Institute of Pathology, Charité - Universitätsmedizin Berlin, 10117 Berlin, Germany.
Genome Med. 2012 Apr 30;4(4):37. doi: 10.1186/gm336.
Breast cancer is the most common cancer in women worldwide, and the development of new technologies for better understanding of the molecular changes involved in breast cancer progression is essential. Metabolic changes precede overt phenotypic changes, because cellular regulation ultimately affects the use of small-molecule substrates for cell division, growth or environmental changes such as hypoxia. Differences in metabolism between normal cells and cancer cells have been identified. Because small alterations in enzyme concentrations or activities can cause large changes in overall metabolite levels, the metabolome can be regarded as the amplified output of a biological system. The metabolome coverage in human breast cancer tissues can be maximized by combining different technologies for metabolic profiling. Researchers are investigating alterations in the steady state concentrations of metabolites that reflect amplified changes in genetic control of metabolism. Metabolomic results can be used to classify breast cancer on the basis of tumor biology, to identify new prognostic and predictive markers and to discover new targets for future therapeutic interventions. Here, we examine recent results, including those from the European FP7 project METAcancer consortium, that show that integrated metabolomic analyses can provide information on the stage, subtype and grade of breast tumors and give mechanistic insights. We predict an intensified use of metabolomic screens in clinical and preclinical studies focusing on the onset and progression of tumor development.
乳腺癌是全世界女性最常见的癌症，开发新技术以更好地了解乳腺癌进展中涉及的分子变化至关重要。代谢变化先于明显的表型变化，因为细胞调节最终会影响细胞分裂、生长或缺氧等环境变化中小分子底物的利用。已经确定了正常细胞和癌细胞之间的代谢差异。由于酶浓度或活性的微小变化可能导致整体代谢物水平的巨大变化，因此代谢组可以被视为生物系统的放大输出。通过结合不同的代谢谱分析技术，可以最大限度地提高人类乳腺癌组织中的代谢组覆盖范围。研究人员正在研究反映代谢遗传控制放大变化的代谢物稳态浓度的改变。代谢组学结果可用于根据肿瘤生物学对乳腺癌进行分类，鉴定新的预后和预测标志物，并发现未来治疗干预的新靶点。在这里，我们检查了最近的研究结果，包括来自欧洲 FP7 项目 METAcancer 联盟的结果，这些结果表明，综合代谢组学分析可以提供有关乳腺癌肿瘤阶段、亚型和分级的信息，并提供机制见解。我们预测在专注于肿瘤发展的起始和进展的临床前和临床研究中，代谢组学筛查的使用将会增加。
