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Cancer Metab. 2016 Jun 27;4:12. doi: 10.1186/s40170-016-0152-x. eCollection 2016.

Metabolic clusters of breast cancer in relation to gene- and protein expression subtypes.

Author information

1
Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway ; K.G. Jebsen Center for Breast Cancer Research, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.
2
Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
3
Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway ; St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
4
Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway ; Department of Food Science, Faculty of Science and Technology, Aarhus University, Årslev, Denmark.
5
K.G. Jebsen Center for Breast Cancer Research, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway ; Department of Cancer Genetics, Institute for Cancer Research Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway.
6
K.G. Jebsen Center for Breast Cancer Research, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway ; Department of Computer Science, University of Oslo, Oslo, Norway ; Centre for Cancer Biomedicine, University of Oslo, Oslo, Norway.
7
Section for Breast and Endocrine Surgery, Oslo University Hospital, Ullevål, Oslo Norway.
8
Department of Pathology, Oslo University Hospital, Oslo, Norway.
9
Department of Systems Biology, The University of Texas M.D. Anderson Cancer Center, Houston, TX USA.
10
K.G. Jebsen Center for Breast Cancer Research, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway ; Department of Research, Vestre Viken, Drammen, Norway.

Abstract

BACKGROUND:

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.

KEYWORDS:

Breast cancer subgroups; Extracellular matrix; HR MAS MRS; Metabolic cluster; Metabolomics

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