Abstract 1140: Digital twins for in vivo metabolic flux estimations in brain cancer patients
Authors: Baharan Meghdadi, Wajd N. Al-Holou, Andrew J. Scott, Anjali Mittal, Ningning Liang, Sravya Palavalasa, Abhinav Achreja, Nathan Qi, Alexandra O'Brien, Vijay Tarnal, Weihua Zhou, Theodore S. Lawrence, Costas A. Lyssiotis, Daniel R. Wahl, Deepak Nagrath
Journal: Cancer Research (2025)

Abstract Understanding how cancer cells rewire their metabolism for biosynthetic growth and treatment resistance is important in metabolic targeted therapies for patients. A technique that we use to understand cancer metabolism in glioma patients is 13C-isotope tracing which can serve as a surrogate of metabolic activity in metabolic modeling. However, quantifying the contribution of various anabolic routes of metabolites is difficult in brain cancer patients due to limitations of tissue sampling, heterogeneity of tumor microenvironment and nonsteady state conditions. We overcame this challenge by implementing a digital twin framework that mimics U13C-glucose tracing data using first-principles-based metabolic flux analysis and implementing a machine learning model to predict fluxes. To find mechanistic insights that can be used in metabolic therapies, we trained a graph neural network to predict absolute metabolic fluxes of central carbon metabolism, purine metabolism and serine metabolism. Our model suggested that both cortex and glioma have high demand of glucose uptake, however, gliomas divert glucose more toward nucleotide synthesis while cortex utilizes glucose more toward serine synthesis and TCA cycle. We experimentally validate these models through direct perturbation of metabolic activity in mouse models to predict treatment responses for: (1) pharmacologic inhibition of de novo GMP synthesis and (2) environmental serine restriction. Our GMP synthesis model predicted elevated GMP synthesis in GBM compared to cortex in a mouse model and discriminated between sources of GMP synthesis in patients. These findings allow prediction of potential responders to mycophenolate mofetil (MMF), which inhibits de novo but not salvage GMP synthesis. Furthermore, we previously showed that circulating serine uptake is higher in GBM than normal cortex, and a serine- and glycine-restricted diet slows tumor growth in mice. While de novo serine synthesis, circulating and microenvironmental-derived serine could account for serine sources in GBM, our model can distinguish between serine sources in patients, hence potentially predict response to dietary serine depletion. Thus, we have shown that our model may identify patients who will benefit from MMF treatment or a serine- and glycine-restricted diet, potentially enabling administration of highly effective personalized metabolic treatments for GBM patients. Citation Format: Baharan Meghdadi, Wajd N. Al-Holou, Andrew J. Scott, Anjali Mittal, Ningning Liang, Sravya Palavalasa, Abhinav Achreja, Nathan Qi, Alexandra O'Brien, Vijay Tarnal, Weihua Zhou, Theodore S. Lawrence, Costas A. Lyssiotis, Daniel R. Wahl, Deepak Nagrath. Digital twins for in vivo metabolic flux estimations in brain cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1140.