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.

中文翻译：

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乳腺癌的代谢簇与基因和蛋白质表达亚型相关


背景乳腺癌的异质性生物学导致预后和治疗反应的高度多样性，即使对于具有相似临床诊断、组织学和疾病阶段的患者也是如此。识别导致这种异质性的机制可能会揭示新的癌症靶标或用于治疗分层的临床相关亚组。在这项研究中，我们合并了乳腺癌患者的代谢物、蛋白质和基因表达数据，以在分子水平上检查异质性。方法该研究包括来自 228 名未经治疗的乳腺癌患者的原发肿瘤样本。采用高分辨率魔角旋转磁共振波谱 (HR MAS MRS) 提取肿瘤代谢谱，进一步用于分层聚类分析，产生三个显着不同的代谢簇（Mc1、Mc2 和 Mc3）。这些簇进一步与基因和蛋白质表达数据相结合。结果我们的结果揭示了三个代谢簇的代谢特征的明显差异。最有趣的差异是，Mc1 的甘油磷酸胆碱 (GPC) 和磷酸胆碱 (PCho) 水平最高，Mc2 的葡萄糖水平最高，Mc3 的乳酸和丙氨酸水平最高。代谢物和基因表达数据的综合途径分析揭示了簇之间糖酵解/糖异生和甘油磷脂代谢的差异。所有三个簇在按乳腺癌相关蛋白的表达分类的蛋白亚型分布上均存在显着差异。与胶原蛋白和细胞外基质相关的基因在 Mc1 中下调，因此在 Mc2 和 Mc3 中上调，从而支撑了代谢簇内蛋白质亚型的差异。 遗传亚型均匀分布在三个代谢簇中，因此有助于对乳腺癌异质性进行额外的解释。结论在未经治疗的乳腺癌患者的原发性肿瘤中检测到了三个自然发生的乳腺癌代谢簇。这些簇表达了乳腺癌相关蛋白以及与细胞外基质相关的基因和已知在癌症中异常的代谢途径的差异。代谢活性与基因和蛋白质表达相结合的分析提供了有关乳腺肿瘤异质性的新信息，重要的是，代谢差异表明这些簇可能对不同的代谢靶向药物敏感。