology in CRC.","manuscriptTitle":"Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-31 03:49:33","doi":"10.21203/rs.3.rs-7028381/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-23T05:41:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-23T01:43:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"187781544664538369631833377700207771200","date":"2025-09-10T10:03:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121912548222493534559595990511573697828","date":"2025-09-08T13:24:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-21T08:33:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140883640201413855274399134343764046388","date":"2025-08-12T02:09:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233288237228238435136846288565806559440","date":"2025-07-29T10:16:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-29T02:06:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-03T07:17:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-03T07:17:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biochemical Genetics","date":"2025-07-02T10:35:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biochemical-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bigi","sideBox":"Learn more about [Biochemical Genetics](http://link.springer.com/journal/10528)","snPcode":"10528","submissionUrl":"https://submission.nature.com/new-submission/10528/3","title":"Biochemical Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"cacd9f21-6b0d-4d5e-9211-889296a90baf","owner":[],"postedDate":"July 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:01:30+00:00","versionOfRecord":{"articleIdentity":"rs-7028381","link":"https://doi.org/10.1007/s10528-026-11368-4","journal":{"identity":"biochemical-genetics","isVorOnly":false,"title":"Biochemical Genetics"},"publishedOn":"2026-04-11 15:57:50","publishedOnDateReadable":"April 11th, 2026"},"versionCreatedAt":"2025-07-31 03:49:33","video":"","vorDoi":"10.1007/s10528-026-11368-4","vorDoiUrl":"https://doi.org/10.1007/s10528-026-11368-4","workflowStages":[]},"version":"v1","identity":"rs-7028381","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7028381","identity":"rs-7028381","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}