A Predictive Model for Selective Targeting of the Warburg Effect through GAPDH Inhibition with a Natural Product.
Cell Metab
; 26(4): 648-659.e8, 2017 Oct 03.
Article
em En
| MEDLINE
| ID: mdl-28918937
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Inibidores Enzimáticos
/
Glucose
/
Gliceraldeído-3-Fosfato Desidrogenases
/
Glicólise
/
Neoplasias
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Animals
/
Humans
Idioma:
En
Revista:
Cell Metab
Ano de publicação:
2017
Tipo de documento:
Article