Reference : Identifying and targeting cancer-specific metabolism with network-based drug target p...
Scientific journals : Article
Life sciences : Multidisciplinary, general & others
Systems Biomedicine
http://hdl.handle.net/10993/39999
Identifying and targeting cancer-specific metabolism with network-based drug target prediction
English
Pacheco, Maria mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
Bintener, Tamara Jean Rita mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
Ternes, Dominik mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
Kulms, Dagmar [Technical University Dresden > Department of Dermatology > Experimental Dermatology > ; Technical University Dresden > Center for Regenerative Therapies]
Haan, Serge mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
Letellier, Elisabeth mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
Sauter, Thomas mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
May-2019
EBioMedicine
Elsevier
43
May 2019
98-106
Yes
International
2352-3964
Amsterdam
Netherlands
[en] Metabolic modelling ; Cancer ; Machine learning ; Drug repurposing
[en] 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.
Researchers ; Students
http://hdl.handle.net/10993/39999
10.1016/j.ebiom.2019.04.046
https://www.sciencedirect.com/science/article/pii/S2352396419302853

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