Abstract 2072: Computational modeling of metabolic variations in tumor microenvironment

Abstract

Abstract Dysregulation of metabolic pathways is a hallmark of cancer. Despite a plethora of knowledge on the core components of metabolic pathways we have gained, there are still major gaps in our understanding of the integrated behavior and metabolic heterogeneity of cells in the context of their microenvironment. Essentially, metabolic behavior can be determined by different factors and vary dramatically from cell to cell due to their high plasticity, driven by the need to cope with various dynamic metabolic requirements. Large amount of single-cell, spatial or tissue multi-omics data obtained from disease tissue has been proven to be endowed with the potential to deliver information on a cell functioning state and its underlying phenotypic switches.We have recently developed single-cell flux estimation analysis (scFEA) to predict sample-wise metabolic fluxome by using single-cell or bulk transcriptomics data. We further developed a web portal scFLUX.org, empowered by our scFEA method, expanding the analytic ability to both human and mouse metabolic networks. We also developed a probabilistic model named Michaelis-Menten-based Flux Estimation Analysis (mmFEA) for statistical inference of flux changes between conditions or cell types based on matched or unmatched gene expression data, metabolomics data, and partially observed kinetic parameters. These methods provide the following unmet capabilities to study metabolic variations in cancer: (1) reconstruction of cell or tissue specific and subcellular-resolution metabolic network, (2) estimation of cell-/sample-wise metabolic flux by considering metabolic imbalance, metabolic exchange between cells, and shifted redox, pH and energy balance in disease microenvironment, (3) a systematic evaluation of functional impact of variations in gene expression, genetic/epigenetic variations, metabolite availability and network structure on the context specific metabolic network and flux, (4) estimate the impact of each gene on flux by using a first-order derivative, (5) perturbation analysis to predict how alterations in enzymes or metabolites may affect the metabolic flux, and (6) identification of the samples and sub-network of a distinct metabolic shift.The methods were validated on matched scRNA-seq, metabolomics and fluxomics data. We demonstrated the function of this computational framework on pan-cancer transcriptomics and proteomics data, and single cell and spatial transcriptomics data. Citation Format: Tingbo Guo, Haiqi Zhu, Xiao Wang, Jia Wang, Xinyu Zhou, Yuhui Wei, Pengtao Dang, Chi Zhang, Sha Cao. Computational modeling of metabolic variations in tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2072.