A Robust Framework for Predicting Mutation Effects on Transcription Factor Binding: Insights from Mutational Signatures in 560 Breast Cancer Genomes

A Robust Framework for Predicting Mutation Effects on Transcription Factor Binding: Insights from Mutational Signatures in 560 Breast Cancer Genomes | 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 A Robust Framework for Predicting Mutation Effects on Transcription Factor Binding: Insights from Mutational Signatures in 560 Breast Cancer Genomes Hüseyin Hilmi Kılınç, Burçak Otlu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8907367/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background: A vast majority of somatic mutations in cancer reside in noncoding regions, yet systematically predicting their functional impact on gene regulation remains a significant challenge. These variants often enforce their effects by altering the binding affinity of transcription factors (TFs) to cis-regulatory elements. However, a critical gap exists in linking specific mutational processes to the disruption of gene regulatory networks at a systems level. Results: In this study, we present a comprehensive in silico pipeline centered on k-mer-based linear regression models to quantify TF binding affinity. Our framework produced 403 high-confidence TF models trained on high-throughput ChIP-seq and PBM datasets. We applied this pipeline to 3.5 million somatic mutations from 560 breast cancer whole genomes to predict gain- or loss-of-function (GOF/LOF) binding perturbations. These predictions were integrated with mutational signature analysis and curated gene sets, utilizing Activity-by-Contact model-based enhancer-gene maps to link variants to their target genes. Our analysis revealed that distinct mutational processes exert non-random, directional effects on specific TF families. The APOBEC-associated signatures (SBS2 and SBS13) were strongly enriched for GOF events in the Myb/SANT and FOX families, while the aging-associated signature SBS1 was enriched for LOF events in the Ets family members. Furthermore, predicted perturbations at putative enhancers were significantly linked to key oncogenes and tumor suppressor genes, with GOF and LOF events (e.g., FOXA1 and BRCA1/2, respectively). In breast cancer samples, the basal-like TNBC subtype exhibited that SBS3-driven GOF enrichments for the CXXC family converged on MYC target gene programs, while SBS39-driven LOF events for the same family converged on DNA Repair pathways. Conclusions: Our framework provides a robust and scalable approach for prioritizing and interpreting the functional consequences of somatic mutations in terms of TF perturbations. We demonstrate that specific mutational processes systematically rewire the gene regulatory landscape in a subtype-specific manner, offering novel mechanisms for transcriptional deregulation in breast cancer. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Genetics Breast Cancer Cancer Genomics Transcription Factors Mutational Signatures Machine Learning Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryforManuscript.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 12 May, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers agreed at journal 17 Mar, 2026 Reviewers invited by journal 10 Mar, 2026 Editor invited by journal 06 Mar, 2026 Editor assigned by journal 18 Feb, 2026 Submission checks completed at journal 18 Feb, 2026 First submitted to journal 18 Feb, 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. 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. 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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-8907367","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":605175507,"identity":"56065b9b-d6c8-4725-a7a5-ec9cb6def5cf","order_by":0,"name":"Hüseyin Hilmi Kılınç","email":"","orcid":"","institution":"Middle East Technical University","correspondingAuthor":false,"prefix":"","firstName":"Hüseyin","middleName":"Hilmi","lastName":"Kılınç","suffix":""},{"id":605175510,"identity":"a3c1efe2-d418-467d-89bd-c86ab6183526","order_by":1,"name":"Burçak Otlu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACAzB5gEEOymcmXosxVDWQYCNSS2ID0VrMJXIffvhxxiZ9bXv/MQmGCuvEBvneB3i1WM5IN5bsuZGWu+3MYTYJhjPpiQ1s7Ab4HXYjjY2B58Ph3G03ktkkGNsOA7UQcBlIC+OfD4fTze4/Bmr5R6QWZp4bhxPMbjADtTQQocWy5xmztMyZNMNtZ5KNLRKOpRu3saXh12LOnsb48c0xG3mz4wcf3vhQYy3bz3wMvxZUkMBAOFpGwSgYBaNgFBABAFygQjxKVb7VAAAAAElFTkSuQmCC","orcid":"","institution":"Middle East Technical University","correspondingAuthor":true,"prefix":"","firstName":"Burçak","middleName":"","lastName":"Otlu","suffix":""}],"badges":[],"createdAt":"2026-02-18 08:39:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8907367/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8907367/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104781037,"identity":"4030bfe3-2ae7-4c65-bb8c-cdfa9bb41f2e","added_by":"auto","created_at":"2026-03-17 07:54:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":18375740,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8907367/v1_covered_dc24bc73-9ba2-4a07-b673-deda8a8911ea.pdf"},{"id":104539319,"identity":"e451be15-3911-40a0-b006-b5ff0c896fbf","added_by":"auto","created_at":"2026-03-13 05:12:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2382574,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryforManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8907367/v1/acbbd728928534eaa7ac3c54.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Robust Framework for Predicting Mutation Effects on Transcription Factor Binding: Insights from Mutational Signatures in 560 Breast Cancer Genomes","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Breast Cancer, Cancer Genomics, Transcription Factors, Mutational Signatures, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-8907367/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8907367/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Background: A vast majority of somatic mutations in cancer reside in noncoding regions, yet systematically predicting their functional impact on gene regulation remains a significant challenge. These variants often enforce their effects by altering the binding affinity of transcription factors (TFs) to cis-regulatory elements. However, a critical gap exists in linking specific mutational processes to the disruption of gene regulatory networks at a systems level.\n\nResults: In this study, we present a comprehensive in silico pipeline centered on k-mer-based linear regression models to quantify TF binding affinity. Our framework produced 403 high-confidence TF models trained on high-throughput ChIP-seq and PBM datasets. We applied this pipeline to 3.5 million somatic mutations from 560 breast cancer whole genomes to predict gain- or loss-of-function (GOF/LOF) binding perturbations. These predictions were integrated with mutational signature analysis and curated gene sets, utilizing Activity-by-Contact model-based enhancer-gene maps to link variants to their target genes. Our analysis revealed that distinct mutational processes exert non-random, directional effects on specific TF families. The APOBEC-associated signatures (SBS2\nand SBS13) were strongly enriched for GOF events in the Myb/SANT and FOX families, while the aging-associated signature SBS1 was enriched for LOF events in the Ets family members. Furthermore, predicted perturbations at putative enhancers were significantly linked to key oncogenes and tumor suppressor genes, with GOF and LOF events (e.g., FOXA1 and BRCA1/2, respectively). In breast cancer samples, the basal-like TNBC subtype exhibited that SBS3-driven GOF enrichments for the CXXC family converged on MYC target gene programs, while SBS39-driven LOF events for the same family converged on DNA Repair pathways.\n\nConclusions: Our framework provides a robust and scalable approach for prioritizing and interpreting the functional consequences of somatic mutations in terms of TF perturbations. We demonstrate that specific mutational processes systematically rewire the gene regulatory landscape in a subtype-specific manner, offering novel mechanisms for transcriptional deregulation in breast cancer.","manuscriptTitle":"A Robust Framework for Predicting Mutation Effects on Transcription Factor Binding: Insights from Mutational Signatures in 560 Breast Cancer Genomes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-13 05:12:39","doi":"10.21203/rs.3.rs-8907367/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"50080070076445944170922316275553546652","date":"2026-05-12T09:56:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168322674126244019940557109265941786709","date":"2026-04-07T18:44:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114407292387566768509255556452235251906","date":"2026-04-02T07:57:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22104006354565252432979691288017134784","date":"2026-03-17T19:18:35+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-10T14:27:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-06T12:50:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-19T04:14:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-19T04:13:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-18T08:29:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f62f7e6b-871d-4746-a21a-909aef28408e","owner":[],"postedDate":"March 13th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"50080070076445944170922316275553546652","date":"2026-05-12T09:56:44+00:00","index":86,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64405246,"name":"Biological sciences/Cancer"},{"id":64405247,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":64405248,"name":"Biological sciences/Genetics"}],"tags":[],"updatedAt":"2026-03-13T05:12:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-13 05:12:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8907367","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8907367","identity":"rs-8907367","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}