The Blavatnik School of Computer Science, The Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
Cancer Res. 2012 Nov 15;72(22):5712-20. doi: 10.1158/0008-5472.CAN-12-2215. Epub 2012 Sep 17.
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.
代谢异常是癌症的一个标志，但整体代谢通量测量仍然很少。为了弥补这一差距，我们开发了一种新的代谢表型分析（MPA）方法，该方法基于转录组学或蛋白质组学数据在人类基因组规模代谢模型中的整合来推断代谢表型。MPA 被应用于基于大量临床样本的基因表达进行首次乳腺癌代谢的全基因组规模研究。该模型正确预测了细胞系的增长率、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。在体外进行了实验验证。该分析揭示了乳腺癌中一种与亚型无关的“去或生长”二分法，其中随着肿瘤进化出转移能力，增殖率下降。MPA 还确定了一种化学计量权衡，将观察到的增殖率降低与不断增长的需要解毒活性氧联系起来。最后，发现丝氨酸和谷氨酰胺代谢之间存在基本的化学计量权衡，这为雌激素受体（ER）（+）与 ER（-）肿瘤代谢提供了一个新的标志。总之，我们的发现极大地扩展了对乳腺癌核心代谢异常及其影响的认识。
