Targeted cancer therapies that use genetics are successful, but principles for selectively targeting tumor metabolism that is also dependent on the environment remain unknown. We now show that differences in rate-controlling enzymes during the Warburg effect (WE), the most prominent hallmark of cancer cell metabolism, can be used to predict a response to targeting glucose metabolism. We establish a natural product, koningic acid (KA), to be a selective inhibitor of GAPDH, an enzyme we characterize to have differential control properties over metabolism during the WE. With machine learning and integrated pharmacogenomics and metabolomics, we demonstrate that KA efficacy is not determined by the status of individual genes, but by the quantitative extent of the WE, leading to a therapeutic window in vivo. Thus, the basis of targeting the WE can be encoded by molecular principles that extend beyond the status of individual genes.

中文翻译：

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通过天然产物GAPDH抑制选择性靶向Warburg效应的预测模型。

使用遗传学的靶向癌症疗法是成功的，但是选择性靶向肿瘤代谢的原理（也取决于环境）仍然未知。我们现在显示，在Warburg效应（WE）（癌细胞代谢的最显着特征）期间，速率控制酶的差异可用于预测对靶向葡萄糖代谢的反应。我们建立了天然产物康宁酸（KA），作为GAPDH的选择性抑制剂，GAPDH是一种酶，我们表征为在WE期间对代谢具有不同的控制特性。通过机器学习以及整合的药物基因组学和代谢组学，我们证明了KA的疗效不是由单个基因的状态决定的，而是由WE的定量程度决定的，从而导致了体内治疗窗口的发展。因此，