Abstract C049: Computational Methods for Central and Lipid Metabolism Networks Analysis to Reveals Flux Change in PDAC

Abstract

Abstract Pancreatic ductal adenocarcinoma (PDAC) is among the most challenging cancers to treat due to its aggressive nature and the difficulties in early detection. The disease progresses rapidly from stage T1 to T4, leaving a narrow window for effective intervention. This swift progression is closely tied to significant metabolic alterations, and current studies have highlighted specific metabolic changes. This includes elevated glutamine metabolism and altered aspartate and alpha-ketoglutarate production to support uncontrolled tumor growth; and elevated lysophosphatidylcholines secretion by cancer-associated fibroblasts to support tumor progression. These insights underscore the need for a holistic understanding of PDAC metabolism. Hence, there is an urgent need for a comprehensive and high-resolution analysis of PDAC's metabolic landscape remains unexplored. To computationally characterize the PDAC metabolism and fully leverage the large-scale transcriptomics with matched or unmatched metabolomics data, we developed an algorithm based on the Michaelis-Menten equation to estimate distribution-wise variations in mass-carrying metabolic networks using gene expression and metabolomics data. We start by manually curating a PDAC-specific metabolic network that focuses on central metabolism, glutamine-glutamate metabolism, and lipid metabolism. We then gathered comprehensive kinetic parameters, predicted enzyme activity from transcriptomics data, and conducted metabolomic profiling to determine metabolic compound concentrations. By applying Monte Carlo sampling methods, we achieved an overall balance of metabolic flux, enabling us to model reaction rates accurately and predict metabolic shifts. This approach holds potential to provide a comprehensive perspective on the rewired metabolism in PDAC, inspiring the development of metabolic targets or diet interventions for therapeutic treatment. Citation Format: Sha Cao, Haiqi Zhu, Yijie Wang, Melissa Fishel, Chi Zhang. Computational Methods for Central and Lipid Metabolism Networks Analysis to Reveals Flux Change in PDAC [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C049.