Dai Chen, Arceo Jennifer, Arnold James, Sreekumar Arun, Dovichi Norman J, Li Jun, Littlepage Laurie E
1Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN 46556 USA.
2Harper Cancer Research Institute, University of Notre Dame, 1234 N Notre Dame Avenue, South Bend, IN 46617 USA.
Cancer Metab. 2018 Apr 3;6:5. doi: 10.1186/s40170-018-0175-6. eCollection 2018.
The complex yet interrelated connections between cancer metabolism and oncogenic driver genes are relatively unexplored but have the potential to identify novel biomarkers and drug targets with prognostic and therapeutic value. The goal of this study was to identify global metabolic profiles of breast tumors isolated from multiple transgenic mouse models and to identify unique metabolic signatures driven by these oncogenes.
Using mass spectrometry (GC-MS, LC-MS/MS, and capillary zone electrophoresis (CZE)-MS platforms), we quantified and compared the levels of 374 metabolites in breast tissue from normal and transgenic mouse breast cancer models overexpressing a panel of oncogenes (PyMT, PyMT-DB, Wnt1, Neu, and C3-TAg). We also compared the mouse metabolomics data to published human metabolomics data already linked to clinical data.
Through analysis of our metabolomics data, we identified metabolic differences between normal and tumor breast tissues as well as metabolic differences unique to each initiating oncogene. We also quantified the metabolic profiles of the mammary fat pad versus mammary epithelium by CZE-MS/MS. However, the differences between the tissues did not account for the majority of the metabolic differences between the normal mammary gland and breast tumor tissues. Therefore, the differences between the cohorts were unlikely due to cellular heterogeneity. Of the mouse models used in this study, C3-TAg was the only cohort with a tumor metabolic signature composed of ten metabolites that had significant prognostic value in breast cancer patients. Gene expression analysis identified candidate genes that may contribute to the metabolic reprogramming.
This study identifies oncogene-induced metabolic reprogramming within mouse breast tumors and compares the results to that of human breast tumors, providing a unique look at the relationship between and clinical value of oncogene initiation and metabolism during breast cancer.
癌症代谢与致癌驱动基因之间复杂而相互关联的联系尚未得到充分探索，但有潜力识别出具有预后和治疗价值的新型生物标志物及药物靶点。本研究的目的是确定从多个转基因小鼠模型中分离出的乳腺肿瘤的整体代谢谱，并识别由这些致癌基因驱动的独特代谢特征。
我们使用质谱（气相色谱 - 质谱联用、液相色谱 - 串联质谱联用以及毛细管区带电泳 - 质谱联用平台），对正常和过表达一组致癌基因（PyMT、PyMT - DB、Wnt1、Neu和C3 - TAg）的转基因小鼠乳腺癌模型的乳腺组织中的374种代谢物水平进行了定量和比较。我们还将小鼠代谢组学数据与已与临床数据相关联的已发表的人类代谢组学数据进行了比较。
通过对我们的代谢组学数据进行分析，我们确定了正常乳腺组织与肿瘤乳腺组织之间的代谢差异以及每个起始致癌基因特有的代谢差异。我们还通过毛细管区带电泳 - 串联质谱联用对乳腺脂肪垫与乳腺上皮的代谢谱进行了定量。然而，组织之间的差异并不能解释正常乳腺组织与乳腺肿瘤组织之间大部分的代谢差异。因此，不同组之间的差异不太可能是由于细胞异质性导致的。在本研究中使用的小鼠模型中，C3 - TAg是唯一一组具有由十种代谢物组成的肿瘤代谢特征的组，这些代谢物在乳腺癌患者中具有显著的预后价值。基因表达分析确定了可能导致代谢重编程的候选基因。
本研究确定了小鼠乳腺肿瘤中致癌基因诱导的代谢重编程，并将结果与人类乳腺肿瘤的结果进行了比较，为乳腺癌发生过程中致癌基因启动与代谢之间的关系及其临床价值提供了独特的见解。
