Merging metabolic modeling and imaging for screening therapeutic targets in colorectal cancer
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-...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | npj Systems Biology and Applications |
| Online Access: | https://doi.org/10.1038/s41540-025-00494-1 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Niki Tavakoli |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9adbc84446124443935660fef3357853 |
| institution | Kabale University |
| issn | 2056-7189 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Systems Biology and Applications |
| spelling | doaj-art-9adbc84446124443935660fef33578532025-02-02T12:30:22ZengNature Portfolionpj Systems Biology and Applications2056-71892025-01-0111111710.1038/s41540-025-00494-1Merging metabolic modeling and imaging for screening therapeutic targets in colorectal cancerNiki Tavakoli0Emma J. Fong1Abigail Coleman2Yu-Kai Huang3Mathias Bigger4Michael E. Doche5Seungil Kim6Heinz-Josef Lenz7Nicholas A. Graham8Paul Macklin9Stacey D. Finley10Shannon M. Mumenthaler11Alfred E. Mann Department of Biomedical Engineering, University of Southern CaliforniaEllison Medical InstituteEllison Medical InstituteEllison Medical InstituteEllison Medical InstituteEllison Medical InstituteEllison Medical InstituteDivision of Medical Oncology, Norris Comprehensive Cancer Center, University of Southern CaliforniaMork Family Department of Chemical Engineering and Materials Science, University of Southern CaliforniaDepartment of Intelligent Systems Engineering, Indiana UniversityAlfred E. Mann Department of Biomedical Engineering, University of Southern CaliforniaAlfred E. Mann Department of Biomedical Engineering, University of Southern CaliforniaAbstract 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.https://doi.org/10.1038/s41540-025-00494-1 |
| spellingShingle | 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 Merging metabolic modeling and imaging for screening therapeutic targets in colorectal cancer npj Systems Biology and Applications |
| title | Merging metabolic modeling and imaging for screening therapeutic targets in colorectal cancer |
| title_full | Merging metabolic modeling and imaging for screening therapeutic targets in colorectal cancer |
| title_fullStr | Merging metabolic modeling and imaging for screening therapeutic targets in colorectal cancer |
| title_full_unstemmed | Merging metabolic modeling and imaging for screening therapeutic targets in colorectal cancer |
| title_short | Merging metabolic modeling and imaging for screening therapeutic targets in colorectal cancer |
| title_sort | merging metabolic modeling and imaging for screening therapeutic targets in colorectal cancer |
| url | https://doi.org/10.1038/s41540-025-00494-1 |
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