A Predictive Model for Selective Targeting of the Warburg Effect through GAPDH Inhibition with a Natural Product

作者：Liberti Maria V; Dai Ziwei; Wardell Suzanne E; Baccile Joshua A; Liu Xiaojing; Gao Xia; Baldi Robert; Mehrmohamadi Mahya; Johnson Marc O; Madhukar Neel S; Shestov Alexander A; Chio Iok I Christine; Elemento Olivier; Rathmell Jeffrey C; Schroeder Frank C; McDonnell Donald P; Locasale Jason W^*

来源：Cell Metabolism, 2017, 26(4): 648-+.

DOI：10.1016/j.cmet.2017.08.017

Targeted cancer therapies that use genetics are successful, but principles for selectively targeting tumor metabolism that is also dependent on the environment remain unknown. Wenow 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 pharmaco-genomics 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.

• 出版日期2017-10-3