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

Publication ,  Journal Article
Liberti, MV; Dai, Z; Wardell, SE; Baccile, JA; Liu, X; Gao, X; Baldi, R; Mehrmohamadi, M; Johnson, MO; Madhukar, NS; Shestov, AA; Chio, IIC ...
Published in: Cell metabolism
October 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.

Duke Scholars

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Published In

Cell metabolism

DOI

EISSN

1932-7420

ISSN

1550-4131

Publication Date

October 2017

Volume

26

Issue

4

Start / End Page

648 / 659.e8

Related Subject Headings

  • Systems Biology
  • Sesquiterpenes
  • Neoplasms
  • Molecular Targeted Therapy
  • Models, Biological
  • Mice, Inbred C57BL
  • Metabolomics
  • Metabolic Flux Analysis
  • Machine Learning
  • Humans
 

Citation

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Chicago
ICMJE
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Liberti, M. V., Dai, Z., Wardell, S. E., Baccile, J. A., Liu, X., Gao, X., … Locasale, J. W. (2017). A Predictive Model for Selective Targeting of the Warburg Effect through GAPDH Inhibition with a Natural Product. Cell Metabolism, 26(4), 648-659.e8. https://doi.org/10.1016/j.cmet.2017.08.017
Liberti, Maria V., Ziwei Dai, Suzanne E. Wardell, Joshua A. Baccile, Xiaojing Liu, Xia Gao, Robert Baldi, et al. “A Predictive Model for Selective Targeting of the Warburg Effect through GAPDH Inhibition with a Natural Product.Cell Metabolism 26, no. 4 (October 2017): 648-659.e8. https://doi.org/10.1016/j.cmet.2017.08.017.
Liberti MV, Dai Z, Wardell SE, Baccile JA, Liu X, Gao X, et al. A Predictive Model for Selective Targeting of the Warburg Effect through GAPDH Inhibition with a Natural Product. Cell metabolism. 2017 Oct;26(4):648-659.e8.
Liberti, Maria V., et al. “A Predictive Model for Selective Targeting of the Warburg Effect through GAPDH Inhibition with a Natural Product.Cell Metabolism, vol. 26, no. 4, Oct. 2017, pp. 648-659.e8. Epmc, doi:10.1016/j.cmet.2017.08.017.
Liberti MV, Dai Z, Wardell SE, Baccile JA, Liu X, Gao X, Baldi R, Mehrmohamadi M, Johnson MO, Madhukar NS, Shestov AA, Chio IIC, Elemento O, Rathmell JC, Schroeder FC, McDonnell DP, Locasale JW. A Predictive Model for Selective Targeting of the Warburg Effect through GAPDH Inhibition with a Natural Product. Cell metabolism. 2017 Oct;26(4):648-659.e8.
Journal cover image

Published In

Cell metabolism

DOI

EISSN

1932-7420

ISSN

1550-4131

Publication Date

October 2017

Volume

26

Issue

4

Start / End Page

648 / 659.e8

Related Subject Headings

  • Systems Biology
  • Sesquiterpenes
  • Neoplasms
  • Molecular Targeted Therapy
  • Models, Biological
  • Mice, Inbred C57BL
  • Metabolomics
  • Metabolic Flux Analysis
  • Machine Learning
  • Humans