Laboratory of Human Carcinogenesis, Center for Cancer Research (CCR), NCI, NIH, Bethesda, Maryland.
Department of Molecular and Cellular Biology, Verna and Marrs McLean Department of Biochemistry and Alkek Center for Molecular Discovery, Baylor College of Medicine, Houston, Texas.
Clin Cancer Res. 2019 Oct 1;25(19):5972-5983. doi: 10.1158/1078-0432.CCR-19-0094. Epub 2019 Jul 11.
Metabolomics is a discovery tool for novel associations of metabolites with disease. Here, we interrogated the metabolome of human breast tumors to describe metabolites whose accumulation affects tumor biology.
We applied large-scale metabolomics followed by absolute quantification and machine learning-based feature selection using LASSO to identify metabolites that show a robust association with tumor biology and disease outcome. Key observations were validated with the analysis of an independent dataset and cell culture experiments.
LASSO-based feature selection revealed an association of tumor glycochenodeoxycholate levels with improved breast cancer survival, which was confirmed using a Cox proportional hazards model. Absolute quantification of four bile acids, including glycochenodeoxycholate and microbiome-derived deoxycholate, corroborated the accumulation of bile acids in breast tumors. Levels of glycochenodeoxycholate and other bile acids showed an inverse association with the proliferation score in tumors and the expression of cell-cycle and G-M checkpoint genes, which was corroborated with cell culture experiments. Moreover, tumor levels of these bile acids markedly correlated with metabolites in the steroid metabolism pathway and increased expression of key genes in this pathway, suggesting that bile acids may interfere with hormonal pathways in the breast. Finally, a proteome analysis identified the complement and coagulation cascade as being upregulated in glycochenodeoxycholate-high tumors.
We describe the unexpected accumulation of liver- and microbiome-derived bile acids in breast tumors. Tumors with increased bile acids show decreased proliferation, thus fall into a good prognosis category, and exhibit significant changes in steroid metabolism.
代谢组学是一种发现代谢物与疾病之间新关联的工具。在这里,我们研究了人类乳腺肿瘤的代谢组学,以描述影响肿瘤生物学的代谢物的积累。
我们应用了大规模代谢组学,然后使用基于 LASSO 的绝对定量和机器学习特征选择,以识别与肿瘤生物学和疾病结果具有稳健关联的代谢物。关键观察结果通过对独立数据集的分析和细胞培养实验进行了验证。
基于 LASSO 的特征选择揭示了肿瘤甘氨胆酸水平与改善乳腺癌生存之间的关联,这通过 Cox 比例风险模型得到了证实。四种胆汁酸(包括甘氨胆酸和微生物群衍生的脱氧胆酸)的绝对定量证实了胆汁酸在乳腺肿瘤中的积累。甘氨胆酸和其他胆汁酸的水平与肿瘤中的增殖评分和细胞周期和 G2-M 检查点基因的表达呈负相关,这与细胞培养实验结果一致。此外,这些胆汁酸在肿瘤中的水平与类固醇代谢途径中的代谢物显著相关,并增加了该途径中的关键基因的表达,表明胆汁酸可能干扰乳腺中的激素途径。最后,蛋白质组分析发现补体和凝血级联在甘氨胆酸高的肿瘤中上调。
我们描述了肝源性和微生物群衍生的胆汁酸在乳腺肿瘤中的意外积累。胆汁酸增加的肿瘤增殖减少,因此归入预后良好的类别,并表现出类固醇代谢的显著变化。