Integrative Multiomic and Deep Learning Profiling of KRAS Mutant and Wildtype Genomes in Pancreatic Tumorigenesis

Integrative Multiomic and Deep Learning Profiling of KRAS Mutant and Wildtype Genomes in Pancreatic Tumorigenesis | 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 Integrative Multiomic and Deep Learning Profiling of KRAS Mutant and Wildtype Genomes in Pancreatic Tumorigenesis Zarlish Attique, Hafiz Muhammad Faraz Azhar, Adnan Ahmed Ansari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7335783/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 Pancreatic ductal adenocarcinoma exhibits high intertumoral heterogeneity, with KRAS mutations as the dominant oncogenic driver. However, a subset of these tumors retains a wild-type KRAS genotype yet progresses through alternative molecular mechanisms. Deciphering how these divergent tumors converge on shared malignant outcomes is crucial for precision oncology. We conducted an integrative multiomic analysis across whole-exome sequencing, RNA-Seq, methylation profiling, and proteomic data. Gene regulatory network (GRN) reconstruction, centrality analysis, T-Test and functional clustering were performed. A deep neural network model was developed for stratifying and validating KRAS-mutant and wild-type tumors based on identified transcriptomic signatures. KRAS-mutant tumors harbored canonical hotspot mutations (G12D, G12V, G12R). In contrast, KRAS-wildtype co-occurring with disruptive variants in TP53, CDKN2A, and SMAD4 highlighting a genomically unstable landscape and displayed enrichment of damaging variants in GNAS, with upregulation of alternative pathways involving hormonal and neuropeptidergic signaling. Multiomic integration identified TFAP2A (LFC: 5.124), and LCN2 (LFC: 4.835) as hyperactive effectors in KRAS-mutants and wildtype, supported by high mRNA and hypomethylated values. Wild-type tumors showed marked upregulation of CARTPT (LFC: 7.535) suggesting adaptive reliance on endocrine and immune modulation. Network analysis revealed seven core functional modules, with CAV1(LFC:2.25) emerging as central hubs in therapy resistance and EMT-metabolic signaling and found to have expression in both lung and liver metastasis. Sustained expression of CAV1 and the conserved nature of GRN seed node variants reinforce their contribution to metastatic evolution. A DNN trained on GRN-prioritized biomarkers achieved AUC = 0.94, accurately stratifying KRAS status and correlating with patient survival (HR = 0.46, p = 0.0021). Despite differing upstream mutations, KRAS-mutant and wild-type PDAC tumors converge on shared transcriptional and epigenetic programs that promote malignancy. These findings emphasize the role of regulatory convergence in tumor evolution and GRN-defined hubs as robust, mutation-agnostic therapeutic targets. Epigenetics & Genomics KRAS-wildtype KRAS-mutant multiomic data epigenetics somatic mutations CARTPT deep neural network Full Text Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryMaterialPancreaticCancer.docx 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. 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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-7335783","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498156760,"identity":"01f35a5a-becb-4287-b58a-b0effdd8d1b2","order_by":0,"name":"Zarlish Attique","email":"","orcid":"","institution":"National Center for Bioinformatics, Quaid-i-Azam University, Islamabad","correspondingAuthor":false,"prefix":"","firstName":"Zarlish","middleName":"","lastName":"Attique","suffix":""},{"id":498156761,"identity":"7e203893-f77f-400e-a5b6-0499a601fbde","order_by":1,"name":"Hafiz Muhammad Faraz 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Tumorigenesis\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Quaid-i-Azam University","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":"KRAS-wildtype, KRAS-mutant, multiomic data, epigenetics, somatic mutations, CARTPT, deep neural network","lastPublishedDoi":"10.21203/rs.3.rs-7335783/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7335783/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePancreatic ductal adenocarcinoma exhibits high intertumoral heterogeneity, with KRAS mutations as the dominant oncogenic driver. However, a subset of these tumors retains a wild-type KRAS genotype yet progresses through alternative molecular mechanisms. Deciphering how these divergent tumors converge on shared malignant outcomes is crucial for precision oncology. We conducted an integrative multiomic analysis across whole-exome sequencing, RNA-Seq, methylation profiling, and proteomic data. Gene regulatory network (GRN) reconstruction, centrality analysis, T-Test and functional clustering were performed. A deep neural network model was developed for stratifying and validating KRAS-mutant and wild-type tumors based on identified transcriptomic signatures. KRAS-mutant tumors harbored canonical hotspot mutations (G12D, G12V, G12R). In contrast, KRAS-wildtype co-occurring with disruptive variants in TP53, CDKN2A, and SMAD4 highlighting a genomically unstable landscape and displayed enrichment of damaging variants in GNAS, with upregulation of alternative pathways involving hormonal and neuropeptidergic signaling. Multiomic integration identified TFAP2A (LFC: 5.124), and LCN2 (LFC: 4.835) as hyperactive effectors in KRAS-mutants and wildtype, supported by high mRNA and hypomethylated values. Wild-type tumors showed marked upregulation of CARTPT (LFC: 7.535) suggesting adaptive reliance on endocrine and immune modulation. Network analysis revealed seven core functional modules, with CAV1(LFC:2.25) emerging as central hubs in therapy resistance and EMT-metabolic signaling and found to have expression in both lung and liver metastasis. Sustained expression of CAV1 and the conserved nature of GRN seed node variants reinforce their contribution to metastatic evolution. A DNN trained on GRN-prioritized biomarkers achieved AUC\u0026thinsp;=\u0026thinsp;0.94, accurately stratifying KRAS status and correlating with patient survival (HR\u0026thinsp;=\u0026thinsp;0.46, p\u0026thinsp;=\u0026thinsp;0.0021). Despite differing upstream mutations, KRAS-mutant and wild-type PDAC tumors converge on shared transcriptional and epigenetic programs that promote malignancy. These findings emphasize the role of regulatory convergence in tumor evolution and GRN-defined hubs as robust, mutation-agnostic therapeutic targets.\u003c/p\u003e","manuscriptTitle":"Integrative Multiomic and Deep Learning Profiling of KRAS Mutant and Wildtype Genomes in Pancreatic Tumorigenesis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 11:26:28","doi":"10.21203/rs.3.rs-7335783/v1","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 Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cec5766a-491c-4912-bae4-7ecd7d43ec4b","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52917444,"name":"Epigenetics \u0026 Genomics"}],"tags":[],"updatedAt":"2025-08-12T11:26:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-12 11:26:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7335783","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7335783","identity":"rs-7335783","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}