Application of Tracer-Based Metabolomics and Flux Analysis in Targeted Cancer Drug Design

  • Marta CascanteEmail author
  • Vitaly Selivanov
  • Antonio Ramos-Montoya
Part of the Methods in Pharmacology and Toxicology book series (MIPT)


Metabolic profiling using stable-isotope tracer technology enables the measurement of substrate redistribution within major metabolic pathways in living cells. This technique has demonstrated that transformed human cells present acute metabolic shifts and that some anticancer drugs induce their effects by forcing the reversion of these metabolic changes. This chapter introduces the application of tracer-based metabolomics and flux analysis in the design of new anticancer therapies, and discusses differential metabolic adaptations of cancer cells that can be new complementary targets in the design of rational combinational treatments in chemotherapy.

Key words

Metabolic Control Analysis Cancer Therapy Metabolic Profiling 









Mass spectrometry


Nuclear magnetic resonance


Pentose phosphate pathway













This work was supported by Grants SAF2011-25726 and ISCIII-RTICC (RD06/0020/0046) from the Spanish government and the European Union FEDER funds), the AGAUR-Generalitat de Catalunya (grant 2009SGR1308, 2009 CTP 00026 and Icrea Academia award 2010 granted to M. Cascante), and the European Commission (FP7-ITN) METAFLUX grant agreement n°264780.


  1. 1.
    Schlotterbeck G, Ross A, Dieterle F, Senn H. Metabolic profiling technologies for biomarker discovery in biomedicine and drug development. Pharmacogenomics. 2006;7(7):1055–75.PubMedCrossRefGoogle Scholar
  2. 2.
    Schnackenberg LK, Beger RD. Monitoring the health to disease continuum with global metabolic profiling and systems biology. Pharmacogenomics. 2006;7(7):1077–86.PubMedCrossRefGoogle Scholar
  3. 3.
    Bailey JE. Reflections on the scope and the future of metabolic engineering and its connections to functional genomics and drug discovery. Metab Eng. 2001;3(2):111–4.PubMedCrossRefGoogle Scholar
  4. 4.
    Oksman-Caldentey KM, Saito K. Integrating genomics and metabolomics for engineering plant metabolic pathways. Curr Opin Biotechnol. 2005;16(2):174–9.PubMedCrossRefGoogle Scholar
  5. 5.
    German JB, Hammock BD, Watkins SM. Metabolomics: building on a century of biochemistry to guide human health. Metabolomics. 2005;1(1):3–9.PubMedCentralPubMedCrossRefGoogle Scholar
  6. 6.
    van der Greef J, Hankemeier T, McBurney RN. Metabolomics-based systems biology and personalized medicine: moving towards n = 1 clinical trials? Pharmacogenomics. 2006;7(7):1087–94.PubMedCrossRefGoogle Scholar
  7. 7.
    Westerhoff HV, Koster JG, Van Workum M, Rudd KE. On the control of gene expression. In: Cornish-Bowden A, editor. Control of metabolic processes. New York, NY: Plenum; 1990. p. 399–412.CrossRefGoogle Scholar
  8. 8.
    Cascante M, Boros LG, Comin-Anduix B, de Atauri P, Centelles JJ, Lee PW. Metabolic control analysis in drug discovery and disease. Nat Biotechnol. 2002;20(3):243–9.PubMedCrossRefGoogle Scholar
  9. 9.
    Dang CV, Semenza GL. Oncogenic alterations of metabolism. Trends Biochem Sci. 1999;24(2):68–72.PubMedCrossRefGoogle Scholar
  10. 10.
    Pelicano H, Martin DS, Xu RH, Huang P. Glycolysis inhibition for anticancer treatment. Oncogene. 2006;25(34):4633–46.PubMedCrossRefGoogle Scholar
  11. 11.
    Ramos-Montoya A, Lee W-NP, Bassilian S, Lim S, Trebukhina RV, Kazhyna MV, Ciudad CJ, Noé V, Centelles JJ, Cascante M. Pentose phosphate cycle oxidative and non-oxidative balance: a new vulnerable target for overcoming drug resistance in cancer. Int J Cancer. 2006;119(12):2733–41.PubMedCrossRefGoogle Scholar
  12. 12.
    Boros LG, Serkova NJ, Cascante MS, Lee W-NP. Use of metabolic pathway flux information in targeted cancer drug design. Drug Discov Today Ther Strat. 2004;1(4):435–43.CrossRefGoogle Scholar
  13. 13.
    Boros LG, Cascante M, Paul Lee W-N. Metabolic profiling of cell growth and death in cancer: applications in drug discovery. Drug Discov Today. 2002;7(6):364–72.PubMedCrossRefGoogle Scholar
  14. 14.
    Schnackenberg LK. Global metabolic profiling and its role in systems biology to advance personalized medicine in the 21st century. Expert Rev Mol Diagn. 2007;7(3):247–59.PubMedCrossRefGoogle Scholar
  15. 15.
    Mathupala SP, Rempel A, Pedersen PL. Aberrant glycolytic metabolism of cancer cells: a remarkable coordination of genetic, transcriptional, post-translational, and mutational events that lead to a critical role for type II hexokinase. J Bioenerg Biomembr. 1997;29(4):339–43.PubMedCrossRefGoogle Scholar
  16. 16.
    Vousden KH, Lane DP. p53 in health and disease. Nat Rev Mol Cell Biol. 2007;8(4):275–83.PubMedCrossRefGoogle Scholar
  17. 17.
    Vizan P, Boros LG, Figueras A, Capella G, Mangues R, Bassilian S, Lim S, Lee WN, Cascante M. K-ras codon-specific mutations produce distinctive metabolic phenotypes in NIH3T3 mice [corrected] fibroblasts. Cancer Res. 2005;65(13):5512–5.PubMedCrossRefGoogle Scholar
  18. 18.
    Halabe Bucay A. The biological significance of cancer: mitochondria as a cause of cancer and the inhibition of glycolysis with citrate as a cancer treatment. Med Hypotheses. 2007;69(4):826–8.PubMedCrossRefGoogle Scholar
  19. 19.
    Cascante M, Ortega F, Marti E. New insights into our understanding of the regulation and organization of cell factories. Trends Biotechnol. 2000;18(5):181–2.PubMedCrossRefGoogle Scholar
  20. 20.
    Bowden AC. Metabolic control analysis in biotechnology and medicine. Nat Biotechnol. 1999;17(7):641–3.PubMedCrossRefGoogle Scholar
  21. 21.
    Baggetto LG. Biochemical, genetic, and metabolic adaptations of tumor cells that express the typical multidrug-resistance phenotype. Reversion by new therapies. J Bioenerg Biomembr. 1997;29(4):401–13.PubMedCrossRefGoogle Scholar
  22. 22.
    Bailey JE. Lessons from metabolic engineering for functional genomics and drug discovery. Nat Biotechnol. 1999;17(7):616–8.PubMedCrossRefGoogle Scholar
  23. 23.
    Huang S. Rational drug discovery: what can we learn from regulatory networks? Drug Discov Today. 2002;7(20 Suppl):S163–9.PubMedCrossRefGoogle Scholar
  24. 24.
    Kitano H. Cancer as a robust system: implications for anticancer therapy. Nat Rev Cancer. 2004;4(3):227–35.PubMedCrossRefGoogle Scholar
  25. 25.
    Kitano H. A robustness-based approach to systems-oriented drug design. Nat Rev Drug Discov. 2007;6(3):202–10.PubMedCrossRefGoogle Scholar
  26. 26.
    Mazurek S, Boschek CB, Hugo F, Eigenbrodt E. Pyruvate kinase type M2 and its role in tumor growth and spreading. Semin Cancer Biol. 2005;15(4):300–8.PubMedCrossRefGoogle Scholar
  27. 27.
    Yasuda S, Arii S, Mori A, Isobe N, Yang W, Oe H, Fujimoto A, Yonenaga Y, Sakashita H, Imamura M. Hexokinase II and VEGF expression in liver tumors: correlation with hypoxia-inducible factor 1 alpha and its significance. J Hepatol. 2004;40(1):117–23.PubMedCrossRefGoogle Scholar
  28. 28.
    Coy JF, Dressler D, Wilde J, Schubert P. Mutations in the transketolase-like gene TKTL1: clinical implications for neurodegenerative diseases, diabetes and cancer. Clin Lab. 2005;51(5–6):257–73.PubMedGoogle Scholar
  29. 29.
    Langbein S, Zerilli M, Zur Hausen A, Staiger W, Rensch-Boschert K, Lukan N, Popa J, Ternullo MP, Steidler A, Weiss C, Grobholz R, Willeke F, Alken P, Stassi G, Schubert P, Coy JF. Expression of transketolase TKTL1 predicts colon and urothelial cancer patient survival: Warburg effect reinterpreted. Br J Cancer. 2006;94(4):578–85.PubMedCentralPubMedCrossRefGoogle Scholar
  30. 30.
    Stetak A, Veress R, Ovadi J, Csermely P, Keri G, Ullrich A. Nuclear translocation of the tumor marker pyruvate kinase M2 induces programmed cell death. Cancer Res. 2007;67(4):1602–8.PubMedCrossRefGoogle Scholar
  31. 31.
    Poulsen HS, Frederiksen P. Glucose-6-phosphate dehydrogenase activity in human breast cancer. Lack of association with oestrogen receptor content. Acta Pathol Microbiol Scand A. 1981;89(4):263–70.PubMedGoogle Scholar
  32. 32.
    Kuo W, Lin J, Tang TK. Human glucose-6-phosphate dehydrogenase (G6PD) gene transforms NIH 3T3 cells and induces tumors in nude mice. Int J Cancer. 2000;85(6):857–64.PubMedCrossRefGoogle Scholar
  33. 33.
    Comin-Anduix B, Boren J, Martinez S, Moro C, Centelles JJ, Trebukhina R, Petushok N, Lee WN, Boros LG, Cascante M. The effect of thiamine supplementation on tumour proliferation. A metabolic control analysis study. Eur J Biochem. 2001;268(15):4177–82.PubMedCrossRefGoogle Scholar
  34. 34.
    Boros LG, Puigjaner J, Cascante M, Lee WN, Brandes JL, Bassilian S, Yusuf FI, Williams RD, Muscarella P, Melvin WS, Schirmer WJ. Oxythiamine and dehydroepiandrosterone inhibit the nonoxidative synthesis of ribose and tumor cell proliferation. Cancer Res. 1997;57(19):4242–8.PubMedGoogle Scholar
  35. 35.
    Savageau MA. Biochemical system analysis: nonlinear systems analysis. Reading, MA: Addison-Wesley; 1976.Google Scholar
  36. 36.
    Voit EO. Computational analysis of biochemical systems: a practical guide for biochemists and molecular biologists. Cambridge, UK: Cambridge University Press; 2000. 544pp.Google Scholar
  37. 37.
    Fell DA. Understanding the control of metabolism. London, UK: Portland Press; 1997. p. 300.Google Scholar
  38. 38.
    Cascante M, Franco R, Canela EI. Use of implicit methods from general sensitivity theory to develop a systematic approach to metabolic control. I. Unbranched pathways. Math Biosci. 1989;94(2):271–88.PubMedCrossRefGoogle Scholar
  39. 39.
    Cascante M, Franco R, Canela EI. Use of implicit methods from general sensitivity theory to develop a systematic approach to metabolic control. II. Complex systems. Math Biosci. 1989;94(2):289–309.PubMedCrossRefGoogle Scholar
  40. 40.
    Cornish-Bowden A, Cárdenas ML. Technological and medical implications of metabolic control analysis. Dordrecht, The Netherlands: Kluwer; 2000.CrossRefGoogle Scholar
  41. 41.
    Boren J, Ramos-Montoya A, de Atauri P, Comin-Anduix B, Cortes A, Centelles JJ, Frederiks WM, Van Noorden CJ, Cascante M. Metabolic control analysis aimed at the ribose synthesis pathways of tumor cells: a new strategy for antitumor drug development. Mol Biol Rep. 2002;29(1–2):7–12.PubMedCrossRefGoogle Scholar
  42. 42.
    Selivanov VA, Sukhomlin T, Centelles JJ, Lee PW, Cascante M. Integration of enzyme kinetic models and isotopomer distribution analysis for studies of in situ cell operation. BMC Neurosci. 2006;7 Suppl 1:S7.PubMedCentralPubMedCrossRefGoogle Scholar
  43. 43.
    Schmidt K, Carlsen M, Nielsen J, Villadsen J. Modeling isotopomer distributions in biochemical networks using isotopomer mapping matrices. Biotechnol Bioeng. 1997;55:831–40.PubMedCrossRefGoogle Scholar
  44. 44.
    Marx A, de Graaf A, Wiechert W, Eggeling L, Sahm H. Determination of the fluxes in the central metabolism of Corynebacterium glutamicum by nuclear magnetic resonance spectroscopy combined with metabolic balancing. Biotechnol Bioeng. 1996;49:111–29.PubMedCrossRefGoogle Scholar
  45. 45.
    Zupke C, Stephanopoulos G. Modeling of isotope distributions and intracellular fluxes in metabolic networks using atom mapping matrices. Biotechnol Prog. 1994;10:489–98.CrossRefGoogle Scholar
  46. 46.
    Wiechert W, Mollney M, Isermann N, Wurzel M, de Graaf A. Bidirectional reaction steps in metabolic networks: III. Explicit solution and analysis of isotopomer labeling systems. Biotechnol Bioeng. 1999;66:69–85.PubMedCrossRefGoogle Scholar
  47. 47.
    Wiechert W, de Graaf A. In vivo stationary flux analysis by 13C labeling experiments. Adv Biochem Eng Biotechnol. 1996;54:109–54.PubMedGoogle Scholar
  48. 48.
    Wiechert W, de Graaf A. Bidirectional reaction steps in metabolic networks: I. Modeling and simulation of carbon isotope labeling experiments. Biotechnol Bioeng. 1997;55:101–17.PubMedCrossRefGoogle Scholar
  49. 49.
    Wiechert W, Siefke C, de Graaf A, Marx A. Bidirectional reaction steps in metabolic networks: II. Flux estimation and statistical analysis. Biotechnol Bioeng. 1997;55:118–35.PubMedCrossRefGoogle Scholar
  50. 50.
    Mulquiney P, Kuchel P. Modelling metabolism with mathematica. Boca Raton, FL: CRC Press; 2003.CrossRefGoogle Scholar
  51. 51.
    Selivanov VA, Puigjaner J, Sillero A, Centelles JJ, Ramos-Montoya A, Lee PW, Cascante M. An optimized algorithm for flux estimation from isotopomer distribution in glucose metabolites. Bioinformatics. 2004;20(18):3387–97.PubMedCrossRefGoogle Scholar
  52. 52.
    Selivanov VA, Meshalkina LE, Solovjeva ON, Kuchel PW, Ramos-Montoya A, Kochetov GA, Lee PW, Cascante M. Rapid simulation and analysis of isotopomer distributions using constraints based on enzyme mechanisms: an example from HT29 cancer cells. Bioinformatics. 2005;21(17):3558–64.PubMedCrossRefGoogle Scholar
  53. 53.
    Selivanov VA, Marin S, Lee PW, Cascante M. Software for dynamic analysis of tracer-based metabolomic data: estimation of metabolic fluxes and their statistical analysis. Bioinformatics. 2006;22(22):2806–12.PubMedCrossRefGoogle Scholar
  54. 54.
    Cornish-Bowden A. Fundamentals of enzyme kinetics. 3rd ed. London, UK: Portland Press; 2004.Google Scholar
  55. 55.
    Boren J, Lee WN, Bassilian S, Centelles JJ, Lim S, Ahmed S, Boros LG, Cascante M. The stable isotope-based dynamic metabolic profile of butyrate-induced HT29 cell differentiation. J Biol Chem. 2003;278(31):28395–402.PubMedCrossRefGoogle Scholar
  56. 56.
    Matito C, Mastorakou F, Centelles JJ, Torres JL, Cascante M. Antiproliferative effect of antioxidant polyphenols from grape in murine Hepa-1c1c7. Eur J Nutr. 2003;42(1):43–9.PubMedCrossRefGoogle Scholar
  57. 57.
    Mollinedo F, Gajate C, Martin-Santamaria S, Gago F. ET-18-OCH3 (edelfosine): a selective antitumour lipid targeting apoptosis through intracellular activation of Fas/CD95 death receptor. Curr Med Chem. 2004;11(24):3163–84.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Marta Cascante
    • 1
    Email author
  • Vitaly Selivanov
    • 1
  • Antonio Ramos-Montoya
    • 1
  1. 1.Department of Biochemistry and Molecular Biology, Associated Unit to CSICInstitute of Biomedicine of University of Barcelona (IBUB) and IDIBAPS (Institut d’Investigacions Biomèdiques August Pi i Sunyer)BarcelonaSpain

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