Cancer cells are adept at reprogramming energy metabolism, and the precise manifestation of this metabolic reprogramming exhibits heterogeneity across individuals (and from cell to cell). In this study, we analyzed the metabolic differences between interpersonal heterogeneous cancer phenotypes. We used divergence analysis on gene expression data of 1156 breast normal and tumor samples from The Cancer Genome Atlas (TCGA) and integrated this information with a genome-scale reconstruction of human metabolism to generate personalized, context-specific metabolic networks. Using this approach, we classified the samples into four distinct groups based on their metabolic profiles. Enrichment analysis of the subsystems indicated that amino acid metabolism, fatty acid oxidation, citric acid cycle, androgen and estrogen metabolism, and reactive oxygen species (ROS) detoxification distinguished these four groups. Additionally, we developed a workflow to identify potential drugs that can selectively target genes associated with the reactions of interest. MG-132 (a proteasome inhibitor) and OSU-03012 (a celecoxib derivative) were the top-ranking drugs identified from our analysis and known to have anti-tumor activity. Our approach has the potential to provide mechanistic insights into cancer-specific metabolic dependencies, ultimately enabling the identification of potential drug targets for each patient independently, contributing to a rational personalized medicine approach.

中文翻译：

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识别乳腺癌中的个性化代谢特征

癌细胞善于重编程能量代谢，这种代谢重编程的精确表现在个体之间（以及细胞与细胞之间）表现出异质性。在这项研究中，我们分析了人际异质癌症表型之间的代谢差异。我们对来自癌症基因组图谱（TCGA）的1156例正常乳腺和肿瘤样品的基因表达数据进行了发散分析，并将此信息与人类代谢的基因组规模重建相结合，以生成个性化的，针对特定环境的代谢网络。使用这种方法，我们根据其代谢谱将样品分为四个不同的组。对子系统的富集分析表明，氨基酸代谢，脂肪酸氧化，柠檬酸循环，雄激素和雌激素代谢，活性氧（ROS）排毒区分了这四个组。此外，我们开发了一种工作流程，以识别可以选择性靶向与目标反应相关的基因的潜在药物。MG-132（蛋白酶体抑制剂）和OSU-03012（塞来昔布衍生物）是我们分析中确定的顶级药物，已知具有抗肿瘤活性。我们的方法有可能提供对特定于癌症的代谢依赖性的机制性见解，最终使每位患者能够独立识别潜在的药物靶标，从而为合理的个性化药物治疗方法做出了贡献。MG-132（蛋白酶体抑制剂）和OSU-03012（塞来昔布衍生物）是我们分析中确定的顶级药物，已知具有抗肿瘤活性。我们的方法有可能提供对特定于癌症的代谢依赖性的机制性见解，最终使每位患者能够独立识别潜在的药物靶标，从而为合理的个性化药物治疗方法做出了贡献。MG-132（蛋白酶体抑制剂）和OSU-03012（塞来昔布衍生物）是我们分析中确定的顶级药物，已知具有抗肿瘤活性。我们的方法有可能提供对特定于癌症的代谢依赖性的机制性见解，最终使每位患者能够独立识别潜在的药物靶标，从而为合理的个性化药物治疗方法做出了贡献。