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

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

  • Cell Metab. 2017 Oct 3;26(4):648-659.e8. doi: 10.1016/j.cmet.2017.08.017.
Maria V Liberti 1,  Ziwei Dai 2,  Suzanne E Wardell 2,  Joshua A Baccile 3,  Xiaojing Liu 2,  Xia Gao 2,  Robert Baldi 2,  Mahya Mehrmohamadi 1,  Marc O Johnson 4,  Neel S Madhukar 5,  Alexander A Shestov 6,  Iok I Christine Chio 7,  Olivier Elemento 5,  Jeffrey C Rathmell 4,  Frank C Schroeder 3,  Donald P McDonnell 2,  Jason W Locasale 8
Affiliations

Affiliations

  • 1 Department of Pharmacology and Cancer Biology, Duke Cancer Institute, Duke University School of Medicine, Durham, NC 27710, USA; Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA.
  • 2 Department of Pharmacology and Cancer Biology, Duke Cancer Institute, Duke University School of Medicine, Durham, NC 27710, USA.
  • 3 Boyce Thompson Institute and Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853, USA.
  • 4 Department of Pathology, Microbiology, and Immunology, Vanderbilt Center for Immunobiology, Vanderbilt University Medical Center, Nashville, TN 37232, USA.
  • 5 Department of Physiology and Biophysics, Meyer Cancer Center, Institute for Precision Medicine and Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY 10021, USA.
  • 6 Molecular Imaging and Metabolomics Lab, Radiology Department, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
  • 7 Cold Spring Harbor Laboratory, Lustgarten Foundation Pancreatic Cancer Research Laboratory, Cold Spring Harbor, NY 11724, USA.
  • 8 Department of Pharmacology and Cancer Biology, Duke Cancer Institute, Duke University School of Medicine, Durham, NC 27710, USA. Electronic address: [email protected].
PMID: 28918937 DOI: 10.1016/j.cmet.2017.08.017
Abstract

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.

Keywords

Warburg effect; cancer metabolism; glucose metabolism; metabolic control analysis; metabolic flux analysis; metabolomics; natural product; pharmacogenomics; precision medicine; systems biology.

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