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

Niki Tavakoli; Emma J Fong; Abigail Coleman; Yu-Kai Huang; Mathias Bigger; Michael E Doche; Seungil Kim; Heinz-Josef Lenz; Nicholas A Graham; Paul Macklin; Stacey D Finley; Shannon M Mumenthaler

doi:10.1038/s41540-025-00494-1

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

NPJ Syst Biol Appl. 2025 Jan 28;11(1):12. doi: 10.1038/s41540-025-00494-1.

Authors

Niki Tavakoli^#¹, Emma J Fong^#², Abigail Coleman², Yu-Kai Huang², Mathias Bigger^{2

3}, Michael E Doche², Seungil Kim², Heinz-Josef Lenz⁴, Nicholas A Graham³, Paul Macklin⁵, Stacey D Finley^{6

7

8}, Shannon M Mumenthaler^{9

10

11}

Affiliations

¹ Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA.
² Ellison Medical Institute, Los Angeles, CA, 90064, USA.
³ Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA.
⁴ Division of Medical Oncology, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90033, USA.
⁵ Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, 46202, USA.
⁶ Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA. sfinley@usc.edu.
⁷ Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA. sfinley@usc.edu.
⁸ Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, 90089, USA. sfinley@usc.edu.
⁹ Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA. smumenthaler@emila.org.
¹⁰ Ellison Medical Institute, Los Angeles, CA, 90064, USA. smumenthaler@emila.org.
¹¹ Division of Medical Oncology, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90033, USA. smumenthaler@emila.org.

^# Contributed equally.

Abstract

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.

MeSH terms

Cancer-Associated Fibroblasts / metabolism
Colorectal Neoplasms* / diagnostic imaging
Colorectal Neoplasms* / drug therapy
Colorectal Neoplasms* / metabolism
Colorectal Neoplasms* / pathology
Hexokinase / antagonists & inhibitors
Hexokinase / metabolism
Humans
Machine Learning
Metabolic Networks and Pathways
Models, Biological
Organoids / metabolism
Systems Biology / methods

Substances

Hexokinase

Abstract

MeSH terms

Substances

Grants and funding