Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer

Background Metabolic alterations can serve as targets for diagnosis and cancer therapy. Due to the highly complex regulation of cellular metabolism, definite identification of metabolic pathway alterations remains challenging and requires sophisticated experimentation. Methods We applied a comprehen...

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Bibliographic Details
Published in:British journal of cancer 2020-01, Vol.122 (2), p.233-244
Main Authors: Berndt, Nikolaus, Egners, Antje, Mastrobuoni, Guido, Vvedenskaya, Olga, Fragoulis, Athanassios, Dugourd, Aurélien, Bulik, Sascha, Pietzke, Matthias, Bielow, Chris, van Gassel, Rob, Damink, Steven W. Olde, Erdem, Merve, Saez-Rodriguez, Julio, Holzhütter, Hermann-Georg, Kempa, Stefan, Cramer, Thorsten
Format: Article
Language:English
Subjects:
631/553/2695
631/67/1504/1610/4029
631/67/2327
Animals
Biomedical and Life Sciences
Biomedicine
Cancer Research
Cellular Reprogramming - genetics
Computer Simulation
Disease Models, Animal
Drug Resistance
Drug therapy
Epidemiology
Experimentation
Hepatocytes - metabolism
Humans
Liver
Liver cancer
Liver Neoplasms - genetics
Liver Neoplasms - metabolism
Liver Neoplasms - pathology
Mass Spectrometry
Mass spectroscopy
Mathematical models
Metabolic Networks and Pathways - genetics
Metabolic pathways
Metabolism
Mice
Mice, Transgenic
Molecular Medicine
Oncology
Proteome - genetics
Proteome - metabolism
Proteomes
Proteomics
Tumors
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Summary:Background Metabolic alterations can serve as targets for diagnosis and cancer therapy. Due to the highly complex regulation of cellular metabolism, definite identification of metabolic pathway alterations remains challenging and requires sophisticated experimentation. Methods We applied a comprehensive kinetic model of the central carbon metabolism (CCM) to characterise metabolic reprogramming in murine liver cancer. Results We show that relative differences of protein abundances of metabolic enzymes obtained by mass spectrometry can be used to assess their maximal velocity values. Model simulations predicted tumour-specific alterations of various components of the CCM, a selected number of which were subsequently verified by in vitro and in vivo experiments. Furthermore, we demonstrate the ability of the kinetic model to identify metabolic pathways whose inhibition results in selective tumour cell killing. Conclusions Our systems biology approach establishes that combining cellular experimentation with computer simulations of physiology-based metabolic models enables a comprehensive understanding of deregulated energetics in cancer. We propose that modelling proteomics data from human HCC with our approach will enable an individualised metabolic profiling of tumours and predictions of the efficacy of drug therapies targeting specific metabolic pathways.
ISSN:0007-0920
1532-1827
DOI:10.1038/s41416-019-0659-3