Computational modeling of lesion dynamics in HER2+ breast cancer: integrating gut microbiota diversity into therapy response prediction

Computational modeling of lesion dynamics in HER2+ breast cancer: integrating gut microbiota diversity into therapy response prediction | 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 Computational modeling of lesion dynamics in HER2+ breast cancer: integrating gut microbiota diversity into therapy response prediction Maria Valeria De Bonis, Tiziana Triulzi, Martina Di Modica, Fabio Corsi, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8703071/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Mathematical models based on partial differential equations (PDEs) can be exploited to integrate heterogeneous clinical and biological data for the interpretation of tumor dynamics during systemic therapy. In this study, a PDE-based model of tumor volume evolution was exercised to investigate the predictive role of clinical and microbiota-derived biomarkers in patients with HER2-positive breast cancer undergoing neoadjuvant chemotherapy. Within a retrospective cohort of 15 patients, a training subset of eight was used to identify and optimize a set of virtual parameters describing tumor proliferation and treatment efficacy. Tumor growth rate ( r ) and drug efficiency for the epirubicin–cyclophosphamide branch (ϵ PD1 ) were modeled as functions of baseline Ki67 expression, while a Spearman correlation analysis identified key microbiota features (Firmicutes/Bacteroidetes ratio and the Simpson Diversity Index) associated with treatment response, or drug efficiency of the taxane–trastuzumab branch (ϵ PD2 ). Model robustness was subsequently assessed in an independent testing subset of seven patients. Simulated tumor volume dynamics did not significantly differ from clinical observations and showed strong predictive capability in discriminating therapeutic response (p = 0.0070), correctly identifying all partial responses and 80% of pathological complete responses. After defining appropriate mathematical assumptions, microbiota-informed drug efficiency parameters were shown to effectively capture inter-patient variability in treatment sensitivity. A simplified model of tumor dynamics integrating microbiota-derived variables was thus demonstrated to provide an upfront prediction of neoadjuvant chemotherapy efficacy. Prospective validation in larger cohorts and correlation with established clinical endpoints are now warranted to confirm the model and support patient-specific optimization of therapeutic strategies in HER2-positive breast cancer. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Health sciences/Oncology breast cancer computational modeling gut microbiota Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 19 Feb, 2026 Editor assigned by journal 19 Feb, 2026 Editor invited by journal 05 Feb, 2026 Submission checks completed at journal 04 Feb, 2026 First submitted to journal 04 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. 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-8703071","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":594564302,"identity":"df825fe7-8465-4fc5-bc3f-a0637adb7047","order_by":0,"name":"Maria Valeria De Bonis","email":"","orcid":"","institution":"University of Basilicata","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Valeria","lastName":"De Bonis","suffix":""},{"id":594564303,"identity":"73f87d5f-03cb-458e-aa13-42614f7ec22d","order_by":1,"name":"Tiziana Triulzi","email":"","orcid":"","institution":"Fondazione IRCCS Istituto Nazionale dei Tumori","correspondingAuthor":false,"prefix":"","firstName":"Tiziana","middleName":"","lastName":"Triulzi","suffix":""},{"id":594564304,"identity":"a4ae84b3-8151-487b-9e7b-03db0e9e412d","order_by":2,"name":"Martina Di Modica","email":"","orcid":"","institution":"Fondazione IRCCS Istituto Nazionale dei Tumori","correspondingAuthor":false,"prefix":"","firstName":"Martina","middleName":"Di","lastName":"Modica","suffix":""},{"id":594564305,"identity":"34bdaf26-f52f-4792-8961-1a3ee9e55934","order_by":3,"name":"Fabio Corsi","email":"","orcid":"","institution":"University of Milan","correspondingAuthor":false,"prefix":"","firstName":"Fabio","middleName":"","lastName":"Corsi","suffix":""},{"id":594564306,"identity":"44d99ec1-ff9d-4bea-b2bd-8a46749cd8a5","order_by":4,"name":"Michela Francesconi","email":"","orcid":"","institution":"Fondazione IRCCS Istituto Nazionale dei Tumori","correspondingAuthor":false,"prefix":"","firstName":"Michela","middleName":"","lastName":"Francesconi","suffix":""},{"id":594564307,"identity":"186d6473-996d-4f59-8829-db6270c0bcda","order_by":5,"name":"Graziella Marino","email":"","orcid":"","institution":"Centro di Riferimento Oncologico della Basilicata","correspondingAuthor":false,"prefix":"","firstName":"Graziella","middleName":"","lastName":"Marino","suffix":""},{"id":594564308,"identity":"11e4690a-b8eb-4e9d-88e4-9ac587735a74","order_by":6,"name":"Francesco Schettini","email":"","orcid":"","institution":"Hospital Clínic de Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Francesco","middleName":"","lastName":"Schettini","suffix":""},{"id":594564309,"identity":"278038b5-f746-4c9d-9919-a90b712bad3d","order_by":7,"name":"Elda Tagliabue","email":"","orcid":"","institution":"Fondazione IRCCS Istituto Nazionale dei Tumori","correspondingAuthor":false,"prefix":"","firstName":"Elda","middleName":"","lastName":"Tagliabue","suffix":""},{"id":594564310,"identity":"3542ea4c-1c0d-410d-a7ee-8575d658c41a","order_by":8,"name":"Gianpaolo Ruocco","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYDACZgglAxfgB+LDIAYbAS08cAHJBpgWnHrQtRgcgJmDQ4u8O/OzxwU1Njz8DLzPJD7U2MkZ38g9eLigpo6BT74BqxbDw2zmxjOOpfFINrCbSc44lmxsdiMv4fCMY4dxOsywmcFMmoftMI/BATY2ad6GA4nbbuQYHOZhO4BHC/s3aZ5/SFo2zwBp+VeHU4s8M4+ZNG8bkpYNEkAtvG3MOLUYMPOUSc/sA/qlmY3ZEuQXiTNvDA7P7AO6jS0Buy39x7dJF3yzkeNnb2O8AQox/vYc488F3+rk5JsPYLflACwWmNFkeDDUwmxpwFQ8CkbBKBgFowAVAACcC00S+0I0iwAAAABJRU5ErkJggg==","orcid":"","institution":"University of Basilicata","correspondingAuthor":true,"prefix":"","firstName":"Gianpaolo","middleName":"","lastName":"Ruocco","suffix":""},{"id":594564311,"identity":"872a7117-8413-42e2-a238-94eeaa21b390","order_by":9,"name":"Daniele Generali","email":"","orcid":"","institution":"Istituti Ospitalieri di Cremona","correspondingAuthor":false,"prefix":"","firstName":"Daniele","middleName":"","lastName":"Generali","suffix":""}],"badges":[],"createdAt":"2026-01-26 18:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8703071/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8703071/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103504782,"identity":"1c355844-3500-4d01-b4ea-d6a65f72d6d6","added_by":"auto","created_at":"2026-02-26 13:21:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":716613,"visible":true,"origin":"","legend":"","description":"","filename":"CancerMate7BCfinal020226.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8703071/v1_covered_6c785408-e39f-491a-88f3-01e615983d12.pdf"},{"id":103195024,"identity":"22a24682-6e6a-4eb1-890d-ac4f4d256e8e","added_by":"auto","created_at":"2026-02-23 03:39:51","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":13322,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8703071/v1/4a42b2f9f0b417a344117765.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Computational modeling of lesion dynamics in HER2+ breast cancer: integrating gut microbiota diversity into therapy response prediction","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, computational modeling, gut microbiota","lastPublishedDoi":"10.21203/rs.3.rs-8703071/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8703071/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMathematical models based on partial differential equations (PDEs) can be exploited to integrate heterogeneous clinical and biological data for the interpretation of tumor dynamics during systemic therapy. In this study, a PDE-based model of tumor volume evolution was exercised to investigate the predictive role of clinical and microbiota-derived biomarkers in patients with HER2-positive breast cancer undergoing neoadjuvant chemotherapy.\u003c/p\u003e \u003cp\u003eWithin a retrospective cohort of 15 patients, a training subset of eight was used to identify and optimize a set of virtual parameters describing tumor proliferation and treatment efficacy. Tumor growth rate (\u003cem\u003er\u003c/em\u003e) and drug efficiency for the epirubicin\u0026ndash;cyclophosphamide branch (ϵ\u003csub\u003ePD1\u003c/sub\u003e) were modeled as functions of baseline Ki67 expression, while a Spearman correlation analysis identified key microbiota features (Firmicutes/Bacteroidetes ratio and the Simpson Diversity Index) associated with treatment response, or drug efficiency of the taxane\u0026ndash;trastuzumab branch (ϵ\u003csub\u003ePD2\u003c/sub\u003e).\u003c/p\u003e \u003cp\u003eModel robustness was subsequently assessed in an independent testing subset of seven patients. Simulated tumor volume dynamics did not significantly differ from clinical observations and showed strong predictive capability in discriminating therapeutic response (p\u0026thinsp;=\u0026thinsp;0.0070), correctly identifying all partial responses and 80% of pathological complete responses.\u003c/p\u003e \u003cp\u003eAfter defining appropriate mathematical assumptions, microbiota-informed drug efficiency parameters were shown to effectively capture inter-patient variability in treatment sensitivity. A simplified model of tumor dynamics integrating microbiota-derived variables was thus demonstrated to provide an upfront prediction of neoadjuvant chemotherapy efficacy. Prospective validation in larger cohorts and correlation with established clinical endpoints are now warranted to confirm the model and support patient-specific optimization of therapeutic strategies in HER2-positive breast cancer.\u003c/p\u003e","manuscriptTitle":"Computational modeling of lesion dynamics in HER2+ breast cancer: integrating gut microbiota diversity into therapy response prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 03:39:00","doi":"10.21203/rs.3.rs-8703071/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-02-19T06:44:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-19T06:41:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-06T00:27:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-04T15:10:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-04T15:01:44+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":"9f86ee2b-8f4e-4860-bf19-d043579017cf","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63273951,"name":"Health sciences/Biomarkers"},{"id":63273952,"name":"Biological sciences/Cancer"},{"id":63273953,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":63273954,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-02-23T03:39:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 03:39:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8703071","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8703071","identity":"rs-8703071","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}