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dc.contributor.authorAure, Miriam Ragle
dc.contributor.authorVitelli, Valeria
dc.contributor.authorJernström, Sandra Johanna
dc.contributor.authorKumar, Surendra
dc.contributor.authorKrohn, Marit
dc.contributor.authorDue, Eldri Undlien
dc.contributor.authorHaukaas, Tonje Husby
dc.contributor.authorLeivonen, Suvi-Katri
dc.contributor.authorVollan, Hans Kristian Moen
dc.contributor.authorLuders, Torben
dc.contributor.authorRødland, Einar Andreas
dc.contributor.authorVaske, Charles
dc.contributor.authorZhao, Wei
dc.contributor.authorMøller, Elen Kristine
dc.contributor.authorNord, Silje
dc.contributor.authorGiskeødegård, Guro F.
dc.contributor.authorBathen, Tone Frost
dc.contributor.authorCaldas, Carlos
dc.contributor.authorTramm, Trine
dc.contributor.authorAlsner, Jan
dc.contributor.authorOvergaard, Jens
dc.contributor.authorGeisler, Jürgen
dc.contributor.authorBukholm, Ida Rashida Khan
dc.contributor.authorNaume, Bjørn
dc.contributor.authorSchlichting, Ellen
dc.contributor.authorSauer, Torill
dc.contributor.authorMills, Gordon B.
dc.contributor.authorKåresen, Rolf
dc.contributor.authorMælandsmo, Gunhild
dc.contributor.authorLingjærde, Ole Christian
dc.contributor.authorFrigessi, Arnoldo
dc.contributor.authorKristensen, Vessela N.
dc.contributor.authorBørresen-Dale, Anne-Lise
dc.contributor.authorSahlberg, Kristine Kleivi
dc.contributor.authorOSBREAC, Oslo Breast Cancer Consortium
dc.date.accessioned2017-10-18T06:40:34Z
dc.date.available2017-10-18T06:40:34Z
dc.date.created2017-04-08T12:00:04Z
dc.date.issued2017
dc.identifier.citationBreast Cancer Research. 2017, 19 (44), .nb_NO
dc.identifier.issn1465-542X
dc.identifier.urihttp://hdl.handle.net/11250/2460651
dc.description.abstractBackground Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. Methods Tumor tissue from 425 patients with primary breast cancer from the Oslo2 study was cut and blended, and divided into fractions for DNA, RNA and protein isolation and metabolomics, allowing the acquisition of representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a “cluster-of-clusters” approach with consensus clustering. Results Based on DNA copy number data, tumors were categorized into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and tumors were classified into ten subtypes using integrative clustering. The final consensus clustering that incorporated all aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype; these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significantly different outcomes, and in another a trend was observed. Conclusions The six integrated subtypes identified confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing knowledge of the heterogeneity of the luminal A subtype may add pivotal information to guide therapeutic choices, evidently bringing us closer to improved treatment for this largest subgroup of breast cancer.nb_NO
dc.language.isoengnb_NO
dc.publisherBioMed Centralnb_NO
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleIntegrative clustering reveals a novel split in the luminal A subtype of breast cancer with impact on outcomenb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber18nb_NO
dc.source.volume19nb_NO
dc.source.journalBreast Cancer Researchnb_NO
dc.source.issue44nb_NO
dc.identifier.doi10.1186/s13058-017-0812-y
dc.identifier.cristin1464530
dc.relation.projectNorges forskningsråd: 237718nb_NO
dc.description.localcode© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)nb_NO
cristin.unitcode194,65,25,0
cristin.unitnameInstitutt for sirkulasjon og bildediagnostikk
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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