Shajahan-Haq Ayesha N, Cheema Mehar S, Clarke Robert
Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University School of Medicine, 3970 Reservoir Road NW, Washington, DC 20057, USA.
Metabolites. 2015 Feb 16;5(1):100-18. doi: 10.3390/metabo5010100.
The metabolic profiles of breast cancer cells are different from normal mammary epithelial cells. Breast cancer cells that gain resistance to therapeutic interventions can reprogram their endogenous metabolism in order to adapt and proliferate despite high oxidative stress and hypoxic conditions. Drug resistance in breast cancer, regardless of subgroups, is a major clinical setback. Although recent advances in genomics and proteomics research has given us a glimpse into the heterogeneity that exists even within subgroups, the ability to precisely predict a tumor's response to therapy remains elusive. Metabolomics as a quantitative, high through put technology offers promise towards devising new strategies to establish predictive, diagnostic and prognostic markers of breast cancer. Along with other "omics" technologies that include genomics, transcriptomics, and proteomics, metabolomics fits into the puzzle of a comprehensive systems biology approach to understand drug resistance in breast cancer. In this review, we highlight the challenges facing successful therapeutic treatment of breast cancer and the innovative approaches that metabolomics offers to better understand drug resistance in cancer.
乳腺癌细胞的代谢谱不同于正常乳腺上皮细胞。对治疗干预产生耐药性的乳腺癌细胞可以重新编程其内源代谢,以便在高氧化应激和低氧条件下仍能适应并增殖。无论乳腺癌的亚型如何,耐药性都是一个重大的临床障碍。尽管基因组学和蛋白质组学研究的最新进展让我们得以一窥即使在亚型内部也存在的异质性,但精确预测肿瘤对治疗反应的能力仍然难以捉摸。代谢组学作为一种定量的高通量技术,有望为设计新策略以建立乳腺癌的预测、诊断和预后标志物提供帮助。与包括基因组学、转录组学和蛋白质组学在内的其他“组学”技术一起,代谢组学融入了全面系统生物学方法的拼图中,以理解乳腺癌的耐药性。在本综述中,我们强调了乳腺癌成功治疗面临的挑战以及代谢组学为更好地理解癌症耐药性所提供的创新方法。