The prognostic impact of the tumour stroma fraction: A machine learning-based analysis in 16 human solid tumour types

dc.contributor.authorMicke, Patrick
dc.contributor.authorStrell, Carina
dc.contributor.authorMattsson, Johanna
dc.contributor.authorMartín Bernabé, Alfonso
dc.contributor.authorBrunnström, Hans
dc.contributor.authorHuvila, Jutta
dc.contributor.authorSund, Malin
dc.contributor.authorWärnberg, Fredrik
dc.contributor.authorPonten, Fredrik
dc.contributor.authorGlimelius, Bengt
dc.contributor.authorHrynchyk, Ina
dc.contributor.authorMauchanski, Siarhei
dc.contributor.authorKhelashvili, Salome
dc.contributor.authorGarcia Vicién, Gemma
dc.contributor.authorMolleví, David G.
dc.contributor.authorEdqvist, Per-Henrik
dc.contributor.authorO´Reilly, Aine
dc.contributor.authorCorvigno, Sara
dc.contributor.authorDahlstrand, Hanna
dc.contributor.authorBotling, Johan
dc.contributor.authorSegersten, Ulrika
dc.contributor.authorKrzyzanowska, Agnieszka
dc.contributor.authorBjartell, Anders
dc.contributor.authorElebro, Jacob
dc.contributor.authorHeby, Margareta
dc.contributor.authorLundgren, Sebastian
dc.contributor.authorHedner, Charlotta
dc.contributor.authorBorg, David
dc.contributor.authorBrändstedt, Jenny
dc.contributor.authorSartor, Hanna
dc.contributor.authorMalmström, Per-Uno
dc.contributor.authorJohansson, Martin
dc.contributor.authorNodin, Björn
dc.contributor.authorBackman, Max
dc.contributor.authorLindskog, Cecilia
dc.contributor.authorJirström, Karin
dc.contributor.authorMezheyeuski, Artur
dc.date.accessioned2021-04-23T09:32:15Z
dc.date.available2021-04-23T09:32:15Z
dc.date.issued2021-03-01
dc.date.updated2021-04-22T10:06:35Z
dc.description.abstractBackground: The development of a reactive tumour stroma is a hallmark of tumour progression and pronounced tumour stroma is generally considered to be associated with clinical aggressiveness. The variability between tumour types regarding stroma fraction, and its prognosis associations, have not been systematically analysed. Methods: Using an objective machine-learning method we quantified the tumour stroma in 16 solid cancer types from 2732 patients, representing retrospective tissue collections of surgically resected primary tumours. Image analysis performed tissue segmentation into stromal and epithelial compartment based on pan-cytokeratin staining and autofluorescence patterns. Findings: The stroma fraction was highly variable within and across the tumour types, with kidney cancer showing the lowest and pancreato-biliary type periampullary cancer showing the highest stroma proportion (median 19% and 73% respectively). Adjusted Cox regression models revealed both positive (pancreato-biliary type periampullary cancer and oestrogen negative breast cancer, HR(95%CI)=0.56(0.34-0.92) and HR(95%CI)=0.41(0.17-0.98) respectively) and negative (intestinal type periampullary cancer, HR(95%CI)=3.59(1.49-8.62)) associations of the tumour stroma fraction with survival. Interpretation: Our study provides an objective quantification of the tumour stroma fraction across major types of solid cancer. Findings strongly argue against the commonly promoted view of a general associations between high stroma abundance and poor prognosis. The results also suggest that full exploitation of the prognostic potential of tumour stroma requires analyses that go beyond determination of stroma abundance. Funding: The Swedish Cancer Society, The Lions Cancer Foundation Uppsala, The Swedish Government Grant for Clinical Research, The Mrs Berta Kamprad Foundation, Sweden, Sellanders foundation, P.O.Zetterling Foundation, and The Sjöberg Foundation, Sweden.
dc.format.extent6 p.
dc.format.mimetypeapplication/pdf
dc.identifier.pmid33706249
dc.identifier.urihttps://hdl.handle.net/2445/176675
dc.language.isoeng
dc.publisherElsevier B. V.
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.1016/j.ebiom.2021.103269
dc.relation.ispartofEBioMedicine, 2021, vol. 65
dc.relation.urihttps://doi.org/10.1016/j.ebiom.2021.103269
dc.rightscc by (c) Micke et al., 2021
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.sourceArticles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))
dc.subject.classificationTumors
dc.subject.classificationAprenentatge automàtic
dc.subject.classificationPronòstic mèdic
dc.subject.otherTumors
dc.subject.otherMachine learning
dc.subject.otherPrognosis
dc.titleThe prognostic impact of the tumour stroma fraction: A machine learning-based analysis in 16 human solid tumour types
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion

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