Advertisement

Analysis and Modeling of Metabolism of Cancer

  • Miroslava Cuperlovic-Culf
  • Pier MorinJr
  • Natalie Lefort
Chapter
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 9)

Abstract

Metabolism comprises a set of chemical reactions that are performed in biological systems in order to sustain life. Metabolism is responsible for deriving energy and biomolecules from the cells’ surrounding. Tumour cells’ very high metabolic needs have to be fulfilled under suboptimal conditions. Thus, tumour cells and tissues have a remarkably different metabolism than the tissues that they derive from. Many key oncogenic signaling pathways converge to create this change in order to support growth and survival of cancer cells. Some of these metabolic alterations are initiated by oncogenes and are required for malignant transformation. Altered metabolism allows cancer cells to sustain higher proliferative rates with faster energy and molecular building block production while resisting cell death signals particularly those that are mediated by increased oxidative damage. The very specific metabolic phenotype of cancer provides an interesting avenue for diagnosis and treatment and several examples of such applications are already in place. Novel methods for metabolic profiling, comprised under the term metabolomics, provide tools for collection of data on cancer cell and tissue’s metabolic properties in steady state and as a function of time and/or treatment. The time, i.e. flux data can provide components for creation of more detailed kinetic models of metabolic processes in cancer leading to more information about possible markers as well as platforms for in silico treatment testing. Once a more detailed understanding of the characteristics of cancer metabolism including energy and biomolecules production is in place, further clinical developments will follow.

Keywords

Cancer metabolic phenotype Metabolism modeling Mitochondrial function 

References

  1. 1.
    Ackerstaff E, Glunde K, Bhujwalla ZM (2003) Choline phospholipid metabolism: a target in cancer cells? J Cell Biochem 90:525–533CrossRefGoogle Scholar
  2. 2.
    Al-Saffar NMS, Jackson LE, Raynaud FI, Clarke PA, de Molina AR et al (2010) The phosphoinositide 3-kinase inhibitor PI-103 downregulates choline kinase alpha leading to phosphocholine and total choline decrease detected by magnetic resonance spectroscopy. Cancer Res 70:5507–5517CrossRefGoogle Scholar
  3. 3.
    Astanin S, Preziosi L (2009) Mathematical modeling of the Warburg effect in tumour cords. J Theor Biol 258:578–90CrossRefGoogle Scholar
  4. 4.
    Bathen TF, Jensen LR, Sitter B, Fjosne HE, Halgunset J, Axelson DE, Gribbestad IS, Lundgren S (2007) MR-determined metabolic phenotype of breast cancer in prediction of lymphatic spread, grade, and hormone status. Breast Cancer Res Treat 104(2):181–189Google Scholar
  5. 5.
    Bazil JN, Buzzard GT, Rundell AE (2010) Modeling mitochondrial bioenergetics with integrated volume dynamics. PLoS comput biol 6(1):e1000632MathSciNetCrossRefGoogle Scholar
  6. 6.
    Ben-Yoseph O, Badar-Goffer RS, Morris PG, Bachelard HS (1993) Glycerol 3-phosphate and lactate as indicators of the cerebral cytoplasmic redox state in severe and mild hypoxia respectively: a \(^{13}\)C- and \(^{31}\)P-NMR study. Biochem J 291:915–919Google Scholar
  7. 7.
    Bertini I, Cacciatore S, Jensen BV, Schou JV, Johansen JS, Kruhøffer M, Luchinat C, Nielsen DL, Turano P (2012) Metabolomic NMR fingerprinting to identify and predict survival of patients with metastatic colorectal cancer. Cancer Res 72:356–364CrossRefGoogle Scholar
  8. 8.
    Bhalla K, Hwang BJ, Dewi RE, Ou L, Twaddel W, Fang HB, Vafai SB, Vazquez F, Puigserver P, Boros L, Girnun GD (2011) PGC1\(\alpha \) promotes tumor growth by inducing gene expression programs supporting lipogenesis. Cancer Res 71:6888–6898CrossRefGoogle Scholar
  9. 9.
    Bogin L, Papa MZ, Polak-Charcon S, Degani H (1998) TNF-induced modulations of phospholipid metabolism in human breast cancer cells. Biochim Biophys Acta 1392:217–232CrossRefGoogle Scholar
  10. 10.
    Brockmöller SF, Bucher E, Müller BM, Budczies J, Hilvo M et al (2011) Integration of metabolomics and expression of glycerol-3-phosphate acyltransferase (GPAM) in breast cancer-link to patient survival, hormone receptor status, and metabolic profiling. J Proteome Res 11:850–860CrossRefGoogle Scholar
  11. 11.
    Bross-Walch N, Kuhn T, Moskau D, Zerbe O (2005) Strategies and tools for structure determination of natural products using modern methods of NMR spectroscopy. Chem Biodivers 2:147–177CrossRefGoogle Scholar
  12. 12.
    Cao MD, Sitter B, Bathen TF, Bofin A, Lønning PE et al (2012) Predicting long-term survival and treatment response in breast cancer patients receiving neoadjuvant chemotherapy by MR metabolic profiling. NMR Biomed 25:369–378CrossRefGoogle Scholar
  13. 13.
    Caso G, McNurlan MA, McMillan ND, Eremin O, Garlick PJ (2004) Tumour cell growth in culture: dependence on arginine. Clin Sci (Lond) 107:371–379CrossRefGoogle Scholar
  14. 14.
    Choi C, Ganji SK, Deberardinis RJ, Hatanpaa KJ, Rakheja D, Kovacs Z, Yang XL, Mashimo T, Raisanen JM, Marin-Valencia I, Pascual JM, Madden CJ, Mickey BE, Malloy CR, Bachoo RM, Maher EA. (2012) 2-hydroxyglutarate detection by magnetic resonance spectroscopy in IDH-mutated patients with gliomas. Nat. Med. doi: 10.1038/nm.2682 (Epub ahead of print)
  15. 15.
    Chung CL, Griffiths JR (2008) Oncogenes meet metabolism: from deregulated genes to a broader understanding of tumour physiology. In: Kroemer G, Mumberg D, Keun H, Riefke B, StegerHartmann T, Petersen K (eds) Using metabolomics to monitor anticancer drugs. Ernst schering foundation symposium proceedings, vol 4, pp 55–78Google Scholar
  16. 16.
    Claudino WM, Quattrone A, Biganzoli L, Pestrin M, Bertini I, Di Leo A (2007) Metabolomics: available results, current research projects in breast cancer, and future applications. J Clin Oncol 25:2840–2846CrossRefGoogle Scholar
  17. 17.
    Costello LC, Franklin RB (2005) Why do tumour cells glycolyse? From glycolysis through citrate to lipogenesis. Mol Cell Biochem 280:1–8CrossRefGoogle Scholar
  18. 18.
    DeBerardinis RJ, Lum JJ, Hatzivassiliou G, Thompson CB (2008) The biology of cancer: metabolic reprogramming fuels cell growth and proliferation. Cell Metab 7:11–20CrossRefGoogle Scholar
  19. 19.
    Denkert C, Budczies J, Weichert W, Wohlgemuth G, Scholz M, Kind T, Niesporek S, Noske A, Buckendahl A, Dietel M, Fiehn O (2008) Metabolite profiling of human colon carcinoma-deregulation of TCA cycle and amino acid turnover. Mol Cancer 7:72–86CrossRefGoogle Scholar
  20. 20.
    Deo RC, Hunter L, Lewis GD, Pare G, Vasan RS et al (2010) Interpreting metabolomic profiles using unbiased pathway models. PLoS Comp Biol 6:e1000692CrossRefGoogle Scholar
  21. 21.
    Dowling C, Bollen AW, Noworolski SM et al (2001) Pre-operative proton MR spectroscopic imaging of brain tumors: correlation with histopathologic analysis of resection specimens. Am J Neuroradiol 22:604–612Google Scholar
  22. 22.
    Duarte NC, Becker SA, Jamshidi N, Thiele I, Mo ML et al (2007) Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci USA 104:1777–1782CrossRefGoogle Scholar
  23. 23.
    Dunckley T, Coon KD, Stephan DA (2005) Discovery and development of biomarkers of neurological disease. Drug Discov Today 10:326–334CrossRefGoogle Scholar
  24. 24.
    Erb G, Elbayed K, Piotto M, Raya J, Neuville A, Mohr M, Maitrot D, Kehrli P, Namer IJ (2008) Toward improved grading of malignancy in oligodendrogliomas using metabolomics. Magn Reson Med 59:959–965Google Scholar
  25. 25.
    Ertel A, Tsirigos A, Whitaker-Menezes D, Birbe RC, Pavlides S, Martinez-Outschoorn UE, Pestell RG, Howell A, Sotgia F, Lisanti MP (2012) Is cancer a metabolic rebellion against host aging? In the quest for immortality, tumor cells try to save themselves by boosting mitochondrial metabolism. Cell Cycle 11:253–263CrossRefGoogle Scholar
  26. 26.
    Fan TW, Lane AN, Higashi RM, Farag MA, Gao H, Bousamra M et al (2009) Altered regulation of metabolic pathways in human lung cancer discerned by \(^{13}\)C stable isotope-resolved metabolomics (SIRM). Mol Cancer 8:41CrossRefGoogle Scholar
  27. 27.
    Feala JD, Coquin L, Paternostro G, McCulloch AD (2008) Integrating metabolomics and phenomics with systems models of cardiac hypoxia. Prog Biophys Mol Biol 96:209–225CrossRefGoogle Scholar
  28. 28.
    Florian CL, Preece NE, Bhakoo KK et al (1995) Cell type-specific fingerprinting of meningioma and meningeal cells by proton nuclear magnetic resonance spectroscopy. Cancer Res 55:420–427Google Scholar
  29. 29.
    Folger O, Jerby L, Frezza C, Gottlieb E, Ruppin E, Shlomi T (2011) Predicting selective drug targets in cancer through metabolic networks. Mol Syst Biol 7:501Google Scholar
  30. 30.
    Forseth RR, Schroeder FC (2010) NMR-spectroscopic analysis of mixtures: from structure to function. Curr Opin Chem Biol 15:38–47CrossRefGoogle Scholar
  31. 31.
    Frezza C, Zheng L, Folger O, Rajagopalan KN, MacKenzie ED, Jerby L, Micaroni M, Chaneton B, Adam J, Hedley A, Kalna G, Tomlinson IP, Pollard PJ, Watson DG, Deberardinis RJ, Shlomi T, Ruppin E, Gottlieb E (2011) Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase. Nature 477:225–228CrossRefGoogle Scholar
  32. 32.
    Garfinkel D, Garfinkel L, Pring M, Green SB, Chance B (1970) Computer applications to biochemical kinetics. Annu Rev Biochem 39:473–498CrossRefGoogle Scholar
  33. 33.
    Giovane A, Balestrieri A, Napoli C (2008) New insights into cardiovascular and lipid metabolomics. J Cell Biochem 105:648–654CrossRefGoogle Scholar
  34. 34.
    Giskeødegård GF, Lundgren S, Sitter B, Fjøsne HE, Postma G, Buydens LM, Gribbestad IS, Bathen TF (2012) Lactate and glycine-potential MR biomarkers of prognosis in estrogen receptor-positive breast cancers. NMR Biomed. doi: 10.1002/nbm.2798
  35. 35.
    Gottlieb E, Tomlinson IP (2005) Mitochondrial tumour suppressors: a genetic and biochemical update. Nat Rev Cancer 11:857–866CrossRefGoogle Scholar
  36. 36.
    Gribbestad IS, Sitter B, Lundgren S, Krane J, Axelson D (1999) Metabolite composition in breast tumors examined by proton nuclear magnetic resonance spectroscopy. Anticancer Res 19(3A):1737–1746Google Scholar
  37. 37.
    Griffin JL, Blenkiron C, Valonen PK, Caldas C, Kauppinen RA (2006) High-resolution magic angle spinning \(^{1}\)H NMR spectroscopy and reverse transcription-PCR analysis of apoptosis in a rat glioma. Anal Chem 78:1546–1552CrossRefGoogle Scholar
  38. 38.
    Griffin JL, Nicholls AW (2006) Metabolomics as a functional genomic tool for understanding lipid dysfunction in diabetes, obesity and related disorders. Pharmacogenomics 7:1095–1107CrossRefGoogle Scholar
  39. 39.
    Griffin JL, Shockcor JP (2004) Metabolic profiles of cancer cells. Nat Rev Cancer 4:551–561CrossRefGoogle Scholar
  40. 40.
    Guo JY, Chen HY, Mathew R, Fan J, Strohecker AM, Karsli-Uzunbas G, Kamphorst JJ, Chen G, Lemons JM, Karantza V, Coller HA, Dipaola RS, Gelinas C, Rabinowitz JD, White E (2011) Activated Ras requires autophagy to maintain oxidative metabolism and tumorigenesis. Genes Dev 25:460–470Google Scholar
  41. 41.
    Hakumaki JM, Poptani H, Sandmair AM, Yla-Herttuala S, Kauppinen RA (1999) \(^{1}\)H MRS detects polyunsaturated fatty acid accumulation during gene therapy of glioma: implications for the in vivo detection of apoptosis. Nat Med 5:1323–1327CrossRefGoogle Scholar
  42. 42.
    Hanahan D, Weinberg RA (2000) The hallmarks of cancer. Cell 100:57–70CrossRefGoogle Scholar
  43. 43.
    Holmes E, Tsang TM, Tabrizi SJ (2006) The application of NMR-based metabonomics in neurological disorders. NeuroRx 3:358–372CrossRefGoogle Scholar
  44. 44.
    Hori S, Nishiumi S, Kobayashi K, Shinohara M, Hatakeyama Y, Kotani Y, Hatano N, Maniwa Y, Nishio W, Bamba T, Fukusaki E, Azuma T, Takenawa T, Nishimura Y, Yoshida M (2011) A metabolomic approach to lung cancer. Lung Cancer 74:284–292CrossRefGoogle Scholar
  45. 45.
    Ibrahim SM, Gold R (2005) Genomics, proteomics, metabolomics: What is in a word for multiple sclerosis? Curr Opin Neurol 18:231–235CrossRefGoogle Scholar
  46. 46.
    Ikeda A, Nishiumi S, Shinohara M, Yoshie T, Hatano N, Okuno T, Bamba T, Fukusaki E, Takenawa T, Azuma T, Yoshida M (2011) Serum metabolomics as a novel diagnostic approach for gastrointestinal cancer. Biomed Chromatogr. doi: 10.1002/bmc.1671
  47. 47.
    Jemal A, Siegel R, Xu J, Ward E (2010) Cancer statistics, 2010. CA Cancer J Clin 60:277–300CrossRefGoogle Scholar
  48. 48.
    Jerby L, Shlomi T, Ruppin E (2010) Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol Syst Biol 6:1CrossRefGoogle Scholar
  49. 49.
    Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. (2012) KEGG for integration and interpretation of large-scale molecular datasets. Nucleic Acids Res 40:D109–D114Google Scholar
  50. 50.
    Kanehisa M (1996) Toward pathway engineering: a new database of genetic and molecular pathways. Sci Technol Jpn 59:34–38Google Scholar
  51. 51.
    Kell DB, Knowles JD (2006) The role of modeling in systems biology. In: Szallasi Z, Stelling J, Periwal V (eds) System modeling in cellular biology. MIT Press, CambridgeGoogle Scholar
  52. 52.
    Kim YS, Maruvada P, Milner JA (2008) Metabolomics in biomarker discovery: future uses for cancer prevention. Future Oncol 4:93–102CrossRefGoogle Scholar
  53. 53.
    Kline EE, Treat EG, Averna TA et al (2006) Citrate concentrations in human seminal fluid and expressed prostatic fluid determined via 1H nuclear magnetic resonance spectroscopy outperform prostate specific antigen in prostate cancer detection. J Urol 176:2274–2279CrossRefGoogle Scholar
  54. 54.
    Lewis GD, Asnani A, Gerszten RE (2008) Application of metabolomics to cardiovascular biomarker and pathway discovery. J Am Coll Cardiol 52:117–123CrossRefGoogle Scholar
  55. 55.
    Lewis NE, Schramm G, Bordbar A, Schellenberger J, Andersen MP, Cheng JK, Patel N et al (2010) Large-scale in silico modeling of metabolic interactions between cell types in the human brain. Nat Biotech 28:1279–1285CrossRefGoogle Scholar
  56. 56.
    Li M, Song Y, Cho N, Chang JM, Koo HR, Yi A, Kim H, Park S, Moon WK (2011) An HR-MAS MR metabolomics study on breast tissues obtained with core needle biopsy. PLoS One 6:e25563CrossRefGoogle Scholar
  57. 57.
    Llaneras F, Pico J (2007) An interval approach for dealing with flux distributions and elementary modes activity patterns. J Theor Biol 246:290–308MathSciNetCrossRefGoogle Scholar
  58. 58.
    Locasale JW, Cantley LC (2010) Altered metabolism in cancer. BMC Biol 8:88CrossRefGoogle Scholar
  59. 59.
    Lutz NW (2005) From metabolic to metabolomic NMR spectroscopy of apoptotic cells. Metabolomics 1:251–268CrossRefGoogle Scholar
  60. 60.
    Ma H, Sorokin A, Mazein A, Selkov A, Selkov E et al (2007) The Edinburgh human metabolic network reconstruction and its functional analysis. Mol Syst Biol 3:135CrossRefGoogle Scholar
  61. 61.
    MacBeath G, Saghatelian A (2009) The promise and challenge of omic approaches. Curr Opin Chem Biol 13:501–502CrossRefGoogle Scholar
  62. 62.
    Madsen R, Lundstedt T, Trygg J (2010) Chemometrics in metabolomics-a review in human disease diagnosis. Analytica Chimica Acta 659:23–33CrossRefGoogle Scholar
  63. 63.
    Matoba S, Kang JG, Patino WD, Wragg A, Boehm M, Gavrilova O, Hurley PJ, Bunz F, Hwang PM (2006) p53 regulates mitochondrial respiration. Science 312:1650–1653CrossRefGoogle Scholar
  64. 64.
    Mayr M, Madhu B, Xu Q (2007) Proteomics and metabolomics combined in cardiovascular research. Trends Cardiovasc Med 17:43–48CrossRefGoogle Scholar
  65. 65.
    Mazurek S, Eigenbrodt E (2003) The tumor metabolome. Anticancer Res 23:1149–1154Google Scholar
  66. 66.
    Modica-Napolitano JS, Steele GD Jr, Chen LB (1989) Aberrant mitochondria in two human colon carcinoma cell lines. Cancer Res 49:3369–3373Google Scholar
  67. 67.
    Mullen AR, Wheaton WW, Jin ES, Chen PH, Sullivan LB, Cheng T, Yang Y, Linehan WM, Chandel NS, DeBerardinis RJ (2011) Reductive carboxylation supports growth in tumour cells with defective mitochondria. Nature 481:385–388Google Scholar
  68. 68.
    Nicholson JK, Connelly J, Lindon JC et al (2002) Metabolomics: a platform for studying drug toxicity and gene function. Nat Rev Drug Discov 1:153–161CrossRefGoogle Scholar
  69. 69.
    Nicholson JK, Lindon JC, Holmes E (1999) ‘Metabolomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 29:1181–1189CrossRefGoogle Scholar
  70. 70.
    Nieman KM, Kenny HA, Penicka CV, Ladanyi A, Buell-Gutbrod R, Zillhardt MR, Romero IL, Carey MS, Mills GB, Hotamisligil GS, Yamada SD, Peter ME, Gwin K, Lengyel E (2011) Adipocytes promote ovarian cancer metastasis and provide energy for rapid tumor growth. Nat Med. doi: 10.1038/nm.2492
  71. 71.
    Palsson B (2011) Systems biology: simulation of dynamic network states. Cambridge University Press, CambridgeGoogle Scholar
  72. 72.
    Palsson B (2006) Systems biology: properties of reconstructed networks. Cambridge University Press, CambridgeGoogle Scholar
  73. 73.
    Pirozynski M (2006) 100 years of lung cancer. Respir Med 100:2073–2084CrossRefGoogle Scholar
  74. 74.
    Resendis-Antonio O, Checa A, Encarnacion S (2010) Modeling core metabolism in cancer cells: surveying the topology underlying the Warburg effect. PloS One 5:e12383CrossRefGoogle Scholar
  75. 75.
    Ritchie SA, Ahiahonu PW, Jayasinghe D, Heath D, Liu J, Lu Y, Jin W, Kavianpour A, Yamazaki Y, Khan AM, Hossain M, Su-Myat KK, Wood PL et al (2010) Reduced levels of hydroxylated, polyunsaturated ultra long-chain fatty acids in the serum of colorectal cancer patients: implications for early screening and detection. BMC Med 8:13CrossRefGoogle Scholar
  76. 76.
    Rocha CM, Carrola J, Barros AS, Gil AM, Goodfellow BJ, Carreira IM, Bernardo J, Gomes A, Sousa V, Carvalho L, Duarte IF (2011) Metabolic signatures of lung cancer in biofluids: NMR-based metabonomics of blood plasma. J Proteome Res 10:4314–4324CrossRefGoogle Scholar
  77. 77.
    Roede JR, Park Y, Li S, Strobel FH, Jones DP (2012) Detailed mitochondrial phenotyping by high resolution metabolomics. PLoS One 7:e33020CrossRefGoogle Scholar
  78. 78.
    Samuel JL, Schaub MC, Zaugg M, Mamas M, Dunn WB, Swynghedauw B (2008) Genomics in cardiac metabolism. Cardiovasc Res 79:218–227CrossRefGoogle Scholar
  79. 79.
    Schulz TJ, Thierbach R, Voigt A, Drewes G, Mietzner B, Steinberg P, Pfeiffer AF, Ristow M (2006) Induction of oxidative metabolism by mitochondrial frataxin inhibits cancer growth: Otto Warburg revisited. J Biol Chem 281:977–981CrossRefGoogle Scholar
  80. 80.
    Sébédio JL, Pujos-Guillot E, Ferrara M (2009) Metabolomics in evaluation of glucose disorders. Curr Opin Clin Nutr Metab Care 12:412–418CrossRefGoogle Scholar
  81. 81.
    Seifert EL, Fiehn O, Bezaire V, Bickel DR, Wohlgemuth G, Adams SH, Harper ME (2010) Long-chain fatty acid combustion rate is associated with unique metabolite profiles in skeletal muscle mitochondria. PLoS One 5(3):e9834CrossRefGoogle Scholar
  82. 82.
    Serkova NJ, Spratlin JL, Eckhardt SG (2007) NMR-based metabolomics: translational application and treatment of cancer. Curr Opin Mol Ther 9:572–582Google Scholar
  83. 83.
    Shulman RG, Rothman DL (eds) (2005) Metabolomics by in vivo NMR. Whiley, ChichesterGoogle Scholar
  84. 84.
    Smith AC, Robinson AJ (2011) A metabolic model of the mitochondrion and its use in modeling diseases of the tricarboxylic acid cycle. BMC Syst Biol 2011(5):102CrossRefGoogle Scholar
  85. 85.
    Spratlin JL, Serkova NJ, Eckhardt SG (2009) Clinical applications of metabolomics in oncology: a review. Clin Cancer Res 15:431–440CrossRefGoogle Scholar
  86. 86.
    Stroobants S, Goeminne J, Seegers M, Dimitrijevic S, Dupont P et al (2003) 18FDG-Positron emission tomography for the early prediction of response in advanced soft tissue sarcoma treated with imatinib mesylate (Glivec). Eur J Cancer 39:2012–2020CrossRefGoogle Scholar
  87. 87.
    Swanson MG, Zektzer AS, Tabatabai ZL et al (2006) Quantitative analysis of prostate metabolites using 1H HR-MAS spectroscopy. Mag Reson Med 55:1257–1264CrossRefGoogle Scholar
  88. 88.
    Tate AR, Underwood J, Acosta DM, Julià-Sapé M, Majós C et al (2006) Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra. NMR Biomed 19:411–434CrossRefGoogle Scholar
  89. 89.
    Thiele I, Price ND, Vo TD, Palsson BO (2005) Candidate metabolic network states in human mitochondria. Impact of diabetes, ischemia, and diet. J Biol Chem 280:11683–11695CrossRefGoogle Scholar
  90. 90.
    Vazquez A, Oltvai ZN (2011) Molecular crowding defines a common origin for the Warburg effect in proliferating cells and the lactate threshold in muscle physiology. PLoS One 6:19538CrossRefGoogle Scholar
  91. 91.
    Ward CS, Venkatesh HS, Chaumeil MM, Brandes AH, VanCriekinge M et al (2010) Noninvasive detection of target modulation following phosphatidylinositol 3-kinase inhibition using hyperpolarized 13C magnetic resonance spectroscopy. Cancer Res 70:1296–1305CrossRefGoogle Scholar
  92. 92.
    Watson AD (2006) Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Lipidomics: a global approach to lipid analysis in biological systems. J Lipid Res 47:2101–2111CrossRefGoogle Scholar
  93. 93.
    Whitaker-Menezes D, Martinez-Outschoorn UE, Flomenberg N, Birbe RC, Witkiewicz AK, Howell A, Pavlides S, Tsirigos A, Ertel A, Pestell RG, Broda P, Minetti C, Lisanti MP, Sotgia F (2011) Hyperactivation of oxidative mitochondrial metabolism in epithelial cancer cells in situ: visualizing the therapeutic effects of metformin in tumor tissue. Cell Cycle 10:4047–4064CrossRefGoogle Scholar
  94. 94.
    Wibom C, Surowiec I, Mörén L, Bergström P, Johansson M, Antti H, Bergenheim AT (2010) Metabolomic patterns in glioblastoma and changes during radiotherapy: a clinical microdialysis study. J Proteome Res 9:2909–2919CrossRefGoogle Scholar
  95. 95.
    Zaugg K, Yao Y, Reilly PT, Kannan K, Kiarash R, Mason J, Huang P, Sawyer SK, Fuerth B, Faubert B, Kalliomäki T, Elia A, Luo X, Nadeem V, Bungard D, Yalavarthi S, Growney JD, Wakeham A, Moolani Y, Silvester J, Ten AY, Bakker W, Tsuchihara K, Berger SL, Hill RP, Jones RG, Tsao M, Robinson MO, Thompson CB, Pan G, Mak TW (2011) Carnitine palmitoyltransferase 1C promotes cell survival and tumor growth under conditions of metabolic stress. Genes Dev 25:1041–1051CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Miroslava Cuperlovic-Culf
    • 1
  • Pier MorinJr
    • 2
  • Natalie Lefort
    • 2
    • 3
  1. 1.National Research Council of CanadaInstitute for Information TechnologyMonctonCanada
  2. 2.Department of Chemistry and BiochemistryUniversité de MonctonMonctonCanada
  3. 3.Atlantic Cancer Research InstituteMonctonCanada

Personalised recommendations