Reference : Identifying and targeting cancer-specific metabolism with network-based drug target p...

	Document type :	Scientific journals : Article
	Discipline(s) :	Life sciences : Multidisciplinary, general & others
	Focus Areas :	Systems Biomedicine
	To cite this reference:	http://hdl.handle.net/10993/39999

Title :	Identifying and targeting cancer-specific metabolism with network-based drug target prediction
Language :	English
Author, co-author :	Pacheco, Maria [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
	Bintener, Tamara Jean Rita [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
	Ternes, Dominik [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 [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
	Letellier, Elisabeth [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
	Sauter, Thomas [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
Publication date :	May-2019
Journal title :	EBioMedicine
Publisher :	Elsevier
Volume :	43
Issue/season :	May 2019
Pages :	98-106
Peer reviewed :	Yes
Audience :	International
e-ISSN :	2352-3964
City :	Amsterdam
Country :	Netherlands
Keywords :	[en] Metabolic modelling ; Cancer ; Machine learning ; Drug repurposing
Abstract :	[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.
Target :	Researchers ; Students
Permalink :	http://hdl.handle.net/10993/39999
DOI :	10.1016/j.ebiom.2019.04.046
Other URL :	https://www.sciencedirect.com/science/article/pii/S2352396419302853

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