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

Publication date: Available online 14 September 2017
Source:Cell Metabolism
Author(s): Maria V. Liberti, Ziwei Dai, Suzanne E. Wardell, Joshua A. Baccile, Xiaojing Liu, Xia Gao, Robert Baldi, Mahya Mehrmohamadi, Marc O. Johnson, Neel S. Madhukar, Alexander A. Shestov, Iok I. Christine Chio, Olivier Elemento, Jeffrey C. Rathmell, Frank C. Schroeder, Donald P. McDonnell, Jason W. Locasale
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.

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Teaser

Liberti et al. use metabolic control analysis and multi-omics approaches to show that the enzyme GAPDH is rate limiting for the Warburg effect in cancer cells. They identify a therapeutic window where partial GAPDH inhibition is more selective for highly glycolytic tumors, highlighting how metabolism is an integral part of precision medicine.


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