LAUSR

Cancer cells undergo numerous adaptive processes to sustain growth and survival. One notable mechanism is by rewiring metabolism, most prominently through a phenomenon known as the Warburg effect (WE). The WE is defined by increased glucose consumption and lactate excretion in the presence or absence of oxygen. Although the WE has been extensively studied, efforts to develop successful glycolytic inhibitors have been largely unsuccessful. Targeting cancer metabolism has remained a challenge due to the lack of obvious metabolic biomarkers and difficulties achieving full enzyme inhibition without inducing toxicity in normal tissue. Although targeted cancer therapies that use genetics have been largely successful, principles for selectively targeting tumor metabolism that also depend on the environment remain unknown. In the present study, we employ metabolic control analysis to reveal that glyceraldehyde-3-phosphate dehydrogenase (GAPDH), the sixth enzyme in glycolysis, exhibits differential control properties during the WE and can be used to predict response to targeting glucose metabolism. Using high-performance liquid chromatography coupled to high-resolution mass spectrometry (HPLC-HRMS), we conducted comparative metabolomics to establish a natural product produced by Trichoderma fungi, koningic acid (KA), as a selective inhibitor of GAPDH. We expressed a fungal-derived resistant-GAPDH allele in human cells to show that KA is highly specific for GAPDH. With machine learning, 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 genetic status. Current work focuses on elucidating acquired resistance mechanisms of KA in cancer cells undergoing the WE. Together, these data importantly demonstrate that a complete understanding of pharmacogenomics for cancer therapy likely requires information encoded at the metabolic level. Citation Format: Maria V. Liberti, Ziwei Dai, Suzanne E. Wardell, Joshua A. Baccile, Xiaojing Liu, Xia Gao, Robert Baldi, Mahya Mehrmohamadi, Marc O. Johnson, Neel S. Madhukar, Alexander Shestov, Iok I. C. Chio, Olivier Elemento, Jeffrey C. Rathmell, Frank C. Schroeder, Donald P. McDonnell, Jason W. Locasale. A predictive model for selective targeting of the Warburg effect through GAPDH inhibition with a natural product [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5496.