CrosstalkIn: Crosstalk-aware inference of tumor microenvironment cell infiltration

CrosstalkIn: Crosstalk-aware inference of tumor microenvironment cell infiltration | 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 CrosstalkIn: Crosstalk-aware inference of tumor microenvironment cell infiltration Qianbei Yi, Jiaqi Yuan, Zheng Ye, Peng Xu, Wenbin Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9550942/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background The tumor microenvironment (TME) is a complex ecosystem whose cellular composition and interactions shape tumor progression, therapeutic response, and patient prognosis. However, most bulk deconvolution methods infer cell-type composition while treating cell types independently. Methods In this paper, we develop CrosstalkIn, a crosstalk-aware bulk deconvolution framework that constructs patient-specific cell-cell networks by integrating Gene Ontology (GO)-based functional similarity and protein-protein interaction (PPI)-based molecular relationships. A modified random walk with restart algorithm is then applied to calculate cell infiltration scores (InScores). Results Benchmark results on two flow-cytometry-validated cohorts demonstrate that CrosstalkIn achieves superior deconvolution performance, with the largest or second-largest Spearman correlations for most evaluated cell types. In lower-grade glioma, CrosstalkIn-derived InScores identify survival-associated cell types, generate accurate prognostic risk scores, and stratify patients into distinct survival groups. Across multiple adenocarcinoma cohorts, the risk score shows consistent prognostic value, particularly in advanced-stage patients. In melanoma, CrosstalkIn improves immunotherapy response prediction and identifies biologically interpretable cell-type biomarkers. Conclusion CrosstalkIn provides a robust framework for crosstalk-aware inference of TME cell infiltration from bulk transcriptomic data and supports cancer prognosis and immunotherapy response prediction. Full Text Supplementary Files supplementmaterial.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 04 May, 2026 Reviewers invited by journal 04 May, 2026 Editor assigned by journal 04 May, 2026 First submitted to journal 30 Apr, 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-9550942","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634450380,"identity":"6f1d055e-f7e6-4a87-acdb-9688b94e83bb","order_by":0,"name":"Qianbei Yi","email":"","orcid":"","institution":"Guangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Qianbei","middleName":"","lastName":"Yi","suffix":""},{"id":634450381,"identity":"c4ab8cf7-18ed-4ab6-b99e-e956f318e236","order_by":1,"name":"Jiaqi Yuan","email":"","orcid":"","institution":"Guangzhou 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Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9550942/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9550942/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe tumor microenvironment (TME) is a complex ecosystem whose cellular composition and interactions shape tumor progression, therapeutic response, and patient prognosis. However, most bulk deconvolution methods infer cell-type composition while treating cell types independently.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this paper, we develop CrosstalkIn, a crosstalk-aware bulk deconvolution framework that constructs patient-specific cell-cell networks by integrating Gene Ontology (GO)-based functional similarity and protein-protein interaction (PPI)-based molecular relationships. A modified random walk with restart algorithm is then applied to calculate cell infiltration scores (InScores).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBenchmark results on two flow-cytometry-validated cohorts demonstrate that CrosstalkIn achieves superior deconvolution performance, with the largest or second-largest Spearman correlations for most evaluated cell types. In lower-grade glioma, CrosstalkIn-derived InScores identify survival-associated cell types, generate accurate prognostic risk scores, and stratify patients into distinct survival groups. Across multiple adenocarcinoma cohorts, the risk score shows consistent prognostic value, particularly in advanced-stage patients. In melanoma, CrosstalkIn improves immunotherapy response prediction and identifies biologically interpretable cell-type biomarkers.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCrosstalkIn provides a robust framework for crosstalk-aware inference of TME cell infiltration from bulk transcriptomic data and supports cancer prognosis and immunotherapy response prediction.\u003c/p\u003e","manuscriptTitle":"CrosstalkIn: Crosstalk-aware inference of tumor microenvironment cell infiltration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-13 10:59:19","doi":"10.21203/rs.3.rs-9550942/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-05-05T02:47:21+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-05T02:04:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-04T04:25:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2026-04-30T06:55:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7d3f8a53-736d-409c-b331-03cc7247bce3","owner":[],"postedDate":"May 13th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"","date":"2026-05-05T02:47:21+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-05T02:04:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-04T04:25:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2026-04-30T06:55:20+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T10:59:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-13 10:59:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9550942","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9550942","identity":"rs-9550942","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}