Cancer Metabolism

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

Abstract

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.

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Acknowledgements

Parts of this work were presented in poster format at the Keystone Symposium “Hypoxia: From Basic Mechanisms to Therapeutics” in Dublin, Ireland. A pre-print version of this manuscript was published on the Cold Spring Harbor Laboratory server “bioRxiv”. The excellent technical assistance of Johanna Roth is highly appreciated.

Author information

N.B., A.E., G.M., O.V., A.F., R.v.G., S.O.D., M.P., M.E., H.G.H., S.K. and T.C. performed and/or coordinated experimental work. N.B., A.E., G.M., A.F., A.D., M.P., C.B., R.v.G., M.E., J.S.R. and S.K. performed data analysis. A.E., A.F., M.E. and T.C. performed animal experiments. N.B., A.E., G.M., A.D., J.S.R., H.G.H., S.K. and T.C. prepared the initial manuscript and figures. H.G.H., S.K. and T.C. provided project leadership. All authors contributed to the final manuscript.

Correspondence to Thorsten Cramer.

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Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

Animal procedures were performed in accordance with approved protocols („Landesamt für Gesundheit und Soziales Berlin“ (G 0024/12) and „Landesamt für Natur, Umwelt und Verbraucherschutz Recklinghausen“ (84-02.04.2015.A344, AZ84-02.04.2016.A018 and 84-02.04.2015.A216))

Funding

This work was funded by a grant from the Bundesministerium für Bildung und Forschung to Thorsten Cramer, Stefan Kempa and Hermann-Georg Holzhütter (0316172). Research in the Cramer laboratory was further supported by grants from the Deutsche Forschungsgemeinschaft (Cr 133/2-1 and Cr 133/3-1) and the Deutsche Krebshilfe (109160). Stefan Kempa received funding by the Senate of Berlin, the Berlin Institute of Medical Systems Biology (BIMSB) and the Berlin Institute of Health (BIH). The Q3 European Union Horizon 2020 grant SyMBioSys (MSCA-ITN-2015-ETN #675585) provided financial support for Aurélien Dugourd.

Data availability

The mass spectrometry results (project accession: PXD011162) have been deposited in the EMBL-EBI PRIDE archive (https://www.ebi.ac.uk/pride/archive/login).

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Berndt, N., Egners, A., Mastrobuoni, G. et al. Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer. Br J Cancer 122, 233–244 (2020). https://doi.org/10.1038/s41416-019-0659-3

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