Show simple item record

dc.contributor.authorHaukaas, Tonje Husby
dc.contributor.authorEuceda, Leslie R.
dc.contributor.authorGiskeødegård, Guro F.
dc.contributor.authorLamichhane, Santosh
dc.contributor.authorKrohn, Marit
dc.contributor.authorJernstrøm, Sandra
dc.contributor.authorAure, Miriam Ragle
dc.contributor.authorLingjærde, Ole Christian
dc.contributor.authorSchlichting, Ellen
dc.contributor.authorGarred, Øystein
dc.contributor.authorDue, Eldri Undlien
dc.contributor.authorMills, Gordon B.
dc.contributor.authorSahlberg, Kristine Kleivi
dc.contributor.authorBørresen-Dale, Anne-Lise
dc.contributor.authorBathen, Tone Frost
dc.contributor.author(OSBREAC), Oslo Breast Cancer Consortium
dc.date.accessioned2016-07-06T11:46:23Z
dc.date.accessioned2016-07-08T07:11:36Z
dc.date.available2016-07-06T11:46:23Z
dc.date.available2016-07-08T07:11:36Z
dc.date.issued2016
dc.identifier.citationCancer & Metabolism 2016nb_NO
dc.identifier.issn2049-3002
dc.identifier.urihttp://hdl.handle.net/11250/2396096
dc.description.abstractBackground: The heterogeneous biology of breast cancer leads to high diversity in prognosis and response to treatment, even for patients with similar clinical diagnosis, histology, and stage of disease. Identifying mechanisms contributing to this heterogeneity may reveal new cancer targets or clinically relevant subgroups for treatment stratification. In this study, we have merged metabolite, protein, and gene expression data from breast cancer patients to examine the heterogeneity at a molecular level. Methods: The study included primary tumor samples from 228 non-treated breast cancer patients. High-resolution magic-angle spinning magnetic resonance spectroscopy (HR MAS MRS) was performed to extract the tumors metabolic profiles further used for hierarchical cluster analysis resulting in three significantly different metabolic clusters (Mc1, Mc2, and Mc3). The clusters were further combined with gene and protein expression data. Results: Our result revealed distinct differences in the metabolic profile of the three metabolic clusters. Among the most interesting differences, Mc1 had the highest levels of glycerophosphocholine (GPC) and phosphocholine (PCho), Mc2 had the highest levels of glucose, and Mc3 had the highest levels of lactate and alanine. Integrated pathway analysis of metabolite and gene expression data uncovered differences in glycolysis/gluconeogenesis and glycerophospholipid metabolism between the clusters. All three clusters had significant differences in the distribution of protein subtypes classified by the expression of breast cancer-related proteins. Genes related to collagens and extracellular matrix were downregulated in Mc1 and consequently upregulated in Mc2 and Mc3, underpinning the differences in protein subtypes within the metabolic clusters. Genetic subtypes were evenly distributed among the three metabolic clusters and could therefore contribute to additional explanation of breast cancer heterogeneity. Conclusions: Three naturally occurring metabolic clusters of breast cancer were detected among primary tumors from non-treated breast cancer patients. The clusters expressed differences in breast cancer-related protein as well as genes related to extracellular matrix and metabolic pathways known to be aberrant in cancer. Analyses of metabolic activity combined with gene and protein expression provide new information about the heterogeneity of breast tumors and, importantly, the metabolic differences infer that the clusters may be susceptible to different metabolically targeted drugs.nb_NO
dc.language.isoengnb_NO
dc.publisherBioMed Centralnb_NO
dc.rights.uriMetabolic clusters of breast cancer in relation to gene- and protein expression subtypes
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectMetabolomics – HR MAS MRS – Breast cancer subgroups – Metabolic cluster – Extracellular matrixnb_NO
dc.titleMetabolic clusters of breast cancer in relation to gene- and protein expression subtypesnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.date.updated2016-07-06T11:46:23Z
dc.rights.holder© The Author(s). 2016nb_NO
dc.source.volume4nb_NO
dc.source.journalCancer & Metabolismnb_NO
dc.source.issue12nb_NO
dc.identifier.doi10.1186/s40170-016-0152-x
dc.identifier.cristin1365760
dc.description.localcode© 2016 The Author(s). 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/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.nb_NO


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record