Aberrant metabolism is a hallmark of cancer, but whole metabolomic flux measurements remain scarce. To bridge this gap, we developed a novel metabolic phenotypic analysis (MPA) method that infers metabolic phenotypes based on the integration of transcriptomics or proteomics data within a human genome-scale metabolic model. MPA was applied to conduct the first genome-scale study of breast cancer metabolism based on the gene expression of a large cohort of clinical samples. The modeling correctly predicted cell lines' growth rates, tumor lipid levels, and amino acid biomarkers, outperforming extant metabolic modeling methods. Experimental validation was obtained in vitro. The analysis revealed a subtype-independent "go or grow" dichotomy in breast cancer, where proliferation rates decrease as tumors evolve metastatic capability. MPA also identified a stoichiometric tradeoff that links the observed reduction in proliferation rates to the growing need to detoxify reactive oxygen species. Finally, a fundamental stoichiometric tradeoff between serine and glutamine metabolism was found, presenting a novel hallmark of estrogen receptor (ER)(+) versus ER(-) tumor metabolism. Together, our findings greatly extend insights into core metabolic aberrations and their impact in breast cancer.

译文

异常代谢是癌症的标志,但整个代谢组通量测量仍然很少。为了弥合这一差距,我们开发了一种新颖的代谢表型分析 (MPA) 方法,该方法基于人类基因组规模代谢模型中转录组学或蛋白质组学数据的整合来推断代谢表型。根据大量临床样本的基因表达,将MPA应用于乳腺癌代谢的首次基因组规模研究。该模型正确预测了细胞系的生长速率,肿瘤脂质水平和氨基酸生物标志物,优于现有的代谢建模方法。在体外获得了实验验证。分析揭示了乳腺癌中独立于亚型的 “去或生长” 二分法,随着肿瘤发展转移能力,增殖率降低。MPA还确定了化学计量的权衡,该权衡将观察到的增殖速率降低与对活性氧的解毒需求的增长联系起来。最后,发现丝氨酸和谷氨酰胺代谢之间的基本化学计量权衡,呈现了雌激素受体 (ER)() 与ER(-) 肿瘤代谢的新标志。总之,我们的发现极大地扩展了对核心代谢异常及其对乳腺癌影响的见解。

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