Abstract
BackgroundThe 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.MethodsThe 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.ResultsOur 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.ConclusionsThree 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s40170-016-0152-x) contains supplementary material, which is available to authorized users.
Highlights
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
The samples were cut into three sections where the edges of the two outer pieces were used for histological evaluation (including estrogen receptor (ER) status and tumor cell percentage), and an adequate part of the mid pieces were used for HR MAS magnetic resonance spectroscopy (MRS) experiments to obtain metabolic profiles
From the spectral data of 228 breast tumors, hierarchical clustering gave a dendrogram divided in three metabolic clusters (Mc) (Fig. 1a) Mc1, Mc2, and Mc3
Summary
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. Breast cancer accounts for 25 % of newly diagnosed cancers and 15 % of cancer deaths among women worldwide [1] It is a heterogeneous disease [2] with high diversity in prognosis and response to treatment. Searching for genetic features causing the variation in breast cancers, Perou et al used gene expression analyses followed by hierarchical clustering and defined naturally occurring molecular subtypes [4, 6] These subtypes are named basal-like, luminal A, luminal B, Erb-B2+ (Her enriched), and normal-like, and are found to be associated with tumor characteristics and clinical outcome; patients with basal-like tumors having the shortest and luminal A the longest relapse-free survival [6]. A centroid-based method called prediction analysis of microarrays 50 (PAM50), which uses the expression of 50 genes to classify breast cancer into these five intrinsic subtypes was later established and is broadly implemented [7]
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