Abstract 2349: A phenotypic model for cancer metabolism

Abstract

Abstract Understanding cancer metabolism is crucial for deciphering various cancer hallmarks, including metastasis and immunosuppression. This study investigated the intricate interplay of catabolic processes involving glucose, fatty acids, and glutamine in cancer cells. Recent evidence highlights the dual reliance of cancer cells on glycolysis and oxidative phosphorylation (OXPHOS), challenging the simplistic Warburg effect. Fatty acids and glutamine also emerged as vital contributors to tumorigenesis. Despite these advances, a comprehensive model encompassing both catabolic and anabolic processes, especially glutamine metabolism, is lacking. A metabolic network model integrates catabolic and anabolic modes for glucose, fatty acids, and glutamine, incorporating genetic regulation involving AMPK, HIF-1, and MYC. Equations capture the temporal dynamics of regulatory proteins (pAMPK and HIF-1) and metabolites (mtROS and noxROS), modeling metabolic flux for glucose and glutamine uptake, fatty acid, acetyl-CoA utilization, and activities of glucose oxidation, glycolysis, and FAO. The regulatory network outlines competition for resources, gene regulation, chemical intermediates, cross-regulation among gene regulators, and regulation of metabolic ingredient uptake. The phenotypic model focusing on AMPK, HIF-1, and MYC reveals metabolic states: OXPHOS (O), glycolysis (W), hybrid (W/O), and low/low, characterized by varying levels of AMPK and HIF-1, influencing pathway activities and anabolic processes. Parameter randomization analysis shows state robustness, especially in MYC overexpressing cancer cells. Analyzing TCGA data reveals differential expression of glutamine metabolism genes. The "W" state exhibits enhanced glutamine uptake, the "O" state heightened glutamine oxidation, and the "W/O" state displays both, aligning with the model's predictions. Bifurcation analyses of AMPK, HIF1, and MYC shed light on state stability and transitions, providing insights into regulatory mechanisms. The low/low phenotype, inactive in both OXPHOS and glycolysis, raises questions about significance and therapy resistance. The study explores this phenotype further, unraveling molecular underpinnings and therapy resistance implications. A deeper understanding could reveal novel therapeutic targets, enhancing comprehension of cancer cell behavior. This study introduces a minimal network model capturing essential features of cancer metabolism, replicating experimental observations of metabolic heterogeneity. The model predicts diverse cancer cell phenotypes based on MYC expression levels, emphasizing the importance of considering both catabolic and anabolic processes. Citation Format: Javier Villela-Castrejon, Jason T. George, Dongya Jia, Herbert Levine, José N. Onuchic, Benny Abraham Kaipparettu. A phenotypic model for cancer metabolism [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2349.