期刊
EBIOMEDICINE
卷 43, 期 -, 页码 98-106出版社
ELSEVIER
DOI: 10.1016/j.ebiom.2019.04.046
关键词
Metabolic modelling; Cancer; Machine learning; Drug repurposing
资金
- European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska- Curie grant [642295]
- Luxembourg National Research Fund (FNR)
- German Federal Ministry of Education and Research (BMBF) within the project MelanomSensitivity [BMBF/BM/7643621]
- Fondation Cancer [F1R-LSC-PAU-13HY2C]
- Fondation du Pelican de Mie and Pierre Hippert-Faber under the aegis of the Fondation de Luxembourg
- CORE [C16/BM/11282028]
- POC grant [PoC18/12554295]
Background: Metabolic rewiring allows cancer cells to sustain high proliferation rates. Thus. targeting only the cancer-specific cellular metabolism will safeguard healthy tissues. Methods: We developed the very efficient FASTCORMICS RNA-seq workflow (rFASTCORMICS) to build 10,005 high-resolution metabolic models from the TCGA dataset to capture metabolic rewiring strategies in cancer cells. Colorectal cancer (CRC) was used as a test case for a repurposing workflow based on rFASTCORMICS. Findings: Alternative pathways that are not required for proliferation or survival tend Lobe shut down and, therefore, tumours display cancer-specific essential genes that are significantly enriched for known drug targets. We identified naftifine, ketoconazole, and mimosine as new potential CRC drugs, which were experimentally validated. Interpretation: The here presented rFASTCORMICS workflow successfully reconstructs a metabolic model based on RNA-seq data and successfully predicted drug targets and drugs not yet indicted for colorectal cancer. (C) 2019 The Authors. Published by Elsevier B.V.
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