Merging metabolic modeling and imaging for screening therapeutic targets in colorectal cancer

作者：Niki Tavakoli, Emma J. Fong, Abigail Coleman, Yukai Huang, Mathias Bigger, Michael E. Doche, Seungil Kim, Heinz‐Josef Lenz, Nicholas A. Graham, Paul Macklin, Stacey D. Finley, Shannon M. Mumenthaler · 发表于：npj Systems Biology and Applications · 年份：2025 · DOI：10.1038/s41540-025-00494-1 · 被引用次数：12 · 研究领域：Cancer, Hypoxia, and Metabolism、Diet and metabolism studies、Metabolomics and Mass Spectrometry Studies

Cancer-associated fibroblasts (CAFs) play a key role in metabolic reprogramming and are well-established contributors to drug resistance in colorectal cancer (CRC). To exploit this metabolic crosstalk, we integrated a systems biology approach that identified key metabolic targets in a data-driven method and validated them experimentally. This process involved a novel machine learning-based method to computationally screen, in a high-throughput manner, the effects of enzyme perturbations predicted by a computational model of CRC metabolism. This approach reveals the network-wide effects of metabolic perturbations. Our results highlighted hexokinase (HK) as a crucial target, which subsequently became our focus for experimental validation using patient-derived tumor organoids (PDTOs). Through metabolic imaging and viability assays, we found that PDTOs cultured in CAF-conditioned media exhibited increased sensitivity to HK inhibition, confirming the model predictions. Our approach emphasizes the critical role of integrating computational and experimental techniques in exploring and exploiting CRC-CAF crosstalk.