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Merging Metabolic Modeling and Imaging for Screening Therapeutic Targets in Colorectal Cancer.

Authors :
Tavakoli N
Fong EJ
Coleman A
Huang YK
Bigger M
Doche ME
Kim S
Lenz HJ
Graham NA
Macklin P
Finley SD
Mumenthaler SM
Source :
BioRxiv : the preprint server for biology [bioRxiv] 2024 Sep 22. Date of Electronic Publication: 2024 Sep 22.
Publication Year :
2024

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 the 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.<br />Competing Interests: Declaration of interests The authors declare no conflict of interest.

Details

Language :
English
ISSN :
2692-8205
Database :
MEDLINE
Journal :
BioRxiv : the preprint server for biology
Publication Type :
Academic Journal
Accession number :
38826317
Full Text :
https://doi.org/10.1101/2024.05.24.595756