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

Cell metabolism | Oct 3, 2017

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.

Pubmed ID: 28918937 RIS Download

Mesh terms: Animals | Cell Line, Tumor | Enzyme Inhibitors | Glucose | Glyceraldehyde-3-Phosphate Dehydrogenases | Glycolysis | Humans | Machine Learning | Metabolic Flux Analysis | Metabolomics | Mice, Inbred C57BL | Models, Biological | Molecular Targeted Therapy | Neoplasms | Sesquiterpenes | Systems Biology

Data used in this publication

None found

Associated grants

  • Agency: NCI NIH HHS, Id: R01 CA193256
  • Agency: NCI NIH HHS, Id: R01 CA174643
  • Agency: NIDDK NIH HHS, Id: R01 DK105550
  • Agency: NCI NIH HHS, Id: R00 CA168997
  • Agency: NIGMS NIH HHS, Id: T32 GM008500
  • Agency: NIGMS NIH HHS, Id: T32 GM007273
  • Agency: NHLBI NIH HHS, Id: R01 HL136664
  • Agency: NCI NIH HHS, Id: F99 CA222986

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