Cancer Metabolism

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



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.


We applied a comprehensive kinetic model of the central carbon metabolism (CCM) to characterise metabolic reprogramming in murine liver cancer.


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.


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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5


  1. 1.

    Garraway, L. A. & Lander, E. S. Lessons from the cancer genome. Cell. 153, 17–37 (2013).

  2. 2.

    Ma, J., Ward, E. M., Siegel, R. L., Jemal, A. Temporal trends in mortality in the United States, 1969–2013. Jama. 314, 1731–1739 (2015).

  3. 3.

    Fojo, T. & Parkinson, D. R. Biologically targeted cancer therapy and marginal benefits: are we making too much of too little or are we achieving too little by giving too much? Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 16, 5972–5980 (2010).

  4. 4.

    McIntyre, A. & Harris, A. L. Metabolic and hypoxic adaptation to anti-angiogenic therapy: a target for induced essentiality. EMBO Mol. Med. 7, 368–379 (2015).

  5. 5.

    Niewerth, D., Jansen, G., Assaraf, Y. G., Zweegman, S., Kaspers, G. J. & Cloos, J. Molecular basis of resistance to proteasome inhibitors in hematological malignancies. Drug Resis. Updates Rev. Comment. Antimicrob. Anticancer Chemother. 18, 18–35 (2015).

  6. 6.

    Waller, L. P., Deshpande, V. & Pyrsopoulos, N. Hepatocellular carcinoma: a comprehensive review. World J. Hepatol. 7, 2648–2663 (2015).

  7. 7.

    Schulze, A. & Harris, A. L. How cancer metabolism is tuned for proliferation and vulnerable to disruption. Nature. 491, 364–373 (2012).

  8. 8.

    Pavlova, N. N. & Thompson, C. B. The emerging hallmarks of cancer metabolism. Cell Metab. 23, 27–47 (2016).

  9. 9.

    Nielsen, J. Systems biology of metabolism: a driver for developing personalized and precision medicine. Cell Metab. 25, 572–579 (2017).

  10. 10.

    Cazzaniga, P., Damiani, C., Besozzi, D., Colombo, R., Nobile, M. S., Gaglio, D. et al. Computational strategies for a system-level understanding of metabolism. Metabolites. 4, 1034–1087 (2014).

  11. 11.

    Weinberg, R. Point: Hypotheses first. Nature 464, (678–678 (2010)..

  12. 12.

    Bulik, S., Holzhutter, H. G. & Berndt, N. The relative importance of kinetic mechanisms and variable enzyme abundances for the regulation of hepatic glucose metabolism - insights from mathematical modeling. BMC Biol. 14, 15 (2016).

  13. 13.

    Berndt, N., Bulik, S., Wallach, I., Wunsch, T., Konig, M., Stockmann, M. et al. HEPATOKIN1 is a biochemistry-based model of liver metabolism for applications in medicine and pharmacology. Nat. Commun. 9, 2386 (2018).

  14. 14.

    Dubois, N., Bennoun, M., Allemand, I., Molina, T., Grimber, G., udet-Monsac, M. et al. Time-course development of differentiated hepatocarcinoma and lung metastasis in transgenic mice. J. Hepatol. 13, 227–239 (1991).

  15. 15.

    Ohno, K. & Maier, P. Cultured rat hepatocytes adapt their cellular glycolytic activity and adenylate energy status to tissue oxygen tension: influences of extracellular matrix components, insulin and glucagon. J. Cell. Physiol. 160, 358–366 (1994).

  16. 16.

    Rappsilber, J., Ishihama, Y. & Mann, M. Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics. Anal. Chem. 75, 663–670 (2003).

  17. 17.

    Hesse, D., Jaschke, A., Kanzleiter, T., Witte, N., Augustin, R., Hommel, A. et al. GTPase ARFRP1 is essential for normal hepatic glycogen storage and insulin-like growth factor 1 secretion. Mol. Cell. Biol. 32, 4363–4374 (2012).

  18. 18.

    de Graaf, I. A., Olinga, P., de Jager, M. H., Merema, M. T., de Kanter, R., van de Kerkhof, E. G. et al. Preparation and incubation of precision-cut liver and intestinal slices for application in drug metabolism and toxicity studies. Nat. Protoc. 5, 1540–1551 (2010).

  19. 19.

    Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

  20. 20.

    Cox, J. & Mann, M. Quantitative, high-resolution proteomics for data-driven systems biology. Ann. Rev. Biochem. 80, 273–299 (2011).

  21. 21.

    Cox, J., Hein, M. Y., Luber, C. A., Paron, I., Nagaraj, N. & Mann, M. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol. Cell. Proteom. 13, 2513–2526 (2014).

  22. 22.

    Bielow, C., Mastrobuoni, G. & Kempa, S. Proteomics quality control: quality control software for MaxQuant results. J. Proteome Res. 15, 777–787 (2016).

  23. 23.

    Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

  24. 24.

    Bares V. G. X. gskb: Gene Set data for pathway analysis in mouse. R package version 1.2.0. Bioconductor. 2015.

  25. 25.

    Varemo, L., Nielsen, J. & Nookaew, I. Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods. Nucleic Acids Res. 41, 4378–4391 (2013).

  26. 26.

    Berndt, N. & Holzhutter, H. G. Dynamic metabolic zonation of the hepatic glucose metabolism is accomplished by sinusoidal plasma gradients of nutrients and hormones. Front. Physiol. 9, 1786 (2018).

  27. 27.

    Coyle, C., Cafferty, F. H., Vale, C. & Langley, R. E. Metformin as an adjuvant treatment for cancer: a systematic review and meta-analysis. Ann. Oncol. Off. J. Euro. Soc. Med. Oncol. 27, 2184–2195 (2016).

  28. 28.

    Wheaton, W. W., Weinberg, S. E., Hamanaka, R. B., Soberanes, S., Sullivan, L. B., Anso, E. et al. Metformin inhibits mitochondrial complex I of cancer cells to reduce tumorigenesis. eLife 3, e02242 (2014).

  29. 29.

    Madiraju, A. K., Erion, D. M., Rahimi, Y., Zhang, X. M., Braddock, D. T., Albright, R. A. et al. Metformin suppresses gluconeogenesis by inhibiting mitochondrial glycerophosphate dehydrogenase. Nature 510, 542–546 (2014).

  30. 30.

    Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

  31. 31.

    Yu, H., Wang, F., Lin, L., Cao, W., Liu, Y., Qin, L. et al. Mapping and analyzing the human liver proteome: progress and potential. Expert Rev. Proteom. 13, 833–843 (2016).

  32. 32.

    Uhlén, M., Fagerberg, L., Hallström, B. M., Lindskog, C., Oksvold, P., Mardinoglu, A. et al. Tissue-based map of the human proteome. Science 347, (2015).

  33. 33.

    Opdam, S., Richelle, A., Kellman, B., Li, S., Zielinski, D. C. & Lewis, N. E. A systematic evaluation of methods for tailoring genome-scale metabolic models. Cell Syst. 4, 318–329.e316 (2017).

  34. 34.

    Yizhak, K., Chaneton, B., Gottlieb, E. & Ruppin, E. Modeling cancer metabolism on a genome scale. Mol. Syst. Biol. 11, 817 (2015).

  35. 35.

    Mu, X., Espanol-Suner, R., Mederacke, I., Affo, S., Manco, R., Sempoux, C. et al. Hepatocellular carcinoma originates from hepatocytes and not from the progenitor/biliary compartment. J. Clin. Invest. 125, 3891–3903 (2015).

  36. 36.

    Bard-Chapeau, E. A., Nguyen, A. T., Rust, A. G., Sayadi, A., Lee, P., Chua, B. Q. et al. Transposon mutagenesis identifies genes driving hepatocellular carcinoma in a chronic hepatitis B mouse model. Nat. Genet. 46, 24–32 (2014).

  37. 37.

    Fan, T., Rong, Z., Dong, J., Li J., Wang, K., Wang, X. et al. Metabolomic and transcriptomic profiling of hepatocellular carcinomas in Hras12V transgenic mice. CancerMed. 6, 2370–2384 (2017).

  38. 38.

    Dolezal, J. M., Wang, H., Kulkarni, S., Jackson, L., Lu, J., Ranganathan, S. et al. Sequential adaptive changes in a c-Myc-driven model of hepatocellular carcinoma. J. Biol. Chem. 292, 10068–10086 (2017).

  39. 39.

    Budhu, A., Roessler, S., Zhao, X., Yu, Z., Forgues, M., Ji, J. et al. Integrated metabolite and gene expression profiles identify lipid biomarkers associated with progression of hepatocellular carcinoma and patient outcomes. Gastroenterology 144, 1066–1075.e1061 (2013).

  40. 40.

    Beyoglu, D., Imbeaud, S., Maurhofer, O., Bioulac-Sage, P., Zucman-Rossi, J., Dufour, J. F. et al. Tissue metabolomics of hepatocellular carcinoma: tumor energy metabolism and the role of transcriptomic classification. Hepatology (Baltimore, Md) 58, 229–238 (2013).

  41. 41.

    Yang, Y., Li, C., Nie, X., Feng, X., Chen, W., Yue, Y. et al. Metabonomic studies of human hepatocellular carcinoma using high-resolution magic-angle spinning 1H NMR spectroscopy in conjunction with multivariate data analysis. J. Proteome Res. 6, 2605–2614 (2007).

  42. 42.

    Mavri-Damelin, D., Eaton, S., Damelin, L. H., Rees, M., Hodgson, H. J. & Selden, C. Ornithine transcarbamylase and arginase I deficiency are responsible for diminished urea cycle function in the human hepatoblastoma cell line HepG2. Int. J. Biochem. Cell Biol. 39, 555–564 (2007).

  43. 43.

    Butler, S. L., Dong, H., Cardona, D., Jia, M., Zheng, R., Zhu, H. et al. The antigen for Hep Par 1 antibody is the urea cycle enzyme carbamoyl phosphate synthetase 1. Lab. Invest. J.Tech. Methods Pathol. 88, 78–88 (2008).

  44. 44.

    Nwosu, Z. C., Megger, D. A., Hammad, S., Sitek, B., Roessler, S., Ebert, M. P. et al. Identification of the consistently altered metabolic targets in human hepatocellular carcinoma. Cell. Mol. Gastroenterol. Hepatol. 4, 303–323.e301 (2017).

  45. 45.

    Yan, B. C., Gong, C., Song, J., Krausz, T., Tretiakova, M., Hyjek, E. et al. Arginase-1: a new immunohistochemical marker of hepatocytes and hepatocellular neoplasms. Am. J. Surg. Pathol. 34, 1147–1154 (2010).

  46. 46.

    Berndt, N. & Holzhutter, H. G. Mathematical modeling of cellular metabolism. Recent Results Cancer Res. 207, 221–232 (2016).

  47. 47.

    Beyoglu, D. & Idle, J. R. The metabolomic window into hepatobiliary disease. J. hepatol. 59, 842–858 (2013).

  48. 48.

    Evans, J. M., Donnelly, L. A., Emslie-Smith, A. M., Alessi, D. R. & Morris, A. D. Metformin and reduced risk of cancer in diabetic patients. BMJ (Clinical research ed) 330, 1304–1305 (2005)..

  49. 49.

    Miyoshi, H., Kato, K., Iwama, H., Maeda, E., Sakamoto, T., Fujita, K. et al. Effect of the anti-diabetic drug metformin in hepatocellular carcinoma in vitro and in vivo. Int. J. Oncol. 45, 322–332 (2014).

Download references


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.

Ethics declarations

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))


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 (

Additional information

Note: This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution 4.0 International (CC BY 4.0).

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation

Further reading