In Search of the Perfect Model: How Cancer Cell Lines Relate to Native Cancers

preprint OA: closed
Full text JSON View at publisher
AI-generated summary by claude@2026-07, 2026-07-16

This study compared copy number variation profiles between cancer cell lines and native cancers using machine learning, finding cell lines retain similar CNV profiles and recommend cell lines best representing specific cancer types for in vitro research.

One-sentence paraphrase of the abstract; not a substitute for reading it. No clinical advice. How this works

AI-generated deep summary by claude@2026-07, 2026-07-16 · read from full text

This paper examined how closely commonly used cancer cell lines match native cancers by comparing large collections of copy number variation (CNV) profiles and using machine learning to predict cell-line classifications. Across analyses, the authors report that cell lines accumulate more mutations than native cancers while retaining similar CNV profiles, and that both groups show CNV alterations involving relevant oncogenes and tumor suppressor genes. They also use the observed similarities and model predictions to provide recommendations for cell lines with good potential to represent selected cancer types in in vitro studies, while noting that the work is framed as a preprint under peer-review status. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

Abstract

Abstract Cancer cell lines are frequently used in biological and translational research to study cellular mechanisms and explore treatment options. However, cancer cell lines may display mutational profiles divergent from native cancers or may be misidentified or contaminated. We explored how similar cancer cell lines are to native cancers to find the most suitable representations for the corresponding diseases by utilising large collections of copy number variation (CNV) profiles and applied machine learning (ML) algorithms to predict cell line classifications. Our results confirm that cancer cell lines indeed accumulate more mutations compared to native cancers but retain similar CNV profiles. We demonstrate that many relevant oncogenes and tumor suppressor genes are altered by CNV events in both cancers and their corresponding cell lines. Based on the similarities between the two groups and the predictions of the ML model, we provide some recommendations about cell lines with good potential to represent selected cancer types in in vitro studies.
Full text 10,378 characters · extracted from preprint-html · click to expand
In Search of the Perfect Model: How Cancer Cell Lines Relate to Native Cancers | 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 In Search of the Perfect Model: How Cancer Cell Lines Relate to Native Cancers Rahel Paloots, Ziying Yang, Michael Baudis This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4424943/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 Cancer cell lines are frequently used in biological and translational research to study cellular mechanisms and explore treatment options. However, cancer cell lines may display mutational profiles divergent from native cancers or may be misidentified or contaminated. We explored how similar cancer cell lines are to native cancers to find the most suitable representations for the corresponding diseases by utilising large collections of copy number variation (CNV) profiles and applied machine learning (ML) algorithms to predict cell line classifications. Our results confirm that cancer cell lines indeed accumulate more mutations compared to native cancers but retain similar CNV profiles. We demonstrate that many relevant oncogenes and tumor suppressor genes are altered by CNV events in both cancers and their corresponding cell lines. Based on the similarities between the two groups and the predictions of the ML model, we provide some recommendations about cell lines with good potential to represent selected cancer types in in vitro studies. CNV cancer cancer cell lines Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 May, 2024 Editor assigned by journal 29 May, 2024 Submission checks completed at journal 29 May, 2024 First submitted to journal 15 May, 2024 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-4424943","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":308228778,"identity":"57bc1296-f9eb-4295-bea8-b345a54d7015","order_by":0,"name":"Rahel Paloots","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYFACHgaGBwwMMmD2ByBmYydGSwKYZGZgnAHSwkyKFmYekAAhLebtZw8+SNxhx8PP3n/4s82vbfJ8QNs+fMzBrUXmTF6yQeKZZB7JnsNs0rl9tw3bgLZJztyGW4sEQ46ZRGIbM4/BjWQ25tye24xALWzMvPi08L8x/5HYVg/SwvzZsue2PWEtEjlmDIlth0FaGKQZftxOJELLG2OJxDPHQX4xk+xtuJ3cxszYjN8v/DmGHz7uqJbjZ298/OHHn9u289ubD374iEcLGDA2wBhtKFxitDD8Iax4FIyCUTAKRh4AANGeSqRmVfyeAAAAAElFTkSuQmCC","orcid":"","institution":"University of Zurich","correspondingAuthor":true,"prefix":"","firstName":"Rahel","middleName":"","lastName":"Paloots","suffix":""},{"id":308228779,"identity":"941b9830-d264-412b-8777-c3e40947bfab","order_by":1,"name":"Ziying Yang","email":"","orcid":"","institution":"University of Zurich","correspondingAuthor":false,"prefix":"","firstName":"Ziying","middleName":"","lastName":"Yang","suffix":""},{"id":308228780,"identity":"d25f4989-1748-413c-b1ad-2619f478059d","order_by":2,"name":"Michael Baudis","email":"","orcid":"","institution":"University of Zurich","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Baudis","suffix":""}],"badges":[],"createdAt":"2024-05-15 11:48:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4424943/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4424943/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58082744,"identity":"6d324430-42b4-4799-ac34-658980a0c139","added_by":"auto","created_at":"2024-06-11 01:39:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3843105,"visible":true,"origin":"","legend":"","description":"","filename":"CellLinevsTumorpaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4424943/v1_covered_5ea1e349-8e3a-4f97-ac61-53b577ff9ef7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"In Search of the Perfect Model: How Cancer Cell Lines Relate to Native Cancers","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":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"CNV, cancer, cancer cell lines","lastPublishedDoi":"10.21203/rs.3.rs-4424943/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4424943/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Cancer cell lines are frequently used in biological and translational research to study cellular mechanisms and explore treatment options. However, cancer cell lines may display mutational profiles divergent from native cancers or may be misidentified or contaminated. We explored how similar cancer cell lines are to native cancers to find the most suitable representations for the corresponding diseases by utilising large collections of copy number variation (CNV) profiles and applied machine learning (ML) algorithms to predict cell line classifications.\nOur results confirm that cancer cell lines indeed accumulate more mutations compared to native cancers but retain similar CNV profiles. We demonstrate that many relevant oncogenes and tumor suppressor genes are altered by CNV events in both cancers and their corresponding cell lines. Based on the similarities between the two groups and the predictions of the ML model, we provide some recommendations about cell lines with good potential to represent selected cancer types in in vitro studies.","manuscriptTitle":"In Search of the Perfect Model: How Cancer Cell Lines Relate to Native Cancers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-11 01:30:58","doi":"10.21203/rs.3.rs-4424943/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-29T13:13:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-29T06:52:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-29T06:52:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2024-05-15T11:47:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b5d1e949-3ce7-4373-bf8d-74b314d7cf0c","owner":[],"postedDate":"June 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-09-24T07:23:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-11 01:30:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4424943","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4424943","identity":"rs-4424943","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

Ask this paper AI returns verbatim quotes from the full text · source: preprint-html

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2024) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00