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

Overview

MeSH Major

  • Gene Expression Regulation, Neoplastic
  • MicroRNAs
  • Multiple Myeloma
  • Plasma Cells
  • RNA, Messenger

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┬ávivo. Thus, the basis of targeting the WE can be encoded by molecular principles that extend beyond the status of individual genes.

publication date

  • January 2017

Research

keywords

  • Academic Article

Identity

Language

  • eng

PubMed Central ID

  • PMC5629112

Digital Object Identifier (DOI)

  • 10.1016/j.cmet.2017.08.017

PubMed ID

  • 28918937

Additional Document Info