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 to be 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. FUND: This study was supported by the University of Luxembourg (IRP grant scheme; R-AGR-0755-12), the Luxembourg National Research Fund (FNR PRIDE PRIDE15/10675146/CANBIO), the Fondation Cancer (Luxembourg), the European Union's Horizon2020 research and innovation programme under the Marie Sklodowska- Curie grant agreement No 642295 (MEL-PLEX), and the German Federal Ministry of Education and Research (BMBF) within the project MelanomSensitivity (BMBF/BM/7643621).
Keywords: Cancer; Drug repurposing; Machine learning; Metabolic modelling.
Copyright © 2019. Published by Elsevier B.V.